From d87717f3cff3fb8132eba9690d08a67aeb3f450c Mon Sep 17 00:00:00 2001 From: Nikita Date: Sun, 3 Aug 2025 16:10:38 +0000 Subject: [PATCH 01/43] feat: replace Whisper with Smart-Turn VAD, preserve diarization --- requirements_dev.txt | Bin 1242 -> 1310 bytes src/libs/smart_turn | 1 + src/preprocess/preprocess.py | 352 ++++++++++++++--------------------- src/separation/separation.py | 27 ++- use_meta_1000h.sh | 31 ++- use_meta_100h.sh | 47 ++++- use_meta_2000h.sh | 30 ++- use_meta_500h.sh | 35 +++- 8 files changed, 276 insertions(+), 247 deletions(-) create mode 160000 src/libs/smart_turn diff --git a/requirements_dev.txt b/requirements_dev.txt index 64a91c5ae6da8bd670c282cae73155b28fc03b0f..d1df2b76b5295f5fde3f50b638ef368a6e84c073 100644 GIT binary patch delta 70 zcmcb`Ige|D8{_6Q#%D~11q_)C1q`VS$qb1MB@DViGLu1{p_m~T$SMMhlrWS6S$Pb4 R4Cz3*lF5!t#*=+m`T%)C5X%4n delta 44 wcmbQob&GR@8>4g@Ln1>lLkUAFLlJ{6LpehRLne@40OD_UWPHsuc>+rx00BD-=>Px# diff --git a/src/libs/smart_turn b/src/libs/smart_turn new file mode 160000 index 0000000..5d35b39 --- /dev/null +++ b/src/libs/smart_turn @@ -0,0 +1 @@ +Subproject commit 5d35b39cc7c465722c2108fa9f657e129b70252b diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index eccbb21..c726eed 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -2,18 +2,18 @@ import os import multiprocessing from concurrent.futures import ProcessPoolExecutor, as_completed -from typing import Any, List, Tuple +from typing import Dict, List, Tuple, Any import torch import torchaudio from loguru import logger from tqdm import tqdm -from faster_whisper import WhisperModel from dotenv import load_dotenv from huggingface_hub import login from src.utils import load_config +from src.libs.smart_turn.offline_svad import OfflineVAD def get_audio_paths(directory: str) -> List[str]: audio_paths = [] @@ -23,179 +23,132 @@ def get_audio_paths(directory: str) -> List[str]: continue if os.path.isdir(full_path): audio_paths.extend(get_audio_paths(full_path)) - elif entry.endswith(".mp3") : + elif entry.endswith(".mp3"): audio_paths.append(full_path) return audio_paths -def get_whisper_segments( - model: Any, - path_audio: str, - beam_size: int = 5 -) -> Tuple[List[float], List[float], List[str]]: +def postprocess_vad_result(vad_result: List[Dict[str, Any]], duration: float = 15.0) -> Tuple[List[float], List[float]]: + speech_intervals = [ + {'start_time': item['start_time'], 'end_time': item['end_time']} + for item in vad_result if item['prediction'] == 1 + ] - segments, info = model.transcribe( - path_audio, - beam_size=beam_size, - ) + if not speech_intervals: + return [], [] timesteps_starts = [] timesteps_ends = [] - - timestamps_text = [] - - for segment in segments: - timesteps_starts.append(segment.start) - timesteps_ends.append(segment.end + 0.05) - timestamps_text.append(segment.text) - - assert len(timesteps_starts) == len(timesteps_ends) == len(timestamps_text), "Mismatch in timestamps lengths." - return timesteps_starts, timesteps_ends, timestamps_text - - -def get_piece_idx( - timesteps_starts: List[float], - timesteps_ends: List[float], - duration: float = 15.0, -) -> List[Tuple[int, int]]: - """ - Groups speech segments into optimal chunks respecting duration constraints, - returning indices of the original segments rather than time intervals. - - Processes speech segments to identify optimal groupings that: - 1. Do not exceed specified maximum duration when combined - 2. Contain complete consecutive speech segments - 3. Maintain natural segmentation boundaries - - Args: - timesteps_starts: Start times of speech segments (seconds) - timesteps_ends: End times of speech segments (seconds) - duration: Maximum allowed combined duration in seconds (default: 15.0) - - Returns: - List of tuples representing segment index ranges: - - Each tuple contains (start_index, end_index) of segments in original lists - - Index ranges reference positions in timesteps_starts/timesteps_ends - - Resulting combined segments would be between duration//3 and duration seconds - - Maintains original segment order - - Example: - Input segments: - starts = [0.0, 2.5, 5.5, 12.5] - ends = [2.15, 5.15, 12.15, 18.15] - - Output with duration=15: - [(0, 2), (3, 3)] # First 3 segments grouped, last segment standalone - """ - - if not timesteps_starts or not timesteps_ends: - return [] - - pieces = [] - n = len(timesteps_starts) - m = len(timesteps_ends) - start_idx = 0 - while start_idx < n: - left_s = timesteps_starts[start_idx] - end_idx = start_idx - temp_duration = 0.0 + current_start = speech_intervals[0]['start_time'] + current_end = speech_intervals[0]['end_time'] - while end_idx < m: - right_s = timesteps_ends[end_idx] - temp_duration = right_s - left_s - if temp_duration > duration: - break - end_idx += 1 + for interval in speech_intervals[1:]: + next_start = interval['start_time'] + next_end = interval['end_time'] - if end_idx > start_idx: - current_duration = timesteps_ends[end_idx - 1] - left_s - if (current_duration >= duration / 3) and (current_duration <= duration): - pieces.append((start_idx, end_idx - 1)) + if next_end - current_start <= duration: + current_end = next_end + else: + if duration / 3 <= current_end - current_start <= duration: + timesteps_starts.append(current_start) + timesteps_ends.append(current_end) + current_start = next_start + current_end = next_end - start_idx = max(end_idx, start_idx + 1) + if duration / 3 <= current_end - current_start <= duration: + timesteps_starts.append(current_start) + timesteps_ends.append(current_end) - return pieces + return timesteps_starts, timesteps_ends def cut_audio( audio: torch.Tensor, sr: int, - pieces: List[Tuple[int, int]], - satrt_timestamps: List[float], + start_timestamps: List[float], end_timestamps: List[float], - text_segments: List[str], output_folder: str, album_id: str, episode_id: str, format: str = 'mp3', - duration:float = 15.0 + duration: float = 15.0 ): try: os.makedirs(output_folder, exist_ok=True) - for i, (start_idx, end_idx) in enumerate(pieces): - - start_time = satrt_timestamps[start_idx] - end_time = end_timestamps[end_idx] - - if end_time - start_time <= duration / 3 : + segments_created = 0 + for start_time, end_time in zip(start_timestamps, end_timestamps): + if end_time - start_time <= duration / 3: continue start_sample = int(start_time * sr) end_sample = int(end_time * sr) end_sample = min(audio.shape[-1], end_sample) - assert end_sample > start_sample + if end_sample <= start_sample: + continue segment = audio[:, start_sample:end_sample] output_audio_filename = f"{start_time:.2f}_{end_time:.2f}_{album_id}_{episode_id}.{format}" - output_whisper_filename = f"{start_time:.2f}_{end_time:.2f}_{album_id}_{episode_id}_whisper.txt" - output_audio_path = os.path.join(output_folder, output_audio_filename) - output_whisper_path = os.path.join(output_folder, output_whisper_filename) - - whisper_text = ' '.join(text_segments[start_idx:end_idx + 1]) - with open(output_whisper_path, 'w', encoding='utf-8') as f: - f.write(whisper_text) - + torchaudio.save(output_audio_path, segment, sr) + segments_created += 1 - logger.success(f"The folder has been processed : {output_folder}") + logger.success(f"Processed {segments_created} segments: {output_folder}") except Exception as e: - logger.error(f"Error : {e}") + logger.error(f"Error in cut_audio: {e}") raise -def process_audio_file( - path_audio: str, - duration: float, - beam_size: int -): - album_id = os.path.basename(os.path.dirname(path_audio)) - episode_id = os.path.splitext(os.path.basename(path_audio))[0] +def init_vad_process(gpu_id: int, vad_args: Dict[str, Any]): + global smart_vad + + if torch.cuda.is_available(): + device = f"cuda:{gpu_id}" + torch.cuda.set_device(device) + else: + device = "cpu" + logger.warning(f"No GPU available, using CPU for process") - logger.info(f"Processing: Album={album_id}, Episode={episode_id}") + smart_vad = OfflineVAD( + silero_vad_threshold=vad_args['silero_vad_threshold'], + smart_vad_threshold=vad_args['smart_vad_threshold'], + smart_vad_path=vad_args['smart_vad_path'], + device=device + ) + logger.info(f"VAD initialized on {device}") +def process_audio_file(path_audio: str, duration: float): + global smart_vad + + album_id = os.path.basename(os.path.dirname(path_audio)) + episode_id = os.path.splitext(os.path.basename(path_audio))[0] episode_folder = os.path.join(os.path.dirname(path_audio), episode_id) try: audio, sr = torchaudio.load(path_audio) - if audio.shape[-1] / sr <= duration: return except Exception as e: - os.remove(path_audio) - logger.info(f"broken file {path_audio}: {e}") + logger.error(f"Broken file {path_audio}: {e}") + if os.path.exists(path_audio): + os.remove(path_audio) return - + try: - timesteps_starts, timesteps_ends, timestamps_text = get_whisper_segments(model, path_audio, beam_size) - pieces = get_piece_idx(timesteps_starts, timesteps_ends, duration) + vad_result = smart_vad.process_file(path_audio) + import pickle + with open('data.pkl', 'wb') as file: + pickle.dump(vad_result, file) + timesteps_starts, timesteps_ends = postprocess_vad_result(vad_result, duration=duration) + + if not timesteps_starts: + logger.warning(f"No speech segments found in {path_audio}") + return cut_audio( audio=audio, sr=sr, - pieces=pieces, - satrt_timestamps=timesteps_starts, + start_timestamps=timesteps_starts, end_timestamps=timesteps_ends, - text_segments=timestamps_text, output_folder=episode_folder, album_id=album_id, episode_id=episode_id, @@ -203,124 +156,101 @@ def process_audio_file( duration=duration ) - except Exception as e: logger.error(f"Processing error {path_audio}: {e}") - torch.cuda.empty_cache() + finally: + if torch.cuda.is_available(): + torch.cuda.empty_cache() - - if len(os.listdir(episode_folder)) > 0 or len(pieces) == 0 : # the audio was cut or we couldn't cut it considering our length + if os.path.exists(episode_folder) and os.listdir(episode_folder): os.remove(path_audio) - logger.info(f"Temporary file deleted: {path_audio}") - -def init_process( - whisper_model: str, - device: str, - compute_type: str = 'float16', - device_index = [0] -): - - global model - model = WhisperModel( - whisper_model, - device=device, - compute_type=compute_type, - device_index=device_index, - ) - + logger.info(f"Original file deleted: {path_audio}") def main(args): load_dotenv() - hf_key = os.getenv("HF_TOKEN") + hf_key=os.environ.get('HF_TOKEN') login(token=hf_key) config = load_config(args.config_path, 'preprocess') - podcasts_path = args.podcasts_path if args.podcasts_path else config.get('podcasts_path', '../../../podcasts') - duration = args.duration if args.duration else config.get('duration', 15) - num_workers = args.num_workers if args.num_workers else config.get('num_workers', 4) - whisper_model = args.whisper_model if args.whisper_model else config.get('whisper_model', 'large-v3') - compute_type = args.compute_type if args.compute_type else config.get('compute_type', 'float16') - beam_size = args.beam_size if args.beam_size else config.get('beam_size', 5) - device = 'cuda' - available_gpu_ids = list(range(torch.cuda.device_count())) - - num_gpus = len(available_gpu_ids) + podcasts_path = args.podcasts_path or config.get('podcasts_path', '../../../podcasts') + duration = args.duration or config.get('duration', 15) + num_workers = args.num_workers or config.get('num_workers', 4) + smart_vad_model = args.smart_vad_model or config.get('smart_vad_model', "pipecat-ai/smart-turn-v2") + num_gpus = torch.cuda.device_count() + available_gpus = list(range(num_gpus)) if num_gpus == 0: logger.error("No GPUs available. Exiting.") return + audio_paths = get_audio_paths(podcasts_path) + if not audio_paths: + logger.info("No audio files found.") + return - max_workers = min(num_workers, os.cpu_count()) - - logger.info( - f""" - Using parms - podcasts_path:{podcasts_path} - whisper_model:{whisper_model} - duration:{duration} - num_workers:{num_workers} - devices:{available_gpu_ids} - compute_type:{compute_type} - beam_size:{beam_size} + vad_args = { + 'silero_vad_threshold': 0.5, + 'smart_vad_threshold': 0.5, + 'smart_vad_path': smart_vad_model + } + + logger.info(f""" + Starting processing with: + Podcasts path: {podcasts_path} + Smart VAD model: {smart_vad_model} + Segment duration: {duration} seconds + Workers per GPU: {num_workers} + Available GPUs: {available_gpus} + Files to process: {len(audio_paths)} """) - with ProcessPoolExecutor( - max_workers=max_workers, - initializer=init_process, - initargs=(whisper_model, device, compute_type, available_gpu_ids, ) - ) as executor: - futures = [ - executor.submit(process_audio_file, path_audio, duration, beam_size) - for path_audio in audio_paths - ] - for future in tqdm(as_completed(futures), total=len(futures)): + all_futures = [] + executors = [] + + for gpu_id in available_gpus: + executor = ProcessPoolExecutor( + max_workers=num_workers, + initializer=init_vad_process, + initargs=(gpu_id, vad_args) + ) + + executors.append(executor) + + files_for_gpu = [] + for i, path in enumerate(audio_paths): + if i % len(available_gpus) == gpu_id % len(available_gpus): + files_for_gpu.append(path) + + logger.info(f"GPU {gpu_id}: processing {len(files_for_gpu)} files") + + for path in files_for_gpu: + future = executor.submit(process_audio_file, path, duration) + all_futures.append(future) + + with tqdm(total=len(all_futures), desc="Processing podcasts") as pbar: + for future in as_completed(all_futures): try: future.result() except Exception as e: logger.error(f"Error processing file: {e}") + finally: + pbar.update(1) + + for executor in executors: + executor.shutdown() if __name__ == "__main__": torchaudio.set_audio_backend('soundfile') multiprocessing.set_start_method('spawn', force=True) - parser = argparse.ArgumentParser(description="Process audio files using whisper model.") - parser.add_argument( - "--config_path", - help="Path to YAML configuration file", - type=str, - ) - parser.add_argument( - "--podcasts_path", - help="Path to podcasts folder", - type=str, - ) - parser.add_argument( - "--whisper_model", - help="name of model", - type=str, - ) - parser.add_argument( - "--compute_type", - help="compute type", - type=str, - ) - parser.add_argument( - "--beam_size", - help="beam size", - type=int, - ) - parser.add_argument( - "--duration", - help="Duration in seconds", - type=int, - ) - parser.add_argument( - "--num_workers", - help="Number of workers", - type=int, - ) - + + parser = argparse.ArgumentParser(description="Process audio files using smart-turn VAD model.") + parser.add_argument("--config_path", type=str, help="Path to YAML configuration file") + parser.add_argument("--podcasts_path", type=str, help="Path to podcasts folder") + parser.add_argument("--smart_vad_model", type=str, help="Name of smart-turn model") + parser.add_argument("--duration", type=int, help="Target segment duration in seconds") + parser.add_argument("--num_workers", type=int, help="Number of worker processes per GPU") + args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/separation/separation.py b/src/separation/separation.py index 3cdca60..cae68e7 100644 --- a/src/separation/separation.py +++ b/src/separation/separation.py @@ -66,7 +66,10 @@ def process_audio(self, audio_path: str) -> Dict: audio_frames, sr, audio = self._preprocess_audio(str(audio_path), frame_duration) is_mono = True - is_mono = self._check_single_speaker(audio, sr) + is_mono = self._check_single_speaker( + waveform=audio, + sr=sr, + audio_path=audio_path) avg_out = self._nisqua_predict(audio_frames, sr) NOI, COL, DISC, LOUD, MOS = avg_out @@ -105,18 +108,23 @@ def _preprocess_audio(self, audio_path: str, frame_duration: int): return frames, sr, audio - def _check_single_speaker(self, waveform: torch.Tensor, sr: int) -> bool: + def _check_single_speaker(self, waveform: torch.Tensor, sr: int, audio_path: str) -> bool: try: diarization = self.diarization_model({ "waveform": waveform.unsqueeze(0), "sample_rate": sr }) + base_path = os.path.splitext(audio_path)[0] + rttm_path = base_path + '.rttm' + + self._save_rttm_file( + diarization=diarization, + rttm_path=rttm_path + ) + is_single_speaker = len({speaker for _, _, speaker in diarization.itertracks(yield_label=True)}) == 1 - if not is_single_speaker and self.one_speaker: - audio_path = str(waveform.audio_path) - base_path = os.path.splitext(audio_path)[0] - + if not is_single_speaker and self.one_speaker: for ext in ['.mp3', '_giga.txt', '_punct.txt', '_accent.txt', '_e.txt', '_e_phonemes.txt']: file_path = base_path + ext if os.path.exists(file_path): @@ -140,7 +148,10 @@ def _nisqua_predict(self, frames: List[torch.Tensor], sr: int) -> np.ndarray: outputs.append(out[0].cpu().numpy()) return np.mean(outputs, axis=0) - + + def _save_rttm_file(self, diarization, rttm_path): + with open(rttm_path, "w") as rttm: + diarization.write_rttm(rttm) def _worker_initializer( one_speaker: bool, @@ -296,4 +307,4 @@ def main(args): ) args = parser.parse_args() - main(args) + main(args) \ No newline at end of file diff --git a/use_meta_1000h.sh b/use_meta_1000h.sh index d68f6a8..b47688e 100644 --- a/use_meta_1000h.sh +++ b/use_meta_1000h.sh @@ -1,5 +1,4 @@ -# bin/bash - +#!/bin/bash activate_venv() { local venv_path=$1 @@ -8,18 +7,36 @@ activate_venv() { exit 1 fi source "$venv_path/bin/activate" - echo "Activated: $(which python)" + echo "Activated virtual environment: $(which python)" } -wget https://huggingface.co/datasets/MTUCI/Balalaika1000H/resolve/main/Balalaika1000H.parquet -wget https://huggingface.co/datasets/MTUCI/Balalaika1000H/resolve/main/Balalaika1000H.pkl +download_if_not_exists() { + local url=$1 + local filename=$2 + + if [ ! -f "$filename" ]; then + echo "Downloading $filename..." + wget "$url" -O "$filename" || { + echo "Error: Failed to download $filename" + exit 1 + } + else + echo "$filename already exists, skipping download." + fi +} PODCASTS_PATH="Balalaika1000H" PICKLE_PATH="Balalaika1000H.pkl" PARQUET_PATH="Balalaika1000H.parquet" NUM_WORKERS=4 +PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika1000H/resolve/main/Balalaika1000H.pkl" +PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika1000H/resolve/main/Balalaika1000H.parquet" + +download_if_not_exists "$PICKLE_URL" "$PICKLE_PATH" +download_if_not_exists "$PARQUET_URL" "$PARQUET_PATH" + activate_venv ".user_venv" -bash src/download/download_prepared.sh $PODCASTS_PATH $PICKLE_PATH $NUM_WORKERS -bash src/recovery_from_meta_yamls.sh $PODCASTS_PATH $PARQUET_PATH $NUM_WORKERS \ No newline at end of file +bash src/download/download_prepared.sh "$PODCASTS_PATH" "$PICKLE_PATH" "$NUM_WORKERS" +bash src/recovery_from_meta_yamls.sh "$PODCASTS_PATH" "$PARQUET_PATH" "$NUM_WORKERS" diff --git a/use_meta_100h.sh b/use_meta_100h.sh index d670ecf..8733b92 100644 --- a/use_meta_100h.sh +++ b/use_meta_100h.sh @@ -1,5 +1,6 @@ -# bin/bash +#!/bin/bash +# Функция активации виртуального окружения activate_venv() { local venv_path=$1 if [ ! -f "$venv_path/bin/activate" ]; then @@ -7,18 +8,52 @@ activate_venv() { exit 1 fi source "$venv_path/bin/activate" - echo "Activated: $(which python)" + echo "Activated virtual environment: $(which python)" } -wget https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.parquet -wget https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.pkl +# Функция для безопасной загрузки файлов +download_if_not_exists() { + local url=$1 + local filename=$2 + + if [ ! -f "$filename" ]; then + echo "Downloading $filename..." + wget "$url" -O "$filename" || { + echo "Error: Failed to download $filename" + exit 1 + } + else + echo "$filename already exists, skipping download." + fi +} +# Основные параметры PODCASTS_PATH="Balalaika100H" PICKLE_PATH="Balalaika100H.pkl" PARQUET_PATH="Balalaika100H.parquet" NUM_WORKERS=4 +# URL для загрузки +PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.pkl" +PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.parquet" + +# Скачиваем файлы (если их нет) +download_if_not_exists "$PICKLE_URL" "$PICKLE_PATH" +download_if_not_exists "$PARQUET_URL" "$PARQUET_PATH" + +# Активируем виртуальное окружение activate_venv ".user_venv" -bash src/download/download_prepared.sh $PODCASTS_PATH $PICKLE_PATH $NUM_WORKERS -bash src/recovery_from_meta_yamls.sh $PODCASTS_PATH $PARQUET_PATH $NUM_WORKERS \ No newline at end of file +# Запускаем обработку +echo "Starting processing..." +bash src/download/download_prepared.sh "$PODCASTS_PATH" "$PICKLE_PATH" "$NUM_WORKERS" || { + echo "Error in download_prepared.sh" + exit 1 +} + +bash src/recovery_from_meta_yamls.sh "$PODCASTS_PATH" "$PARQUET_PATH" "$NUM_WORKERS" || { + echo "Error in recovery_from_meta_yamls.sh" + exit 1 +} + +echo "All operations completed successfully." \ No newline at end of file diff --git a/use_meta_2000h.sh b/use_meta_2000h.sh index 87efed1..43b9b16 100644 --- a/use_meta_2000h.sh +++ b/use_meta_2000h.sh @@ -1,4 +1,4 @@ -# bin/bash +#!/bin/bash activate_venv() { local venv_path=$1 @@ -7,18 +7,36 @@ activate_venv() { exit 1 fi source "$venv_path/bin/activate" - echo "Activated: $(which python)" + echo "Activated virtual environment: $(which python)" } -wget https://huggingface.co/datasets/MTUCI/Balalaika2000H/resolve/main/Balalaika2000H.parquet -wget https://huggingface.co/datasets/MTUCI/Balalaika2000H/resolve/main/Balalaika2000H.pkl +download_if_not_exists() { + local url=$1 + local filename=$2 + + if [ ! -f "$filename" ]; then + echo "Downloading $filename..." + wget "$url" -O "$filename" || { + echo "Error: Failed to download $filename" + exit 1 + } + else + echo "$filename already exists, skipping download." + fi +} PODCASTS_PATH="Balalaika2000H" PICKLE_PATH="Balalaika2000H.pkl" PARQUET_PATH="Balalaika2000H.parquet" NUM_WORKERS=4 +PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika2000H/resolve/main/Balalaika2000H.pkl" +PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika2000H/resolve/main/Balalaika2000H.parquet" + +download_if_not_exists "$PICKLE_URL" "$PICKLE_PATH" +download_if_not_exists "$PARQUET_URL" "$PARQUET_PATH" + activate_venv ".user_venv" -bash src/download/download_prepared.sh $PODCASTS_PATH $PICKLE_PATH $NUM_WORKERS -bash src/recovery_from_meta_yamls.sh $PODCASTS_PATH $PARQUET_PATH $NUM_WORKERS \ No newline at end of file +bash src/download/download_prepared.sh "$PODCASTS_PATH" "$PICKLE_PATH" "$NUM_WORKERS" +bash src/recovery_from_meta_yamls.sh "$PODCASTS_PATH" "$PARQUET_PATH" "$NUM_WORKERS" diff --git a/use_meta_500h.sh b/use_meta_500h.sh index 62a6dd2..56ac524 100644 --- a/use_meta_500h.sh +++ b/use_meta_500h.sh @@ -1,5 +1,4 @@ -# bin/bash - +#!/bin/bash activate_venv() { local venv_path=$1 @@ -8,18 +7,36 @@ activate_venv() { exit 1 fi source "$venv_path/bin/activate" - echo "Activated: $(which python)" + echo "Activated virtual environment: $(which python)" } -wget https://huggingface.co/datasets/MTUCI/Balalaika500H/resolve/main/Balalaika500H.parquet -wget https://huggingface.co/datasets/MTUCI/Balalaika500H/resolve/main/Balalaika500H.pkl - -activate_venv ".user_venv" +download_if_not_exists() { + local url=$1 + local filename=$2 + + if [ ! -f "$filename" ]; then + echo "Downloading $filename..." + wget "$url" -O "$filename" || { + echo "Error: Failed to download $filename" + exit 1 + } + else + echo "$filename already exists, skipping download." + fi +} PODCASTS_PATH="Balalaika500H" PICKLE_PATH="Balalaika500H.pkl" PARQUET_PATH="Balalaika500H.parquet" NUM_WORKERS=4 -bash src/download/download_prepared.sh $PODCASTS_PATH $PICKLE_PATH $NUM_WORKERS -bash src/recovery_from_meta_yamls.sh $PODCASTS_PATH $PARQUET_PATH $NUM_WORKERS \ No newline at end of file +PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.pkl" +PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.parquet" + +download_if_not_exists "$PICKLE_URL" "$PICKLE_PATH" +download_if_not_exists "$PARQUET_URL" "$PARQUET_PATH" + +activate_venv ".user_venv" + +bash src/download/download_prepared.sh "$PODCASTS_PATH" "$PICKLE_PATH" "$NUM_WORKERS" +bash src/recovery_from_meta_yamls.sh "$PODCASTS_PATH" "$PARQUET_PATH" "$NUM_WORKERS" From 8cbfa104928bb794b53cf97c5dc7d9ad35f5dd7b Mon Sep 17 00:00:00 2001 From: Nikita Date: Sat, 9 Aug 2025 23:36:10 +0000 Subject: [PATCH 02/43] feat(transcription): add new ASR models + ROVER integration --- .gitignore | 1 + configs/config.yaml | 40 +-- requirements_dev.txt | Bin 1310 -> 1456 bytes src/accents/accents.py | 2 +- src/classification/classification.py | 4 +- src/collate.py | 5 +- src/phonemizer/phonemizer.py | 2 +- src/punctuation/punctuation.py | 4 +- src/transcription/transcription.py | 322 ++++++++++-------------- src/transcription/transcription_args.sh | 4 +- src/transcription/transcripton_base.py | 286 +++++++++++++++++++++ use_meta_100h.sh | 7 - 12 files changed, 445 insertions(+), 232 deletions(-) create mode 100644 src/transcription/transcripton_base.py diff --git a/.gitignore b/.gitignore index af0b09c..33ed064 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ *.csv +.torch_hub/* *.mp3 *.wav test.py diff --git a/configs/config.yaml b/configs/config.yaml index 13fef0f..40c7411 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,39 +1,39 @@ download: podcasts_path: /home/nikita/balalaika - episodes_limit: 1 - num_workers: 4 - podcasts_urls_file: '' + episodes_limit: 1 # limit on downloading episodes from one playlist + num_workers: 1 + podcasts_urls_file: '/home/nikita/yapoddataset/20000_more_albums.pkl' # pkl with urls preprocess: podcasts_path: /home/nikita/balalaika # full path to the folder where the podcasts are located - duration: 15 - num_workers: 2 - whisper_model: 'large-v3' - compute_type: 'float16' - beam_size: 5 + duration: 15 # in seconds (each sample will be no longer than this time.) + num_workers: 1 + smart_vad_model: pipecat-ai/smart-turn-v2 + separation: podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - num_workers: 4 + num_workers: 1 nisqa_config: "configs/nisqa_s.yaml" - one_speaker: False + one_speaker: False # When this option is enabled, only single-speaker audio recordings are downloaded. transcription: podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - model_name: "rnnt" - num_workers: 4 # per one gpu - with_timestamps: False # ctc only support - lm_path: '' # path to lm.bin + num_workers: 1 # per one gpu + model_names: ['vosk', 'tone', 'giga_rnnt', 'giga_ctc', 'giga_ctc_lm'] + vosk_path: /home/nikita/yapoddataset/models/vosk-model-ru + lm_path: '/home/nikita/yapoddataset/kenlm.bin' # path to lm.bin + with_timestamps: True punctuation: podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - model_name: "RUPunct/RUPunct_big" + model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 4 # per one gpu accent: podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - num_workers: 8 - model_name: turbo3.1 + num_workers: 4 # per one gpu + model_name: turbo3.1 # ruAccent model name phonemizer: podcasts_path: /home/nikita/balalaika # full path to the folder where the text files are located @@ -41,9 +41,9 @@ phonemizer: classification: podcasts_path: /home/nikita/balalaika - num_workers: 8 - threshold: 0.8 - model_path: models/voxblink2_samresnet100_ft + num_workers: 4 # per one gpu + threshold: 0.6 + model_path: models/voxblink2_samresnet100_ft # .pt format model from https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md diff --git a/requirements_dev.txt b/requirements_dev.txt index d1df2b76b5295f5fde3f50b638ef368a6e84c073..40a4c2fe1a4cba0f73a6814930d2d8a7be53d9c4 100644 GIT binary patch delta 162 zcmbQowSjws5|eo@LncEW5GOK}GNb_MdxGqpE4+ty3GG#zs zF+=udStfoaT@WXeAsMJN8%UP`m6ib6i43_6`V1jZjj0TJ4Cx@*$%;(IlP9nkOjcvk flMeyv%mLzTplW@f2}MA5InV*R5R)cvX2}KsZW1A4 delta 66 zcmdnMJ&$XH64T~5CN3t;0)|Y60)|wEWQIhB5(Zr$naQBfP|T1EWEFu$N*GFkth~vE P%+`}nuoz4ZVvPd;0J9Im diff --git a/src/accents/accents.py b/src/accents/accents.py index 4ce94a9..3ef8fd2 100644 --- a/src/accents/accents.py +++ b/src/accents/accents.py @@ -79,7 +79,7 @@ def main(args): devices:{available_gpu_ids} """) - valid_text_files = get_valid_txt_paths(podcast_path) + valid_text_files = get_valid_txt_paths(podcast_path)[:5] files_for_each_gpu = [[] for _ in range(num_gpus)] for i, path in enumerate(valid_text_files): diff --git a/src/classification/classification.py b/src/classification/classification.py index eccfe25..bf0897d 100644 --- a/src/classification/classification.py +++ b/src/classification/classification.py @@ -119,7 +119,7 @@ def merge_clusters(self): comp_embs = torch.stack(comp_cluster["embeddings"]) comp_avg = comp_embs.mean(dim=0) sim = self.norm_cos_sim(base_avg, comp_avg) - if sim >= self.threshold: + if sim >= 0.9: base_cluster["embeddings"].extend(comp_cluster["embeddings"]) base_cluster["paths"].extend(comp_cluster["paths"]) @@ -225,7 +225,7 @@ def precompute_embeddings( def main(args): config = load_config(args.config_path, 'classification') podcasts_path = config.get('podcasts_path', '../../../podcasts') if args.podcasts_path is None else args.podcasts_path - threshold = config.get('threshold', 0.85) if args.threshold is None else args.threshold + threshold = config.get('threshold', 0.7) if args.threshold is None else args.threshold model_path = config.get('model_path', '/models/voxblink2_samresnet100_ft') if args.model_path is None else args.model_path num_workers = config.get('num_workers', 8) if args.num_workers is None else args.num_workers diff --git a/src/collate.py b/src/collate.py index 79dc767..0c7f905 100644 --- a/src/collate.py +++ b/src/collate.py @@ -16,10 +16,9 @@ def process_audio_file(audio_path_str: str, base_path: Path) -> Dict[str, Option file_types = { 'accent': '_accent.txt', - 'giga': '_giga.txt', + 'rover': '_rover.txt', 'punct': '_punct.txt', - 'whisper': '_whisper.txt', - 'phonemes': '_giga_phonemes.txt' + 'phonemes': '_rover_phonemes.txt' } results = {'audio_path': audio_path_str} diff --git a/src/phonemizer/phonemizer.py b/src/phonemizer/phonemizer.py index 31784dd..b129f68 100644 --- a/src/phonemizer/phonemizer.py +++ b/src/phonemizer/phonemizer.py @@ -33,7 +33,7 @@ def process_text(text_path: Path): f.write(" ".join(phonemes)) def get_valid_text_paths(src_path: str) -> List[Path]: - all_paths = get_txt_paths(src_path, '_giga.txt') + all_paths = get_txt_paths(src_path, '_rover.txt') valid_paths = [] for giga_path in all_paths: diff --git a/src/punctuation/punctuation.py b/src/punctuation/punctuation.py index 78075f9..1cef1e3 100644 --- a/src/punctuation/punctuation.py +++ b/src/punctuation/punctuation.py @@ -39,7 +39,7 @@ def make_punct_txt( src_text = read_file_content(path) - punct_path = path.with_name(path.name.replace("_giga.txt", "_punct.txt")) + punct_path = path.with_name(path.name.replace("_rover.txt", "_punct.txt")) if str(path).endswith('_punct.txt') or str(path).endswith('_accent.txt') or os.path.exists(punct_path): return @@ -61,7 +61,7 @@ def get_valid_txt_paths(src_path: str) -> List[str]: valid_paths = [] for audio_path in all_audio_paths: - giga_path = audio_path.with_name(audio_path.stem + "_giga.txt") + giga_path = audio_path.with_name(audio_path.stem + "_rover.txt") punct_path = audio_path.with_name(audio_path.stem + "_punct.txt") if os.path.exists(giga_path) and not os.path.exists(punct_path): diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 3a8ef7a..6b61aea 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -6,249 +6,183 @@ from pathlib import Path from typing import List, Tuple -import gigaam -import pyctcdecode import torch import torchaudio from loguru import logger from tqdm import tqdm +from src.transcription.transcripton_base import * from src.utils import get_audio_paths, load_config -# Global variables for each worker process model = None -decoder = None -# Frame size in milliseconds for the GigaAM model, crucial for timestamp calculation -GIGA_AM_FRAME_SIZE_MS = 40 - -def init_process( - model_name: str, - device_str: str, - lm_path: str, - with_timestamps: bool, -): +def init_worker(model_name: str, device_str: str, **kwargs): """ - Initializes the model and, if needed, the CTC decoder for each worker process. + Initializes the appropriate ASR model wrapper for each worker process. """ - global model, decoder - logger.info(f"Initializing worker on {device_str}...") - if not (with_timestamps and 'ctc' in model_name): - logger.info("Timestamp generation requested CTC model. Decoder will not be initialized.") - with_timestamps=False - - model = gigaam.load_model(model_name, device=device_str) - - if with_timestamps: - if not lm_path: - logger.warning("Timestamp generation requested without an LM path. Decoder will not be initialized.") - decoder = None - return - - logger.info(f"Building CTC decoder with LM: {lm_path}") - try: - vocab = model.decoding.tokenizer.vocab - decoder = pyctcdecode.build_ctcdecoder( - vocab, - lm_path, - alpha=0.5, - beta=1.0, - ) - except Exception as e: - logger.error(f"Failed to build CTC decoder: {e}") - decoder = None - - -def to_simple_timestamps(word_timestamps: List[Tuple[str, Tuple[int, int]]]) -> str: - output_lines = [] - sec_per_frame = GIGA_AM_FRAME_SIZE_MS / 1000.0 - for word, (start_frame, end_frame) in word_timestamps: - start_time = start_frame * sec_per_frame - end_time = end_frame * sec_per_frame - output_lines.append(f"{word} {start_time:.3f} {end_time:.3f}") - return "\n".join(output_lines) + global model + logger.info(f"Initializing worker for model '{model_name}' on {device_str}...") + + try: + if 'giga' in model_name: + model = GigaAMWrapper(model_id=model_name, device=device_str, lm_path=kwargs.get('lm_path')) + elif 'tone' in model_name: + model = ToneWrapper(model_id=model_name, device=device_str) + elif 'vosk' in model_name: + model = VoskWrapper(model_id=kwargs.get('vosk_path'), device=device_str) + else: + raise ValueError(f"Unknown model type for '{model_name}'") + logger.info(f"Worker initialized successfully for model '{model_name}' on {device_str}.") + except Exception as e: + logger.error(f"Failed to initialize worker for model '{model_name}' on {device_str}: {e}") + model = None -def make_txt_and_tst(path: Path, with_timestamps: bool): +def process_file(path: Path, model_name_for_output: str, with_timestamps: bool): """ - Transcribes an audio file. If with_timestamps is True and the decoder is available, - it generates both a .txt (transcription) and a .tstt (timestamps) file. - Otherwise, it only generates the .txt file. + Transcribes a single audio file using the globally initialized model. + Generates .txt and optionally .tst files. """ - txt_path = path.with_name(f"{path.stem}_giga.txt") - tst_path = path.with_name(f"{path.stem}_giga.tst") - - if os.path.exists(txt_path): + global model + if model is None: + logger.error(f"Model is not initialized in this worker. Skipping {path.name}.") return - # Timestamp-enabled path using the CTC decoder - if not (with_timestamps and decoder): - text = model.transcribe(str(path)) - with open(txt_path, "w", encoding="utf-8") as f: - f.write(text) - return - try: - wav, sr = torchaudio.load(path) + txt_path = path.with_name(f"{path.stem}_{model_name_for_output}.txt") + tst_path = path.with_name(f"{path.stem}_{model_name_for_output}.tst") - if wav.shape[0] > 1: - wav = wav.mean(dim=0).unsqueeze(0) - - wav = torchaudio.functional.resample(wav, sr, 16000) - length = torch.full([1], wav.shape[-1]) - - encoded, _ = model.forward(wav.to(model._device), length.to(model._device)) - logitst = model.head(encoded).squeeze(0).detach().cpu().numpy() - - # Use decode_beams to get timestamps - beams = decoder.decode_beams(logitst, beam_width=100) - - # The top beam result with timestamps is typically at index 0 - best_beam = beams[0] - word_timestamps = best_beam[2] + if txt_path.exists(): + logger.info(f"Skipping already transcribed file: {txt_path}") + return + + try: + if with_timestamps: + plain_text, tst_content = model.transcribe_with_timestamps(str(path)) + else: + plain_text = model.transcribe(str(path)) + tst_content = "" - # 1. Save the plain text transcription to _giga.txt - plain_text = best_beam[0] with open(txt_path, "w", encoding="utf-8") as f: f.write(plain_text) - - # 2. Save the timestamps to _giga.tst - tst_content = to_simple_timestamps(word_timestamps) - with open(tst_path, "w", encoding="utf-8") as f: - f.write(tst_content) + if with_timestamps and tst_content: + with open(tst_path, "w", encoding="utf-8") as f: + f.write(tst_content) except Exception as e: - logger.error(f"Error processing {path} with timestamps: {e}") - # Fallback to simple transcription if timestamping fails - text = model.transcribe(str(path)) - with open(txt_path, "w", encoding="utf-8") as f: - f.write(text) + logger.error(f"Failed to process {path.name}: {e}") -def get_valid_audio_paths(src_path: str) -> List[Path]: +def get_valid_audio_paths(src_path: str, model_name_for_output: str) -> List[Path]: """ - Getst all audio paths and filters out those that have already been transcribed. + Gets all audio paths and filters out those that have already been transcribed. """ all_audio_paths = get_audio_paths(src_path) - valid_paths = [] - for audio_path in all_audio_paths: - giga_path = audio_path.with_name(audio_path.stem + "_giga.txt") - if not giga_path.exists(): - valid_paths.append(audio_path) + + valid_paths = [ + p for p in all_audio_paths + if not p.with_name(f"{p.stem}_{model_name_for_output}.txt").exists() + ] return valid_paths def main(args): - config = load_config(args.config_path, 'transcription') - - model_name = args.model_name if args.model_name else config.get('model_name', 'rnnt') - num_workers_per_gpu = args.num_workers if args.num_workers else config.get('num_workers', 4) - src_path = args.podcasts_path if args.podcasts_path else config.get('podcasts_path', '../../../balalaika') - lm_path = args.lm_path if args.lm_path else config.get('lm_path', 'ru.lm.bin') - with_timestamps = args.with_timestamps if args.with_timestamps else config.get('with_timestamps', 'False') + config = load_config(args.config_path, 'transcription') if args.config_path else {} - if with_timestamps and not lm_path: - raise ValueError("Language model path (--lm_path) is required when using --with_timestamps.") + model_names = args.model_names or config.get('model_names', ['giga_rnnt']) + num_workers_per_gpu = args.num_workers or config.get('num_workers', 1) + src_path = args.podcasts_path or config.get('podcasts_path', '.') + lm_path = args.lm_path or config.get('lm_path', None) + with_timestamps = args.with_timestamps or config.get('with_timestamps', False) + vosk_path = args.vosk_path or config.get('vosk_path', None) - all_audio_paths = get_valid_audio_paths(src_path) - logger.info(f"Found {len(all_audio_paths)} audio files to process.") + logger.info(f"Starting transcription run for models: {model_names}") available_gpu_ids = list(range(torch.cuda.device_count())) - num_gpus = len(available_gpu_ids) + if not available_gpu_ids: + logger.warning("No CUDA GPUs detected. Using CPU. This will be slow.") + raise - if num_gpus == 0: - logger.error("No GPUs available. Exiting.") - return + num_devices = len(available_gpu_ids) + + for model_name in model_names: + logger.info(f"--- Processing model: {model_name} ---") - logger.info( - f""" - Starting transcription with parameters: - Source Path: {src_path} - Model Name: {model_name} - Timestamps Enabled: {with_timestamps} - Language Model Path: {lm_path} - Number of GPUs: {num_gpus} (IDs: {available_gpu_ids}) - Workers per GPU: {num_workers_per_gpu} - Total Worker Processes: {num_gpus * num_workers_per_gpu} - """ - ) - - files_for_each_gpu = [[] for _ in range(num_gpus)] - for i, path in enumerate(all_audio_paths): - gpu_assignment_index = i % num_gpus - files_for_each_gpu[gpu_assignment_index].append(path) - - all_futures = [] - executors = [] - - task_fn = partial(make_txt_and_tst, with_timestamps=with_timestamps) - - for i, gpu_id in enumerate(available_gpu_ids): - device_str = f'cuda:{gpu_id}' - files_for_this_gpu = files_for_each_gpu[i] - - if not files_for_this_gpu: + model_name_for_output = 'vosk' if 'vosk' in model_name else model_name + timestamps_supported = 'giga_ctc' in model_name or 'tone' in model_name or 'vosk' in model_name + + current_with_timestamps = with_timestamps and timestamps_supported + if with_timestamps and not timestamps_supported: + logger.warning(f"Timestamps requested but not supported for model '{model_name}'. Disabling.") + + all_audio_paths = get_valid_audio_paths(src_path, model_name_for_output) + if not all_audio_paths: + logger.info(f"No new audio files to process for model '{model_name}'. Skipping.") continue + + logger.info(f"Found {len(all_audio_paths)} new audio files to process for '{model_name}'.") + + files_for_each_device = [[] for _ in range(num_devices)] + for i, path in enumerate(all_audio_paths): + device_assignment_index = i % num_devices + files_for_each_device[device_assignment_index].append(path) - logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_gpu)} files.") + all_futures = [] + executors = [] - executor = ProcessPoolExecutor( - max_workers=num_workers_per_gpu, - initializer=init_process, - initargs=(model_name, device_str, lm_path, with_timestamps), - ) - executors.append(executor) + init_kwargs = {'lm_path': lm_path, 'vosk_path': vosk_path} + task_fn = partial(process_file, model_name_for_output=model_name_for_output, with_timestamps=current_with_timestamps) - for path in files_for_this_gpu: - future = executor.submit(task_fn, path) - all_futures.append(future) + for i, device_id in enumerate(available_gpu_ids): + device_str = f'cuda:{device_id}' if device_id != 'cpu' else 'cpu' + files_for_this_device = files_for_each_device[i] + + if not files_for_this_device: + continue + + logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") + + initializer_fn = partial(init_worker, model_name, device_str, **init_kwargs) + + executor = ProcessPoolExecutor( + max_workers=num_workers_per_gpu, + initializer=initializer_fn, + mp_context=multiprocessing.get_context('spawn') + ) + executors.append(executor) + + for path in files_for_this_device: + future = executor.submit(task_fn, path) + all_futures.append(future) + + for future in tqdm(as_completed(all_futures), total=len(all_futures), desc=f"Transcribing ({model_name})"): + try: + future.result() + except Exception as e: + logger.error(f"A task encountered an error: {e}") + + for executor in executors: + executor.shutdown(wait=True) + + logger.info(f"Finished processing model: {model_name}") - for future in tqdm(as_completed(all_futures), total=len(all_futures), desc="Overall Transcription Progress"): - try: - future.result() - except Exception as e: - logger.error(f"A task processing encountered an error: {e}") + rover_wrapper = ROVERWrapper(podcasts_path=src_path) + rover_wrapper.aggregate_and_save() - for executor in executors: - executor.shutdown() + logger.info(f"Finished processing ROVER") if __name__ == "__main__": multiprocessing.set_start_method('spawn', force=True) torchaudio.set_audio_backend('soundfile') - parser = argparse.ArgumentParser( - description="Transcribe audio files in parallel using multiple GPUs." - ) - parser.add_argument( - "--config_path", - type=str, - help="Path to the configuration YAML file." - ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to the directory containing audio files (e.g., MP3s)." - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of worker processes per GPU for parallel processing." - ) - parser.add_argument( - "--model_name", - type=str, - help="Name of the model to use for transcription (e.g., 'rnnt', 'ctc')." - ) - parser.add_argument( - "--lm_path", - type=str, - help="Path to the language model binary file (e.g., 'ru.lm.bin') required for timestamps." - ) - parser.add_argument( - '--with_timestamps', - type=bool, - help="Enable to generate tst files with word timestamps." - ) + parser = argparse.ArgumentParser(description="Transcribe audio files in parallel using multiple GPUs/CPUs.") + parser.add_argument("--config_path", type=str, help="Path to the configuration YAML file.") + parser.add_argument("--podcasts_path", type=str, help="Path to the directory containing audio files.") + parser.add_argument("--num_workers", type=int, help="Number of worker processes per device.") + parser.add_argument("--model_names", nargs='+', help="One or more model names to use (e.g., 'giga_ctc_lm' 'tone' 'vosk').") + parser.add_argument("--lm_path", type=str, help="Path to the KenLM language model binary file for GigaAM-CTC.") + parser.add_argument('--vosk_path', type=str, help="Path to the Vosk model directory.") + parser.add_argument('--with_timestamps', action='store_true', help="Enable to generate .tst files with word timestamps (if supported by model).") args = parser.parse_args() - main(args) \ No newline at end of file + main(args) diff --git a/src/transcription/transcription_args.sh b/src/transcription/transcription_args.sh index 633cc29..333075a 100644 --- a/src/transcription/transcription_args.sh +++ b/src/transcription/transcription_args.sh @@ -16,13 +16,13 @@ SCRIPT_DIR=$(dirname "$(realpath "$0")") PODCASTS_PATH="/home/nikita/Balalaika100H" NUM_WORKERS=1 -MODEL_NAME="ctc" +MODEL_NAMES=('giga_ctc' 'giga_rnnt' 'vosk' 'ton') LM_PATH="/home/nikita/yapoddataset/ru.lm.bin" WITH_TIMESTAMPS=True python -m src.transcription.transcription \ --podcasts_path "$PODCASTS_PATH" \ --num_workers "$NUM_WORKERS" \ - --model_name "$MODEL_NAME" \ + --model_names "$MODEL_NAMES" \ --lm_path "$LM_PATH" \ --with_timestamps "$WITH_TIMESTAMPS" diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py new file mode 100644 index 0000000..bf9e516 --- /dev/null +++ b/src/transcription/transcripton_base.py @@ -0,0 +1,286 @@ +import json +from abc import ABC, abstractmethod +from typing import Tuple, List +from pathlib import Path +from collections import defaultdict + +import torch +import torchaudio +import numpy as np +import miniaudio +from loguru import logger +import pyctcdecode +import gigaam +from tone import StreamingCTCPipeline, TextPhrase +from crowdkit.aggregation import ROVER +import pandas as pd +import sherpa_onnx +import wave +from src.utils import get_audio_paths, read_file_content + +class ASRWrapper(ABC): + """Abstract Base Class for ASR model wrappers.""" + + @abstractmethod + def __init__(self, model_id: str, device: str, **kwargs): + """Initializes the model.""" + pass + + @abstractmethod + def transcribe(self, audio_path: str) -> str: + """Transcribes an audio file and returns the text.""" + pass + + @abstractmethod + def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: + """Transcribes an audio file and returns the text and timestamps content.""" + pass + + +class GigaAMWrapper(ASRWrapper): + """Wrapper for GigaAM models (CTC and RNN-T).""" + def __init__(self, model_id: str, device: str, **kwargs): + logger.info(f"Initializing GigaAM model '{model_id}' on {device}") + self.model_id = model_id + self.device = device + self.model_type = 'ctc' if 'ctc' in model_id else 'rnnt' + self.use_lm = 'lm' in model_id + self.model = gigaam.load_model(self.model_type, device=device) + self.target_sr = 16_000 + self.decoder = None + + # Frame duration for timestamp calculation + self.GIGA_AM_FRAME_SIZE_MS = 40 + self.sec_per_frame = self.GIGA_AM_FRAME_SIZE_MS / 1000.0 + + if self.use_lm: + if self.model_type != 'ctc': + logger.warning(f"LM decoding is only supported for CTC models, but model is {self.model_type}. Ignoring LM.") + self.use_lm = False + elif 'lm_path' not in kwargs: + logger.error("'lm_path' is required for GigaAM with LM but was not provided.") + self.use_lm = False + else: + self._init_lm(kwargs['lm_path']) + + def _init_lm(self, lm_path: str, alpha: float = 0.5, beta: float = 1.0): + logger.info(f"Building CTC decoder with LM: {lm_path}") + try: + vocab = self.model.decoding.tokenizer.vocab + self.decoder = pyctcdecode.build_ctcdecoder( + vocab, + kenlm_model_path=lm_path, + alpha=alpha, + beta=beta, + ) + except Exception as e: + logger.error(f"Failed to build CTC decoder: {e}") + self.decoder = None + + def transcribe(self, audio_path: str) -> str: + if self.use_lm and self.decoder: + text, _ = self.transcribe_with_timestamps(audio_path) + return text + + text = self.model.transcribe(audio_path) + return text + + def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: + if self.model_type != 'ctc': + logger.info(f"Timestamp generation is only available for CTC models. Model '{self.model_id}' is RNN-T. Returning empty timestamps.") + text = self.transcribe(audio_path) + return text, "" + + if self.model_type == 'rnnt' or self.decoder is None: + text = self.transcribe(audio_path) + return text, "" + + audio = self._read_audio(audio_path) + length = torch.tensor([audio.shape[-1]], device=self.device) + + encoded, _ = self.model.forward(audio.to(self.device), length) + logits = self.model.head(encoded).squeeze(0).detach().cpu().numpy() + + beams = self.decoder.decode_beams(logits, beam_width=100) + + best_beam = beams[0] + result_text = best_beam[0] + word_timestamps_raw = best_beam[2] + + word_timestamps_formatted = self._to_simple_timestamps(word_timestamps_raw) + + return result_text, word_timestamps_formatted + + def _read_audio(self, path_to_file: str) -> torch.Tensor: + audio, sr = torchaudio.load(path_to_file) + if sr != self.target_sr: + audio = torchaudio.functional.resample(audio, sr, self.target_sr) + + if audio.dim() > 1 and audio.size(0) > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + + return audio + + def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int]]]) -> str: + output_lines = [] + for word, (start_frame, end_frame) in word_timestamps: + start_time = start_frame * self.sec_per_frame + end_time = end_frame * self.sec_per_frame + output_lines.append(f"{word} {start_time:.3f} {end_time:.3f}") + return "\n".join(output_lines) + + +class ToneWrapper(ASRWrapper): + """Wrapper for the Tone streaming CTC model.""" + def __init__(self, model_id: str, device: str, **kwargs): + logger.info(f"Initializing Tone model '{model_id}' on {device}") + self.tone_pipeline = StreamingCTCPipeline.from_hugging_face() + self.device = 'cpu' # Tone is CPU-based + self.target_sr = 8_000 + + def transcribe(self, audio_path: str) -> str: + audio = self._read_audio(audio_path) + phrases = self.tone_pipeline.forward_offline(audio) + result_text = ' '.join([phrase.text for phrase in phrases]).strip() + return result_text + + def _read_audio(self, path_to_file: str) -> np.ndarray: + audio = miniaudio.decode_file(str(path_to_file), nchannels=1, sample_rate=8000) + assert audio.sample_rate == 8000 + assert audio.nchannels == 1 + return np.asarray(audio.samples, dtype=np.int16).astype(np.int32) + + def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: + audio_tensor = self._read_audio(audio_path) + phrases: List[TextPhrase] = self.tone_pipeline.forward_offline(audio_tensor) + full_text_parts = [] + timestamp_lines = [] + + # TODO: add logic for word alignment + for phrase in phrases: + if phrase.text: + full_text_parts.append(phrase.text) + timestamp_lines.append(f"{phrase.text} {phrase.start_time:.3f} {phrase.end_time:.3f}") + + result_text = " ".join(full_text_parts).strip() + result_timestamps = "\n".join(timestamp_lines) + + return result_text, result_timestamps + + +class VoskWrapper(ASRWrapper): + """Wrapper for Vosk-style models using sherpa-onnx.""" + def __init__(self, model_id: str, device: str = 'cpu', **kwargs): + logger.info(f"Initializing sherpa-onnx based ASR model from '{model_id}' on {device}") + model_path = Path(model_id) + encoder_path = str(model_path / "am-onnx" / "encoder.onnx") + decoder_path = str(model_path / "am-onnx" / "decoder.onnx") + joiner_path = str(model_path / "am-onnx" / "joiner.onnx") + tokens_path = str(model_path / "lang" / "tokens.txt") + self.target_sr = 16000 + + if not all(Path(p).exists() for p in [encoder_path, decoder_path, joiner_path, tokens_path]): + raise FileNotFoundError(f"One or more required model files not found in {model_path}") + + self.recognizer = sherpa_onnx.OfflineRecognizer.from_transducer( + encoder=encoder_path, + decoder=decoder_path, + joiner=joiner_path, + tokens=tokens_path, + num_threads=0, + provider=device, + sample_rate=self.target_sr, + decoding_method="modified_beam_search" + ) + logger.info(f"sherpa-onnx recognizer initialized successfully on {device}.") + + def transcribe(self, audio_path: str) -> str: + audio = self._read_audio(audio_path) + s = self.recognizer.create_stream() + s.accept_waveform(self.target_sr, audio) + self.recognizer.decode_stream(s) + return s.result.text.strip() + + def _read_audio(self, path_to_file: str) -> Tuple[np.ndarray, int]: + """Reads audio and returns a float32 numpy array for sherpa-onnx.""" + audio, sr = torchaudio.load(path_to_file) + + if sr != self.target_sr: + audio = torchaudio.functional.resample(audio, sr, self.target_sr) + + if audio.dim() > 1 and audio.size(0) > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + + audio_samples = audio.numpy().squeeze().astype(np.float32) + + if np.max(np.abs(audio_samples)) > 1.0: + audio_samples = audio_samples / 32768.0 + + return audio_samples + + def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: + """Transcribes an audio file and returns the text and word-level timestamps.""" + # TODO: add logic for word alignment + audio = self._read_audio(audio_path) + s = self.recognizer.create_stream() + s.accept_waveform(self.target_sr, audio) + self.recognizer.decode_stream(s) + + result = s.result + full_text = result.text.strip() + + timestamp_lines = [] + for token, timestamp in zip(result.tokens, result.timestamps): + timestamp_lines.append(f"{token} {timestamp:.3f} {timestamp:.3f}") + + result_timestamps = "\n".join(timestamp_lines) + + return full_text, result_timestamps + +class ROVERWrapper: + def __init__(self, podcasts_path: str): + self.podcasts_path = Path(podcasts_path) + self.tokenizer = lambda s: s.lower().split() + self.detokenizer = lambda tokens: ' '.join(tokens) + self.rover_aggregator = ROVER(self.tokenizer, self.detokenizer) + + def _collate_transcriptions(self) -> pd.DataFrame: + records = [] + transcription_groups = defaultdict(list) + + txt_files = list(self.podcasts_path.rglob("*.txt")) + for txt_path in txt_files: + base_name = txt_path.stem.rsplit('_', 1)[0] + audio_path = txt_path.with_name(f"{base_name}.mp3") + if audio_path.exists(): + transcription_groups[audio_path].append(txt_path) + + for audio_path, txt_paths in transcription_groups.items(): + for txt_path in txt_paths: + try: + model_name = txt_path.stem.replace(f"{audio_path.stem}_", "") + text = read_file_content(txt_path) + if text: + records.append({ + 'task': str(audio_path), + 'worker': model_name, + 'text': text + }) + except Exception as e: + print(f"Error reading the file {txt_path}: {e}") + + return pd.DataFrame(records) + + def aggregate_and_save(self): + df = self._collate_transcriptions() + df['text'] = df['text'].str.lower() + result = self.rover_aggregator.fit_predict(df) + + for task_path, agg_text in result.items(): + audio_path = Path(task_path) + output_path = audio_path.with_name(f"{audio_path.stem}_rover.txt") + + with open(output_path, "w", encoding="utf-8") as f: + f.write(agg_text) + + diff --git a/use_meta_100h.sh b/use_meta_100h.sh index 8733b92..6babb67 100644 --- a/use_meta_100h.sh +++ b/use_meta_100h.sh @@ -1,6 +1,5 @@ #!/bin/bash -# Функция активации виртуального окружения activate_venv() { local venv_path=$1 if [ ! -f "$venv_path/bin/activate" ]; then @@ -11,7 +10,6 @@ activate_venv() { echo "Activated virtual environment: $(which python)" } -# Функция для безопасной загрузки файлов download_if_not_exists() { local url=$1 local filename=$2 @@ -27,24 +25,19 @@ download_if_not_exists() { fi } -# Основные параметры PODCASTS_PATH="Balalaika100H" PICKLE_PATH="Balalaika100H.pkl" PARQUET_PATH="Balalaika100H.parquet" NUM_WORKERS=4 -# URL для загрузки PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.pkl" PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.parquet" -# Скачиваем файлы (если их нет) download_if_not_exists "$PICKLE_URL" "$PICKLE_PATH" download_if_not_exists "$PARQUET_URL" "$PARQUET_PATH" -# Активируем виртуальное окружение activate_venv ".user_venv" -# Запускаем обработку echo "Starting processing..." bash src/download/download_prepared.sh "$PODCASTS_PATH" "$PICKLE_PATH" "$NUM_WORKERS" || { echo "Error in download_prepared.sh" From 2cc427c7060cbe69a65bf5e777ebcde6174d3665 Mon Sep 17 00:00:00 2001 From: Nikita Date: Thu, 18 Sep 2025 20:06:07 +0000 Subject: [PATCH 03/43] feat(speech-processing): major pipeline overhaul - Integrated Vosk for speech recognition - Optimized tone processing module - Replaced NISQA-s to NISQA - Removed deprecated audio clustering section --- .gitignore | 1 + base.sh | 7 +- configs/config.yaml | 69 +-- configs/nisqa_s.yaml | 45 -- create_dev_env.sh | 12 - requirements_dev.txt | Bin 1456 -> 1474 bytes src/accents/accents.py | 2 +- src/accents/accents_yaml.sh | 2 +- src/classification/README.md | 36 -- src/classification/classification.py | 328 --------------- src/classification/classification_args.sh | 23 - src/classification/classification_yaml.sh | 22 - src/classification/emb/embeder.py | 106 ----- src/collate.py | 10 +- src/collate_yamls.sh | 2 +- src/download/download_prepared.sh | 2 +- src/download/download_yaml.sh | 2 +- src/libs/NISQA | 1 + src/libs/nisqa/__init__.py | 0 src/libs/nisqa/core/__init__.py | 0 src/libs/nisqa/core/model_torch.py | 340 --------------- src/libs/nisqa/utils/__init__.py | 0 src/libs/nisqa/utils/process_utils.py | 124 ------ src/phonemizer/phonemizer_yaml.sh | 2 +- src/preprocess/preprocess.py | 177 +++++--- src/preprocess/preprocess_yaml.sh | 2 +- src/punctuation/punctuation_yaml.sh | 2 +- src/recovery_from_meta_yamls.sh | 2 +- src/separation/diarization.py | 256 +++++++++++ src/separation/model.py | 128 ++++++ src/separation/music_detect.py | 216 ++++++++++ src/separation/nisqa.py | 130 ++++++ src/separation/separation.py | 310 -------------- src/separation/separation_yaml.sh | 4 +- src/transcription/transcription.py | 97 ++--- src/transcription/transcription_yaml.sh | 2 +- src/transcription/transcripton_base.py | 464 ++++++++++++++------ src/utils.py | 6 +- src/utils_asr.py | 490 ++++++++++++++++++++++ use_meta_500h.sh | 4 +- 40 files changed, 1794 insertions(+), 1632 deletions(-) delete mode 100644 configs/nisqa_s.yaml delete mode 100644 src/classification/README.md delete mode 100644 src/classification/classification.py delete mode 100644 src/classification/classification_args.sh delete mode 100644 src/classification/classification_yaml.sh delete mode 100644 src/classification/emb/embeder.py create mode 160000 src/libs/NISQA delete mode 100644 src/libs/nisqa/__init__.py delete mode 100644 src/libs/nisqa/core/__init__.py delete mode 100644 src/libs/nisqa/core/model_torch.py delete mode 100644 src/libs/nisqa/utils/__init__.py delete mode 100644 src/libs/nisqa/utils/process_utils.py create mode 100644 src/separation/diarization.py create mode 100644 src/separation/model.py create mode 100644 src/separation/music_detect.py create mode 100644 src/separation/nisqa.py delete mode 100644 src/separation/separation.py create mode 100644 src/utils_asr.py diff --git a/.gitignore b/.gitignore index 33ed064..bf54f1a 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ chromadb cl_*.py *.ipynb *.bin +temp/* .env __pycache__/ diff --git a/base.sh b/base.sh index 8aed625..c6c3acb 100644 --- a/base.sh +++ b/base.sh @@ -19,15 +19,14 @@ fi CONFIG_PATH=$(realpath "$1") SCRIPTS=( - "./src/download/download_yaml.sh" + # "./src/download/download_yaml.sh" "./src/preprocess/preprocess_yaml.sh" "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" "./src/punctuation/punctuation_yaml.sh" "./src/accents/accents_yaml.sh" - "./src/phonemizer/phonemizer_yaml.sh" - "./src/classification/classification_yaml.sh" - "./src/collate_yamls.sh" + # "./src/phonemizer/phonemizer_yaml.sh" + # "./src/collate_yamls.sh" ) activate_venv ".dev_venv" diff --git a/configs/config.yaml b/configs/config.yaml index 40c7411..5abcea1 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,49 +1,60 @@ download: - podcasts_path: /home/nikita/balalaika + podcasts_path: /mnt/disk1tb/radio-v4 episodes_limit: 1 # limit on downloading episodes from one playlist num_workers: 1 podcasts_urls_file: '/home/nikita/yapoddataset/20000_more_albums.pkl' # pkl with urls preprocess: - podcasts_path: /home/nikita/balalaika # full path to the folder where the podcasts are located + podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) - num_workers: 1 - smart_vad_model: pipecat-ai/smart-turn-v2 + num_workers: 2 + smart_vad_model: /home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx - separation: - podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - num_workers: 1 - nisqa_config: "configs/nisqa_s.yaml" - one_speaker: False # When this option is enabled, only single-speaker audio recordings are downloaded. + podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located + # diar parms + num_workers_diarization: 4 + one_speaker: True # When this option is enabled, only single-speaker audio recordings are downloaded. + # nisqa parms + bs: 32 + num_workers_nisqa: 12 + # audio detect + num_workers_detect: 4 + checkpoint_path: /home/nikita/audio_detect/wavlm-endpointing-model/checkpoint-3000/model.safetensors # /home/nikita/yapoddataset/models/audio_detect/model.safetensors + threshold: 0.2442 transcription: - podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - num_workers: 1 # per one gpu - model_names: ['vosk', 'tone', 'giga_rnnt', 'giga_ctc', 'giga_ctc_lm'] - vosk_path: /home/nikita/yapoddataset/models/vosk-model-ru - lm_path: '/home/nikita/yapoddataset/kenlm.bin' # path to lm.bin + podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located with_timestamps: True + model_names: ['giga_ctc_lm', 'vosk', 'tone', 'giga_ctc', 'giga_rnnt'] # 'tone' ['vosk','tone'] + + giga: + num_workers: 4 # per one gpu + batch_size: 16 # if supported + lm_path: '/home/nikita/yapoddataset/kenlm.bin' # path to lm.bin for giga ctc model + + vosk: + vosk_path: /home/nikita/yapoddataset/models/vosk-model-ru + num_workers: 4 # per one gpu + batch_size: 12 # if supported + beam_size: 4 + lm_scale: 0.2 + ngram_lm_scale: -0.1 + + tone: + num_workers: 4 # per one gpu + batch_size: 16 # if supported punctuation: - podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name - num_workers: 4 # per one gpu + num_workers: 2 # per one gpu accent: - podcasts_path: /home/nikita/balalaika # full path to the folder where the chopped podcasts are located - num_workers: 4 # per one gpu + podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located + num_workers: 2 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /home/nikita/balalaika # full path to the folder where the text files are located - num_workers: 4 # per one gpu - -classification: - podcasts_path: /home/nikita/balalaika - num_workers: 4 # per one gpu - threshold: 0.6 - model_path: models/voxblink2_samresnet100_ft # .pt format model from https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md - - - + podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the text files are located + num_workers: 2 # per one gpu diff --git a/configs/nisqa_s.yaml b/configs/nisqa_s.yaml deleted file mode 100644 index 7599c96..0000000 --- a/configs/nisqa_s.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Mel-Specs options -ms_sr: null # resample speech signal to 'ms_sr' -ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples -ms_n_fft: 960 # fft size -ms_hop_length: 480 # hop length of fft windowing -ms_win_length: 960 # fft window length, will be padded with zeros to match 'ms_n_fft' -ms_n_mels: 48 # number of Mel bands -ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) -ms_seg_hop_length: 3 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used -ms_max_length: 1300 # spec length for training only (in bins). if samples of different duration are used in dataloader they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. change if you want to train on different samples - - -# CNN parameters -cnn_c_out_1: 16 # number of output channels of first convolutional layer -cnn_c_out_2: 32 # number of output channels of the second convolutional layer -cnn_c_out_3: 64 # number of output channels of the last four convolutional layer -cnn_kernel_size: !!python/tuple [3,3] -cnn_dropout: 0.2 -cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted -cnn_pool_1: [24,7] # output dimensions of first adaptive pooling ('adaptive' CNN only) -cnn_pool_2: [12,5] # output dimensions of second adaptive pooling ('adaptive' CNN only) -cnn_pool_3: [6,3] # output dimensions of third adaptive pooling ('adaptive' CNN only) - -# LSTM parameters -td_lstm_h: 128 # number of LSTM hidden units -td_lstm_num_layers: 1 # LSTM depth -td_lstm_dropout: 0 -td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 - -# Arguments for inference -ckp: models/nisqa_s.tar # checkpoint that will be used for inference -inf_device: cuda # device for inference runs -warmup: False # warmup run before inference; usually is not needed on CPU runs - -#Microphone inference specifics -frame: 2 # framesize for file/mic capture in seconds -updates: null # if null, metrics will be calculated over whole available frame (every [frame] seconds); if int - metrics will be calculated every n bins (which equivalent to sr / ms_n_fft * n seconds, for updates=1 it will be 48000/960*1 = 20ms) -sd_device: null # check mic device ID in sounddevice or leave null to use default input device (most probably your system default mic) -sd_dump: null # set this to filename if you want to dump mic signal into file - - - - - diff --git a/create_dev_env.sh b/create_dev_env.sh index e185bda..e2dfab2 100644 --- a/create_dev_env.sh +++ b/create_dev_env.sh @@ -40,16 +40,4 @@ create_venv_env() { fi } -if ! command -v python &> /dev/null; then - echo "Python not found! Please install Python 3.10+" - exit 1 -fi - -python -c "import sys; exit(0 if sys.version_info >= (3,10) else 1)" || { - echo "Requires Python 3.10 or newer!" - exit 1 -} - -# create_venv_env ".main_venv" "requirements_main.txt" -# create_venv_env ".support_venv" "requirements_support.txt" create_venv_env ".dev_venv" "requirements_dev.txt" \ No newline at end of file diff --git a/requirements_dev.txt b/requirements_dev.txt index 40a4c2fe1a4cba0f73a6814930d2d8a7be53d9c4..66d57f6eb088900a8d1967cc48f52ffcdf44570d 100644 GIT binary patch delta 26 hcmdnMeTaL55)*F@LncEKLlHwhLoq|*W?81m%m86^2EG6Q delta 12 TcmX@ay@7j!64PcAraoo>9L@vR diff --git a/src/accents/accents.py b/src/accents/accents.py index 3ef8fd2..4ce94a9 100644 --- a/src/accents/accents.py +++ b/src/accents/accents.py @@ -79,7 +79,7 @@ def main(args): devices:{available_gpu_ids} """) - valid_text_files = get_valid_txt_paths(podcast_path)[:5] + valid_text_files = get_valid_txt_paths(podcast_path) files_for_each_gpu = [[] for _ in range(num_gpus)] for i, path in enumerate(valid_text_files): diff --git a/src/accents/accents_yaml.sh b/src/accents/accents_yaml.sh index 0fbd946..d38c522 100644 --- a/src/accents/accents_yaml.sh +++ b/src/accents/accents_yaml.sh @@ -19,4 +19,4 @@ CONFIG_PATH=$(realpath "$1") activate_venv ".dev_venv" -python3 -m src.accents.accents --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.accents.accents --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/classification/README.md b/src/classification/README.md deleted file mode 100644 index d18981f..0000000 --- a/src/classification/README.md +++ /dev/null @@ -1,36 +0,0 @@ -## Usage/Examples - -### Running the Code via Command-Line Arguments -You can modify the parameters directly in the shell script (e.g., `classification/classification_args.sh`) and then run it: -~~~sh -sh classification/classification_args.sh -~~~ - -### Running the Code via Config File -Example: -~~~sh -bash classification/classification_yaml.sh -~~~ - -## Explanation of Parameters - -- `--config_path`: Path to the YAML configuration file. -- `--podcasts_path`: Path to the podcast folder. This folder must contain the `results.csv` file with metadata about the podcast segments. -- `--threshold`: Similarity threshold for clustering speaker embeddings (default: 0.8). Higher values result in stricter clustering. -- `--model_path`: Embedder model path or identifier (default: `"voxblink2_samresnet100_ft"`). This model is used to generate speaker embeddings. -- `--device`: Device to run the embedder model (e.g., `cuda` or `cpu`). - -## Output Structure - -After execution, a CSV file named `clustering_result.csv` will be generated in the specified podcasts folder. This file includes the original metadata along with an additional column `speaker` that indicates the assigned speaker cluster ID for each audio segment. - - -### File Descriptions -- **`results.csv`**: The input CSV file located in the podcasts folder containing metadata for each podcast segment. This file should include a column `IsMono` used to filter segments for clustering. -- **`clustering_result.csv`**: The output CSV file containing the original metadata along with an additional `speaker` column. Each entry in this column represents the speaker cluster ID assigned to that segment. - -## Important Notice - -- Ensure that the `results.csv` file exists in the specified podcasts folder and contains the required data (e.g., the `IsMono` column) for proper clustering. -- The clustering process is applied only to segments marked as mono (i.e., `IsMono == True`). - diff --git a/src/classification/classification.py b/src/classification/classification.py deleted file mode 100644 index bf0897d..0000000 --- a/src/classification/classification.py +++ /dev/null @@ -1,328 +0,0 @@ -import argparse -import os -from concurrent.futures import ProcessPoolExecutor, as_completed -import multiprocessing as mp - -import numpy as np -import pandas as pd -import torch -import torch.nn.functional as F -from loguru import logger -from tqdm import tqdm - -from src.classification.emb.embeder import ResNetEmbedder -from src.utils import load_config - -class SpeakerClustering: - def __init__( - self, - podcasts_path: str, - threshold=0.85, - model_path: str = 'voxblink2_samresnet100_ft', - ): - - self.podcasts_path = podcasts_path - df_path = os.path.join(podcasts_path, 'results.csv') - self.full_df = pd.read_csv(df_path) - - if 'fullness' not in self.full_df.columns: - self.full_df['fullness'] = np.nan - - self.df = self.full_df[self.full_df['is_mono'] == True].copy() - self.threshold = threshold - self.model_path = model_path - self.all_clusters = [] # {"embeddings": list, "paths": list} - - def norm_cos_sim(self, emb1: torch.Tensor, emb2: torch.Tensor) -> float: - if emb1.dim() == 1: - emb1 = emb1.unsqueeze(0) - if emb2.dim() == 1: - emb2 = emb2.unsqueeze(0) - - cosine_score = F.cosine_similarity( - F.normalize(emb1, p=2, dim=1), - F.normalize(emb2, p=2, dim=1), - dim=1 - ) - cosine_score = cosine_score.item() - return (cosine_score + 1.0) / 2 # normalize: [-1, 1] => [0, 1] - - def load_embedding(self, audio_path: str) -> torch.Tensor: - emb_path = audio_path.rsplit('.', 1)[0] + '.emb' - if os.path.exists(emb_path): - try: - emb, _ = torch.load(emb_path) - return emb - except Exception as e: - logger.error(f"Failed to load embedding from {emb_path}: {str(e)}") - - def cluster_mono_podcast(self, podcast_id: int): - group = self.df[self.df['podcast_id'] == podcast_id] - temp_clusters = [] - - for _, row in group.iterrows(): - audio_path = os.path.join( - self.podcasts_path, - row["audio_path"] - ) - emb = self.load_embedding(audio_path) - - if emb is None: - continue - - best_similarity = 0.0 - best_cluster = None - - for cluster in temp_clusters: - cluster_embs = torch.stack(cluster["embeddings"]) - avg_emb = cluster_embs.mean(dim=0) - - sim = self.norm_cos_sim(emb, avg_emb) - if sim > best_similarity: - best_similarity = sim - best_cluster = cluster - - if best_similarity >= self.threshold and best_cluster is not None: - best_cluster["embeddings"].append(emb) - best_cluster["paths"].append(row["audio_path"]) - else: - new_cluster = { - "embeddings": [emb], - "paths": [row["audio_path"]] - } - temp_clusters.append(new_cluster) - - self.all_clusters.extend(temp_clusters) - - def cluster_all_podcast(self): - for podcast_id in tqdm(self.df['podcast_id'].unique()): - self.cluster_mono_podcast(int(podcast_id)) - - def merge_clusters(self): - changed = True - while changed: - changed = False - new_clusters = [] - used = [False] * len(self.all_clusters) - - for i in range(len(self.all_clusters)): - if used[i]: - continue - base_cluster = self.all_clusters[i] - base_embs = torch.stack(base_cluster["embeddings"]) - base_avg = base_embs.mean(dim=0) - - for j in range(i + 1, len(self.all_clusters)): - if used[j]: - continue - comp_cluster = self.all_clusters[j] - comp_embs = torch.stack(comp_cluster["embeddings"]) - comp_avg = comp_embs.mean(dim=0) - sim = self.norm_cos_sim(base_avg, comp_avg) - if sim >= 0.9: - base_cluster["embeddings"].extend(comp_cluster["embeddings"]) - base_cluster["paths"].extend(comp_cluster["paths"]) - - base_embs = torch.stack(base_cluster["embeddings"]) - base_avg = base_embs.mean(dim=0) - used[j] = True - changed = True - new_clusters.append(base_cluster) - used[i] = True - self.all_clusters = new_clusters - - for cluster_id, cluster in enumerate(self.all_clusters): - cluster['id'] = cluster_id - - return self.all_clusters - - def assign_cluster_ids(self): - cluster_mapping = {} - for cluster in self.all_clusters: - for path in cluster["paths"]: - cluster_mapping[path] = cluster["id"] - - self.full_df["speaker"] = ( - self.full_df["audio_path"] - .map(cluster_mapping) - .astype(pd.Int64Dtype()) - ) - -def init_worker(model_path, device_str): - global worker_embedder - worker_embedder = ResNetEmbedder(model_path, device_str) - -def compute_embedding_for_file(audio_path): - try: - emb, fullness = worker_embedder(audio_path) - if fullness: - emb_path = audio_path.rsplit('.', 1)[0] + '.emb' - torch.save((emb, fullness), emb_path) - return audio_path, fullness - return None - except Exception as e: - logger.error(f"Error processing {audio_path}: {str(e)}") - return None - -def precompute_embeddings( - audio_files: list, - model_path: str, - num_workers_per_gpu: int = 8 -) -> dict: - num_gpus = torch.cuda.device_count() - available_gpu_ids = list(range(num_gpus)) - - files_for_each_gpu = [[] for _ in range(num_gpus)] - for i, path in enumerate(audio_files): - gpu_assignment_index = i % num_gpus - files_for_each_gpu[gpu_assignment_index].append(path) - - all_futures = [] - executors = [] - fullness_results = {} - - logger.info(f"Starting embedding computation on {num_gpus} GPUs with {num_workers_per_gpu} workers per GPU") - - for i, gpu_id in enumerate(available_gpu_ids): - device_str = f'cuda:{gpu_id}' - files_for_this_gpu = files_for_each_gpu[i] - - if not files_for_this_gpu: - continue - - logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_gpu)} files") - - executor = ProcessPoolExecutor( - max_workers=num_workers_per_gpu, - initializer=init_worker, - initargs=(model_path, device_str) - ) - executors.append(executor) - - for path in files_for_this_gpu: - future = executor.submit(compute_embedding_for_file, path) - all_futures.append(future) - - logger.info(f"Submitted {len(all_futures)} tasks across {len(executors)} GPUs") - - completed_count = 0 - for future in tqdm(as_completed(all_futures), total=len(all_futures), desc="Computing embeddings"): - try: - result = future.result() - if result: - audio_path, fullness = result - fullness_results[audio_path] = fullness - completed_count += 1 - except Exception as e: - logger.error(f"Task failed: {str(e)}") - - for executor in executors: - executor.shutdown() - - logger.info(f"Completed {completed_count}/{len(all_futures)} embedding computations") - return fullness_results - -def main(args): - config = load_config(args.config_path, 'classification') - podcasts_path = config.get('podcasts_path', '../../../podcasts') if args.podcasts_path is None else args.podcasts_path - threshold = config.get('threshold', 0.7) if args.threshold is None else args.threshold - model_path = config.get('model_path', '/models/voxblink2_samresnet100_ft') if args.model_path is None else args.model_path - num_workers = config.get('num_workers', 8) if args.num_workers is None else args.num_workers - - available_gpu_ids = list(range(torch.cuda.device_count())) - num_gpus = len(available_gpu_ids) - - if num_gpus == 0: - logger.error("No GPUs available. Exiting.") - return - - logger.info( - f""" - Used params: - podcasts path: {podcasts_path} - threshold: {threshold} - emb model path: {model_path} - list available devices: {available_gpu_ids} - workers per GPU: {num_workers} - """ - ) - - sc = SpeakerClustering( - podcasts_path=podcasts_path, - threshold=threshold, - model_path=model_path, - ) - - audio_files = [ - os.path.join(podcasts_path, path) - for path in sc.df['audio_path'].unique().tolist() - ] - - missing_files = [] - for path in audio_files: - emb_path = os.path.join(podcasts_path,path.rsplit('.', 1)[0] + '.emb') - if not os.path.exists(emb_path): - missing_files.append(path) - - if missing_files: - logger.info(f"Found {len(missing_files)} missing embeddings, starting computation...") - fullness_results = precompute_embeddings( - missing_files, - model_path, - num_workers_per_gpu=num_workers - ) - - for path, fullness in fullness_results.items(): - relative_path = os.path.relpath(path, podcasts_path) - mask = sc.full_df['audio_path'] == relative_path - sc.full_df.loc[mask, 'fullness'] = fullness - - mask_df = sc.df['audio_path'] == relative_path - sc.df.loc[mask_df, 'fullness'] = fullness - - updated_csv_path = os.path.join(podcasts_path, 'results.csv') - sc.full_df.to_csv(updated_csv_path, index=False) - logger.info(f"Updated results.csv with fullness values") - else: - logger.info("All embeddings already precomputed") - - sc.cluster_all_podcast() - sc.merge_clusters() - sc.assign_cluster_ids() - - output_path = os.path.join(podcasts_path, 'results.csv') - sc.full_df.to_csv(output_path, index=False) - logger.info(f"Clustered DataFrame saved to {output_path}") - -if __name__ == '__main__': - mp.set_start_method('spawn', force=True) - - parser = argparse.ArgumentParser(description="Classification speaker") - parser.add_argument( - "--config_path", - type=str, - help="Path to the configuration file" - ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to the podcast folder" - ) - parser.add_argument( - "--threshold", - type=float, - help="Threshold for clustering" - ) - parser.add_argument( - "--model_path", - type=str, - help="embedder model path" - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of workers per GPU" - ) - - args = parser.parse_args() - main(args) \ No newline at end of file diff --git a/src/classification/classification_args.sh b/src/classification/classification_args.sh deleted file mode 100644 index 3b2f2ba..0000000 --- a/src/classification/classification_args.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - - -activate_venv ".dev_venv" - -MODEL_PATH="$SCRIPT_DIR/voxblink2_samresnet100_ft" -PODCASTS_PATH=""../../../podcasts"" -THRESHOLD=0.8 - -python -m src.classificatoin.classificatoin \ - --podcasts_path "$PODCASTS_PATH" \ - --model_path "$MODEL_PATH" \ - --threshold "$THRESHOLD" \ diff --git a/src/classification/classification_yaml.sh b/src/classification/classification_yaml.sh deleted file mode 100644 index 06cec60..0000000 --- a/src/classification/classification_yaml.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - -if [ -z "${1:-}" ]; then - echo "Usage: $0 " - exit 1 -fi - -CONFIG_PATH=$(realpath "$1") - -activate_venv ".dev_venv" - -python3 -m src.classification.classification --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/classification/emb/embeder.py b/src/classification/emb/embeder.py deleted file mode 100644 index 1e1f124..0000000 --- a/src/classification/emb/embeder.py +++ /dev/null @@ -1,106 +0,0 @@ -import os -import yaml -import torchaudio.compliance.kaldi as kaldi -import torch -import torchaudio -from silero_vad import load_silero_vad, get_speech_timestamps -from loguru import logger - -from wespeaker.models.speaker_model import get_speaker_model -from wespeaker.utils.checkpoint import load_checkpoint - -class ResNetEmbedder: - def __init__(self, model_path, device, resample_rate=16000, use_vad=True): - self.device = device - self.resample_rate = resample_rate - self.use_vad = use_vad - - config_path = os.path.join(model_path, 'config.yaml') - model_file = os.path.join(model_path, 'avg_model.pt') - - if not os.path.exists(config_path): - raise FileNotFoundError(f"Config file {config_path} not found") - if not os.path.exists(model_file): - raise FileNotFoundError(f"Model file {model_file} not found") - - with open(config_path, 'r') as fin: - configs = yaml.safe_load(fin) - - self.model = get_speaker_model(configs['model'])(**configs['model_args']) - load_checkpoint(self.model, model_file) - self.model.to(self.device).eval() - - if self.use_vad: - self.vad_model = load_silero_vad() - - def __call__(self, path): - wav = self._preprocess(path) - - if wav is None: - return None, None - - duration = wav.shape[-1] / self.resample_rate - fullness = duration / self.total_duration - - feats = self.compute_fbank(wav) - feats = feats.unsqueeze(0).to(self.device) - - with torch.no_grad(): - outputs = self.model(feats) - outputs = outputs[-1] if isinstance(outputs, tuple) else outputs - - return outputs[0].detach().cpu(), fullness - - def _preprocess(self, path): - try: - waveform, sr = torchaudio.load(path) - if sr != self.resample_rate: - waveform = torchaudio.functional.resample( - waveform, - orig_freq=sr, - new_freq=self.resample_rate - ) - self.total_duration = waveform.shape[-1] / self.resample_rate - except Exception as e: - logger.error(f"failed to load {path} - {e}") - return None - - if waveform.shape[0] > 1: - waveform = torch.mean(waveform, dim=0, keepdim=True) - - if self.use_vad: - speech_segments = get_speech_timestamps( - waveform.squeeze(0), - self.vad_model, - threshold=0.4, - return_seconds=False - ) - - if len(speech_segments) == 0: # empty audio - return - - segments = [waveform[:, seg['start']:seg['end']] - for seg in speech_segments] - - waveform = torch.cat(segments, dim=-1) - - return waveform - - def compute_fbank(self, - waveform, - sample_rate=16000, - num_mel_bins=80, - frame_length=25, - frame_shift=10, - cmn=True - )->torch.Tensor: - - feat = kaldi.fbank(waveform, - num_mel_bins=num_mel_bins, - frame_length=frame_length, - frame_shift=frame_shift, - sample_frequency=sample_rate) - if cmn: - feat = feat - torch.mean(feat, 0) - - return feat \ No newline at end of file diff --git a/src/collate.py b/src/collate.py index 0c7f905..63ce5ac 100644 --- a/src/collate.py +++ b/src/collate.py @@ -21,7 +21,7 @@ def process_audio_file(audio_path_str: str, base_path: Path) -> Dict[str, Option 'phonemes': '_rover_phonemes.txt' } - results = {'audio_path': audio_path_str} + results = {'filepath': audio_path_str} for key, suffix in file_types.items(): file_path = base_path / dir_path / f"{base_name}{suffix}" results[key] = read_file_content(file_path) @@ -37,10 +37,10 @@ def main(args): ) - df = pd.read_csv(base_path / "results.csv") - df.drop_duplicates(subset='audio_path', inplace=True) + df = pd.read_csv(base_path / "balalaika.csv") + df.drop_duplicates(subset='filepath', inplace=True) - audio_paths = df['audio_path'].tolist() + audio_paths = df['filepath'].tolist() results = [] num_workers = 32 @@ -64,7 +64,7 @@ def main(args): extracted_df = pd.DataFrame(results) - final_df = pd.merge(df, extracted_df, on='audio_path', how='left') + final_df = pd.merge(df, extracted_df, on='filepath', how='left') output_path = base_path / "balalaika.parquet" final_df.to_parquet(output_path, engine='pyarrow', index=False) diff --git a/src/collate_yamls.sh b/src/collate_yamls.sh index 95c59bf..cf72fc8 100644 --- a/src/collate_yamls.sh +++ b/src/collate_yamls.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.collate --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.collate --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/download/download_prepared.sh b/src/download/download_prepared.sh index 2af56e0..decbe55 100644 --- a/src/download/download_prepared.sh +++ b/src/download/download_prepared.sh @@ -18,4 +18,4 @@ activate_venv ".user_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.download.download_prepared --pickle_path "$PICKLE_PATH" --podcasts_path "$PODCASTS_PATH" --num_workers "$NUM_WORKERS" \ No newline at end of file +taskset -c 0-24 python3 -m src.download.download_prepared --pickle_path "$PICKLE_PATH" --podcasts_path "$PODCASTS_PATH" --num_workers "$NUM_WORKERS" \ No newline at end of file diff --git a/src/download/download_yaml.sh b/src/download/download_yaml.sh index 16a57b8..fff1f63 100644 --- a/src/download/download_yaml.sh +++ b/src/download/download_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.download.download --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.download.download --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/libs/NISQA b/src/libs/NISQA new file mode 160000 index 0000000..fe84f0f --- /dev/null +++ b/src/libs/NISQA @@ -0,0 +1 @@ +Subproject commit fe84f0f252abec382b24367d5b22498a7ce34dbb diff --git a/src/libs/nisqa/__init__.py b/src/libs/nisqa/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/libs/nisqa/core/__init__.py b/src/libs/nisqa/core/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/libs/nisqa/core/model_torch.py b/src/libs/nisqa/core/model_torch.py deleted file mode 100644 index ed3f69f..0000000 --- a/src/libs/nisqa/core/model_torch.py +++ /dev/null @@ -1,340 +0,0 @@ -# --------------------------------------------------------------------- -# This file is based on code from the NISQA-s project by Deep Intelligence: -# https://github.com/deepvk/NISQA-s -# -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0 -# --------------------------------------------------------------------- - -import os - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence - - -class PoolAvg(torch.nn.Module): - """ - PoolAvg: Average pooling that consideres masked time-steps. - """ - - def __init__(self, d_input, output_size): - super().__init__() - - self.linear = nn.Linear(d_input, output_size) - - def forward(self, x, n_wins): - mask = torch.arange(x.shape[1])[None, :] < n_wins[:, None].to("cpu").to(torch.long) - mask = ~mask.unsqueeze(2).to(x.device) - x.masked_fill_(mask, 0.0) - - x = torch.div(x.sum(1), n_wins.unsqueeze(1)) - - x = self.linear(x) - - return x - - -class AdaptCNN(nn.Module): - """ - AdaptCNN: CNN with adaptive maxpooling that can be used as framewise model. - Overall, it has six convolutional layers. This CNN module is more flexible - than the StandardCNN that requires a fixed input dimension of 48x15. - """ - - def __init__( - self, - input_channels, - c_out_1, - c_out_2, - c_out_3, - kernel_size, - dropout, - pool_1, - pool_2, - pool_3, - fc_out_h=20, - ): - super().__init__() - self.name = "CNN_adapt" - - self.input_channels = input_channels - self.c_out_1 = c_out_1 - self.c_out_2 = c_out_2 - self.c_out_3 = c_out_3 - self.kernel_size = kernel_size - self.pool_1 = pool_1 - self.pool_2 = pool_2 - self.pool_3 = pool_3 - self.dropout_rate = dropout - self.fc_out_h = fc_out_h - - self.dropout = nn.Dropout2d(p=self.dropout_rate) - - if isinstance(self.kernel_size, int): - self.kernel_size = (self.kernel_size, self.kernel_size) - - self.kernel_size_last = (self.kernel_size[0], self.pool_3[1]) - - if self.kernel_size[1] == 1: - self.cnn_pad = (1, 0) - else: - self.cnn_pad = (1, 1) - - self.conv1 = nn.Conv2d(self.input_channels, self.c_out_1, self.kernel_size, padding=self.cnn_pad) - - self.bn1 = nn.BatchNorm2d(self.conv1.out_channels) - - self.conv2 = nn.Conv2d(self.conv1.out_channels, self.c_out_2, self.kernel_size, padding=self.cnn_pad) - - self.bn2 = nn.BatchNorm2d(self.conv2.out_channels) - - self.conv3 = nn.Conv2d(self.conv2.out_channels, self.c_out_3, self.kernel_size, padding=self.cnn_pad) - - self.bn3 = nn.BatchNorm2d(self.conv3.out_channels) - - self.conv4 = nn.Conv2d(self.conv3.out_channels, self.c_out_3, self.kernel_size, padding=self.cnn_pad) - - self.bn4 = nn.BatchNorm2d(self.conv4.out_channels) - - self.conv5 = nn.Conv2d(self.conv4.out_channels, self.c_out_3, self.kernel_size, padding=self.cnn_pad) - - self.bn5 = nn.BatchNorm2d(self.conv5.out_channels) - - self.conv6 = nn.Conv2d(self.conv5.out_channels, self.c_out_3, self.kernel_size_last, padding=(1, 0)) - - self.bn6 = nn.BatchNorm2d(self.conv6.out_channels) - - if self.fc_out_h: - self.fc = nn.Linear(self.conv6.out_channels * self.pool_3[0], self.fc_out_h) - self.fan_out = self.fc_out_h - else: - self.fan_out = self.conv6.out_channels * self.pool_3[0] - - def forward(self, x, n_wins): - x_packed = pack_padded_sequence(x, n_wins.cpu(), batch_first=True, enforce_sorted=False) - - x = F.relu(self.bn1(self.conv1(x_packed.data))) - x = F.adaptive_max_pool2d(x, output_size=self.pool_1) - - x = F.relu(self.bn2(self.conv2(x))) - x = F.adaptive_max_pool2d(x, output_size=self.pool_2) - x = self.dropout(x) - - x = F.relu(self.bn3(self.conv3(x))) - x = self.dropout(x) - - x = F.relu(self.bn4(self.conv4(x))) - x = F.adaptive_max_pool2d(x, output_size=self.pool_3) - x = self.dropout(x) - - x = F.relu(self.bn5(self.conv5(x))) - x = self.dropout(x) - - x = F.relu(self.bn6(self.conv6(x))) - - x = x.view(-1, self.conv6.out_channels * self.pool_3[0]) - - if self.fc_out_h: - x = self.fc(x) - - x = x_packed._replace(data=x) - - x, _ = pad_packed_sequence(x, batch_first=True, padding_value=0.0, total_length=n_wins.max()) - return x - - -class LSTM(nn.Module): - def __init__(self, input_size, lstm_h=128, num_layers=2, dropout=0.1, bidirectional=True): - super().__init__() - - self.lstm = nn.LSTM( - input_size=input_size, - hidden_size=lstm_h, - num_layers=num_layers, - dropout=dropout, - batch_first=True, - bidirectional=bidirectional, - ) - if bidirectional: - num_directions = 2 - else: - num_directions = 1 - self.fan_out = num_directions * lstm_h - - def forward(self, x, n_wins, h0=None, c0=None): - x = pack_padded_sequence(x, n_wins.cpu(), batch_first=True, enforce_sorted=False) - self.lstm.flatten_parameters() - x, (h, c) = self.lstm(x, (h0, c0)) - x, _ = pad_packed_sequence(x, batch_first=True, padding_value=0.0, total_length=n_wins.max()) - return x, h, c - - -class NISQA_DIM(nn.Module): - """ - NISQA_DIM: The main speech quality model with speech quality dimension - estimation (MOS, Noisiness, Coloration, Discontinuity, and Loudness). - """ - - def __init__( - self, - cnn_c_out_1=16, - cnn_c_out_2=32, - cnn_c_out_3=64, - cnn_kernel_size=3, - cnn_dropout=0.2, - cnn_pool_1=[24, 7], - cnn_pool_2=[12, 5], - cnn_pool_3=[6, 3], - cnn_fc_out_h=None, - td_lstm_h=128, - td_lstm_num_layers=1, - td_lstm_dropout=0, - td_lstm_bidirectional=True, - ): - super().__init__() - - self.name = "NISQA_DIM" - - self.cnn = AdaptCNN( - input_channels=1, - c_out_1=cnn_c_out_1, - c_out_2=cnn_c_out_2, - c_out_3=cnn_c_out_3, - kernel_size=cnn_kernel_size, - dropout=cnn_dropout, - pool_1=cnn_pool_1, - pool_2=cnn_pool_2, - pool_3=cnn_pool_3, - fc_out_h=cnn_fc_out_h, - ) - - self.time_dependency = LSTM( - input_size=self.cnn.fan_out, - lstm_h=td_lstm_h, - num_layers=td_lstm_num_layers, - dropout=td_lstm_dropout, - bidirectional=td_lstm_bidirectional, - ) - - self.pool_layers = nn.ModuleList( - [ - PoolAvg( - self.time_dependency.fan_out, - output_size=1, - ) - for _ in range(5) - ] - ) - - def forward(self, x, n_wins, h0, c0): - x = self.cnn(x, n_wins) - x, h, c = self.time_dependency(x, n_wins, h0, c0) - out = [mod(x, n_wins) for mod in self.pool_layers] - out = torch.cat(out, dim=1) - - return out, h, c - - -def loadModel(args): - """ - Loads the Pytorch models with given input arguments. - """ - # if True overwrite input arguments from pretrained model - if torch.cuda.is_available(): - dev = torch.device("cuda") - else: - dev = torch.device("cpu") - - if "tr_device" in args: - if args["tr_device"] == "cpu": - dev = torch.device("cpu") - elif args["tr_device"] == "cuda": - dev = torch.device("cuda") - print("Device: {}".format(dev)) - - if "tr_parallel" in args: - if (dev == torch.device("cpu")) and args["tr_parallel"]: - args["tr_parallel"] == False - print("Using CPU -> tr_parallel set to False") - if args["pretrained_model"]: - if os.path.isabs(args["pretrained_model"]): - model_path = os.path.join(args["pretrained_model"]) - else: - model_path = os.path.join(os.getcwd(), args["pretrained_model"]) - checkpoint = torch.load(model_path, map_location=dev) - - # update checkpoint arguments with new arguments - checkpoint["args"].update(args) - args = checkpoint["args"] - - args["dim"] = True - args["csv_mos_train"] = None # column names hardcoded for dim models - args["csv_mos_val"] = None - - args["double_ended"] = False - args["csv_ref"] = None - - # Load Model - model_args = { - "cnn_c_out_1": args["cnn_c_out_1"], - "cnn_c_out_2": args["cnn_c_out_2"], - "cnn_c_out_3": args["cnn_c_out_3"], - "cnn_kernel_size": args["cnn_kernel_size"], - "cnn_dropout": args["cnn_dropout"], - "cnn_pool_1": args["cnn_pool_1"], - "cnn_pool_2": args["cnn_pool_2"], - "cnn_pool_3": args["cnn_pool_3"], - "cnn_fc_out_h": args["cnn_fc_out_h"], - "td_lstm_h": args["td_lstm_h"], - "td_lstm_num_layers": args["td_lstm_num_layers"], - "td_lstm_dropout": args["td_lstm_dropout"], - "td_lstm_bidirectional": args["td_lstm_bidirectional"], - } - - model = NISQA_DIM(**model_args) - - # Load weights if pretrained model is used ------------------------------------ - if args["pretrained_model"]: - missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model_state_dict"], strict=True) - print("Loaded pretrained model from " + args["pretrained_model"]) - if missing_keys: - print("missing_keys:") - print(missing_keys) - if unexpected_keys: - print("unexpected_keys:") - print(unexpected_keys) - return model, dev, model_args - - -def model_init(args): - model = NISQA_DIM( - cnn_c_out_1=args["cnn_c_out_1"], - cnn_c_out_2=args["cnn_c_out_2"], - cnn_c_out_3=args["cnn_c_out_3"], - cnn_kernel_size=args["cnn_kernel_size"], - cnn_dropout=args["cnn_dropout"], - cnn_pool_1=args["cnn_pool_1"], - cnn_pool_2=args["cnn_pool_2"], - cnn_pool_3=args["cnn_pool_3"], - cnn_fc_out_h=args["cnn_fc_out_h"], - td_lstm_h=args["td_lstm_h"], - td_lstm_num_layers=args["td_lstm_num_layers"], - td_lstm_dropout=args["td_lstm_num_layers"], - td_lstm_bidirectional=args["td_lstm_bidirectional"], - ) - - ckp = torch.load(args["ckp"], map_location="cpu", weights_only=False) - model.load_state_dict(ckp["model_state_dict"], strict=True) - model = model.to(torch.device(args["inf_device"])) - model.eval() - - # init lstm states - directions = 2 if args["td_lstm_bidirectional"] else 1 - h0 = torch.zeros(args["td_lstm_num_layers"] * directions, 1, args["td_lstm_h"]) - c0 = torch.zeros(args["td_lstm_num_layers"] * directions, 1, args["td_lstm_h"]) - - return model, h0, c0 diff --git a/src/libs/nisqa/utils/__init__.py b/src/libs/nisqa/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/libs/nisqa/utils/process_utils.py b/src/libs/nisqa/utils/process_utils.py deleted file mode 100644 index 5906e1f..0000000 --- a/src/libs/nisqa/utils/process_utils.py +++ /dev/null @@ -1,124 +0,0 @@ -# --------------------------------------------------------------------- -# This file is based on code from the NISQA-s project by Deep Intelligence: -# https://github.com/deepvk/NISQA-s -# -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0 -# --------------------------------------------------------------------- -import numpy as np -import torch -import torchaudio as ta - - -def get_ta_melspec( - y, - sr=48e3, - n_fft=1024, - hop_length=80, - win_length=170, - n_mels=32, - fmax=16e3, # for the sake of consistency with original librosa implementation -): - """ - Calculate mel-spectrograms with torchaudio (librosa-like). - """ - if isinstance(y, str): - try: - y, sr = ta.load(y) - y = y[0] - except: - raise ValueError("Could not load file {}".format(y)) - - melSpec = ta.transforms.MelSpectrogram( - sample_rate=int(sr), - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window_fn=torch.hann_window, - center=True, - pad_mode="reflect", - power=1.0, - n_mels=n_mels, - norm=None, - mel_scale="htk", - ) - - S = melSpec(y) - - spec = ta.functional.amplitude_to_DB(S, amin=1e-4, top_db=80.0, multiplier=20.0, db_multiplier=0.0) - return spec - - -def segment_specs(x, seg_length=15, seg_hop=3, max_length=None): - """ - Segment a spectrogram into "seg_length" wide spectrogram segments. - Instead of using only the frequency bin of the current time step, - the neighboring bins are included as input to the CNN. For example - for a seg_length of 7, the previous 3 and the follwing 3 frequency - bins are included. - - A spectrogram with input size [H x W] will be segmented to: - [W-(seg_length-1) x C x H x seg_length], where W is the width of the - original mel-spec (corresponding to the length of the speech signal), - H is the height of the mel-spec (corresponding to the number of mel bands), - C is the number of CNN input Channels (always one in our case). - """ - n_wins = x.shape[1] - (seg_length - 1) - transposed_x = x.transpose(1, 0) - - unfolded_x = transposed_x.unfold(0, seg_length, 1) - expanded_x = unfolded_x.unsqueeze(1) - x = expanded_x[::seg_hop, :] - n_wins = int(np.ceil(n_wins / seg_hop)) - if max_length is not None: - if max_length < x.shape[0]: - raise ValueError( - "n_wins {} > max_length {}. Increase max window length ms_max_segments!".format(x.shape[0], max_length) - ) - x_padded = torch.zeros((max_length, x.shape[1], x.shape[2], x.shape[3])) - x_padded[:n_wins, :] = x - x = x_padded - - return x, np.array(n_wins) - - -def process(audio, sr, model, h0, c0, args): - audio = get_ta_melspec( - torch.as_tensor(audio).float().cpu(), - sr, - args["ms_n_fft"], - args["ms_hop_length"], - args["ms_win_length"], - args["ms_n_mels"], - args["ms_fmax"], - ) - device = args['inf_device'] - audio, n_wins = segment_specs(audio, args["ms_seg_length"], args["ms_seg_hop_length"]) - np.set_printoptions(precision=3) - if args["updates"]: - n_wins = args["updates"] - audio = torch.split(audio, args["updates"], dim=0) - for seg in audio: - if seg.shape[0] < n_wins: - to_pad = torch.zeros(audio[0].shape) - to_pad[: seg.shape[0], :] = seg - seg = to_pad - with torch.no_grad(): - out, h0, c0 = model( - seg.unsqueeze(0).float().to(device), - torch.as_tensor(n_wins).unsqueeze(0).to(device), - h0.to(device), - c0.to(device) - ) - else: - with torch.no_grad(): - out, h0, c0 = model( - audio.unsqueeze(0).float().to(device), - torch.as_tensor(n_wins).unsqueeze(0).to(device), - h0.to(device), - c0.to(device) - ) - - return out, h0, c0 diff --git a/src/phonemizer/phonemizer_yaml.sh b/src/phonemizer/phonemizer_yaml.sh index 2991021..ed2be54 100644 --- a/src/phonemizer/phonemizer_yaml.sh +++ b/src/phonemizer/phonemizer_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.phonemizer.phonemizer --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.phonemizer.phonemizer --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index c726eed..1b4c045 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -12,54 +12,126 @@ from huggingface_hub import login -from src.utils import load_config +from src.utils import load_config, get_audio_paths from src.libs.smart_turn.offline_svad import OfflineVAD -def get_audio_paths(directory: str) -> List[str]: - audio_paths = [] - for entry in os.listdir(directory): - full_path = os.path.join(directory, entry) - if len(os.path.basename(full_path).split('_')) == 4: - continue - if os.path.isdir(full_path): - audio_paths.extend(get_audio_paths(full_path)) - elif entry.endswith(".mp3"): - audio_paths.append(full_path) - return audio_paths - -def postprocess_vad_result(vad_result: List[Dict[str, Any]], duration: float = 15.0) -> Tuple[List[float], List[float]]: - speech_intervals = [ - {'start_time': item['start_time'], 'end_time': item['end_time']} - for item in vad_result if item['prediction'] == 1 - ] - - if not speech_intervals: +from typing import List, Dict, Any, Tuple + +def postprocess_vad_result( + vad_result: List[Dict[str, Any]], + duration: float = 15.0, + min_duration: float = 1.0, + gap_threshold: float = 30.0 +) -> Tuple[List[float], List[float]]: + """ + Сбалансированный алгоритм для постобработки результатов VAD. + Приоритет: законченные фразы, но заполняются большие промежутки между ними. + """ + if not vad_result: return [], [] - timesteps_starts = [] - timesteps_ends = [] - - current_start = speech_intervals[0]['start_time'] - current_end = speech_intervals[0]['end_time'] - - for interval in speech_intervals[1:]: - next_start = interval['start_time'] - next_end = interval['end_time'] - - if next_end - current_start <= duration: - current_end = next_end - else: - if duration / 3 <= current_end - current_start <= duration: - timesteps_starts.append(current_start) - timesteps_ends.append(current_end) - current_start = next_start - current_end = next_end - - if duration / 3 <= current_end - current_start <= duration: - timesteps_starts.append(current_start) - timesteps_ends.append(current_end) + # Этап 1: Сохраняем все семантически законченные фразы (prediction=1) + primary_segments = [] + + # Итерируем по результатам и объединяем последовательные "законченные" сегменты + current_segment_start = None + + for i, item in enumerate(vad_result): + if item['prediction'] == 1: + if current_segment_start is None: + # Начало нового потенциального сегмента + current_segment_start = item['start_time'] + # Конец текущего потенциального сегмента + segment_end = item['end_time'] + segment_duration = segment_end - current_segment_start + + # Проверяем, не превышаем ли мы максимальную длительность + if segment_duration > duration: + # Сохраняем предыдущий сегмент и начинаем новый + if i > 0 and vad_result[i-1]['prediction'] == 1: + primary_segments.append((current_segment_start, vad_result[i-1]['end_time'])) + current_segment_start = item['start_time'] + + # Сохраняем последний накопленный сегмент + if current_segment_start is not None: + last_item = vad_result[-1] + primary_segments.append((current_segment_start, last_item['end_time'])) + + # Фильтруем сегменты по длительности + primary_segments = [(s, e) for s, e in primary_segments if min_duration <= e - s <= duration] + + # Сортируем и убираем дубликаты + primary_segments = sorted(list(set(primary_segments)), key=lambda x: x[0]) - return timesteps_starts, timesteps_ends + if not primary_segments: + return [], [] + + # Этап 2: Заполняем большие промежутки между сохранёнными сегментами + filled_segments = [] + last_end_time = 0.0 + + for start, end in primary_segments: + gap_duration = start - last_end_time + if gap_duration > gap_threshold: + filled_segments.extend(_fill_gap(vad_result, last_end_time, start, duration, min_duration)) + last_end_time = end + + # Проверяем промежуток в конце + audio_end = vad_result[-1]['end_time'] + if audio_end - last_end_time > gap_threshold: + filled_segments.extend(_fill_gap(vad_result, last_end_time, audio_end, duration, min_duration)) + + # Объединяем итоговые сегменты + all_segments = primary_segments + filled_segments + all_segments = sorted(list(set(all_segments)), key=lambda x: x[0]) + + all_starts = [s for s, e in all_segments] + all_ends = [e for s, e in all_segments] + + return all_starts, all_ends + + +def _fill_gap( + vad_result: List[Dict[str, Any]], + gap_start: float, + gap_end: float, + duration: float, + min_duration: float +) -> List[Tuple[float, float]]: + """ + Вспомогательная функция: разбивает промежуток [gap_start, gap_end] на сегменты + длиной [min_duration, duration], используя интервалы из vad_result. + """ + filled_segments = [] + + current_time = gap_start + while current_time < gap_end: + # Находим следующий интервал, который начинается после current_time + next_interval_index = -1 + for i, item in enumerate(vad_result): + if item['start_time'] >= current_time: + next_interval_index = i + break + + if next_interval_index == -1: + break + + seg_start = vad_result[next_interval_index]['start_time'] + seg_end = vad_result[next_interval_index]['end_time'] + + # Объединяем интервалы, пока не превысим max_duration или не дойдем до конца промежутка + for j in range(next_interval_index + 1, len(vad_result)): + if vad_result[j]['end_time'] - seg_start > duration or vad_result[j]['end_time'] > gap_end: + break + seg_end = vad_result[j]['end_time'] + + seg_duration = seg_end - seg_start + if min_duration <= seg_duration <= duration: + filled_segments.append((seg_start, seg_end)) + + current_time = seg_end + + return filled_segments def cut_audio( audio: torch.Tensor, @@ -124,7 +196,10 @@ def process_audio_file(path_audio: str, duration: float): episode_folder = os.path.join(os.path.dirname(path_audio), episode_id) try: + # TODO: don't forget to remove the code audio, sr = torchaudio.load(path_audio) + if audio.shape[-1] / sr <= 5: + logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") if audio.shape[-1] / sr <= duration: return except Exception as e: @@ -135,13 +210,13 @@ def process_audio_file(path_audio: str, duration: float): try: vad_result = smart_vad.process_file(path_audio) - import pickle - with open('data.pkl', 'wb') as file: - pickle.dump(vad_result, file) + # import pickle + # with open('data.pkl', 'wb') as file: + # pickle.dump(vad_result, file) timesteps_starts, timesteps_ends = postprocess_vad_result(vad_result, duration=duration) - if not timesteps_starts: - logger.warning(f"No speech segments found in {path_audio}") + logger.warning(f"No speech segments found in {path_audio}, removed") + os.remove(path_audio) return cut_audio( @@ -177,7 +252,7 @@ def main(args): duration = args.duration or config.get('duration', 15) num_workers = args.num_workers or config.get('num_workers', 4) smart_vad_model = args.smart_vad_model or config.get('smart_vad_model', "pipecat-ai/smart-turn-v2") - + num_gpus = torch.cuda.device_count() available_gpus = list(range(num_gpus)) if num_gpus == 0: @@ -191,8 +266,8 @@ def main(args): return vad_args = { - 'silero_vad_threshold': 0.5, - 'smart_vad_threshold': 0.5, + 'silero_vad_threshold': 0.4, + 'smart_vad_threshold': 0.4, 'smart_vad_path': smart_vad_model } diff --git a/src/preprocess/preprocess_yaml.sh b/src/preprocess/preprocess_yaml.sh index acaf32d..651c91c 100644 --- a/src/preprocess/preprocess_yaml.sh +++ b/src/preprocess/preprocess_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/punctuation/punctuation_yaml.sh b/src/punctuation/punctuation_yaml.sh index c298b63..938b383 100644 --- a/src/punctuation/punctuation_yaml.sh +++ b/src/punctuation/punctuation_yaml.sh @@ -20,4 +20,4 @@ CONFIG_PATH=$(realpath "$1") activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.punctuation.punctuation --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.punctuation.punctuation --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/recovery_from_meta_yamls.sh b/src/recovery_from_meta_yamls.sh index 591034e..2779063 100644 --- a/src/recovery_from_meta_yamls.sh +++ b/src/recovery_from_meta_yamls.sh @@ -19,4 +19,4 @@ activate_venv "$VENV_PATH" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.recovery_from_meta --podcasts_path $PODCASTS_PATH --parquet_path $PARQUET_PATH --num_workers $NUM_WORKERS \ No newline at end of file +taskset -с 0-24 python3 -m src.recovery_from_meta --podcasts_path $PODCASTS_PATH --parquet_path $PARQUET_PATH --num_workers $NUM_WORKERS \ No newline at end of file diff --git a/src/separation/diarization.py b/src/separation/diarization.py new file mode 100644 index 0000000..3f627a8 --- /dev/null +++ b/src/separation/diarization.py @@ -0,0 +1,256 @@ +import argparse +import multiprocessing +import os +from concurrent.futures import ProcessPoolExecutor, as_completed +from functools import partial +from pathlib import Path +from typing import Dict, List + +import pandas as pd +import torch +import torchaudio +from dotenv import load_dotenv +from loguru import logger +from pyannote.audio import Pipeline +from tqdm import tqdm + +from src.utils import get_audio_paths, load_config + +diarization_pipeline = None + +def init_worker(hf_token: str, device_str: str): + global diarization_pipeline + logger.info(f"Initializing worker for diarization on {device_str}...") + + try: + torch.cuda.set_device(device_str) + diarization_pipeline = Pipeline.from_pretrained( + "pyannote/speaker-diarization-3.1", + use_auth_token=hf_token + ).to(torch.device(device_str)) + logger.info(f"Worker initialized successfully for diarization on {device_str}.") + except Exception as e: + logger.error(f"Failed to initialize worker on {device_str}: {e}") + diarization_pipeline = None + +def _save_rttm_file(diarization, rttm_path: str): + with open(rttm_path, "w") as rttm_file: + diarization.write_rttm(rttm_file) + +def process_file(path: Path, one_speaker: bool) -> Dict: + + global diarization_pipeline + if diarization_pipeline is None: + logger.error(f"Diarization pipeline is not initialized. Skipping {path.name}.") + return None + + try: + # 1. Preprocess audio: Load, convert to mono + audio, sr = torchaudio.load(path) + if audio.shape[0] > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + + # 2. Perform diarization + diarization = diarization_pipeline({ + "waveform": audio, + "sample_rate": sr + }) + + # 3. Save RTTM and check speaker count + base_path_str = str(path.with_suffix('')) + rttm_path = base_path_str + '.rttm' + _save_rttm_file(diarization=diarization, rttm_path=rttm_path) + + num_speakers = len({speaker for _, _, speaker in diarization.itertracks(yield_label=True)}) + is_single_speaker = num_speakers == 1 + + # 4. Handle multi-speaker files if one_speaker mode is enabled + if not is_single_speaker and one_speaker: + logger.info(f"Multiple speakers ({num_speakers}) detected in {path.name}. Deleting associated files.") + for ext in ['.mp3', '_giga.txt', '_punct.txt', '_accent.txt', '_e.txt', '_e_phonemes.txt', '.rttm']: + file_to_delete = Path(base_path_str + ext) + if file_to_delete.exists(): + file_to_delete.unlink() + logger.info(f"Deleted {file_to_delete}") + + # 5. Extract metadata from the filename + file_parts = path.name.split('_') + playlist_id = file_parts[-2] if len(file_parts) > 1 else 'N/A' + podcast_id = file_parts[-1].split('.')[0] if len(file_parts) > 0 else 'N/A' + + return { + 'filepath': str(path), + 'is_single_speaker': is_single_speaker, + 'playlist_id': playlist_id, + 'podcast_id': podcast_id, + 'start': file_parts[0] if len(file_parts) > 0 else 'N/A', + 'end': file_parts[1] if len(file_parts) > 1 else 'N/A' + } + + except Exception as e: + logger.error(f"Failed to process {path.name}: {e}") + return None + finally: + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: str) -> List[Path]: + all_audio_paths = get_audio_paths(podcasts_path) + processed_audio_paths = [] + + if result_csv_path.exists(): + logger.info(f"Resuming from existing results file: {result_csv_path}") + df = pd.read_csv(result_csv_path) + processed_audio_paths = df.set_index('filepath').to_dict('index') + + unprocessed_paths = [ + Path(path) for path in processed_audio_paths.keys() + if not isinstance( + processed_audio_paths[path].get('is_single_speaker'), + bool + ) + ] + + return unprocessed_paths + + +def main(args): + load_dotenv() + hf_token = os.getenv("HF_TOKEN") + config = load_config(args.config_path, 'separation') + + podcasts_path = args.podcasts_path or config.get('podcasts_path', './data') + one_speaker = args.one_speaker or config.get('one_speaker', False) + num_workers_per_gpu = args.num_workers or config.get('num_workers_diarization', 1) + + result_csv_path = Path(podcasts_path) / 'balalaika.csv' + available_gpu_ids = list(range(torch.cuda.device_count())) + if not available_gpu_ids: + logger.error("No GPUs available. This script requires at least one GPU. Exiting.") + return + + logger.info(f""" + --- Starting Diarization --- + Podcasts path: {podcasts_path} + One speaker mode: {one_speaker} + Workers per GPU: {num_workers_per_gpu} + GPUs detected: {len(available_gpu_ids)} + -------------------------- + """) + + all_audio_paths = get_unprocessed_audio_paths( + podcasts_path, + result_csv_path + ) + + if not all_audio_paths: + logger.success("All audio files have already been processed.") + return + + logger.info(f"Found {len(all_audio_paths)} new audio files to process.") + + num_devices = len(available_gpu_ids) + files_for_each_device = [[] for _ in range(num_devices)] + for i, path in enumerate(all_audio_paths): + device_assignment_index = i % num_devices + files_for_each_device[device_assignment_index].append(path) + + all_futures = [] + executors = [] + task_fn = partial(process_file, one_speaker=one_speaker) + + for i, device_id in enumerate(available_gpu_ids): + device_str = f'cuda:{device_id}' + files_for_this_device = files_for_each_device[i] + + if not files_for_this_device: + continue + + logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") + + initializer_fn = partial(init_worker, hf_token=hf_token, device_str=device_str) + + executor = ProcessPoolExecutor( + max_workers=num_workers_per_gpu, + initializer=initializer_fn, + mp_context=multiprocessing.get_context('spawn') + ) + executors.append(executor) + + for path in files_for_this_device: + future = executor.submit(task_fn, path) + all_futures.append(future) + + results = [] + for future in tqdm(as_completed(all_futures), total=len(all_futures), desc="Processing audio files"): + try: + result = future.result() + if result: + results.append(result) + except Exception as e: + logger.error(f"A task encountered an error: {e}") + + for executor in executors: + executor.shutdown(wait=True) + + if results: + try: + new_results_df = pd.DataFrame(results) + main_df = pd.read_csv(result_csv_path) + + main_df = main_df.set_index('filepath') + new_results_df = new_results_df.set_index('filepath') + + updated_df = new_results_df.combine_first(main_df) + + updated_df = updated_df.reset_index() + + original_cols = main_df.reset_index().columns.tolist() + new_cols = [col for col in updated_df.columns if col not in original_cols] + final_order = original_cols + new_cols + final_order_unique = list(dict.fromkeys(final_order)) + updated_df = updated_df[final_order_unique] + + if 'Unnamed: 0' in updated_df.columns: + updated_df = updated_df.drop('Unnamed: 0', axis=1) + + updated_df.to_csv(result_csv_path, index=False) + logger.success(f"Processing complete. Updated/added diarization for {len(results)} rows in {result_csv_path}") + + except FileNotFoundError: + logger.error(f"Error: The result file {result_csv_path} was not found. Please run the NISQA script first.") + except Exception as e: + logger.error(f"An error occurred while updating the CSV file: {e}") + else: + logger.warning("Processing finished, but no new results were generated.") + + +if __name__ == "__main__": + multiprocessing.set_start_method('spawn', force=True) + torchaudio.set_audio_backend('soundfile') + + parser = argparse.ArgumentParser(description="Perform speaker diarization on audio files using multiple GPUs.") + parser.add_argument( + "--config_path", + type=str, + help="Path to the main YAML configuration file." + ) + parser.add_argument( + "--podcasts_path", + type=str, + help="Path to the directory containing audio files." + ) + parser.add_argument( + "--one_speaker", + action='store_true', + help="Enable one-speaker mode. If set, files with multiple speakers will be deleted." + ) + parser.add_argument( + "--num_workers", + type=int, + help="Number of worker processes to spawn per GPU." + ) + + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/src/separation/model.py b/src/separation/model.py new file mode 100644 index 0000000..0894d18 --- /dev/null +++ b/src/separation/model.py @@ -0,0 +1,128 @@ +import torch +from torch import nn +import torch.nn.functional as F +import torch.nn as nn +from transformers import AutoModel + + +import torch +from torch import nn +import torch.nn.functional as F + +class BCEWithLogitsLossLS(nn.Module): + def __init__(self, label_smoothing=0.1, pos_weight=None, reduction='mean'): + super(BCEWithLogitsLossLS, self).__init__() + assert 0 <= label_smoothing < 1, "label_smoothing value must be between 0 and 1." + self.label_smoothing = label_smoothing + self.reduction = reduction + self.bce_with_logits = nn.BCEWithLogitsLoss(pos_weight=pos_weight, reduction=reduction) + + def forward(self, input, target): + if self.label_smoothing > 0: + positive_smoothed_labels = 1.0 - self.label_smoothing + negative_smoothed_labels = self.label_smoothing + target = target * positive_smoothed_labels + \ + (1 - target) * negative_smoothed_labels + + loss = self.bce_with_logits(input, target) + return loss + +class WavLMForEndpointing(nn.Module): + def __init__(self, config, n_trainable_layers=6): + super().__init__() + self.wavlm = AutoModel.from_pretrained('microsoft/wavlm-base-plus', config=config) + self.config = config + self.n_trainable_layers = n_trainable_layers + + for param in self.wavlm.parameters(): + param.requires_grad = False + + if self.n_trainable_layers > 0: + for i in range(self.n_trainable_layers): + for param in self.wavlm.encoder.layers[-(i+1)].parameters(): + param.requires_grad = True + + self.pool_attention = nn.Sequential( + nn.Linear(config.hidden_size, 256), + nn.Tanh(), + nn.Linear(256, 1) + ) + + self.classifier = nn.Sequential( + nn.Linear(config.hidden_size, 256), + nn.LayerNorm(256), + nn.GELU(), + nn.Dropout(0.1), + nn.Linear(256, 64), + nn.LayerNorm(64), + nn.GELU(), + nn.Linear(64, 1) + ) + + for module in self.classifier: + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.1) + if module.bias is not None: + module.bias.data.zero_() + + for module in self.pool_attention: + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.1) + if module.bias is not None: + module.bias.data.zero_() + + def attention_pool(self, hidden_states, attention_mask): + attention_weights = self.pool_attention(hidden_states) + + if attention_mask is None: + raise ValueError("attention_mask must be provided for attention pooling") + + attention_weights = attention_weights + ( + (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 + ) + + attention_weights = F.softmax(attention_weights, dim=1) + + # Apply attention to hidden states + weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) + + return weighted_sum + + def forward(self, input_values, attention_mask=None, labels=None): + outputs = self.wavlm(input_values, attention_mask=attention_mask) + hidden_states = outputs[0] + + if attention_mask is not None: + input_length = attention_mask.size(1) + hidden_length = hidden_states.size(1) + ratio = input_length / hidden_length + indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() + attention_mask = attention_mask[:, indices] + attention_mask = attention_mask.bool() + else: + attention_mask = None + + pooled = self.attention_pool(hidden_states, attention_mask) + + logits = self.classifier(pooled) + + if torch.isnan(logits).any(): + raise ValueError("NaN values detected in logits") + + if labels is not None: + pos_weight = ((labels == 0).sum() / (labels == 1).sum()).clamp(min=0.1, max=10.0) + loss_fct = BCEWithLogitsLossLS(pos_weight=pos_weight) + labels = labels.float() + loss = loss_fct(logits.view(-1), labels.view(-1)) + + l2_lambda = 0.01 + l2_reg = torch.tensor(0., device=logits.device) + for param in self.classifier.parameters(): + l2_reg += torch.norm(param) + loss += l2_lambda * l2_reg + + probs = torch.sigmoid(logits.detach()) + return {"loss": loss, "logits": probs} + + probs = torch.sigmoid(logits) + return {"logits": probs} \ No newline at end of file diff --git a/src/separation/music_detect.py b/src/separation/music_detect.py new file mode 100644 index 0000000..eb90bf9 --- /dev/null +++ b/src/separation/music_detect.py @@ -0,0 +1,216 @@ +import argparse +import multiprocessing +import os +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +from typing import Optional + +import torch +import torchaudio +import transformers +from loguru import logger +from safetensors import safe_open +from tqdm import tqdm + +from src.separation.model import WavLMForEndpointing +from src.utils import get_audio_paths, load_config + +g_model = None +g_processor = None +g_device = None +g_threshold = None + + +def init_process( + model_name: str, + checkpoint_path: str, + device: str, + threshold: float +) -> None: + """ + Initializes the model, processor, device, and threshold for a worker process. + This function runs once for each process in the ProcessPoolExecutor. + """ + global g_model, g_processor, g_device, g_threshold + + logger.info(f"Initializing the process on the device {device}...") + + g_device = device + g_threshold = threshold + + try: + g_processor = transformers.AutoFeatureExtractor.from_pretrained(model_name) + config = transformers.AutoConfig.from_pretrained(model_name) + model = WavLMForEndpointing(config) + + with safe_open(checkpoint_path, framework="pt", device="cpu") as f: + state_dict = {key: f.get_tensor(key) for key in f.keys()} + + # Load the state dict and then move the model to the correct device + model.load_state_dict(state_dict) + model.to(g_device) + model.eval() + + g_model = model + logger.info(f"The process on the {device} device has been successfully initialized.") + except Exception as e: + logger.error(f"Error initializing the process on the device {device}: {e}") + # Re-raise the exception to prevent the process from starting incorrectly + raise + + +def process_audio_file(audio_path: Path) -> Optional[str]: + """ + Processes a single audio file to check if it should be removed. + Returns the path of the deleted file if removed, otherwise returns None. + """ + global g_model, g_processor, g_device, g_threshold + + # If globals aren't initialized, something went wrong with init_process + if g_model is None or g_processor is None or g_device is None: + logger.error(f"Global variables are not initialized in the process.") + return None + + try: + waveform, sample_rate = torchaudio.load(audio_path) + + # TODO: don't forget to remove the code + + if (waveform.shape[-1] / sample_rate) <= 5: + os.remove(audio_path) + logger.info(f"{audio_path} -- removed {waveform.shape[-1] / sample_rate}") + + # TODO: don't forget to remove the code + + # Resample if not 16kHz + if sample_rate != 16000: + resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000) + waveform = resampler(waveform) + + # Convert stereo to mono if necessary + if waveform.shape[0] > 1: + waveform = torch.mean(waveform, dim=0, keepdim=True) + + # Ensure waveform is on the correct device for processing + waveform = waveform.to(g_device) + + inputs = g_processor( + waveform.squeeze().cpu().numpy(), # Use .cpu().numpy() to handle device + sampling_rate=16000, + return_tensors="pt", + padding=False, + truncation=False, + ) + + # Move input tensors to the correct device + inputs = {key: inputs[key].to(g_device) for key in inputs.keys()} + + with torch.no_grad(): + result = g_model(**inputs) + + # Check the condition and remove the file + if result['logits'][0][0].item() > g_threshold: + # Use os.remove for a more direct delete operation + # Add a log message to confirm the action + logger.info(f"Удаление файла: {audio_path} (score: {result['logits'][0][0].item():.4f})") + os.remove(audio_path) + return str(audio_path) # Return the path of the removed file + + # Return None if the file is not removed + return None + + except Exception as e: + logger.error(f"Error in file processing{audio_path}: {e}") + return None # Return None in case of an error + + +def main(args): + """ + Main function to orchestrate the multiprocessing and audio processing. + """ + config = load_config(args.config_path, 'separation') + + num_workers_per_gpu = config.get('num_workers_detect', 2) + model_name = 'microsoft/wavlm-base-plus' + checkpoint_path = config.get('checkpoint_path', '/path/to/your/model.safetensors') + audio_data_path = config.get('podcasts_path', '/path/to/your/audio') + threshold = config.get('threshold', 0.5) + + all_audio_files = get_audio_paths(audio_data_path) + if not all_audio_files: + logger.warning(f"Audio files not found in {audio_data_path}") + return + + if not torch.cuda.is_available(): + logger.error("There are no available GPUs. Exit.") + return + + available_gpu_ids = list(range(torch.cuda.device_count())) + num_gpus = len(available_gpu_ids) + + logger.info( + f""" + Starting audio processing: + - Data path: {audio_data_path} + - Model: {model_name} + - Checkpoint: {checkpoint_path} + - Number of GPUs: {num_gpus} (IDs: {available_gpu_ids}) + - Workers on GPU: {num_workers_per_gpu} + - Total workers: {num_gpus * num_workers_per_gpu} + - Total files to process: {len(all_audio_files)} + - Threshold {threshold} + """ + ) + + # Use a dictionary to map GPU IDs to a list of files for better clarity + files_per_gpu = {gpu_id: [] for gpu_id in available_gpu_ids} + for i, path in enumerate(all_audio_files): + gpu_assignment_index = i % num_gpus + gpu_id = available_gpu_ids[gpu_assignment_index] + files_per_gpu[gpu_id].append(path) + + all_futures = [] + executors = [] + + for gpu_id, files_for_this_gpu in files_per_gpu.items(): + if not files_for_this_gpu: + continue + + device_str = f'cuda:{gpu_id}' + logger.info(f"Creating a ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_gpu)} files.") + + executor = ProcessPoolExecutor( + max_workers=num_workers_per_gpu, + initializer=init_process, + initargs=(model_name, checkpoint_path, device_str, threshold) + ) + executors.append(executor) + + for path in files_for_this_gpu: + future = executor.submit(process_audio_file, path) + all_futures.append(future) + + logger.info(f"All {len(all_futures)} issues have been sent for processing. Waiting for completion...") + + deleted_files_count = 0 + with tqdm(total=len(all_futures), desc="Audio file processing") as pbar: + for future in as_completed(all_futures): + result = future.result() + if result: + deleted_files_count += 1 + pbar.update(1) + + for executor in executors: + executor.shutdown() + + logger.info(f"Processing is completed. Total deleted {deleted_files_count} files.") + + +if __name__ == "__main__": + # It's good practice to set the start method at the top level + multiprocessing.set_start_method("spawn", force=True) + + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, required=True) + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/src/separation/nisqa.py b/src/separation/nisqa.py new file mode 100644 index 0000000..f28ee25 --- /dev/null +++ b/src/separation/nisqa.py @@ -0,0 +1,130 @@ +import argparse +import sys +from pathlib import Path +from typing import List + +import pandas as pd +import yaml +from loguru import logger + +from src.utils import get_audio_paths, load_config +from src.libs.NISQA.run_predict import run_nisqa_with_config + + +def _create_input_csv(audio_files: List[Path], temp_dir: Path) -> Path: + csv_file_path = temp_dir / 'input_audio_list.csv' + df = pd.DataFrame({'filepath': [p.resolve() for p in audio_files]}) + df.to_csv(csv_file_path, index=False) + logger.info(f"A temporary CSV file has been created: {csv_file_path}") + return csv_file_path + + +def _save_results(output_dir: Path, final_output_path: Path): + source_file = output_dir / 'NISQA_results.csv' + if not source_file.exists(): + logger.warning(f"The NISQA output file was not found: {source_file}") + return + + final_output_path.parent.mkdir(parents=True, exist_ok=True) + try: + new_df = pd.read_csv(source_file) + + if final_output_path.exists(): + existing_df = pd.read_csv(final_output_path) + new_files_df = new_df[~new_df['filepath'].isin(existing_df['filepath'])] + + if not new_files_df.empty: + result_df = pd.concat([existing_df, new_files_df], ignore_index=True) + logger.success(f"Added {len(new_files_df)} new NISQA results.") + else: + result_df = existing_df + logger.info("There are no new files to add from NISQA.") + else: + result_df = new_df + + if 'Unnamed: 0' in result_df.columns: + result_df = result_df.drop('Unnamed: 0', axis=1) + + result_df.to_csv(final_output_path, index=False) + logger.success(f"Results are saved in: {final_output_path}") + + except Exception as e: + logger.error(f"Could not process and save the results: {e}") + + +def get_unprocessed_audio_paths(podcasts_path: Path, result_csv_path: Path) -> List[Path]: + all_audio_paths = get_audio_paths(str(podcasts_path)) + + if not result_csv_path.exists(): + logger.info("The results file was not found. All found audio files are processed.") + return all_audio_paths + + logger.info(f"Checking an existing results file: {result_csv_path}") + df = pd.read_csv(result_csv_path) + processed_audio_paths = set(df['filepath'].astype(str).to_list()) + + unprocessed_paths = [ + path for path in all_audio_paths + if str(path.resolve()) not in processed_audio_paths + ] + + return unprocessed_paths + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, required=True) + args = parser.parse_args() + + config = load_config(args.config_path, 'separation') + + podcasts_path = Path(config.get('podcasts_path', '.')) + bs = config.get('bs', 16) + num_workers = config.get('num_workers_nisqa', 2) + + nisqa_dir = Path('./src/libs/NISQA').resolve() + pretrained_model = nisqa_dir / 'weights' / 'nisqa.tar' + + final_output_path = podcasts_path / 'balalaika.csv' + temp_dir = final_output_path.parent / 'nisqa_temp' + output_dir = temp_dir / 'nisqa_results' + + output_dir.mkdir(parents=True, exist_ok=True) + + unprocessed_paths = get_unprocessed_audio_paths( + podcasts_path=podcasts_path, + result_csv_path=final_output_path + ) + + if not unprocessed_paths: + logger.warning("Не найдено аудиофайлов для обработки. Выход.") + return + + logger.info(f' Found {len(unprocessed_paths)} files to process.') + csv_file_path = _create_input_csv(unprocessed_paths, temp_dir) + + nisqa_config = { + 'mode': 'predict_csv', + 'pretrained_model': str(pretrained_model), + 'csv_file': str(csv_file_path), + 'csv_deg': 'filepath', + 'output_dir': str(output_dir), + 'bs': bs, + 'num_workers': num_workers, + 'ms_channel': None + } + + try: + logger.info(f"Launching NISQA with configuration: {nisqa_config}") + + run_nisqa_with_config(nisqa_config) + + logger.info("Обработка NISQA завершена. Сохранение результатов...") + _save_results(output_dir=output_dir, final_output_path=final_output_path) + + except Exception as e: + logger.error(f"Произошла ошибка во время выполнения NISQA: {e}", exc_info=True) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/separation/separation.py b/src/separation/separation.py deleted file mode 100644 index cae68e7..0000000 --- a/src/separation/separation.py +++ /dev/null @@ -1,310 +0,0 @@ -import argparse -import os -import multiprocessing -from concurrent.futures import ProcessPoolExecutor, as_completed -from pathlib import Path -from typing import Dict, List -import yaml - -import numpy as np -import pandas as pd -import torch -import torchaudio -from dotenv import load_dotenv -from loguru import logger -from pyannote.audio import Pipeline -from tqdm import tqdm - -from src.libs.nisqa.core.model_torch import model_init -from src.libs.nisqa.utils.process_utils import process -from src.utils import load_config, get_audio_paths - -_global_worker = None - -class Worker: - def __init__( - self, - one_speaker: bool, - nisqa_config_path: str, - gpu_id: int, - hf_token: str, - ): - self.one_speaker = one_speaker - self.nisqa_config_path = nisqa_config_path - self.hf_token = hf_token - self.device = f"cuda:{gpu_id}" - self._init_models() - - def _init_models(self): - - torch.cuda.set_device(self.device) - - try: - with open(self.nisqa_config_path, "r") as f: - args_yaml = yaml.load(f, Loader=yaml.FullLoader) - - self.nisqa_device = self.device - args = {**args_yaml, "inf_device": self.device} - - self.nisqa_model, self.h0, self.c0 = model_init(args) - self.nisqa_args = args - except Exception as e: - logger.warning(f"{e} on {self.device}.") - - - try: - self.diarization_model = Pipeline.from_pretrained( - "pyannote/speaker-diarization-3.1", - use_auth_token=self.hf_token - ).to(torch.device(self.device)) - except Exception as e: - logger.warning(f"{e} on {self.device}.") - - def process_audio(self, audio_path: str) -> Dict: - audio_path = Path(audio_path) - frame_duration = self.nisqa_args.get("frame") - audio_frames, sr, audio = self._preprocess_audio(str(audio_path), frame_duration) - - is_mono = True - is_mono = self._check_single_speaker( - waveform=audio, - sr=sr, - audio_path=audio_path) - - avg_out = self._nisqua_predict(audio_frames, sr) - NOI, COL, DISC, LOUD, MOS = avg_out - - file_parts = audio_path.name.split('_') - playlist_id = file_parts[-2] if len(file_parts) > 0 else 'N/A' - podcast_id = file_parts[-1].split('.')[0] if len(file_parts) > 1 else 'N/A' - - return { - 'audio_path': '/'.join(audio_path.parts[-3:]), - 'is_mono': is_mono, - 'NOI': NOI, - 'COL': COL, - 'DISC': DISC, - 'LOUD': LOUD, - 'MOS': MOS, - 'playlist_id': playlist_id, - 'podcast_id': podcast_id, - 'start': file_parts[0] if len(file_parts) > 0 else 'N/A', - 'end': file_parts[1] if len(file_parts) > 1 else 'N/A' - } - - - def _preprocess_audio(self, audio_path: str, frame_duration: int): - audio, sr = torchaudio.load(audio_path) - if audio.shape[0] != 1: - audio = torch.mean(audio, dim=0, keepdim=True) - audio = audio.squeeze(0) - - audio = audio.to(self.device) - frame_size = int(sr * frame_duration) - if len(audio) % frame_size != 0: - padding = frame_size - (len(audio) % frame_size) - audio = torch.cat([audio, torch.zeros(padding, device=self.device)]) - frames = torch.split(audio, frame_size) - - return frames, sr, audio - - def _check_single_speaker(self, waveform: torch.Tensor, sr: int, audio_path: str) -> bool: - try: - diarization = self.diarization_model({ - "waveform": waveform.unsqueeze(0), - "sample_rate": sr - }) - base_path = os.path.splitext(audio_path)[0] - rttm_path = base_path + '.rttm' - - self._save_rttm_file( - diarization=diarization, - rttm_path=rttm_path - ) - - is_single_speaker = len({speaker for _, _, speaker in diarization.itertracks(yield_label=True)}) == 1 - - if not is_single_speaker and self.one_speaker: - for ext in ['.mp3', '_giga.txt', '_punct.txt', '_accent.txt', '_e.txt', '_e_phonemes.txt']: - file_path = base_path + ext - if os.path.exists(file_path): - os.remove(file_path) - logger.info(f"Deleted {file_path} due to multiple speakers detected") - - return is_single_speaker - - except Exception as e: - logger.error(f"Diarization error on {self.device}: {e}") - return True - - - def _nisqua_predict(self, frames: List[torch.Tensor], sr: int) -> np.ndarray: - - outputs = [] - h, c = self.h0.clone().to(self.device), self.c0.clone().to(self.device) - - for frame in frames: - out, h, c = process(frame.to(self.device), sr, self.nisqa_model, h, c, self.nisqa_args) - outputs.append(out[0].cpu().numpy()) - - return np.mean(outputs, axis=0) - - def _save_rttm_file(self, diarization, rttm_path): - with open(rttm_path, "w") as rttm: - diarization.write_rttm(rttm) - -def _worker_initializer( - one_speaker: bool, - nisqa_config_path: str, - hf_token: str, - gpu_id_assignment_queue - ): - global _global_worker - gpu_id = None - try: - if not gpu_id_assignment_queue.empty(): - gpu_id = gpu_id_assignment_queue.get() - - _global_worker = Worker( - one_speaker=one_speaker, - nisqa_config_path=nisqa_config_path, - gpu_id=gpu_id, - hf_token=hf_token - ) - except Exception as e: - logger.error(f"Failed to initialize worker process on {f'GPU {gpu_id}' if gpu_id is not None else 'CPU'}: {e}") - -def _process_audio_task(audio_path: str) -> Dict: - global _global_worker - - if _global_worker is None: - logger.error(f"Worker not initialized in this process. Cannot process {audio_path}.") - try: - result = _global_worker.process_audio(audio_path) - return result - except Exception as e: - logger.error(f"Error during audio processing for {audio_path} by worker on {_global_worker.device}: {e}") - return None - finally: - if _global_worker and _global_worker.device.startswith("cuda"): - torch.cuda.empty_cache() - -def main(args): - load_dotenv() - hf_token = os.getenv("HF_TOKEN") - config = load_config(args.config_path, 'separation') - - podcasts_path = args.podcasts_path if args.podcasts_path else config.get('podcasts_path', '/../../../balalaika') - one_speaker = args.one_speaker if args.one_speaker else config.get('one_speaker', False) - num_workers = args.num_workers if args.num_workers else config.get('num_workers', 4) - - nisqa_config_path = args.nisqa_config if args.nisqa_config else config.get('nisqa_config', '') - num_gpus = torch.cuda.device_count() - - if num_gpus == 0: - logger.error("No GPUs available. Exiting.") - return - - actual_max_workers = num_gpus - gpu_ids_available = list(range(num_gpus)) - - logger.info(f""" - Using params: - Podcasts path: {podcasts_path} - One speaker : {one_speaker} - num_workers : {num_workers} - Number of GPUs detected: {num_gpus} - """) - - audio_paths = get_audio_paths(podcasts_path) - result_csv_path = Path(podcasts_path) / 'results.csv' - - - if os.path.exists(result_csv_path): - logger.info('csv file exists') - df = pd.read_csv(result_csv_path) - processed_audio_paths = set(df['audio_path'].tolist()) - else: - processed_audio_paths = set() - logger.info(f'csv does not exist: found {len(audio_paths) - len(processed_audio_paths)} files') - - audio_paths_to_process = [ - audio_path for audio_path in audio_paths - if str('/'.join(Path(audio_path).parts[-3:])) not in processed_audio_paths - ] - - - if not audio_paths: - logger.error(f"No audio files found in {podcasts_path}") - return - - results = [] - num_workers_per_gpu = num_workers - manager = multiprocessing.Manager() - gpu_id_assignment_queue = manager.Queue() - - for gid in gpu_ids_available: - for _ in range(num_workers_per_gpu): - gpu_id_assignment_queue.put(gid) - - actual_max_workers = len(gpu_ids_available) * num_workers_per_gpu - - if actual_max_workers == 0: - actual_max_workers = 1 - if gpu_id_assignment_queue.empty(): - gpu_id_assignment_queue.put(None) - - with ProcessPoolExecutor( - max_workers=actual_max_workers, - mp_context=multiprocessing.get_context('spawn'), - initializer=_worker_initializer, - initargs=(one_speaker, nisqa_config_path, hf_token, gpu_id_assignment_queue) - ) as executor: - futures = [executor.submit(_process_audio_task, str(path)) for path in audio_paths_to_process] - - with tqdm(total=len(audio_paths_to_process), desc="Processing files") as pbar: - for future in as_completed(futures): - result = future.result() - if result: - results.append(result) - pbar.update(1) - - csv_path = Path(podcasts_path) / "results.csv" - pd.DataFrame(results).to_csv( - csv_path, - mode='a', - header=not csv_path.exists(), - index=False - ) - logger.success(f"Processing completed successfully. Results saved to {result_csv_path}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Process audio files using multiple GPUs") - parser.add_argument( - "--config_path", - type=str, - help="Path to the YAML configuration file." - ) - parser.add_argument( - "--nisqa_config", - type=str, - help="Path to the NISQA YAML configuration file." - ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to the directory containing podcast audio files." - ) - parser.add_argument( - "--one_speaker", - type=bool, - help="Boolean flag to indicate if only one speaker is expected per audio file" - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of worker processes per GPU for parallel processing." - ) - - args = parser.parse_args() - main(args) \ No newline at end of file diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index a3053f1..ee9b1d3 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -21,4 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.separation.separation --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" +taskset -c 0-24 python3 -m src.separation.nisqa --config_path "$CONFIG_PATH" +taskset -c 0-24 python3 -m src.separation.diarization --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 6b61aea..658e163 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -15,6 +15,7 @@ from src.utils import get_audio_paths, load_config model = None +SUPPORTED_TIME_STAMPS = ['giga_ctc_lm', 'tone'] def init_worker(model_name: str, device_str: str, **kwargs): """ @@ -25,11 +26,11 @@ def init_worker(model_name: str, device_str: str, **kwargs): try: if 'giga' in model_name: - model = GigaAMWrapper(model_id=model_name, device=device_str, lm_path=kwargs.get('lm_path')) + model = GigaAMWrapper(model_id=model_name, device=device_str, **kwargs) elif 'tone' in model_name: - model = ToneWrapper(model_id=model_name, device=device_str) + model = ToneWrapper(model_id=model_name, device=device_str, **kwargs) elif 'vosk' in model_name: - model = VoskWrapper(model_id=kwargs.get('vosk_path'), device=device_str) + model = VOSKCUDAWrapper(model_id=kwargs.get('vosk_path'), device=device_str, **kwargs) else: raise ValueError(f"Unknown model type for '{model_name}'") logger.info(f"Worker initialized successfully for model '{model_name}' on {device_str}.") @@ -38,38 +39,41 @@ def init_worker(model_name: str, device_str: str, **kwargs): model = None -def process_file(path: Path, model_name_for_output: str, with_timestamps: bool): +def process_batch(paths: List[Path], model_name_for_output: str, with_timestamps: bool): """ - Transcribes a single audio file using the globally initialized model. + Transcribes a batch of audio files using the globally initialized model. Generates .txt and optionally .tst files. """ global model if model is None: - logger.error(f"Model is not initialized in this worker. Skipping {path.name}.") + logger.error(f"Model is not initialized in this worker. Skipping batch starting with {paths[0].name}.") return - txt_path = path.with_name(f"{path.stem}_{model_name_for_output}.txt") - tst_path = path.with_name(f"{path.stem}_{model_name_for_output}.tst") - - if txt_path.exists(): - logger.info(f"Skipping already transcribed file: {txt_path}") - return + txt_paths = [path.with_name(f"{path.stem}_{model_name_for_output}.txt") for path in paths] + tst_paths = [path.with_name(f"{path.stem}_{model_name_for_output}.tst") for path in paths] + audio_paths_str = [str(p) for p in paths] try: if with_timestamps: - plain_text, tst_content = model.transcribe_with_timestamps(str(path)) + plain_texts, tst_contents = model.transcribe_batch_with_timestamps(audio_paths_str) else: - plain_text = model.transcribe(str(path)) - tst_content = "" + plain_texts = model.transcribe_batch(audio_paths_str) + + + # Transcribe + for txt_path, plain_text in zip(txt_paths, plain_texts): + with open(txt_path, "w", encoding="utf-8") as f: + f.write(plain_text) - with open(txt_path, "w", encoding="utf-8") as f: - f.write(plain_text) + # Timestamps + if with_timestamps and tst_contents: + for tst_path, tst_content in zip(tst_paths, tst_contents): + if tst_content: + with open(tst_path, "w", encoding="utf-8") as f: + f.write(tst_content) - if with_timestamps and tst_content: - with open(tst_path, "w", encoding="utf-8") as f: - f.write(tst_content) except Exception as e: - logger.error(f"Failed to process {path.name}: {e}") + logger.error(f"Failed to process batch starting with {paths[0].name}: {e}") def get_valid_audio_paths(src_path: str, model_name_for_output: str) -> List[Path]: @@ -86,14 +90,9 @@ def get_valid_audio_paths(src_path: str, model_name_for_output: str) -> List[Pat def main(args): - config = load_config(args.config_path, 'transcription') if args.config_path else {} - - model_names = args.model_names or config.get('model_names', ['giga_rnnt']) - num_workers_per_gpu = args.num_workers or config.get('num_workers', 1) - src_path = args.podcasts_path or config.get('podcasts_path', '.') - lm_path = args.lm_path or config.get('lm_path', None) - with_timestamps = args.with_timestamps or config.get('with_timestamps', False) - vosk_path = args.vosk_path or config.get('vosk_path', None) + config = load_config(args.config_path, 'transcription') + model_names = config.get('model_names', ['giga_rnnt']) + src_path = config.get('podcasts_path', '.') logger.info(f"Starting transcription run for models: {model_names}") @@ -103,18 +102,21 @@ def main(args): raise num_devices = len(available_gpu_ids) - for model_name in model_names: logger.info(f"--- Processing model: {model_name} ---") - + model_config = config.get('giga') if 'giga' in model_name else config.get(model_name) + num_workers_per_gpu = model_config.get('num_workers') + batch_size = model_config.get('batch_size') model_name_for_output = 'vosk' if 'vosk' in model_name else model_name - timestamps_supported = 'giga_ctc' in model_name or 'tone' in model_name or 'vosk' in model_name - + with_timestamps = config.get('with_timestamps') + timestamps_supported = model_name in SUPPORTED_TIME_STAMPS current_with_timestamps = with_timestamps and timestamps_supported + if with_timestamps and not timestamps_supported: logger.warning(f"Timestamps requested but not supported for model '{model_name}'. Disabling.") all_audio_paths = get_valid_audio_paths(src_path, model_name_for_output) + if not all_audio_paths: logger.info(f"No new audio files to process for model '{model_name}'. Skipping.") continue @@ -123,15 +125,12 @@ def main(args): files_for_each_device = [[] for _ in range(num_devices)] for i, path in enumerate(all_audio_paths): - device_assignment_index = i % num_devices - files_for_each_device[device_assignment_index].append(path) + files_for_each_device[i % num_devices].append(path) all_futures = [] executors = [] - init_kwargs = {'lm_path': lm_path, 'vosk_path': vosk_path} - task_fn = partial(process_file, model_name_for_output=model_name_for_output, with_timestamps=current_with_timestamps) - + task_fn = partial(process_batch, model_name_for_output=model_name_for_output, with_timestamps=current_with_timestamps) for i, device_id in enumerate(available_gpu_ids): device_str = f'cuda:{device_id}' if device_id != 'cpu' else 'cpu' files_for_this_device = files_for_each_device[i] @@ -141,7 +140,7 @@ def main(args): logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") - initializer_fn = partial(init_worker, model_name, device_str, **init_kwargs) + initializer_fn = partial(init_worker, model_name, device_str, **model_config) executor = ProcessPoolExecutor( max_workers=num_workers_per_gpu, @@ -150,9 +149,11 @@ def main(args): ) executors.append(executor) - for path in files_for_this_device: - future = executor.submit(task_fn, path) - all_futures.append(future) + for j in range(0, len(files_for_this_device), batch_size): + batch = files_for_this_device[j:j + batch_size] + if batch: + future = executor.submit(task_fn, batch) + all_futures.append(future) for future in tqdm(as_completed(all_futures), total=len(all_futures), desc=f"Transcribing ({model_name})"): try: @@ -165,7 +166,8 @@ def main(args): logger.info(f"Finished processing model: {model_name}") - rover_wrapper = ROVERWrapper(podcasts_path=src_path) + logger.info(f"Starting ROVER processing") + rover_wrapper = ROVERWrapper(podcasts_path=src_path, model_names=model_names) rover_wrapper.aggregate_and_save() logger.info(f"Finished processing ROVER") @@ -173,16 +175,7 @@ def main(args): if __name__ == "__main__": multiprocessing.set_start_method('spawn', force=True) - torchaudio.set_audio_backend('soundfile') - parser = argparse.ArgumentParser(description="Transcribe audio files in parallel using multiple GPUs/CPUs.") parser.add_argument("--config_path", type=str, help="Path to the configuration YAML file.") - parser.add_argument("--podcasts_path", type=str, help="Path to the directory containing audio files.") - parser.add_argument("--num_workers", type=int, help="Number of worker processes per device.") - parser.add_argument("--model_names", nargs='+', help="One or more model names to use (e.g., 'giga_ctc_lm' 'tone' 'vosk').") - parser.add_argument("--lm_path", type=str, help="Path to the KenLM language model binary file for GigaAM-CTC.") - parser.add_argument('--vosk_path', type=str, help="Path to the Vosk model directory.") - parser.add_argument('--with_timestamps', action='store_true', help="Enable to generate .tst files with word timestamps (if supported by model).") - args = parser.parse_args() main(args) diff --git a/src/transcription/transcription_yaml.sh b/src/transcription/transcription_yaml.sh index e221468..5735cb0 100644 --- a/src/transcription/transcription_yaml.sh +++ b/src/transcription/transcription_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.transcription.transcription --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 0-24 python3 -m src.transcription.transcription --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py index bf9e516..b31450d 100644 --- a/src/transcription/transcripton_base.py +++ b/src/transcription/transcripton_base.py @@ -1,69 +1,105 @@ -import json +import math +import random from abc import ABC, abstractmethod -from typing import Tuple, List -from pathlib import Path from collections import defaultdict +from pathlib import Path +from typing import List, Tuple +import time +import gigaam +import kaldifeat +import miniaudio +import numpy as np +import pandas as pd +import pyctcdecode +import sentencepiece as spm +import sherpa_onnx import torch import torchaudio -import numpy as np -import miniaudio +from crowdkit.aggregation import ROVER from loguru import logger -import pyctcdecode -import gigaam +from torch.nn.utils.rnn import pad_sequence from tone import StreamingCTCPipeline, TextPhrase -from crowdkit.aggregation import ROVER -import pandas as pd -import sherpa_onnx -import wave -from src.utils import get_audio_paths, read_file_content +from tqdm import tqdm + +from src.utils import read_file_content, get_audio_paths +from src.utils_asr import (AttributeDict, LmScorer, NgramLm, + modified_beam_search_LODR) + class ASRWrapper(ABC): """Abstract Base Class for ASR model wrappers.""" @abstractmethod def __init__(self, model_id: str, device: str, **kwargs): - """Initializes the model.""" - pass + """ + Initializes the ASR model. + + Args: + model_id (str): The identifier for the model. + device (str): The device for computations (e.g., 'cuda:0'). + **kwargs: Additional arguments for model configuration. + """ + self.model_id = model_id + self.device = device @abstractmethod - def transcribe(self, audio_path: str) -> str: - """Transcribes an audio file and returns the text.""" + def transcribe_batch(self, audio_paths: List[str]) -> List[str]: + """ + Transcribes a batch of audio files and returns the texts. + + Args: + audio_paths (List[str]): A list of paths to the audio files. + + Returns: + List[str]: A list of transcribed texts. + """ pass @abstractmethod - def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: - """Transcribes an audio file and returns the text and timestamps content.""" + def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: + """ + Transcribes a batch of audio files, returning texts and timestamps. + + Args: + audio_paths (List[str]): A list of paths to the audio files. + + Returns: + Tuple[List[str], List[str]]: A tuple of two lists: + - A list of transcribed texts. + - A list of strings with timestamps for each audio. + """ pass class GigaAMWrapper(ASRWrapper): """Wrapper for GigaAM models (CTC and RNN-T).""" + + GIGA_AM_FRAME_SIZE_MS = 40 # Duration of a single frame in ms for CTC models + def __init__(self, model_id: str, device: str, **kwargs): - logger.info(f"Initializing GigaAM model '{model_id}' on {device}") - self.model_id = model_id - self.device = device - self.model_type = 'ctc' if 'ctc' in model_id else 'rnnt' - self.use_lm = 'lm' in model_id - self.model = gigaam.load_model(self.model_type, device=device) + super().__init__(model_id, device) + logger.info(f"Initializing GigaAM model '{self.model_id}' on device {self.device}") + + self.model_type = 'ctc' if 'ctc' in self.model_id else 'rnnt' + self.use_lm = 'lm' in self.model_id + self.model = gigaam.load_model(self.model_type, device=self.device) self.target_sr = 16_000 self.decoder = None - - # Frame duration for timestamp calculation - self.GIGA_AM_FRAME_SIZE_MS = 40 self.sec_per_frame = self.GIGA_AM_FRAME_SIZE_MS / 1000.0 if self.use_lm: if self.model_type != 'ctc': - logger.warning(f"LM decoding is only supported for CTC models, but model is {self.model_type}. Ignoring LM.") + logger.warning(f"LM decoding is only supported for CTC models, but got {self.model_type}. LM will be ignored.") self.use_lm = False elif 'lm_path' not in kwargs: - logger.error("'lm_path' is required for GigaAM with LM but was not provided.") + logger.error("'lm_path' is required for GigaAM with LM but was not provided. LM will not be used.") self.use_lm = False else: self._init_lm(kwargs['lm_path']) def _init_lm(self, lm_path: str, alpha: float = 0.5, beta: float = 1.0): + """Initializes the CTC decoder with a language model.""" logger.info(f"Building CTC decoder with LM: {lm_path}") try: vocab = self.model.decoding.tokenizer.vocab @@ -76,52 +112,59 @@ def _init_lm(self, lm_path: str, alpha: float = 0.5, beta: float = 1.0): except Exception as e: logger.error(f"Failed to build CTC decoder: {e}") self.decoder = None + self.use_lm = False - def transcribe(self, audio_path: str) -> str: + @torch.inference_mode() + def transcribe_batch(self, audio_paths: List[str]) -> List[str]: + """Transcribes a batch of audio files.""" + # If an LM is used, the transcription logic is in the method with timestamps if self.use_lm and self.decoder: - text, _ = self.transcribe_with_timestamps(audio_path) - return text - - text = self.model.transcribe(audio_path) - return text - - def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: - if self.model_type != 'ctc': - logger.info(f"Timestamp generation is only available for CTC models. Model '{self.model_id}' is RNN-T. Returning empty timestamps.") - text = self.transcribe(audio_path) - return text, "" + texts, _ = self.transcribe_batch_with_timestamps(audio_paths) + return texts - if self.model_type == 'rnnt' or self.decoder is None: - text = self.transcribe(audio_path) - return text, "" + texts = [self.model.transcribe(audio_path) for audio_path in audio_paths] + return texts - audio = self._read_audio(audio_path) - length = torch.tensor([audio.shape[-1]], device=self.device) + @torch.inference_mode() + def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: + """Transcribes audio, returning text and timestamps (only for CTC with LM).""" + if self.model_type != 'ctc' or self.decoder is None: + logger.warning("Timestamps are only available for CTC models with a language model.") + texts = self.transcribe_batch(audio_paths) + # Return empty strings for timestamps + return texts, [''] * len(texts) - encoded, _ = self.model.forward(audio.to(self.device), length) - logits = self.model.head(encoded).squeeze(0).detach().cpu().numpy() - - beams = self.decoder.decode_beams(logits, beam_width=100) - - best_beam = beams[0] - result_text = best_beam[0] - word_timestamps_raw = best_beam[2] + result_texts = [] + result_timestamps = [] - word_timestamps_formatted = self._to_simple_timestamps(word_timestamps_raw) + for audio_path in audio_paths: + audio = self._read_audio(audio_path).to(self.device) + length = torch.tensor([audio.shape[-1]], device=self.device) + + encoded, _ = self.model.forward(audio, length) + logits = self.model.head(encoded).squeeze(0).detach().cpu().numpy() + + beams = self.decoder.decode_beams(logits, beam_width=100) + best_beam = beams[0] + text, _, word_timestamps_raw, _, _ = best_beam + formatted_timestamps = self._to_simple_timestamps(word_timestamps_raw) + + result_texts.append(text) + result_timestamps.append(formatted_timestamps) + + return result_texts, result_timestamps - return result_text, word_timestamps_formatted - def _read_audio(self, path_to_file: str) -> torch.Tensor: + """Reads an audio file, resamples it, and converts it to mono.""" audio, sr = torchaudio.load(path_to_file) if sr != self.target_sr: audio = torchaudio.functional.resample(audio, sr, self.target_sr) - if audio.dim() > 1 and audio.size(0) > 1: audio = torch.mean(audio, dim=0, keepdim=True) - return audio - + def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int]]]) -> str: + """Formats timestamps into a string.""" output_lines = [] for word, (start_frame, end_frame) in word_timestamps: start_time = start_frame * self.sec_per_frame @@ -133,45 +176,164 @@ def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int] class ToneWrapper(ASRWrapper): """Wrapper for the Tone streaming CTC model.""" def __init__(self, model_id: str, device: str, **kwargs): - logger.info(f"Initializing Tone model '{model_id}' on {device}") - self.tone_pipeline = StreamingCTCPipeline.from_hugging_face() - self.device = 'cpu' # Tone is CPU-based + super().__init__(model_id, device) + logger.info(f"Initializing Tone model '{self.model_id}' on device {self.device}") + + if 'cuda' in self.device: + try: + self.device_id = int(self.device.split(':')[-1]) + except (ValueError, IndexError): + logger.error(f"Invalid CUDA device format: {self.device}. Using ID 0.") + self.device_id = 0 + else: + self.device_id = -1 # ID for CPU + + self.tone_pipeline = StreamingCTCPipeline.from_hugging_face(device_id=self.device_id) self.target_sr = 8_000 - def transcribe(self, audio_path: str) -> str: - audio = self._read_audio(audio_path) - phrases = self.tone_pipeline.forward_offline(audio) - result_text = ' '.join([phrase.text for phrase in phrases]).strip() - return result_text - + @torch.inference_mode() + def transcribe_batch(self, audio_paths: List[str]) -> List[str]: + """Transcribes a batch of audio files.""" + result_texts = [] + for audio_path in audio_paths: + audio = self._read_audio(audio_path) + phrases = self.tone_pipeline.forward_offline(audio) + result_text = ' '.join([phrase.text for phrase in phrases]).strip() + result_texts.append(result_text) + return result_texts + def _read_audio(self, path_to_file: str) -> np.ndarray: audio = miniaudio.decode_file(str(path_to_file), nchannels=1, sample_rate=8000) assert audio.sample_rate == 8000 assert audio.nchannels == 1 - return np.asarray(audio.samples, dtype=np.int16).astype(np.int32) - - def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: - audio_tensor = self._read_audio(audio_path) - phrases: List[TextPhrase] = self.tone_pipeline.forward_offline(audio_tensor) - full_text_parts = [] - timestamp_lines = [] + return np.asarray(audio.samples, dtype=np.int16).astype(np.int32) + + @torch.inference_mode() + def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: + """Transcribes audio, returning text and phrase-level timestamps.""" + result_texts = [] + results_timestamps = [] + for audio_path in audio_paths: + audio_tensor = self._read_audio(audio_path) + phrases: List[TextPhrase] = self.tone_pipeline.forward_offline(audio_tensor) + + full_text_parts = [] + timestamp_lines = [] + + for phrase in phrases: + if phrase.text: + full_text_parts.append(phrase.text) + # TODO: add logic for word-level alignment if needed + timestamp_lines.append(f"{phrase.text} {phrase.start_time:.3f} {phrase.end_time:.3f}") - # TODO: add logic for word alignment - for phrase in phrases: - if phrase.text: - full_text_parts.append(phrase.text) - timestamp_lines.append(f"{phrase.text} {phrase.start_time:.3f} {phrase.end_time:.3f}") + result_text = " ".join(full_text_parts).strip() + result_timestamps = "\n".join(timestamp_lines) - result_text = " ".join(full_text_parts).strip() - result_timestamps = "\n".join(timestamp_lines) + result_texts.append(result_text) + results_timestamps.append(result_timestamps) - return result_text, result_timestamps + return result_texts, results_timestamps -class VoskWrapper(ASRWrapper): +class VOSKCUDAWrapper(ASRWrapper): + """Wrapper for a custom VOSK model adapted for CUDA.""" + def __init__(self, model_id: str, device: str, **kwargs): + super().__init__(model_id, device) + logger.info(f"Initializing VOSK CUDA model '{self.model_id}' on device {self.device}") + vosk_path = Path(kwargs['vosk_path']) + self.nn_model_filename = str(vosk_path / 'am' / 'jit_script.pt') + self.ngram_path = str(vosk_path / 'lm' / '2gram.fst.txt') + self.lm_exp_dir_vosk = str(vosk_path / 'lm') + self.bpe_model = str(vosk_path / 'lang' / 'bpe.model') + + self.target_sr = 16_000 + self.beam_size = kwargs['beam_size'] + self.lm_scale = kwargs['lm_scale'] + self.model = torch.jit.load(self.nn_model_filename).to(self.device).eval() + + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.bpe_model) + + opts = kaldifeat.FbankOptions() + opts.device = self.device + opts.frame_opts.dither = 3e-5 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = self.target_sr + opts.mel_opts.num_bins = 80 + opts.mel_opts.high_freq = -400 + self.fbank = kaldifeat.Fbank(opts) + + params = AttributeDict({ + "lm_vocab_size": 500, + "rnn_lm_embedding_dim": 2048, + "rnn_lm_hidden_dim": 2048, + "rnn_lm_num_layers": 3, + "rnn_lm_tie_weights": True, + "lm_epoch": 99, + "lm_exp_dir": self.lm_exp_dir_vosk, + "lm_avg": 1, + }) + + self.LM = LmScorer( + lm_type="rnn", + params=params, + device=self.device, + lm_scale=self.lm_scale, + ).to(self.device).eval() + + self.ngram_lm = NgramLm( + self.ngram_path, + backoff_id=500, + is_binary=False, + ) + self.ngram_lm_scale = kwargs['ngram_lm_scale'] + + @torch.inference_mode() + def transcribe_batch(self, audio_paths: List[str]) -> List[str]: + """Transcribes a batch of audio files.""" + waves = [self._read_audio(path).to(self.device) for path in audio_paths] + features = self.fbank(waves) + feature_lengths = torch.tensor([f.size(0) for f in features], device=self.device) + features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10)) + encoder_out, encoder_out_lens = self.model.encoder( + features=features, + feature_lengths=feature_lengths, + ) + # start_time = time.time() + hyps = modified_beam_search_LODR( + model=self.model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=self.beam_size, + LODR_lm=self.ngram_lm, + LODR_lm_scale=self.ngram_lm_scale, + LM=self.LM, + ) + # print(time.time() - start_time, 'hyps') + result_texts = [self.sp.decode(hyp) for hyp in hyps] + return result_texts + + @torch.inference_mode() + def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: + """This model does not support timestamp generation.""" + texts = self.transcribe_batch(audio_paths) + return texts, [''] * len(audio_paths) + + def _read_audio(self, path_to_file: str) -> torch.Tensor: + """Reads an audio file, resamples it, and converts it to mono.""" + audio, sr = torchaudio.load(path_to_file) + if sr != self.target_sr: + audio = torchaudio.functional.resample(audio, sr, self.target_sr) + if audio.dim() > 1 and audio.size(0) > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + return audio.squeeze(0) + + +class VoskSherpaOnnxWrapper(ASRWrapper): """Wrapper for Vosk-style models using sherpa-onnx.""" def __init__(self, model_id: str, device: str = 'cpu', **kwargs): logger.info(f"Initializing sherpa-onnx based ASR model from '{model_id}' on {device}") + device = 'cpu' model_path = Path(model_id) encoder_path = str(model_path / "am-onnx" / "encoder.onnx") decoder_path = str(model_path / "am-onnx" / "decoder.onnx") @@ -187,19 +349,24 @@ def __init__(self, model_id: str, device: str = 'cpu', **kwargs): decoder=decoder_path, joiner=joiner_path, tokens=tokens_path, - num_threads=0, + num_threads=4, provider=device, sample_rate=self.target_sr, decoding_method="modified_beam_search" ) logger.info(f"sherpa-onnx recognizer initialized successfully on {device}.") - def transcribe(self, audio_path: str) -> str: - audio = self._read_audio(audio_path) - s = self.recognizer.create_stream() - s.accept_waveform(self.target_sr, audio) - self.recognizer.decode_stream(s) - return s.result.text.strip() + @torch.inference_mode() + def transcribe_batch(self, audio_paths: List[str]) -> List[str]: + result_texts = [] + for audio_path in audio_paths: + audio = self._read_audio(audio_path) + s = self.recognizer.create_stream() + s.accept_waveform(self.target_sr, audio) + self.recognizer.decode_stream(s) + result_texts.append(s.result.text.strip()) + + return result_texts def _read_audio(self, path_to_file: str) -> Tuple[np.ndarray, int]: """Reads audio and returns a float32 numpy array for sherpa-onnx.""" @@ -218,48 +385,79 @@ def _read_audio(self, path_to_file: str) -> Tuple[np.ndarray, int]: return audio_samples - def transcribe_with_timestamps(self, audio_path: str) -> Tuple[str, str]: + @torch.inference_mode() + def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: """Transcribes an audio file and returns the text and word-level timestamps.""" - # TODO: add logic for word alignment - audio = self._read_audio(audio_path) - s = self.recognizer.create_stream() - s.accept_waveform(self.target_sr, audio) - self.recognizer.decode_stream(s) - - result = s.result - full_text = result.text.strip() - - timestamp_lines = [] - for token, timestamp in zip(result.tokens, result.timestamps): - timestamp_lines.append(f"{token} {timestamp:.3f} {timestamp:.3f}") + result_texts = [] + results_timestamps = [] + for audio_path in audio_paths: + # TODO: add logic for word alignment + audio = self._read_audio(audio_path) + s = self.recognizer.create_stream() + s.accept_waveform(self.target_sr, audio) + self.recognizer.decode_stream(s) + + result = s.result + full_text = result.text.strip() - result_timestamps = "\n".join(timestamp_lines) + timestamp_lines = [] + for token, timestamp in zip(result.tokens, result.timestamps): + timestamp_lines.append(f"{token} {timestamp:.3f} {timestamp:.3f}") + + result_timestamps = "\n".join(timestamp_lines) + + result_texts.append(full_text) + results_timestamps.append(result_timestamps) - return full_text, result_timestamps + return result_texts, results_timestamps + class ROVERWrapper: - def __init__(self, podcasts_path: str): + """Aggregates transcription results from multiple models using ROVER.""" + + def __init__(self, podcasts_path: str, model_names: List[str]): + """ + Args: + podcasts_path (str): Path to the directory with transcription files. + model_names (List[str]): A list of model suffixes to include in the aggregation. + """ self.podcasts_path = Path(podcasts_path) + self.model_names = model_names + # self.model_names = ['giga_ctc_lm', 'vosk', 'tone', 'giga_ctc', 'giga_rnnt'] self.tokenizer = lambda s: s.lower().split() self.detokenizer = lambda tokens: ' '.join(tokens) self.rover_aggregator = ROVER(self.tokenizer, self.detokenizer) - def _collate_transcriptions(self) -> pd.DataFrame: + def aggregate_and_save(self): + """ + Performs transcription aggregation and saves the results. + + This method now first finds all audio files and then collects the + corresponding transcription files for each model. + """ + logger.info("Starting transcription aggregation based on audio files.") + + all_audio_paths = get_audio_paths(str(self.podcasts_path)) + + if not all_audio_paths: + logger.warning("Audio files not found. Aggregation finished.") + return + records = [] - transcription_groups = defaultdict(list) - - txt_files = list(self.podcasts_path.rglob("*.txt")) - for txt_path in txt_files: - base_name = txt_path.stem.rsplit('_', 1)[0] - audio_path = txt_path.with_name(f"{base_name}.mp3") - if audio_path.exists(): - transcription_groups[audio_path].append(txt_path) + excluded_patterns = ['_rover', '_phonemes', '_accent'] + + for audio_path in tqdm(all_audio_paths, desc="Aggregating transcriptions"): + if any(pattern in audio_path.stem for pattern in excluded_patterns): + continue + + for model_name in self.model_names: + transcript_path = audio_path.with_name(f"{audio_path.stem}_{model_name}.txt") + + if not transcript_path.exists(): + continue - for audio_path, txt_paths in transcription_groups.items(): - for txt_path in txt_paths: try: - model_name = txt_path.stem.replace(f"{audio_path.stem}_", "") - text = read_file_content(txt_path) + text = read_file_content(transcript_path) if text: records.append({ 'task': str(audio_path), @@ -267,20 +465,26 @@ def _collate_transcriptions(self) -> pd.DataFrame: 'text': text }) except Exception as e: - print(f"Error reading the file {txt_path}: {e}") - - return pd.DataFrame(records) + logger.error(f"Error reading file {transcript_path}: {e}") + + df = pd.DataFrame(records) + if df.empty: + logger.warning("No transcriptions found for aggregation. Check file paths and names.") + return - def aggregate_and_save(self): - df = self._collate_transcriptions() df['text'] = df['text'].str.lower() + logger.info(f"Running ROVER on {len(df['task'].unique())} unique audio files...") result = self.rover_aggregator.fit_predict(df) + logger.info("Saving aggregated results...") for task_path, agg_text in result.items(): audio_path = Path(task_path) output_path = audio_path.with_name(f"{audio_path.stem}_rover.txt") - with open(output_path, "w", encoding="utf-8") as f: - f.write(agg_text) - - + try: + with open(output_path, "w", encoding="utf-8") as f: + f.write(agg_text) + except IOError as e: + logger.error(f"Failed to write result to {output_path}: {e}") + + logger.info("Aggregation complete.") \ No newline at end of file diff --git a/src/utils.py b/src/utils.py index bfd3cf6..c15ce3e 100644 --- a/src/utils.py +++ b/src/utils.py @@ -29,15 +29,17 @@ def read_file_content(file_path): except FileNotFoundError: return '' -def get_audio_paths(podcast_path: str) -> List[Path]: +def get_audio_paths(podcast_path: str): podcast_path=Path(podcast_path) return ( list(podcast_path.rglob("*.mp3")) + list(podcast_path.rglob("*.wav")) + list(podcast_path.rglob("*.flac")) + - list(podcast_path.rglob("*.ogg")) + list(podcast_path.rglob("*.ogg")) + + list(podcast_path.rglob("*.opus")) ) + def process_token(token, label): if label == "LOWER_O": return token diff --git a/src/utils_asr.py b/src/utils_asr.py new file mode 100644 index 0000000..3dea75f --- /dev/null +++ b/src/utils_asr.py @@ -0,0 +1,490 @@ + +import argparse +import logging +import math +import random +import os +import sys +import warnings +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple, Union + +import kaldifeat +import sentencepiece as spm +import torch +import torchaudio +from torch.nn.utils.rnn import pad_sequence +from timeit import default_timer as timer + +from icefall import NgramLm, NgramLmStateCost +from icefall.decode import Nbest, one_best_decoding +from icefall.lm_wrapper import LmScorer +from icefall.rnn_lm.model import RnnLmModel +from icefall.transformer_lm.model import TransformerLM +from icefall.utils import AttributeDict +from icefall.lexicon import Lexicon + +import k2 + +def read_sound_files( + filenames: List[str], expected_sample_rate: float = 16000 +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + resampler = torchaudio.transforms.Resample(sample_rate, 16_000) + wav = resampler(wave[0]) + ans.append(wav) + return ans + + + +@dataclass +class Hypothesis: + # The predicted tokens so far. + # Newly predicted tokens are appended to `ys`. + ys: List[int] + + # The log prob of ys. + # It contains only one entry. + log_prob: torch.Tensor + + # timestamp[i] is the frame index after subsampling + # on which ys[i] is decoded + timestamp: List[int] = field(default_factory=list) + + # the lm score for next token given the current ys + lm_score: Optional[torch.Tensor] = None + + # the RNNLM states (h and c in LSTM) + state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None + + # N-gram LM state + state_cost: Optional[NgramLmStateCost] = None + + @property + def key(self) -> str: + """Return a string representation of self.ys""" + return "_".join(map(str, self.ys)) + + +class HypothesisList(object): + def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None: + """ + Args: + data: + A dict of Hypotheses. Its key is its `value.key`. + """ + if data is None: + self._data = {} + else: + self._data = data + + @property + def data(self) -> Dict[str, Hypothesis]: + return self._data + + def add(self, hyp: Hypothesis) -> None: + """Add a Hypothesis to `self`. + + If `hyp` already exists in `self`, its probability is updated using + `log-sum-exp` with the existed one. + + Args: + hyp: + The hypothesis to be added. + """ + key = hyp.key + if key in self: + old_hyp = self._data[key] # shallow copy + torch.logaddexp(old_hyp.log_prob, hyp.log_prob, out=old_hyp.log_prob) + else: + self._data[key] = hyp + + def get_most_probable(self, length_norm: bool = False) -> Hypothesis: + """Get the most probable hypothesis, i.e., the one with + the largest `log_prob`. + + Args: + length_norm: + If True, the `log_prob` of a hypothesis is normalized by the + number of tokens in it. + Returns: + Return the hypothesis that has the largest `log_prob`. + """ + if length_norm: + return max(self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys)) + else: + return max(self._data.values(), key=lambda hyp: hyp.log_prob) + + def remove(self, hyp: Hypothesis) -> None: + """Remove a given hypothesis. + + Caution: + `self` is modified **in-place**. + + Args: + hyp: + The hypothesis to be removed from `self`. + Note: It must be contained in `self`. Otherwise, + an exception is raised. + """ + key = hyp.key + assert key in self, f"{key} does not exist" + del self._data[key] + + def filter(self, threshold: torch.Tensor) -> "HypothesisList": + """Remove all Hypotheses whose log_prob is less than threshold. + + Caution: + `self` is not modified. Instead, a new HypothesisList is returned. + + Returns: + Return a new HypothesisList containing all hypotheses from `self` + with `log_prob` being greater than the given `threshold`. + """ + ans = HypothesisList() + for _, hyp in self._data.items(): + if hyp.log_prob > threshold: + ans.add(hyp) # shallow copy + return ans + + def topk(self, k: int, length_norm: bool = False) -> "HypothesisList": + """Return the top-k hypothesis. + + Args: + length_norm: + If True, the `log_prob` of a hypothesis is normalized by the + number of tokens in it. + """ + hyps = list(self._data.items()) + + if length_norm: + hyps = sorted( + hyps, key=lambda h: h[1].log_prob / len(h[1].ys), reverse=True + )[:k] + else: + hyps = sorted(hyps, key=lambda h: h[1].log_prob, reverse=True)[:k] + + ans = HypothesisList(dict(hyps)) + return ans + + def __contains__(self, key: str): + return key in self._data + + def __iter__(self): + return iter(self._data.values()) + + def __len__(self) -> int: + return len(self._data) + + def __str__(self) -> str: + s = [] + for key in self: + s.append(key) + return ", ".join(s) + + +def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape: + """Return a ragged shape with axes [utt][num_hyps]. + + Args: + hyps: + len(hyps) == batch_size. It contains the current hypothesis for + each utterance in the batch. + Returns: + Return a ragged shape with 2 axes [utt][num_hyps]. Note that + the shape is on CPU. + """ + num_hyps = [len(h) for h in hyps] + + # torch.cumsum() is inclusive sum, so we put a 0 at the beginning + # to get exclusive sum later. + num_hyps.insert(0, 0) + + num_hyps = torch.tensor(num_hyps) + row_splits = torch.cumsum(num_hyps, dim=0, dtype=torch.int32) + ans = k2.ragged.create_ragged_shape2( + row_splits=row_splits, cached_tot_size=row_splits[-1].item() + ) + return ans + + +def modified_beam_search_LODR( + model, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + LODR_lm: NgramLm, + LODR_lm_scale: float, + LM: LmScorer, + beam: int = 4, +) -> List[List[int]]: + """This function implements LODR (https://arxiv.org/abs/2203.16776) with + `modified_beam_search`. It uses a bi-gram language model as the estimate + of the internal language model and subtracts its score during shallow fusion + with an external language model. This implementation uses a RNNLM as the + external language model. + + Args: + model (Transducer): + The transducer model + encoder_out (torch.Tensor): + Encoder output in (N,T,C) + encoder_out_lens (torch.Tensor): + A 1-D tensor of shape (N,), containing the number of + valid frames in encoder_out before padding. + LODR_lm: + A low order n-gram LM, whose score will be subtracted during shallow fusion + LODR_lm_scale: + The scale of the LODR_lm + LM: + A neural net LM, e.g an RNNLM or transformer LM + beam (int, optional): + Beam size. Defaults to 4. + + Returns: + Return a list-of-list of token IDs. ans[i] is the decoding results + for the i-th utterance. + + """ + assert encoder_out.ndim == 3, encoder_out.shape + assert encoder_out.size(0) >= 1, encoder_out.size(0) + assert LM is not None + lm_scale = LM.lm_scale + + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) + + blank_id = model.decoder.blank_id + sos_id = getattr(LM, "sos_id", 1) + unk_id = getattr(model, "unk_id", blank_id) + context_size = model.decoder.context_size + device = next(model.parameters()).device + + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + # get initial lm score and lm state by scoring the "sos" token + sos_token = torch.tensor([[sos_id]]).to(torch.int64).to(device) + lens = torch.tensor([1]).to(device) + init_score, init_states = LM.score_token(sos_token, lens) + + B = [HypothesisList() for _ in range(N)] + for i in range(N): + B[i].add( + Hypothesis( + ys=([-1] * (context_size - 1) + [blank_id]), + log_prob=torch.zeros(1, dtype=torch.float32, device=device), + state=init_states, # state of the NN LM + lm_score=init_score.reshape(-1), + state_cost=NgramLmStateCost( + LODR_lm + ), # state of the source domain ngram + ) + ) + + encoder_out = model.joiner.encoder_proj(packed_encoder_out.data) + + offset = 0 + finalized_B = [] + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] # get batch + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) + # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim) + offset = end + + finalized_B = B[batch_size:] + finalized_B + B = B[:batch_size] + + hyps_shape = get_hyps_shape(B).to(device) + + A = [list(b) for b in B] + B = [HypothesisList() for _ in range(batch_size)] + + ys_log_probs = torch.cat( + [hyp.log_prob.reshape(1, 1) for hyps in A for hyp in hyps] + ) + + decoder_input = torch.tensor( + [hyp.ys[-context_size:] for hyps in A for hyp in hyps], + device=device, + dtype=torch.int64, + ) # (num_hyps, context_size) + + decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1) + decoder_out = model.joiner.decoder_proj(decoder_out) + + current_encoder_out = torch.index_select( + current_encoder_out, + dim=0, + index=hyps_shape.row_ids(1).to(torch.int64), + ) # (num_hyps, 1, 1, encoder_out_dim) + + logits = model.joiner( + current_encoder_out, + decoder_out, + project_input=False, + ) # (num_hyps, 1, 1, vocab_size) + + logits = logits.squeeze(1).squeeze(1) # (num_hyps, vocab_size) + + log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size) + + log_probs.add_(ys_log_probs) + + vocab_size = log_probs.size(-1) + + log_probs = log_probs.reshape(-1) + + row_splits = hyps_shape.row_splits(1) * vocab_size + log_probs_shape = k2.ragged.create_ragged_shape2( + row_splits=row_splits, cached_tot_size=log_probs.numel() + ) + ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs) + """ + for all hyps with a non-blank new token, score this token. + It is a little confusing here because this for-loop + looks very similar to the one below. Here, we go through all + top-k tokens and only add the non-blanks ones to the token_list. + LM will score those tokens given the LM states. Note that + the variable `scores` is the LM score after seeing the new + non-blank token. + """ + token_list = [] + hs = [] + cs = [] + for i in range(batch_size): + topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + topk_hyp_indexes = (topk_indexes // vocab_size).tolist() + topk_token_indexes = (topk_indexes % vocab_size).tolist() + for k in range(len(topk_hyp_indexes)): + hyp_idx = topk_hyp_indexes[k] + hyp = A[i][hyp_idx] + + new_token = topk_token_indexes[k] + if new_token not in (blank_id, unk_id): + if LM.lm_type == "rnn": + token_list.append([new_token]) + # store the LSTM states + hs.append(hyp.state[0]) + cs.append(hyp.state[1]) + else: + # for transformer LM + token_list.append( + [sos_id] + hyp.ys[context_size:] + [new_token] + ) + + # forward NN LM to get new states and scores + if len(token_list) != 0: + x_lens = torch.tensor([len(tokens) for tokens in token_list]).to(device) + if LM.lm_type == "rnn": + tokens_to_score = ( + torch.tensor(token_list).to(torch.int64).to(device).reshape(-1, 1) + ) + hs = torch.cat(hs, dim=1).to(device) + cs = torch.cat(cs, dim=1).to(device) + state = (hs, cs) + else: + # for transformer LM + tokens_list = [torch.tensor(tokens) for tokens in token_list] + tokens_to_score = ( + torch.nn.utils.rnn.pad_sequence( + tokens_list, batch_first=True, padding_value=0.0 + ) + .to(device) + .to(torch.int64) + ) + + state = None + + scores, lm_states = LM.score_token(tokens_to_score, x_lens, state) + + count = 0 # index, used to locate score and lm states + for i in range(batch_size): + topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + topk_hyp_indexes = (topk_indexes // vocab_size).tolist() + topk_token_indexes = (topk_indexes % vocab_size).tolist() + + for k in range(len(topk_hyp_indexes)): + hyp_idx = topk_hyp_indexes[k] + hyp = A[i][hyp_idx] + + ys = hyp.ys[:] + + # current score of hyp + lm_score = hyp.lm_score + state = hyp.state + + hyp_log_prob = topk_log_probs[k] # get score of current hyp + new_token = topk_token_indexes[k] + if new_token not in (blank_id, unk_id): + + ys.append(new_token) + state_cost = hyp.state_cost.forward_one_step(new_token) + + # calculate the score of the latest token + current_ngram_score = state_cost.lm_score - hyp.state_cost.lm_score + + assert current_ngram_score <= 0.0, ( + state_cost.lm_score, + hyp.state_cost.lm_score, + ) + # score = score + TDLM_score - LODR_score + # LODR_LM_scale should be a negative number here + hyp_log_prob += ( + lm_score[new_token] * lm_scale + + LODR_lm_scale * current_ngram_score + ) # add the lm score + + lm_score = scores[count] + if LM.lm_type == "rnn": + state = ( + lm_states[0][:, count, :].unsqueeze(1), + lm_states[1][:, count, :].unsqueeze(1), + ) + count += 1 + else: + state_cost = hyp.state_cost + + new_hyp = Hypothesis( + ys=ys, + log_prob=hyp_log_prob, + state=state, + lm_score=lm_score, + state_cost=state_cost, + ) + B[i].add(new_hyp) + + B = B + finalized_B + best_hyps = [b.get_most_probable(length_norm=True) for b in B] + + sorted_ans = [h.ys[context_size:] for h in best_hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) + + return ans \ No newline at end of file diff --git a/use_meta_500h.sh b/use_meta_500h.sh index 56ac524..3f92148 100644 --- a/use_meta_500h.sh +++ b/use_meta_500h.sh @@ -30,8 +30,8 @@ PICKLE_PATH="Balalaika500H.pkl" PARQUET_PATH="Balalaika500H.parquet" NUM_WORKERS=4 -PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.pkl" -PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika100H/resolve/main/Balalaika100H.parquet" +PICKLE_URL="https://huggingface.co/datasets/MTUCI/Balalaika500H/resolve/main/Balalaika500H.pkl" +PARQUET_URL="https://huggingface.co/datasets/MTUCI/Balalaika500H/resolve/main/Balalaika500H.parquet" download_if_not_exists "$PICKLE_URL" "$PICKLE_PATH" download_if_not_exists "$PARQUET_URL" "$PARQUET_PATH" From 6272fca1afcd706b82aa0e6d56cc95d195775e99 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Sun, 5 Oct 2025 03:15:23 +0000 Subject: [PATCH 04/43] feat: optimizations and updates from pyannote 3.1 to pyannote/speaker-diarization-community-1 --- base.sh | 4 +- configs/config.yaml | 18 +- src/phonemizer/phonemizer.py | 6 + src/preprocess/preprocess.py | 113 ++++++---- src/punctuation/punctuation.py | 5 + src/separation/diarization.py | 8 +- src/separation/model.py | 286 ++++++++++++++++-------- src/separation/music_detect.py | 221 +++++++----------- src/separation/nisqa.py | 4 +- src/separation/separation_yaml.sh | 2 +- src/transcription/transcription.py | 5 + src/transcription/transcription_yaml.sh | 2 +- 12 files changed, 373 insertions(+), 301 deletions(-) diff --git a/base.sh b/base.sh index c6c3acb..4e59096 100644 --- a/base.sh +++ b/base.sh @@ -20,8 +20,8 @@ CONFIG_PATH=$(realpath "$1") SCRIPTS=( # "./src/download/download_yaml.sh" - "./src/preprocess/preprocess_yaml.sh" - "./src/separation/separation_yaml.sh" + # "./src/preprocess/preprocess_yaml.sh" + # "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" "./src/punctuation/punctuation_yaml.sh" "./src/accents/accents_yaml.sh" diff --git a/configs/config.yaml b/configs/config.yaml index 5abcea1..2f80f01 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,14 +1,18 @@ download: podcasts_path: /mnt/disk1tb/radio-v4 - episodes_limit: 1 # limit on downloading episodes from one playlist + episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 1 - podcasts_urls_file: '/home/nikita/yapoddataset/20000_more_albums.pkl' # pkl with urls + podcasts_urls_file: '/home/nikita/yapoddataset/pickls/single.pkl' # pkl with urls preprocess: podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 2 smart_vad_model: /home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx + + silero_vad_threshold: 0.4 + smart_vad_threshold: 0.4 + separation: podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located @@ -19,8 +23,8 @@ separation: bs: 32 num_workers_nisqa: 12 # audio detect - num_workers_detect: 4 - checkpoint_path: /home/nikita/audio_detect/wavlm-endpointing-model/checkpoint-3000/model.safetensors # /home/nikita/yapoddataset/models/audio_detect/model.safetensors + num_workers_detect: 2 + checkpoint_path: /home/nikita/audio_detect/wavlm-endpointing-model/checkpoint-3000/music_detection.safetensors # /home/nikita/yapoddataset/models/audio_detect/model.safetensors threshold: 0.2442 transcription: @@ -29,20 +33,20 @@ transcription: model_names: ['giga_ctc_lm', 'vosk', 'tone', 'giga_ctc', 'giga_rnnt'] # 'tone' ['vosk','tone'] giga: - num_workers: 4 # per one gpu + num_workers: 2 # per one gpu batch_size: 16 # if supported lm_path: '/home/nikita/yapoddataset/kenlm.bin' # path to lm.bin for giga ctc model vosk: vosk_path: /home/nikita/yapoddataset/models/vosk-model-ru - num_workers: 4 # per one gpu + num_workers: 2 # per one gpu batch_size: 12 # if supported beam_size: 4 lm_scale: 0.2 ngram_lm_scale: -0.1 tone: - num_workers: 4 # per one gpu + num_workers: 2 # per one gpu batch_size: 16 # if supported punctuation: diff --git a/src/phonemizer/phonemizer.py b/src/phonemizer/phonemizer.py index b129f68..6dd4e70 100644 --- a/src/phonemizer/phonemizer.py +++ b/src/phonemizer/phonemizer.py @@ -11,6 +11,12 @@ from src.utils import get_txt_paths, load_config, read_file_content +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + g2p_model: Any = None def init_process(device_str: str): diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index 1b4c045..e5e0dc8 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -17,6 +17,28 @@ from typing import List, Dict, Any, Tuple +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + +smart_vad = None + + +def initializer_wrapper(args_dict: Dict[str, Any], gpus_count: int): + """ + Initializer for each worker process. Assigns a GPU and creates a VAD instance. + This function runs once when each worker process is created. + """ + # Get a unique ID for each worker process (note: _identity is an internal API) + worker_id = multiprocessing.current_process()._identity[0] - 1 + # Assign a GPU to the worker in a round-robin fashion + gpu_id = worker_id % gpus_count if gpus_count > 0 else 0 + + # Call the original VAD initialization function for this specific worker + init_vad_process(gpu_id=gpu_id, vad_args=args_dict) + def postprocess_vad_result( vad_result: List[Dict[str, Any]], duration: float = 15.0, @@ -196,10 +218,14 @@ def process_audio_file(path_audio: str, duration: float): episode_folder = os.path.join(os.path.dirname(path_audio), episode_id) try: + # TODO: don't forget to remove the code audio, sr = torchaudio.load(path_audio) if audio.shape[-1] / sr <= 5: logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") + os.remove(path_audio) + # # TODO: don't forget to remove the code + if audio.shape[-1] / sr <= duration: return except Exception as e: @@ -243,80 +269,75 @@ def process_audio_file(path_audio: str, duration: float): def main(args): load_dotenv() - hf_key=os.environ.get('HF_TOKEN') - login(token=hf_key) + hf_key = os.environ.get('HF_TOKEN') + if hf_key: + login(token=hf_key) + else: + logger.warning("HF_TOKEN not found in environment.") config = load_config(args.config_path, 'preprocess') podcasts_path = args.podcasts_path or config.get('podcasts_path', '../../../podcasts') duration = args.duration or config.get('duration', 15) - num_workers = args.num_workers or config.get('num_workers', 4) smart_vad_model = args.smart_vad_model or config.get('smart_vad_model', "pipecat-ai/smart-turn-v2") - + + # --- Начало измененной логики параллелизации --- + + # 1. Определяем количество воркеров НА КАЖДУЮ GPU num_gpus = torch.cuda.device_count() - available_gpus = list(range(num_gpus)) - if num_gpus == 0: - logger.error("No GPUs available. Exiting.") - return - + + # Аргумент --num_workers теперь означает "количество процессов на одну GPU". По умолчанию 1. + workers_per_gpu = config.get('num_workers', 4) + + if num_gpus > 0: + # Общее число процессов = (количество GPU) * (процессов на GPU) + total_workers = num_gpus * workers_per_gpu + logger.info(f"Найдено {num_gpus} GPU. Запускаем по {workers_per_gpu} процесса на каждую.") + logger.info(f"Общее количество рабочих процессов: {total_workers}.") + else: + # В режиме CPU --num_workers задает общее число процессов. + cpu_count = os.cpu_count() + total_workers = workers_per_gpu if args.num_workers else max(1, cpu_count // 2) + logger.warning(f"GPU не найдены. Запускаем {total_workers} процессов на CPU.") audio_paths = get_audio_paths(podcasts_path) if not audio_paths: - logger.info("No audio files found.") + logger.info("Аудиофайлы для обработки не найдены.") return vad_args = { 'silero_vad_threshold': 0.4, 'smart_vad_threshold': 0.4, - 'smart_vad_path': smart_vad_model + 'smart_vad_path': smart_vad_model, } logger.info(f""" - Starting processing with: - Podcasts path: {podcasts_path} - Smart VAD model: {smart_vad_model} - Segment duration: {duration} seconds - Workers per GPU: {num_workers} - Available GPUs: {available_gpus} - Files to process: {len(audio_paths)} + Запуск параллельной обработки: + Путь к подкастам: {podcasts_path} + Модель Smart VAD: {smart_vad_model} + Длительность сегмента: {duration} секунд + Общее число воркеров: {total_workers} + Файлов для обработки: {len(audio_paths)} """) - all_futures = [] - executors = [] - - for gpu_id in available_gpus: - executor = ProcessPoolExecutor( - max_workers=num_workers, - initializer=init_vad_process, - initargs=(gpu_id, vad_args) - ) - executors.append(executor) - - files_for_gpu = [] - for i, path in enumerate(audio_paths): - if i % len(available_gpus) == gpu_id % len(available_gpus): - files_for_gpu.append(path) + with ProcessPoolExecutor( + max_workers=total_workers, + initializer=initializer_wrapper, + initargs=(vad_args, num_gpus) + ) as executor: - logger.info(f"GPU {gpu_id}: processing {len(files_for_gpu)} files") - - for path in files_for_gpu: - future = executor.submit(process_audio_file, path, duration) - all_futures.append(future) + futures = [executor.submit(process_audio_file, path, duration) for path in audio_paths] - with tqdm(total=len(all_futures), desc="Processing podcasts") as pbar: - for future in as_completed(all_futures): + for future in tqdm(as_completed(futures), total=len(audio_paths), desc="Обработка подкастов"): try: future.result() except Exception as e: - logger.error(f"Error processing file: {e}") - finally: - pbar.update(1) + logger.error(f"Задача завершилась с ошибкой: {e}") - for executor in executors: - executor.shutdown() + logger.info("Все файлы обработаны.") -if __name__ == "__main__": +if __name__ == "__main__": torchaudio.set_audio_backend('soundfile') multiprocessing.set_start_method('spawn', force=True) diff --git a/src/punctuation/punctuation.py b/src/punctuation/punctuation.py index 1cef1e3..03ab31b 100644 --- a/src/punctuation/punctuation.py +++ b/src/punctuation/punctuation.py @@ -12,6 +12,11 @@ from src.utils import load_config, get_audio_paths, process_token, read_file_content +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + model = None def init_process( diff --git a/src/separation/diarization.py b/src/separation/diarization.py index 3f627a8..5b9ddb4 100644 --- a/src/separation/diarization.py +++ b/src/separation/diarization.py @@ -16,6 +16,12 @@ from src.utils import get_audio_paths, load_config +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + diarization_pipeline = None def init_worker(hf_token: str, device_str: str): @@ -25,7 +31,7 @@ def init_worker(hf_token: str, device_str: str): try: torch.cuda.set_device(device_str) diarization_pipeline = Pipeline.from_pretrained( - "pyannote/speaker-diarization-3.1", + "pyannote/speaker-diarization-community-1", use_auth_token=hf_token ).to(torch.device(device_str)) logger.info(f"Worker initialized successfully for diarization on {device_str}.") diff --git a/src/separation/model.py b/src/separation/model.py index 0894d18..9669db5 100644 --- a/src/separation/model.py +++ b/src/separation/model.py @@ -1,55 +1,52 @@ import torch -from torch import nn -import torch.nn.functional as F import torch.nn as nn -from transformers import AutoModel - - -import torch -from torch import nn import torch.nn.functional as F - -class BCEWithLogitsLossLS(nn.Module): - def __init__(self, label_smoothing=0.1, pos_weight=None, reduction='mean'): - super(BCEWithLogitsLossLS, self).__init__() - assert 0 <= label_smoothing < 1, "label_smoothing value must be between 0 and 1." - self.label_smoothing = label_smoothing - self.reduction = reduction - self.bce_with_logits = nn.BCEWithLogitsLoss(pos_weight=pos_weight, reduction=reduction) - - def forward(self, input, target): - if self.label_smoothing > 0: - positive_smoothed_labels = 1.0 - self.label_smoothing - negative_smoothed_labels = self.label_smoothing - target = target * positive_smoothed_labels + \ - (1 - target) * negative_smoothed_labels - - loss = self.bce_with_logits(input, target) - return loss - -class WavLMForEndpointing(nn.Module): - def __init__(self, config, n_trainable_layers=6): +from transformers import AutoModel, AutoConfig, AutoFeatureExtractor +import torchaudio +from safetensors import safe_open +from typing import List, Dict +import time + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + +class WavLMForMusicDetection(nn.Module): + """ + Music detection model based on WavLM. + Uses attention pooling + classification head. + Outputs probability that input audio contains music. + Supports batched inference with automatic batching and preprocessing. + EER - 2.5-3 % + """ + def __init__( + self, + base_model_name: str = 'microsoft/wavlm-base-plus', + batch_size: int = 32, + device: str = 'cuda' + ) -> None: super().__init__() - self.wavlm = AutoModel.from_pretrained('microsoft/wavlm-base-plus', config=config) - self.config = config - self.n_trainable_layers = n_trainable_layers + self.config = AutoConfig.from_pretrained(base_model_name) + self.wavlm = AutoModel.from_pretrained(base_model_name, config=self.config) + self.processor = AutoFeatureExtractor.from_pretrained(base_model_name) - for param in self.wavlm.parameters(): - param.requires_grad = False - - if self.n_trainable_layers > 0: - for i in range(self.n_trainable_layers): - for param in self.wavlm.encoder.layers[-(i+1)].parameters(): - param.requires_grad = True + self.batch_size = batch_size + self.device = torch.device(device if torch.cuda.is_available() else 'cpu') + + self.target_sample_rate = self.processor.sampling_rate + # Attention-based pooling head self.pool_attention = nn.Sequential( - nn.Linear(config.hidden_size, 256), + nn.Linear(self.config.hidden_size, 256), nn.Tanh(), nn.Linear(256, 1) ) + # Classification head self.classifier = nn.Sequential( - nn.Linear(config.hidden_size, 256), + nn.Linear(self.config.hidden_size, 256), nn.LayerNorm(256), nn.GELU(), nn.Dropout(0.1), @@ -59,70 +56,161 @@ def __init__(self, config, n_trainable_layers=6): nn.Linear(64, 1) ) - for module in self.classifier: - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=0.1) - if module.bias is not None: - module.bias.data.zero_() + # to device + self.to(self.device) + self.eval() + + def _attention_pool( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor + ) -> torch.Tensor: + """ + Apply attention-based pooling over time dimension. + + Args: + hidden_states (torch.Tensor): [batch_size, seq_len, hidden_size] + attention_mask (torch.Tensor): [batch_size, seq_len] — mask to ignore padding + + Returns: + torch.Tensor: [batch_size, hidden_size] — context vector + """ + + attention_weights = self.pool_attention(hidden_states) # [B, T, 1] + # Mask out padded positions + attention_weights = attention_weights + ( + (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 + ) + + attention_weights = F.softmax(attention_weights, dim=1) # [B, T, 1] - for module in self.pool_attention: - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=0.1) - if module.bias is not None: - module.bias.data.zero_() + # Weighted sum over time + weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) # [B, D] + return weighted_sum + + @torch.inference_mode() + def forward( + self, + input_values: torch.Tensor, + attention_mask: torch.Tensor + ) -> torch.Tensor: + """ + Forward pass for inference. + + Args: + input_values (torch.Tensor): [batch_size, audio_seq_len] — raw audio waveform + attention_mask (torch.Tensor): [batch_size, audio_seq_len] — input mask (1 = real, 0 = pad) + + Returns: + torch.Tensor: [batch_size, 1] — probability that audio contains music + """ + assert isinstance(input_values, torch.Tensor), f"Expected torch.Tensor, got {type(input_values)}" + assert isinstance(attention_mask, torch.Tensor), f"Expected torch.Tensor, got {type(attention_mask)}" + + outputs = self.wavlm(input_values.to(self.device), attention_mask=attention_mask.to(self.device)) + hidden_states = outputs.last_hidden_state # [B, T', D] + + # Align attention mask with downsampled hidden states + input_length = attention_mask.size(1) + hidden_length = hidden_states.size(1) + ratio = input_length / hidden_length + indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() + attention_mask = attention_mask[:, indices] # [B, T'] + attention_mask = attention_mask.bool() + + pooled = self._attention_pool(hidden_states, attention_mask) + logits = self.classifier(pooled) # [B, 1] + + probs = torch.sigmoid(logits) # [B, 1] → probability of MUSIC + return probs + + def _prepare_batches(self, audio_paths: List[str]) -> List[List[str]]: + """ + Split list of audio paths into batches of size `self.batch_size`. + + Args: + audio_paths (List[str]): List of paths to audio files. + + Returns: + List[List[str]]: List of batches, each batch is a list of paths. + """ + batches = [] + current_batch = [] + counter = 0 + + while counter < len(audio_paths): + if len(current_batch) == self.batch_size: + batches.append(current_batch) + current_batch = [] + current_batch.append(audio_paths[counter]) + counter += 1 + + if current_batch: + batches.append(current_batch) + + return batches + + def _preprocess_audio_batch(self, audio_paths: List[str]) -> Dict[str, torch.Tensor]: + """ + Load and preprocess a batch of audio files. + + Args: + audio_paths (List[str]): List of file paths. + + Returns: + Dict with keys: + "input_values": tensor [B, T] + "attention_mask": tensor [B, T] + """ + waveforms = [] + + for audio_path in audio_paths: + waveform, sample_rate = torchaudio.load(audio_path) + + # Resample if needed + if sample_rate != self.target_sample_rate: + resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.target_sample_rate) + waveform = resampler(waveform) + + # Convert to mono + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + + waveforms.append(waveform.squeeze()) + + # Extract features + inputs = self.processor( + [w.numpy() for w in waveforms], + sampling_rate=self.target_sample_rate, + return_tensors="pt", + padding=True, + truncation=False + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} - def attention_pool(self, hidden_states, attention_mask): - attention_weights = self.pool_attention(hidden_states) + return inputs - if attention_mask is None: - raise ValueError("attention_mask must be provided for attention pooling") + def predict_proba(self, audio_paths: List[str]) -> torch.Tensor: + """ + Predict music probability for a list of audio files. - attention_weights = attention_weights + ( - (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 - ) + Args: + audio_paths (List[str]): List of audio file paths. - attention_weights = F.softmax(attention_weights, dim=1) + Returns: + torch.Tensor: [N] — probabilities for each audio file. + """ - # Apply attention to hidden states - weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) + all_probs = [] - return weighted_sum + batches = self._prepare_batches(audio_paths) + + for batch in batches: + inputs = self._preprocess_audio_batch(batch) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + with torch.no_grad(): + probs = self.forward(**inputs).squeeze(-1) # [B] + all_probs.append(probs) - def forward(self, input_values, attention_mask=None, labels=None): - outputs = self.wavlm(input_values, attention_mask=attention_mask) - hidden_states = outputs[0] - - if attention_mask is not None: - input_length = attention_mask.size(1) - hidden_length = hidden_states.size(1) - ratio = input_length / hidden_length - indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() - attention_mask = attention_mask[:, indices] - attention_mask = attention_mask.bool() - else: - attention_mask = None - - pooled = self.attention_pool(hidden_states, attention_mask) - - logits = self.classifier(pooled) - - if torch.isnan(logits).any(): - raise ValueError("NaN values detected in logits") - - if labels is not None: - pos_weight = ((labels == 0).sum() / (labels == 1).sum()).clamp(min=0.1, max=10.0) - loss_fct = BCEWithLogitsLossLS(pos_weight=pos_weight) - labels = labels.float() - loss = loss_fct(logits.view(-1), labels.view(-1)) - - l2_lambda = 0.01 - l2_reg = torch.tensor(0., device=logits.device) - for param in self.classifier.parameters(): - l2_reg += torch.norm(param) - loss += l2_lambda * l2_reg - - probs = torch.sigmoid(logits.detach()) - return {"loss": loss, "logits": probs} - - probs = torch.sigmoid(logits) - return {"logits": probs} \ No newline at end of file + return torch.cat(all_probs, dim=0) \ No newline at end of file diff --git a/src/separation/music_detect.py b/src/separation/music_detect.py index eb90bf9..1d4597f 100644 --- a/src/separation/music_detect.py +++ b/src/separation/music_detect.py @@ -3,135 +3,74 @@ import os from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path -from typing import Optional +from typing import List import torch -import torchaudio -import transformers from loguru import logger from safetensors import safe_open from tqdm import tqdm -from src.separation.model import WavLMForEndpointing +from src.separation.model import WavLMForMusicDetection from src.utils import get_audio_paths, load_config -g_model = None -g_processor = None -g_device = None -g_threshold = None - -def init_process( - model_name: str, +def process_files_on_device( + audio_paths: List[Path], checkpoint_path: str, device: str, - threshold: float -) -> None: - """ - Initializes the model, processor, device, and threshold for a worker process. - This function runs once for each process in the ProcessPoolExecutor. + threshold: float, + batch_size: int +) -> int: """ - global g_model, g_processor, g_device, g_threshold - - logger.info(f"Initializing the process on the device {device}...") + Processes a list of audio files on a single device (GPU). - g_device = device - g_threshold = threshold + This function initializes the model, gets predictions for all files, + and deletes those that exceed the threshold. + Returns: + The number of deleted files. + """ + logger.info(f"Starting process on device {device} for {len(audio_paths)} files...") + deleted_count = 0 + try: - g_processor = transformers.AutoFeatureExtractor.from_pretrained(model_name) - config = transformers.AutoConfig.from_pretrained(model_name) - model = WavLMForEndpointing(config) + # 1. Initialize the model, specifying the device and batch size + model = WavLMForMusicDetection(batch_size=batch_size, device=device) + # 2. Load the weights with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - state_dict = {key: f.get_tensor(key) for key in f.keys()} - - # Load the state dict and then move the model to the correct device + state_dict = {k: f.get_tensor(k) for k in f.keys()} model.load_state_dict(state_dict) - model.to(g_device) - model.eval() - g_model = model - logger.info(f"The process on the {device} device has been successfully initialized.") - except Exception as e: - logger.error(f"Error initializing the process on the device {device}: {e}") - # Re-raise the exception to prevent the process from starting incorrectly - raise + # 3. Get probabilities for all files in a single call + # Convert Path objects to strings, as the model expects + string_paths = [str(p) for p in audio_paths] + probs = model.predict_proba(string_paths) + # 4. Check and delete files + for path, prob in zip(audio_paths, probs): + score = prob.item() + if score > threshold: + logger.info(f"Deleting file: {path} (score: {score:.4f})") + os.remove(path) + deleted_count += 1 -def process_audio_file(audio_path: Path) -> Optional[str]: - """ - Processes a single audio file to check if it should be removed. - Returns the path of the deleted file if removed, otherwise returns None. - """ - global g_model, g_processor, g_device, g_threshold - - # If globals aren't initialized, something went wrong with init_process - if g_model is None or g_processor is None or g_device is None: - logger.error(f"Global variables are not initialized in the process.") - return None - - try: - waveform, sample_rate = torchaudio.load(audio_path) - - # TODO: don't forget to remove the code - - if (waveform.shape[-1] / sample_rate) <= 5: - os.remove(audio_path) - logger.info(f"{audio_path} -- removed {waveform.shape[-1] / sample_rate}") - - # TODO: don't forget to remove the code - - # Resample if not 16kHz - if sample_rate != 16000: - resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000) - waveform = resampler(waveform) - - # Convert stereo to mono if necessary - if waveform.shape[0] > 1: - waveform = torch.mean(waveform, dim=0, keepdim=True) - - # Ensure waveform is on the correct device for processing - waveform = waveform.to(g_device) - - inputs = g_processor( - waveform.squeeze().cpu().numpy(), # Use .cpu().numpy() to handle device - sampling_rate=16000, - return_tensors="pt", - padding=False, - truncation=False, - ) - - # Move input tensors to the correct device - inputs = {key: inputs[key].to(g_device) for key in inputs.keys()} - - with torch.no_grad(): - result = g_model(**inputs) - - # Check the condition and remove the file - if result['logits'][0][0].item() > g_threshold: - # Use os.remove for a more direct delete operation - # Add a log message to confirm the action - logger.info(f"Удаление файла: {audio_path} (score: {result['logits'][0][0].item():.4f})") - os.remove(audio_path) - return str(audio_path) # Return the path of the removed file - - # Return None if the file is not removed - return None + logger.info(f"Process on {device} finished. Deleted {deleted_count} files.") + return deleted_count except Exception as e: - logger.error(f"Error in file processing{audio_path}: {e}") - return None # Return None in case of an error + logger.error(f"Error in process on device {device}: {e}") + return 0 # Return 0 deleted files in case of an error def main(args): """ - Main function to orchestrate the multiprocessing and audio processing. + Main function to orchestrate the multiprocessing of audio files. """ config = load_config(args.config_path, 'separation') - num_workers_per_gpu = config.get('num_workers_detect', 2) - model_name = 'microsoft/wavlm-base-plus' + # Get parameters from the config + batch_size = config.get('batch_size', 32) checkpoint_path = config.get('checkpoint_path', '/path/to/your/model.safetensors') audio_data_path = config.get('podcasts_path', '/path/to/your/audio') threshold = config.get('threshold', 0.5) @@ -142,75 +81,73 @@ def main(args): return if not torch.cuda.is_available(): - logger.error("There are no available GPUs. Exit.") + logger.error("No available GPUs. Exiting.") return available_gpu_ids = list(range(torch.cuda.device_count())) num_gpus = len(available_gpu_ids) + + # We now use 1 worker per GPU + total_workers = num_gpus logger.info( f""" Starting audio processing: - Data path: {audio_data_path} - - Model: {model_name} - Checkpoint: {checkpoint_path} - Number of GPUs: {num_gpus} (IDs: {available_gpu_ids}) - - Workers on GPU: {num_workers_per_gpu} - - Total workers: {num_gpus * num_workers_per_gpu} + - Total workers: {total_workers} - Total files to process: {len(all_audio_files)} - - Threshold {threshold} + - Deletion threshold: {threshold} + - Batch size per GPU: {batch_size} """ ) - # Use a dictionary to map GPU IDs to a list of files for better clarity + # Distribute the files among the available GPUs files_per_gpu = {gpu_id: [] for gpu_id in available_gpu_ids} for i, path in enumerate(all_audio_files): - gpu_assignment_index = i % num_gpus - gpu_id = available_gpu_ids[gpu_assignment_index] + gpu_id = available_gpu_ids[i % num_gpus] files_per_gpu[gpu_id].append(path) - all_futures = [] - executors = [] - - for gpu_id, files_for_this_gpu in files_per_gpu.items(): - if not files_for_this_gpu: - continue - - device_str = f'cuda:{gpu_id}' - logger.info(f"Creating a ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_gpu)} files.") + futures = [] + # Create a single process pool for all GPUs + with ProcessPoolExecutor(max_workers=total_workers) as executor: + for gpu_id, files_for_this_gpu in files_per_gpu.items(): + if not files_for_this_gpu: + continue - executor = ProcessPoolExecutor( - max_workers=num_workers_per_gpu, - initializer=init_process, - initargs=(model_name, checkpoint_path, device_str, threshold) - ) - executors.append(executor) - - for path in files_for_this_gpu: - future = executor.submit(process_audio_file, path) - all_futures.append(future) - - logger.info(f"All {len(all_futures)} issues have been sent for processing. Waiting for completion...") - - deleted_files_count = 0 - with tqdm(total=len(all_futures), desc="Audio file processing") as pbar: - for future in as_completed(all_futures): - result = future.result() - if result: - deleted_files_count += 1 + device_str = f'cuda:{gpu_id}' + logger.info(f"Submitting task for {device_str} with {len(files_for_this_gpu)} files.") + + # Submit one large task for each GPU + future = executor.submit( + process_files_on_device, + files_for_this_gpu, + checkpoint_path, + device_str, + threshold, + batch_size + ) + futures.append(future) + + logger.info(f"All {len(futures)} tasks have been submitted for processing. Awaiting completion...") + + total_deleted_count = 0 + # The progress bar will show the completion of tasks per GPU + with tqdm(total=len(futures), desc="Processing on GPUs") as pbar: + for future in as_completed(futures): + # Collect the number of deleted files from each process + total_deleted_count += future.result() pbar.update(1) - - for executor in executors: - executor.shutdown() - logger.info(f"Processing is completed. Total deleted {deleted_files_count} files.") + logger.info(f"Processing complete. Total files deleted: {total_deleted_count}.") if __name__ == "__main__": - # It's good practice to set the start method at the top level + # It's recommended to set the start method to 'spawn' for CUDA compatibility multiprocessing.set_start_method("spawn", force=True) - parser = argparse.ArgumentParser() - parser.add_argument("--config_path", type=str, required=True) + parser = argparse.ArgumentParser(description="Music detection and removal script.") + parser.add_argument("--config_path", type=str, required=True, help="Path to the config file.") args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/separation/nisqa.py b/src/separation/nisqa.py index f28ee25..cf48098 100644 --- a/src/separation/nisqa.py +++ b/src/separation/nisqa.py @@ -119,11 +119,11 @@ def main(): run_nisqa_with_config(nisqa_config) - logger.info("Обработка NISQA завершена. Сохранение результатов...") + logger.info("NISQA processing is complete. Saving the results...") _save_results(output_dir=output_dir, final_output_path=final_output_path) except Exception as e: - logger.error(f"Произошла ошибка во время выполнения NISQA: {e}", exc_info=True) + logger.error(f"An error occurred during the execution of NISQA: {e}", exc_info=True) if __name__ == "__main__": diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index ee9b1d3..749169d 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -21,6 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -taskset -c 0-24 python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" +# taskset -c 0-24 python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" taskset -c 0-24 python3 -m src.separation.nisqa --config_path "$CONFIG_PATH" taskset -c 0-24 python3 -m src.separation.diarization --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 658e163..b480c89 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -14,6 +14,11 @@ from src.transcription.transcripton_base import * from src.utils import get_audio_paths, load_config +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + model = None SUPPORTED_TIME_STAMPS = ['giga_ctc_lm', 'tone'] diff --git a/src/transcription/transcription_yaml.sh b/src/transcription/transcription_yaml.sh index 5735cb0..0f0e217 100644 --- a/src/transcription/transcription_yaml.sh +++ b/src/transcription/transcription_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -taskset -c 0-24 python3 -m src.transcription.transcription --config_path "$CONFIG_PATH" \ No newline at end of file +taskset -c 25-48 python3 -m src.transcription.transcription --config_path "$CONFIG_PATH" \ No newline at end of file From 29311ee7f34f998efffb02bdeeb529b12427b8f0 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Sun, 5 Oct 2025 03:30:12 +0000 Subject: [PATCH 05/43] feat: removed support for launching via separate bash scripts --- configs/config.yaml | 10 +-- src/accents/accents.py | 27 +++----- src/accents/accents_args.sh | 23 ------- src/download/download.py | 30 ++------- src/download/download_args.sh | 25 -------- src/phonemizer/phonemizer.py | 14 +---- src/phonemizer/phonemizer_args.sh | 23 ------- src/preprocess/preprocess.py | 82 ++++++------------------- src/preprocess/preprocess_args.sh | 33 ---------- src/punctuation/punctuation.py | 21 +------ src/punctuation/punctuation_args.sh | 24 -------- src/separation/diarization.py | 23 +------ src/separation/separation_args.sh | 31 ---------- src/transcription/transcription_args.sh | 28 --------- 14 files changed, 46 insertions(+), 348 deletions(-) delete mode 100644 src/accents/accents_args.sh delete mode 100644 src/download/download_args.sh delete mode 100644 src/phonemizer/phonemizer_args.sh delete mode 100644 src/preprocess/preprocess_args.sh delete mode 100644 src/punctuation/punctuation_args.sh delete mode 100644 src/separation/separation_args.sh delete mode 100644 src/transcription/transcription_args.sh diff --git a/configs/config.yaml b/configs/config.yaml index 2f80f01..4fede07 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -8,12 +8,12 @@ preprocess: podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 2 - smart_vad_model: /home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx + vad_args: + smart_vad_model: /home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx + silero_vad_threshold: 0.4 + smart_vad_threshold: 0.4 - silero_vad_threshold: 0.4 - smart_vad_threshold: 0.4 - - + separation: podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located # diar parms diff --git a/src/accents/accents.py b/src/accents/accents.py index 4ce94a9..3be466c 100644 --- a/src/accents/accents.py +++ b/src/accents/accents.py @@ -12,6 +12,12 @@ from src.utils import load_config, get_txt_paths, read_file_content +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + accentizer = None def init_process( @@ -59,9 +65,9 @@ def get_valid_txt_paths(path: str) -> List[str]: def main(args): config = load_config(args.config_path, 'accent') - num_workers = args.num_workers if args.num_workers else config.get('num_workers', 4) - model_name = args.model_name if args.model_name else config.get('model_name', 'turbo3.1') - podcast_path = args.podcasts_path if args.podcasts_path else config.get('podcasts_path', '../../../balalaika') + num_workers = config.get('num_workers', 4) + model_name = config.get('model_name', 'turbo3.1') + podcast_path = config.get('podcasts_path', '../../../balalaika') available_gpu_ids = list(range(torch.cuda.device_count())) num_gpus = len(available_gpu_ids) @@ -131,21 +137,6 @@ def main(args): type=str, help="Path to config" ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to dataset directory" - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of worker processes" - ) - parser.add_argument( - "--model_name", - type=str, - help="Model version" - ) args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/accents/accents_args.sh b/src/accents/accents_args.sh deleted file mode 100644 index 05202a0..0000000 --- a/src/accents/accents_args.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - - -activate_venv ".dev_venv" - -PODCASTS_PATH="../../../balalaika" -MODEL_NAME="turbo3.1" -NUM_WORKERS=4 - -python3 -m src.accents.accents \ - --podcasts_path "$PODCASTS_PATH" \ - --model_name "$MODEL_NAME" \ - --num_workers "$NUM_WORKERS" diff --git a/src/download/download.py b/src/download/download.py index c352bc4..7a7a939 100644 --- a/src/download/download.py +++ b/src/download/download.py @@ -147,9 +147,9 @@ def main(args): if not client: return - podcasts_path = args.podcasts_path if args.podcasts_path else config.get('podcasts_path','../../../balalaika') - episodes_limit = args.episodes_limit if args.episodes_limit else config.get('episodes_limit',1) - urls_pickle_path = args.podcasts_urls_file if args.podcasts_urls_file else config.get('podcasts_urls_file','alboms.pkl') + podcasts_path = config.get('podcasts_path','../../../balalaika') + episodes_limit = config.get('episodes_limit',1) + urls_pickle_path = config.get('podcasts_urls_file','alboms.pkl') try: with open(urls_pickle_path, 'rb') as file: @@ -196,28 +196,6 @@ def main(args): default="./configs/config.yaml", help="Path to the configuration file" ) - parser.add_argument( - "--podcasts_urls_file", - default=None, - help="Path to the pickle file with album urls" - ) - parser.add_argument( - "--podcasts_path", - default=None, - help="Path for saving podcasts" - ) - parser.add_argument( - "--episodes_limit", - default=None, - type=int, - help="Limit for episodes to download" - ) - parser.add_argument( - "--num_workers", - default=None, - type=int, - help="num workers" - ) - + args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/download/download_args.sh b/src/download/download_args.sh deleted file mode 100644 index f2cf543..0000000 --- a/src/download/download_args.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - -activate_venv ".dev_venv" - -SCRIPT_DIR=$(dirname "$(realpath "$0")") - - -PODCASTS_PATH="../../../balalaika" -EPISODES_LIMIT=2 -NUM_WORKERS=2 - -python3 -m src.download.download \ - --podcasts_path "$PODCASTS_PATH" \ - --episodes_limit "$EPISODES_LIMIT" \ - --num_workers "$NUM_WORKERS" \ No newline at end of file diff --git a/src/phonemizer/phonemizer.py b/src/phonemizer/phonemizer.py index 6dd4e70..5b22c43 100644 --- a/src/phonemizer/phonemizer.py +++ b/src/phonemizer/phonemizer.py @@ -53,8 +53,8 @@ def get_valid_text_paths(src_path: str) -> List[Path]: def main(args): config = load_config(args.config_path, 'phonemizer') - num_workers = args.num_workers if args.num_workers else config.get('num_workers', 4) - src_path_str = args.podcasts_path if args.podcasts_path else config.get('podcasts_path', '../../../podcasts') + num_workers = config.get('num_workers', 4) + src_path_str = config.get('podcasts_path', '../../../podcasts') all_text_paths = get_valid_text_paths(src_path_str) logger.info(f"Found {len(all_text_paths)} text files to process") @@ -122,16 +122,6 @@ def main(args): type=str, help="Path to the configuration YAML file." ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to the directory containing audio files (e.g., MP3s)." - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of worker processes per GPU for parallel processing." - ) args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/phonemizer/phonemizer_args.sh b/src/phonemizer/phonemizer_args.sh deleted file mode 100644 index dc4ccec..0000000 --- a/src/phonemizer/phonemizer_args.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - -activate_venv ".dev_venv" - -SCRIPT_DIR=$(dirname "$(realpath "$0")") -CONFIG_PATH="$SCRIPT_DIR/../../configs/config.yaml" - -PODCASTS_PATH="../../../balalaika" -NUM_WORKERS=8 - -python3 -m src.phonemizer.phonemizer \ - --podcasts_path "$PODCASTS_PATH" \ - --num_workers "$NUM_WORKERS" \ No newline at end of file diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index e5e0dc8..c9fb7c9 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -45,50 +45,38 @@ def postprocess_vad_result( min_duration: float = 1.0, gap_threshold: float = 30.0 ) -> Tuple[List[float], List[float]]: - """ - Сбалансированный алгоритм для постобработки результатов VAD. - Приоритет: законченные фразы, но заполняются большие промежутки между ними. - """ + if not vad_result: return [], [] - # Этап 1: Сохраняем все семантически законченные фразы (prediction=1) primary_segments = [] - # Итерируем по результатам и объединяем последовательные "законченные" сегменты current_segment_start = None for i, item in enumerate(vad_result): if item['prediction'] == 1: if current_segment_start is None: - # Начало нового потенциального сегмента current_segment_start = item['start_time'] - # Конец текущего потенциального сегмента segment_end = item['end_time'] segment_duration = segment_end - current_segment_start - # Проверяем, не превышаем ли мы максимальную длительность if segment_duration > duration: - # Сохраняем предыдущий сегмент и начинаем новый if i > 0 and vad_result[i-1]['prediction'] == 1: primary_segments.append((current_segment_start, vad_result[i-1]['end_time'])) current_segment_start = item['start_time'] - # Сохраняем последний накопленный сегмент if current_segment_start is not None: last_item = vad_result[-1] primary_segments.append((current_segment_start, last_item['end_time'])) - # Фильтруем сегменты по длительности primary_segments = [(s, e) for s, e in primary_segments if min_duration <= e - s <= duration] - # Сортируем и убираем дубликаты primary_segments = sorted(list(set(primary_segments)), key=lambda x: x[0]) if not primary_segments: return [], [] - # Этап 2: Заполняем большие промежутки между сохранёнными сегментами + # stage 2 filled_segments = [] last_end_time = 0.0 @@ -98,12 +86,10 @@ def postprocess_vad_result( filled_segments.extend(_fill_gap(vad_result, last_end_time, start, duration, min_duration)) last_end_time = end - # Проверяем промежуток в конце audio_end = vad_result[-1]['end_time'] if audio_end - last_end_time > gap_threshold: filled_segments.extend(_fill_gap(vad_result, last_end_time, audio_end, duration, min_duration)) - # Объединяем итоговые сегменты all_segments = primary_segments + filled_segments all_segments = sorted(list(set(all_segments)), key=lambda x: x[0]) @@ -120,15 +106,11 @@ def _fill_gap( duration: float, min_duration: float ) -> List[Tuple[float, float]]: - """ - Вспомогательная функция: разбивает промежуток [gap_start, gap_end] на сегменты - длиной [min_duration, duration], используя интервалы из vad_result. - """ + filled_segments = [] current_time = gap_start while current_time < gap_end: - # Находим следующий интервал, который начинается после current_time next_interval_index = -1 for i, item in enumerate(vad_result): if item['start_time'] >= current_time: @@ -141,7 +123,6 @@ def _fill_gap( seg_start = vad_result[next_interval_index]['start_time'] seg_end = vad_result[next_interval_index]['end_time'] - # Объединяем интервалы, пока не превысим max_duration или не дойдем до конца промежутка for j in range(next_interval_index + 1, len(vad_result)): if vad_result[j]['end_time'] - seg_start > duration or vad_result[j]['end_time'] > gap_end: break @@ -236,9 +217,6 @@ def process_audio_file(path_audio: str, duration: float): try: vad_result = smart_vad.process_file(path_audio) - # import pickle - # with open('data.pkl', 'wb') as file: - # pickle.dump(vad_result, file) timesteps_starts, timesteps_ends = postprocess_vad_result(vad_result, duration=duration) if not timesteps_starts: logger.warning(f"No speech segments found in {path_audio}, removed") @@ -277,48 +255,33 @@ def main(args): config = load_config(args.config_path, 'preprocess') - podcasts_path = args.podcasts_path or config.get('podcasts_path', '../../../podcasts') - duration = args.duration or config.get('duration', 15) - smart_vad_model = args.smart_vad_model or config.get('smart_vad_model', "pipecat-ai/smart-turn-v2") + podcasts_path = config.get('podcasts_path', '../../../podcasts') + duration = config.get('duration', 15) + vad_args = config.get('vad_args') - # --- Начало измененной логики параллелизации --- - - # 1. Определяем количество воркеров НА КАЖДУЮ GPU num_gpus = torch.cuda.device_count() - - # Аргумент --num_workers теперь означает "количество процессов на одну GPU". По умолчанию 1. + workers_per_gpu = config.get('num_workers', 4) if num_gpus > 0: - # Общее число процессов = (количество GPU) * (процессов на GPU) total_workers = num_gpus * workers_per_gpu - logger.info(f"Найдено {num_gpus} GPU. Запускаем по {workers_per_gpu} процесса на каждую.") - logger.info(f"Общее количество рабочих процессов: {total_workers}.") - else: - # В режиме CPU --num_workers задает общее число процессов. - cpu_count = os.cpu_count() - total_workers = workers_per_gpu if args.num_workers else max(1, cpu_count // 2) - logger.warning(f"GPU не найдены. Запускаем {total_workers} процессов на CPU.") + logger.info (f"Found {num_gpus} GPU. We run {workers_per_gpu} processes for each one.") + logger.info (f"Total number of work processes: {total_workers}.") audio_paths = get_audio_paths(podcasts_path) if not audio_paths: - logger.info("Аудиофайлы для обработки не найдены.") + logger.info ("No audio files found for processing.") return - vad_args = { - 'silero_vad_threshold': 0.4, - 'smart_vad_threshold': 0.4, - 'smart_vad_path': smart_vad_model, - } - logger.info(f""" - Запуск параллельной обработки: - Путь к подкастам: {podcasts_path} - Модель Smart VAD: {smart_vad_model} - Длительность сегмента: {duration} секунд - Общее число воркеров: {total_workers} - Файлов для обработки: {len(audio_paths)} - """) + logger.info (f""" + Running parallel processing: + The path to Podcasts: {podcasts_path} + Smart VAD model: {smart_vad_model} + Segment duration: {duration} seconds + Total number of workers: {total_workers} + Files to process: {len(audio_paths)} + """) with ProcessPoolExecutor( @@ -333,9 +296,9 @@ def main(args): try: future.result() except Exception as e: - logger.error(f"Задача завершилась с ошибкой: {e}") + logger.error(f"The task ended with an error:{e}") - logger.info("Все файлы обработаны.") + logger.info("All files have been processed.") if __name__ == "__main__": torchaudio.set_audio_backend('soundfile') @@ -343,10 +306,5 @@ def main(args): parser = argparse.ArgumentParser(description="Process audio files using smart-turn VAD model.") parser.add_argument("--config_path", type=str, help="Path to YAML configuration file") - parser.add_argument("--podcasts_path", type=str, help="Path to podcasts folder") - parser.add_argument("--smart_vad_model", type=str, help="Name of smart-turn model") - parser.add_argument("--duration", type=int, help="Target segment duration in seconds") - parser.add_argument("--num_workers", type=int, help="Number of worker processes per GPU") - args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/preprocess/preprocess_args.sh b/src/preprocess/preprocess_args.sh deleted file mode 100644 index 3061350..0000000 --- a/src/preprocess/preprocess_args.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - - -activate_venv ".dev_venv" - -SCRIPT_DIR=$(dirname "$(realpath "$0")") - -PODCASTS_PATH="../../../balalaika" -DURATION=15 -NUM_WORKERS=2 -WHISPER_MODEL="large-v3" -COMPUTE_TYPE="float16" -BEAM_SIZE=5 - -python3 -m src.preprocess.preprocess \ - --podcasts_path "$PODCASTS_PATH" \ - --duration "$DURATION" \ - --num_workers "$NUM_WORKERS" \ - --whisper_model "$WHISPER_MODEL" \ - --compute_type "$COMPUTE_TYPE" \ - --beam_size "$BEAM_SIZE" - - diff --git a/src/punctuation/punctuation.py b/src/punctuation/punctuation.py index 03ab31b..023d883 100644 --- a/src/punctuation/punctuation.py +++ b/src/punctuation/punctuation.py @@ -77,9 +77,9 @@ def get_valid_txt_paths(src_path: str) -> List[str]: def main(args): config = load_config(args.config_path, 'punctuation') - num_workers_per_gpu = args.num_workers if args.num_workers else config.get('num_workers', 4) - model_name = args.model_name if args.model_name else config.get('model_name', 'RUPunct/RUPunct_big') - podcasts_path = args.podcasts_path if args.podcasts_path else config.get('podcasts_path', '../../../balalaika') + num_workers_per_gpu = config.get('num_workers', 4) + model_name = config.get('model_name', 'RUPunct/RUPunct_big') + podcasts_path = config.get('podcasts_path', '../../../balalaika') all_text_files = get_valid_txt_paths(podcasts_path) @@ -146,21 +146,6 @@ def main(args): type=str, help="Path to the configuration file" ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to the dataset directory containing .txt files" - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of worker processes per GPU" - ) - parser.add_argument( - "--model_name", - type=str, - help="Hugging Face NER model name for punctuation" - ) args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/punctuation/punctuation_args.sh b/src/punctuation/punctuation_args.sh deleted file mode 100644 index cc095f9..0000000 --- a/src/punctuation/punctuation_args.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - - -activate_venv ".dev_venv" -SCRIPT_DIR=$(dirname "$(realpath "$0")") - -PODCASTS_PATH="../../../balalaika" -MODEL_NAME="RUPunct/RUPunct_big" -NUM_WORKERS=4 - -python3 -m src.punctuation.punctuation \ - --podcasts_path "$PODCASTS_PATH" \ - --model_name "$MODEL_NAME" \ - --num_workers "$NUM_WORKERS" diff --git a/src/separation/diarization.py b/src/separation/diarization.py index 5b9ddb4..6fe5c5b 100644 --- a/src/separation/diarization.py +++ b/src/separation/diarization.py @@ -126,9 +126,9 @@ def main(args): hf_token = os.getenv("HF_TOKEN") config = load_config(args.config_path, 'separation') - podcasts_path = args.podcasts_path or config.get('podcasts_path', './data') - one_speaker = args.one_speaker or config.get('one_speaker', False) - num_workers_per_gpu = args.num_workers or config.get('num_workers_diarization', 1) + podcasts_path = config.get('podcasts_path', './data') + one_speaker = config.get('one_speaker', False) + num_workers_per_gpu = config.get('num_workers_diarization', 1) result_csv_path = Path(podcasts_path) / 'balalaika.csv' available_gpu_ids = list(range(torch.cuda.device_count())) @@ -137,12 +137,10 @@ def main(args): return logger.info(f""" - --- Starting Diarization --- Podcasts path: {podcasts_path} One speaker mode: {one_speaker} Workers per GPU: {num_workers_per_gpu} GPUs detected: {len(available_gpu_ids)} - -------------------------- """) all_audio_paths = get_unprocessed_audio_paths( @@ -242,21 +240,6 @@ def main(args): type=str, help="Path to the main YAML configuration file." ) - parser.add_argument( - "--podcasts_path", - type=str, - help="Path to the directory containing audio files." - ) - parser.add_argument( - "--one_speaker", - action='store_true', - help="Enable one-speaker mode. If set, files with multiple speakers will be deleted." - ) - parser.add_argument( - "--num_workers", - type=int, - help="Number of worker processes to spawn per GPU." - ) args = parser.parse_args() main(args) \ No newline at end of file diff --git a/src/separation/separation_args.sh b/src/separation/separation_args.sh deleted file mode 100644 index dcbfec6..0000000 --- a/src/separation/separation_args.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - -activate_venv ".dev_venv" - -SCRIPT_DIR=$(dirname "$(realpath "$0")") -CONFIG_PATH="$SCRIPT_DIR/../../configs/config.yaml" -NISQA_CONFIG_PATH="$SCRIPT_DIR/../../configs/nisqa_config.yaml" -PODCASTS_PATH="../../../balalaika" -USE_NISQA="True" -USE_MONO="True" -ONE_SPEAKER="False" -NUM_WORKERS="4" - -python3 -m src.separation.separation \ - --config_path "$CONFIG_PATH" \ - --nisqa_config "$NISQA_CONFIG_PATH" \ - --podcasts_path "$PODCASTS_PATH" \ - --use_nisqa "$USE_NISQA" \ - --use_mono "$USE_MONO" \ - --one_speaker "$ONE_SPEAKER" \ - --num_workers "$NUM_WORKERS" \ No newline at end of file diff --git a/src/transcription/transcription_args.sh b/src/transcription/transcription_args.sh deleted file mode 100644 index 333075a..0000000 --- a/src/transcription/transcription_args.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash - -activate_venv() { - local venv_path=$1 - if [ ! -f "$venv_path/bin/activate" ]; then - echo "Error: Virtual environment not found at $venv_path" - exit 1 - fi - source "$venv_path/bin/activate" - echo "Activated: $(which python)" -} - -activate_venv ".dev_venv" - -SCRIPT_DIR=$(dirname "$(realpath "$0")") - -PODCASTS_PATH="/home/nikita/Balalaika100H" -NUM_WORKERS=1 -MODEL_NAMES=('giga_ctc' 'giga_rnnt' 'vosk' 'ton') -LM_PATH="/home/nikita/yapoddataset/ru.lm.bin" -WITH_TIMESTAMPS=True - -python -m src.transcription.transcription \ - --podcasts_path "$PODCASTS_PATH" \ - --num_workers "$NUM_WORKERS" \ - --model_names "$MODEL_NAMES" \ - --lm_path "$LM_PATH" \ - --with_timestamps "$WITH_TIMESTAMPS" From 24f5a5101ec91161475f0ff7791a1bfb26078342 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Sun, 5 Oct 2025 23:44:39 +0000 Subject: [PATCH 06/43] feat(libs): add NISQA and Smart-VAD libraries as local dependencies --- src/libs/NISQA | 1 - src/libs/NISQA/.gitignore | 145 + src/libs/NISQA/LICENSE | 21 + src/libs/NISQA/README.md | 172 ++ src/libs/NISQA/config/finetune_nisqa.yaml | 53 + .../finetune_nisqa_multidimensional.yaml | 51 + .../config/train_nisqa_cnn_lstm_avg.yaml | 103 + .../NISQA/config/train_nisqa_cnn_sa_ap.yaml | 104 + .../config/train_nisqa_double_ended.yaml | 109 + src/libs/NISQA/env.yml | 24 + src/libs/NISQA/nisqa/NISQA_lib.py | 2334 +++++++++++++++++ src/libs/NISQA/nisqa/NISQA_model.py | 1116 ++++++++ src/libs/NISQA/run_evaluate.py | 69 + src/libs/NISQA/run_predict.py | 35 + src/libs/NISQA/run_train.py | 57 + src/libs/NISQA/weights/LICENSE_model_weights | 437 +++ src/libs/NISQA/weights/nisqa.tar | Bin 0 -> 1051663 bytes src/libs/NISQA/weights/nisqa_mos_only.tar | Bin 0 -> 907215 bytes src/libs/NISQA/weights/nisqa_tts.tar | Bin 0 -> 1237159 bytes src/libs/smart_turn | 1 - src/libs/smart_turn/.gitignore | 2 + src/libs/smart_turn/inference.py | 87 + src/libs/smart_turn/model.py | 98 + src/libs/smart_turn/offline_svad.py | 190 ++ 24 files changed, 5207 insertions(+), 2 deletions(-) delete mode 160000 src/libs/NISQA create mode 100644 src/libs/NISQA/.gitignore create mode 100644 src/libs/NISQA/LICENSE create mode 100644 src/libs/NISQA/README.md create mode 100644 src/libs/NISQA/config/finetune_nisqa.yaml create mode 100644 src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml create mode 100644 src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml create mode 100644 src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml create mode 100644 src/libs/NISQA/config/train_nisqa_double_ended.yaml create mode 100644 src/libs/NISQA/env.yml create mode 100644 src/libs/NISQA/nisqa/NISQA_lib.py create mode 100644 src/libs/NISQA/nisqa/NISQA_model.py create mode 100644 src/libs/NISQA/run_evaluate.py create mode 100644 src/libs/NISQA/run_predict.py create mode 100644 src/libs/NISQA/run_train.py create mode 100644 src/libs/NISQA/weights/LICENSE_model_weights create mode 100644 src/libs/NISQA/weights/nisqa.tar create mode 100644 src/libs/NISQA/weights/nisqa_mos_only.tar create mode 100644 src/libs/NISQA/weights/nisqa_tts.tar delete mode 160000 src/libs/smart_turn create mode 100644 src/libs/smart_turn/.gitignore create mode 100644 src/libs/smart_turn/inference.py create mode 100644 src/libs/smart_turn/model.py create mode 100644 src/libs/smart_turn/offline_svad.py diff --git a/src/libs/NISQA b/src/libs/NISQA deleted file mode 160000 index fe84f0f..0000000 --- a/src/libs/NISQA +++ /dev/null @@ -1 +0,0 @@ -Subproject commit fe84f0f252abec382b24367d5b22498a7ce34dbb diff --git a/src/libs/NISQA/.gitignore b/src/libs/NISQA/.gitignore new file mode 100644 index 0000000..80b5816 --- /dev/null +++ b/src/libs/NISQA/.gitignore @@ -0,0 +1,145 @@ + +# Created by https://www.toptal.com/developers/gitignore/api/python +# Edit at https://www.toptal.com/developers/gitignore?templates=python + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# End of https://www.toptal.com/developers/gitignore/api/python \ No newline at end of file diff --git a/src/libs/NISQA/LICENSE b/src/libs/NISQA/LICENSE new file mode 100644 index 0000000..73b3df7 --- /dev/null +++ b/src/libs/NISQA/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Gabriel Mittag, Quality and Usability Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/src/libs/NISQA/README.md b/src/libs/NISQA/README.md new file mode 100644 index 0000000..2336fd7 --- /dev/null +++ b/src/libs/NISQA/README.md @@ -0,0 +1,172 @@ +# NISQA: Speech Quality and Naturalness Assessment + +*+++ News: The NISQA model has recently been updated to NISQA v2.0. The new version offers multidimensional predictions with higher accuracy and allows for training and finetuning the model.* + +**Speech Quality Prediction:** +NISQA is a deep learning model/framework for speech quality prediction. The NISQA model weights can be used to predict the quality of a speech sample that has been sent through a communication system (e.g telephone or video call). Besides overall speech quality, NISQA also provides predictions for the quality dimensions *Noisiness*, *Coloration*, *Discontinuity*, and *Loudness* to give more insight into the cause of the quality degradation. + +**TTS Naturalness Prediction:** +The NISQA-TTS model weights can be used to estimate the *Naturalness* of synthetic speech generated by a Voice Conversion or Text-To-Speech system (Siri, Alexa, etc.). + +**Training/Finetuning:** +NISQA can be used to train new single-ended or double-ended speech quality prediction models with different deep learning architectures, such as CNN or DFF -> Self-Attention or LSTM -> Attention-Pooling or Max-Pooling. The provided model weights can also be applied to finetune the trained model towards new data or for transfer-learning to a different regression task (e.g. quality estimation of enhanced speech, speaker similarity estimation, or emotion recognition) . + +**Speech Quality Datasets:** +We provide a large corpus of more than 14,000 speech samples with subjective speech quality and speech quality dimension labels. + +## Table of Contents +- [Installation](#installation) +- [Using NISQA](#using-nisqa) + - [Prediction](#prediction) + - [Training](#training) + - [Finetuning / Transfer Learning](#finetuning--transfer-learning) + - [Training a new model](#training-a-new-model) + - [Evaluation](#evaluation) +- [NISQA Corpus](#nisqa-corpus) +- [Paper and License](#paper-and-license) + +More information about the deep learning model structure, the used training datasets, and the training options, see the [NISQA paper](https://arxiv.org/abs/2104.09494) and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/). + + +## Installation + +To install requirements install [Anaconda](https://www.anaconda.com/products/individual) and then use: + +```setup +conda env create -f env.yml +``` + +This will create a new environment with the name "nisqa". Activate this environment to go on: + +```setup2 +conda activate nisqa +``` + + + +## Using NISQA + +We provide examples for using NISQA to predict the quality of speech samples, to train a new speech quality model, and to evaluate the performance of a trained speech quality model. + +There are three different model weights available, the appropriate weights should be loaded depending on the domain: + +| Model | Prediction Output | Domain | Filename | +| --------------------- | --------------------------------------------------------------- | ------------------ | ------------------ | +| NISQA (v2.0) | Overall Quality, Noisiness, Coloration, Discontinuity, Loudness | Transmitted Speech | nisqa.tar | +| NISQA (v2.0) mos only | Overall Quality only (for finetuning/transfer learning) | Transmitted Speech | nisqa_mos_only.tar | +| NISQA-TTS (v1.0) | Naturalness | Synthesized Speech | nisqa_tts.tar | + +### Prediction + +There are three modes available to predict the quality of speech via command line arguments: +* Predict a single file +* Predict all files in a folder +* Predict all files in a CSV table + +**Important:** Select "*nisqa.tar*" to predict the quality of a transmitted speech sample and "*nisqa_tts.tar*" to predict the Naturalness of a synthesized speech sample. + +To predict the quality of a single .wav file use: + +``` +python run_predict.py --mode predict_file --pretrained_model weights/nisqa.tar --deg /path/to/wav/file.wav --output_dir /path/to/dir/with/results +``` +To predict the quality of all .wav files in a folder use: +``` +python run_predict.py --mode predict_dir --pretrained_model weights/nisqa.tar --data_dir /path/to/folder/with/wavs --num_workers 0 --bs 10 --output_dir /path/to/dir/with/results +``` + +To predict the quality of all .wav files listed in a csv table use: +``` +python run_predict.py --mode predict_csv --pretrained_model weights/nisqa.tar --csv_file files.csv --csv_deg column_name_of_filepaths --num_workers 0 --bs 10 --output_dir /path/to/dir/with/results +``` + +The results will be printed to the console and saved to a csv file in a given folder (optional with --output_dir). To speed up the prediction, the number of workers and batch size of the Pytorch Dataloader can be increased (optional with --num_workers and --bs). In case of stereo files --ms_channel can be used to select the audio channel. + +### Training + +#### Finetuning / Transfer Learning + +To use the model weights to finetune the model on a new dataset, only a CSV file with the filenames and labels is needed. The training configuration is controlled from a YAML file and can be started as follows: + +``` +python run_train.py --yaml config/finetune_nisqa.yaml +``` + +- If the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) is used, only two arguments need to updated in the YAML file and you are ready to go: The `data_dir` to the extracted NISQA_Corpus folder and the `output_dir`, where the results should be stored. + +- If you use your own dataset or want to load the NISQA-TTS model, some other updates are needed. + + Your CSV file needs to contain at least three columns with the following names + + - `db` with the individual dataset names for each file + - `filepath_deg` filepath to the degraded WAV file, either absolute paths or relative to the `data_dir` (CSV column name can be changed in YAML) + - `mos` with the target labels (CSV column name can be changed in YAML) + + The `finetune_nisqa.yaml` needs to be updated as follows: + + - `data_dir` path to the main folder, which contains the CSV file and the datasets + - `output_dir` path to output folder with saved model weights and results + - `pretrained_model` filename of the pretrained model, either `nisqa_mos_only.tar` for natural speech or `nisqa_tts.tar` for synthesized speech + - `csv_file` name of the CSV with filepaths and target labels + - `csv_deg` CSV column name that contains filepaths (e.g. `filepath_deg`) + - `csv_mos_train` and `csv_mos_val` CSV column names of the target value (e.g. `mos`) + - `csv_db_train` and `csv_db_val` names of the datasets you want to use for training and validation. Datasets names must be in the `db` column. + +See the comments in the YAML configuration file and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/) (not yet added) for more advanced training options. A good starting point would be to use the NISQA Corpus to get the training started with the standard configuration. + +#### Training a new model + +NISQA can also be used as a framework to train new speech quality models with different deep learning architectures. The general model structure is as follows: + +1. *Framewise model:* CNN or Feedforward network +2. *Time-Dependency* model: Self-Attention or LSTM +3. *Pooling:* Average, Max, Attention or Last-Step-Pooling + +The framewise and time-dependency models can be skipped, for example to train an LSTM model without CNN that uses the last-time step for prediction. Also a second time-dependency stage can be added, for example for LSTM-Self-Attention structure. The model structure can be easily controlled via the YAML configuration file. The training with the standard NISQA model configuration can be started with the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) as follows: + +``` +python run_train.py --yaml config/train_nisqa_cnn_sa_ap.yaml +``` + +If the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) is used, only the `data_dir` needs to be updated to the unzipped NISQA_Corpus folder and the `output_dir` in the YAML file. Otherwise, see the previous [finetuning section](#finetuning-transfer-learning) for updating the YAML file if a custom dataset is applied. + +It is also possible to train any other combination of neural networks, for example, to train a model with LSTM instead of Self-Attention, the `train_nisqa_cnn_lstm_avg.yaml` example configuration file is provided. + +To train a **double-ended** model for full-reference speech quality prediction, the `train_nisqa_double_ended.yaml` configuration file can be used as an example. See the comments in the YAML files and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/) (not yet added) for more details on different possible model structures and advanced training options. + +### Evaluation + +Trained models can be evaluated on a given dataset as follows (can also be used as a conformance test of the model installation): + +``` +python run_evaluate.py +``` + +Before running, the options and paths inside the Python script `run_evaluate.py` should be updated. If the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) is used, only the `data_dir` and `output_dir` paths need to be adjusted. Besides Pearson's Correlation and RMSE, also an RMSE after first-order polynomial mapping is calculated. If a CSV file with per-condition labels is provided, the script will also output per-condition results and RMSE*. Optionally, correlation diagrams can be plotted. The script should return the same results as in the NISQA paper when it is run on the NISQA Corpus. + +## NISQA Corpus + +The NISQA Corpus includes more than 14,000 speech samples with simulated (e.g. codecs, packet-loss, background noise) and live (e.g. mobile phone, Zoom, Skype, WhatsApp) conditions. + +For the download link and more details on the datasets and used source speech samples see the [NISQA Corpus Wiki](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus). + +## Paper and License + +- If you use the **NISQA model** or the **NISQA Corpus** for your research, please cite following paper: + [G. Mittag, B. Naderi, A. Chehadi, and S. Möller “NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets,” in Proc. Interspeech 2021, 2021.](https://www.isca-speech.org/archive/pdfs/interspeech_2021/mittag21_interspeech.pdf) +- Please cite following paper if you use the **NISQA-TTS** model for Naturalness prediction of synthesized speech: + [G. Mittag and S. Moller, “Deep Learning Based Assessment of Synthetic Speech Naturalness,” in Proc. Interspeech 2020, 2020.](https://www.isca-speech.org/archive/Interspeech_2020/abstracts/2382.html) +- Please cite following paper if you use the **double-ended NISQA model**: + [G. Mittag and S. Möller. Full-reference speech quality estimation with attentional Siamese neural networks. In Proc. ICASSP 2020, 2020.](https://ieeexplore.ieee.org/document/9053951) +- The older NISQA (v0.42) model version is described in following paper: + [G. Mittag and S. Möller, “Non-intrusive speech quality assessment for super-wideband speech communication networks,” in Proc. ICASSP 2019, 2019](https://ieeexplore.ieee.org/document/8683770) + +The NISQA code is licensed under [MIT License](LICENSE). + +The model weights (nisqa.tar, nisqa_mos_only.tar, nisqa_tts.tar) are provided under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License](weights/LICENSE_model_weights) + +The NISQA Corpus is provided under the original terms of the used source speech and noise samples. More information can be found in the [NISQA Corpus Wiki](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus). + +Copyright © 2021 Gabriel Mittag +www.qu.tu-berlin.de + diff --git a/src/libs/NISQA/config/finetune_nisqa.yaml b/src/libs/NISQA/config/finetune_nisqa.yaml new file mode 100644 index 0000000..ec811c1 --- /dev/null +++ b/src/libs/NISQA/config/finetune_nisqa.yaml @@ -0,0 +1,53 @@ +# Config example for transfer-learning or finetuning of NISQA or NISQA-TTS: + +# Runname and paths +name: training_run_name # name of current training run +data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files +output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights +pretrained_model: weights/nisqa_mos_only.tar # absolute path to pretrained model | path to pretrained model relative to current folder + +# Dataset options +csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings +csv_con: null # csv-file with per-condition MOS used for evaluation (optional) +csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' +csv_mos_train: mos # csv column name of target training value (usually MOS) +csv_mos_val: mos # csv column name of target validation value (usually MOS) +csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file + - NISQA_TRAIN_SIM + - NISQA_TRAIN_LIVE +csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file + - NISQA_VAL_SIM + - NISQA_VAL_LIVE + +# Training options +tr_epochs: 500 # number of max training epochs +tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs +tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_lr: 0.001 # learning rate of ADAM optimiser +tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs +tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) +tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs +tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) +tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) +tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. +tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights +tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar +ms_max_segments: 1300 # if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples +ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used + +# Bias loss options (optional) +tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' +tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied +tr_bias_anchor_db: null # name of anchor dataset (optional) + + + + + + + + + + + diff --git a/src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml b/src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml new file mode 100644 index 0000000..f4a503e --- /dev/null +++ b/src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml @@ -0,0 +1,51 @@ +# Config example for transfer-learning or finetuning of NISQA with dimension prediction (it requires csv files with MOS, Noisiness, Coloration, Discontinuity, and Loudness labels): + +# Runname and paths +name: training_run_name # name of current training run +data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files +output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights +pretrained_model: weights/nisqa.tar # absolute path to pretrained model | path to pretrained model relative to current folder + +# Dataset options +csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings +csv_con: null # csv-file with per-condition MOS used for evaluation (optional) +csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' +csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file + - NISQA_TRAIN_SIM + - NISQA_TRAIN_LIVE +csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file + - NISQA_VAL_SIM + - NISQA_VAL_LIVE + +# Training options +tr_epochs: 500 # number of max training epochs +tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs +tr_bs: 40 # training dataset mini-batch size +tr_bs_val: 40 # validation dataset mini-batch size +tr_lr: 0.001 # learning rate of ADAM optimiser +tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs +tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) +tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs +tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) +tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) +tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. +tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights +tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar +ms_max_segments: 1300 # if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples +ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used + +# Bias loss options (optional) +tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' +tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied +tr_bias_anchor_db: null # name of anchor dataset (optional) + + + + + + + + + + + diff --git a/src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml b/src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml new file mode 100644 index 0000000..bbeb868 --- /dev/null +++ b/src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml @@ -0,0 +1,103 @@ +# Config example for training single-ended model: CNN->LSTM->Avg-Pooling + +# Runname and paths +name: training_run_name # name of current training run +data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files +output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights +pretrained_model: false + +# Dataset options +csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings +csv_con: null # csv-file with per-condition MOS used for evaluation (optional) +csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' +csv_ref: filepath_ref # csv column name of filepath to reference speech sample, path must be relative to 'data_dir' +csv_mos_train: mos # csv column name of target training value (usually MOS) +csv_mos_val: mos # csv column name of target validation value (usually MOS) +csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file + - NISQA_TRAIN_SIM + - NISQA_TRAIN_LIVE +csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file + - NISQA_VAL_SIM + - NISQA_VAL_LIVE + +# Training options +tr_epochs: 500 # number of max training epochs +tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs +tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_lr: 0.001 # learning rate of ADAM optimiser +tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs +tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) +tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs +tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) +tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) +tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. +tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights +tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar + +# Bias loss options (optional) +tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' +tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied +tr_bias_anchor_db: null # name of anchor dataset (optional) + +# Mel-Specs options +ms_sr: null # resample speech signal to 'ms_sr', (usually not needed, because window length 'ms_win_length' is adjusted automatically for different sample frequencies) +ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples +ms_n_fft: 4096 # padded fft window length (in bins), 4096 still fits 40 ms window length, even for 96kHz sample rate +ms_hop_length: 0.01 # hop length of fft windowing (in seconds) +ms_win_length: 0.02 # fft window length (in seconds), will be padded with zeros to match 'ms_n_fft' +ms_n_mels: 48 # number of Mel bands +ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) +ms_seg_hop_length: 3 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. segment hop length in seconds: ms_hop_length*ms_seg_hop_length +ms_max_segments: 1300 # max segment length (in bins). if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples +ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used + +# Main model +model: NISQA # 'NISQA' single-ended | 'NISQA_DIM' single-ended with multidimension prediction | 'NISQA_DE' double-ended + +# Framewise options (usually CNN) +cnn_model: standard # framewise model, either 'adapt' for CNN with adaptive maxpooling | 'standard' for standard CNN | 'dff' for deep feed-forward network | 'skip' to skip framewise modelling +cnn_c_out_1: 16 # number of output channels of first convolutional layer +cnn_c_out_2: 32 # number of output channels of the second convolutional layer +cnn_c_out_3: 64 # number of output channels of the last four convolutional layer +cnn_kernel_size: !!python/tuple [3,3] +cnn_dropout: 0.2 +cnn_fc_out_h: 20 # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted +cnn_pool_1: null # outpuf dimensions of first adaptive pooling ('adaptive' CNN only) +cnn_pool_2: null # outpuf dimensions of second adaptive pooling ('adaptive' CNN only) +cnn_pool_3: null # outpuf dimensions of third adaptive pooling ('adaptive' CNN only) + +# Time-Depedency options +td: lstm # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) +td_sa_d_model: null # attention network dimension +td_sa_nhead: null # number of heads +td_sa_pos_enc: null # apply positional encoding (no improvement in previous experiments) +td_sa_num_layers: null # self-attention depth +td_sa_h: null # hidden units of self-attention feedforward network +td_sa_dropout: null # self-attention dropout +td_lstm_h: 128 # number of LSTM hidden units +td_lstm_num_layers: 1 # LSTM depth +td_lstm_dropout: 0 +td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 + +# Second Time-Depedency options (optional, for example for LSTM->Self-Attention network) +td_2: skip # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) +td_2_sa_d_model: null # attention network dimension +td_2_sa_nhead: null # number of heads +td_2_sa_pos_enc: null # apply positional encoding (no improvement in previous experiments) +td_2_sa_num_layers: null +td_2_sa_h: null # hidden units of self-attention feedforward network +td_2_sa_dropout: null # self-attention dropout +td_2_lstm_h: null # number of LSTM hidden units +td_2_lstm_num_layers: null # LSTM depth +td_2_lstm_dropout: null +td_2_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 + +# Pooling options +pool: avg # 'att' for Attention-Pooling | 'avg' for average-pooling | 'max' max-pooling | 'last_step' last-step pooling | 'last_step_bi' last-step pooling with BiLSTMs +pool_att_h: null # number of hidden units of attention-pooling feedforward network +pool_att_dropout: null # attention pooling dropout + + + + diff --git a/src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml b/src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml new file mode 100644 index 0000000..9171779 --- /dev/null +++ b/src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml @@ -0,0 +1,104 @@ +# Config example for training a new model with standard NISQA settings + +# Runname and paths +name: training_run_name # name of current training run +data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files +output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights +pretrained_model: false + +# Dataset options +csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings +csv_con: null # csv-file with per-condition MOS used for evaluation (optional) +csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' +csv_mos_train: mos # csv column name of target training value (usually MOS) +csv_mos_val: mos # csv column name of target validation value (usually MOS) +csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file + - NISQA_TRAIN_SIM + - NISQA_TRAIN_LIVE +csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file + - NISQA_VAL_SIM + - NISQA_VAL_LIVE + +# Training options +tr_epochs: 500 # number of max training epochs +tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs +tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_lr: 0.001 # learning rate of ADAM optimiser +tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs +tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) +tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs +tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) +tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) +tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. +tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights +tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar + +# Bias loss options (optional) +tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' +tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied +tr_bias_anchor_db: null # name of anchor dataset (optional) + +# Mel-Specs options +ms_sr: null # resample speech signal to 'ms_sr', (usually not needed, because window length 'ms_win_length' is adjusted automatically for different sample frequencies) +ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples +ms_n_fft: 4096 # padded fft window length (in bins), 4096 still fits 40 ms window length, even for 96kHz sample rate +ms_hop_length: 0.01 # hop length of fft windowing (in seconds) +ms_win_length: 0.02 # fft window length (in seconds), will be padded with zeros to match 'ms_n_fft' +ms_n_mels: 48 # number of Mel bands +ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) +ms_seg_hop_length: 4 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. segment hop length in seconds: ms_hop_length*ms_seg_hop_length +ms_max_segments: 1300 # max segment length (in bins). if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples +ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used + +# Main model +model: NISQA # 'NISQA' single-ended | 'NISQA_DIM' single-ended with multidimension prediction | 'NISQA_DE' double-ended + +# Framewise options (usually CNN) +cnn_model: adapt # framewise model, either 'adapt' for CNN with adaptive maxpooling | 'standard' for standard CNN | 'dff' for deep feed-forward network | 'skip' to skip framewise modelling +cnn_c_out_1: 16 # number of output channels of first convolutional layer +cnn_c_out_2: 32 # number of output channels of the second convolutional layer +cnn_c_out_3: 64 # number of output channels of the last four convolutional layer +cnn_kernel_size: !!python/tuple [3,3] +cnn_dropout: 0.2 +cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted +cnn_pool_1: [24,7] # outpuf dimensions of first adaptive pooling ('adaptive' CNN only) +cnn_pool_2: [12,5] # outpuf dimensions of second adaptive pooling ('adaptive' CNN only) +cnn_pool_3: [6,3] # outpuf dimensions of third adaptive pooling ('adaptive' CNN only) + +# Time-Depedency options +td: self_att # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) +td_sa_d_model: 64 # attention network dimension +td_sa_nhead: 1 # number of heads +td_sa_pos_enc: False # apply positional encoding (no improvement in previous experiments) +td_sa_num_layers: 2 # self-attention depth +td_sa_h: 64 # hidden units of self-attention feedforward network +td_sa_dropout: 0.1 # self-attention dropout +td_lstm_h: null # number of LSTM hidden units +td_lstm_num_layers: null # LSTM depth +td_lstm_dropout: null +td_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 + +# Second Time-Depedency options (optional, for example for LSTM-Self-Attention network) +td_2: skip +td_2_sa_d_model: null +td_2_sa_nhead: null +td_2_sa_pos_enc: null +td_2_sa_num_layers: null +td_2_sa_h: null +td_2_sa_dropout: null +td_2_lstm_h: null +td_2_lstm_num_layers: null +td_2_lstm_dropout: null +td_2_lstm_bidirectional: null + +# Pooling options +pool: att # 'att' for Attention-Pooling | 'avg' for average-pooling | 'max' max-pooling | 'last_step' last-step pooling | 'last_step_bi' last-step pooling with BiLSTMs +pool_att_h: 128 # number of hidden units of attention-pooling feedforward network +pool_att_dropout: 0 # attention pooling dropout + + + + + + diff --git a/src/libs/NISQA/config/train_nisqa_double_ended.yaml b/src/libs/NISQA/config/train_nisqa_double_ended.yaml new file mode 100644 index 0000000..c49a376 --- /dev/null +++ b/src/libs/NISQA/config/train_nisqa_double_ended.yaml @@ -0,0 +1,109 @@ +# Config example for training double-ended model: CNN-SA-Align-Fuse-SA-AP (SA: Self-Attention, AP: Attention-Pooling) + +# Runname and paths +name: training_run_name # name of current training run +data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files +output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights +pretrained_model: false + +# Dataset options +csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings +csv_con: null # csv-file with per-condition MOS used for evaluation (optional) +csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' +csv_ref: filepath_ref # csv column name of filepath to reference speech sample, path must be relative to 'data_dir' +csv_mos_train: mos # csv column name of target training value (usually MOS) +csv_mos_val: mos # csv column name of target validation value (usually MOS) +csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file + - NISQA_TRAIN_SIM + - NISQA_TRAIN_LIVE +csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file + - NISQA_VAL_SIM + - NISQA_VAL_LIVE + +# Training options +tr_epochs: 500 # number of max training epochs +tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs +tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_lr: 0.001 # learning rate of ADAM optimiser +tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs +tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) +tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs +tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) +tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) +tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. +tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights +tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar + +# Bias loss options (optional) +tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' +tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied +tr_bias_anchor_db: null # name of anchor dataset (optional) + +# Mel-Specs options +ms_sr: null # resample speech signal to 'ms_sr', (usually not needed, because window length 'ms_win_length' is adjusted automatically for different sample frequencies) +ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples +ms_n_fft: 4096 # padded fft window length (in bins), 4096 still fits 40 ms window length, even for 96kHz sample rate +ms_hop_length: 0.01 # hop length of fft windowing (in seconds) +ms_win_length: 0.02 # fft window length (in seconds), will be padded with zeros to match 'ms_n_fft' +ms_n_mels: 48 # number of Mel bands +ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) +ms_seg_hop_length: 4 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. segment hop length in seconds: ms_hop_length*ms_seg_hop_length +ms_max_segments: 1300 # max segment length (in bins). if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples +ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used + +# Main model +model: NISQA_DE # 'NISQA' single-ended | 'NISQA_DIM' single-ended with multidimension prediction | 'NISQA_DE' double-ended + +# Framewise options (usually CNN) +cnn_model: adapt # framewise model, either 'adapt' for CNN with adaptive maxpooling | 'standard' for standard CNN | 'dff' for deep feed-forward network | 'skip' to skip framewise modelling +cnn_c_out_1: 16 # number of output channels of first convolutional layer +cnn_c_out_2: 32 # number of output channels of the second convolutional layer +cnn_c_out_3: 64 # number of output channels of the last four convolutional layer +cnn_kernel_size: !!python/tuple [3,3] +cnn_dropout: 0.2 +cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted +cnn_pool_1: [24,7] # outpuf dimensions of first adaptive pooling ('adaptive' CNN only) +cnn_pool_2: [12,5] # outpuf dimensions of second adaptive pooling ('adaptive' CNN only) +cnn_pool_3: [6,3] # outpuf dimensions of third adaptive pooling ('adaptive' CNN only) + +# Time-Depedency options +td: self_att # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) +td_sa_d_model: 64 # attention network dimension +td_sa_nhead: 1 # number of heads +td_sa_pos_enc: False # apply positional encoding (no improvement in previous experiments) +td_sa_num_layers: 2 # self-attention depth +td_sa_h: 64 # hidden units of self-attention feedforward network +td_sa_dropout: 0.1 # self-attention dropout +td_lstm_h: null # number of LSTM hidden units +td_lstm_num_layers: null # LSTM depth +td_lstm_dropout: null +td_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 + +# Second Time-Depedency options (optional, for example for LSTM-Self-Attention network) +td_2: self_att # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) +td_2_sa_d_model: 64 # attention network dimension +td_2_sa_nhead: 1 # number of heads +td_2_sa_pos_enc: False # apply positional encoding (no improvement in previous experiments) +td_2_sa_num_layers: 2 +td_2_sa_h: 64 # hidden units of self-attention feedforward network +td_2_sa_dropout: 0.1 # self-attention dropout +td_2_lstm_h: null # number of LSTM hidden units +td_2_lstm_num_layers: null # LSTM depth +td_2_lstm_dropout: null +td_2_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 + +# Pooling options +pool: att # 'att' for Attention-Pooling | 'avg' for average-pooling | 'max' max-pooling | 'last_step' last-step pooling | 'last_step_bi' last-step pooling with BiLSTMs +pool_att_h: 128 # number of hidden units of attention-pooling feedforward network +pool_att_dropout: 0 # attention pooling dropout + +# Double-Ended options +de_align: cosine # attention mechanism for alignment, 'bahd' | 'luong' | 'dot' | 'cosine' | 'distance' | 'none' +de_align_apply: hard # attention application method for alignment, 'soft' | 'hard' +de_fuse: x/y/- # fusing mechanism, 'x/y/-' | '+/-' | 'x/y' +de_fuse_dim: null # apply fully connected to change fuse output dimension, if 'null' fully conneted is skipped + + + + diff --git a/src/libs/NISQA/env.yml b/src/libs/NISQA/env.yml new file mode 100644 index 0000000..1e70ef4 --- /dev/null +++ b/src/libs/NISQA/env.yml @@ -0,0 +1,24 @@ +name: nisqa +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - cudatoolkit=10.2.89 + - libsndfile=1.0.31 + - pillow=8.4.0 + - pip=21.3.1 + - python=3.9.9 + - pytorch=1.10.1 + - scipy=1.7.3 + - torchvision=0.11.2 + - pip: + - librosa==0.8.1 + - matplotlib==3.5.1 + - numba==0.54.1 + - numpy==1.20.3 + - pandas==1.3.5 + - pyyaml==6.0 + - scikit-learn==1.0.2 + - seaborn==0.11.2 + - tqdm==4.62.3 diff --git a/src/libs/NISQA/nisqa/NISQA_lib.py b/src/libs/NISQA/nisqa/NISQA_lib.py new file mode 100644 index 0000000..59bc2c6 --- /dev/null +++ b/src/libs/NISQA/nisqa/NISQA_lib.py @@ -0,0 +1,2334 @@ +# -*- coding: utf-8 -*- +""" +@author: Gabriel Mittag, TU-Berlin +""" +import os +import multiprocessing +import copy +import math +import time + +import librosa as lb +import numpy as np +import pandas as pd; pd.options.mode.chained_assignment = None +import matplotlib.pyplot as plt + +from tqdm import tqdm +from scipy.stats import pearsonr +from scipy.optimize import minimize + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils.rnn import pad_packed_sequence +from torch.nn.utils.rnn import pack_padded_sequence +from torch.utils.data import DataLoader +from torch.utils.data import Dataset + +# import torchaudio.transforms as T +import torchaudio + + +#%% Models +class NISQA(nn.Module): + ''' + NISQA: The main speech quality model without speech quality dimension + estimation (MOS only). The module loads the submodules for framewise + modelling (e.g. CNN), time-dependency modelling (e.g. Self-Attention + or LSTM), and pooling (e.g. max-pooling or attention-pooling) + ''' + def __init__(self, + ms_seg_length=15, + ms_n_mels=48, + + cnn_model='adapt', + cnn_c_out_1=16, + cnn_c_out_2=32, + cnn_c_out_3=64, + cnn_kernel_size=3, + cnn_dropout=0.2, + cnn_pool_1=[24,7], + cnn_pool_2=[12,5], + cnn_pool_3=[6,3], + cnn_fc_out_h=None, + + td='self_att', + td_sa_d_model=64, + td_sa_nhead=1, + td_sa_pos_enc=None, + td_sa_num_layers=2, + td_sa_h=64, + td_sa_dropout=0.1, + td_lstm_h=128, + td_lstm_num_layers=1, + td_lstm_dropout=0, + td_lstm_bidirectional=True, + + td_2='skip', + td_2_sa_d_model=None, + td_2_sa_nhead=None, + td_2_sa_pos_enc=None, + td_2_sa_num_layers=None, + td_2_sa_h=None, + td_2_sa_dropout=None, + td_2_lstm_h=None, + td_2_lstm_num_layers=None, + td_2_lstm_dropout=None, + td_2_lstm_bidirectional=None, + + pool='att', + pool_att_h=128, + pool_att_dropout=0.1, + + ): + super().__init__() + + self.name = 'NISQA' + + self.cnn = Framewise( + cnn_model, + ms_seg_length=ms_seg_length, + ms_n_mels=ms_n_mels, + c_out_1=cnn_c_out_1, + c_out_2=cnn_c_out_2, + c_out_3=cnn_c_out_3, + kernel_size=cnn_kernel_size, + dropout=cnn_dropout, + pool_1=cnn_pool_1, + pool_2=cnn_pool_2, + pool_3=cnn_pool_3, + fc_out_h=cnn_fc_out_h, + ) + + self.time_dependency = TimeDependency( + input_size=self.cnn.model.fan_out, + td=td, + sa_d_model=td_sa_d_model, + sa_nhead=td_sa_nhead, + sa_pos_enc=td_sa_pos_enc, + sa_num_layers=td_sa_num_layers, + sa_h=td_sa_h, + sa_dropout=td_sa_dropout, + lstm_h=td_lstm_h, + lstm_num_layers=td_lstm_num_layers, + lstm_dropout=td_lstm_dropout, + lstm_bidirectional=td_lstm_bidirectional + ) + + self.time_dependency_2 = TimeDependency( + input_size=self.time_dependency.fan_out, + td=td_2, + sa_d_model=td_2_sa_d_model, + sa_nhead=td_2_sa_nhead, + sa_pos_enc=td_2_sa_pos_enc, + sa_num_layers=td_2_sa_num_layers, + sa_h=td_2_sa_h, + sa_dropout=td_2_sa_dropout, + lstm_h=td_2_lstm_h, + lstm_num_layers=td_2_lstm_num_layers, + lstm_dropout=td_2_lstm_dropout, + lstm_bidirectional=td_2_lstm_bidirectional + ) + + self.pool = Pooling( + self.time_dependency_2.fan_out, + output_size=1, + pool=pool, + att_h=pool_att_h, + att_dropout=pool_att_dropout, + ) + + def forward(self, x, n_wins): + x = self.cnn(x, n_wins) + x, n_wins = self.time_dependency(x, n_wins) + x, n_wins = self.time_dependency_2(x, n_wins) + x = self.pool(x, n_wins) + return x + + + +class NISQA_DIM(nn.Module): + ''' + NISQA_DIM: The main speech quality model with speech quality dimension + estimation (MOS, Noisiness, Coloration, Discontinuity, and Loudness). + The module loads the submodules for framewise modelling (e.g. CNN), + time-dependency modelling (e.g. Self-Attention or LSTM), and pooling + (e.g. max-pooling or attention-pooling) + ''' + def __init__(self, + ms_seg_length=15, + ms_n_mels=48, + + cnn_model='adapt', + cnn_c_out_1=16, + cnn_c_out_2=32, + cnn_c_out_3=64, + cnn_kernel_size=3, + cnn_dropout=0.2, + cnn_pool_1=[24,7], + cnn_pool_2=[12,5], + cnn_pool_3=[6,3], + cnn_fc_out_h=None, + + td='self_att', + td_sa_d_model=64, + td_sa_nhead=1, + td_sa_pos_enc=None, + td_sa_num_layers=2, + td_sa_h=64, + td_sa_dropout=0.1, + td_lstm_h=128, + td_lstm_num_layers=1, + td_lstm_dropout=0, + td_lstm_bidirectional=True, + + td_2='skip', + td_2_sa_d_model=None, + td_2_sa_nhead=None, + td_2_sa_pos_enc=None, + td_2_sa_num_layers=None, + td_2_sa_h=None, + td_2_sa_dropout=None, + td_2_lstm_h=None, + td_2_lstm_num_layers=None, + td_2_lstm_dropout=None, + td_2_lstm_bidirectional=None, + + pool='att', + pool_att_h=128, + pool_att_dropout=0.1, + + ): + super().__init__() + + self.name = 'NISQA_DIM' + + self.cnn = Framewise( + cnn_model, + ms_seg_length=ms_seg_length, + ms_n_mels=ms_n_mels, + c_out_1=cnn_c_out_1, + c_out_2=cnn_c_out_2, + c_out_3=cnn_c_out_3, + kernel_size=cnn_kernel_size, + dropout=cnn_dropout, + pool_1=cnn_pool_1, + pool_2=cnn_pool_2, + pool_3=cnn_pool_3, + fc_out_h=cnn_fc_out_h, + ) + + self.time_dependency = TimeDependency( + input_size=self.cnn.model.fan_out, + td=td, + sa_d_model=td_sa_d_model, + sa_nhead=td_sa_nhead, + sa_pos_enc=td_sa_pos_enc, + sa_num_layers=td_sa_num_layers, + sa_h=td_sa_h, + sa_dropout=td_sa_dropout, + lstm_h=td_lstm_h, + lstm_num_layers=td_lstm_num_layers, + lstm_dropout=td_lstm_dropout, + lstm_bidirectional=td_lstm_bidirectional + ) + + self.time_dependency_2 = TimeDependency( + input_size=self.time_dependency.fan_out, + td=td_2, + sa_d_model=td_2_sa_d_model, + sa_nhead=td_2_sa_nhead, + sa_pos_enc=td_2_sa_pos_enc, + sa_num_layers=td_2_sa_num_layers, + sa_h=td_2_sa_h, + sa_dropout=td_2_sa_dropout, + lstm_h=td_2_lstm_h, + lstm_num_layers=td_2_lstm_num_layers, + lstm_dropout=td_2_lstm_dropout, + lstm_bidirectional=td_2_lstm_bidirectional + ) + + pool = Pooling( + self.time_dependency.fan_out, + output_size=1, + pool=pool, + att_h=pool_att_h, + att_dropout=pool_att_dropout, + ) + + self.pool_layers = self._get_clones(pool, 5) + + def _get_clones(self, module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + def forward(self, x, n_wins): + + x = self.cnn(x, n_wins) + x, n_wins = self.time_dependency(x, n_wins) + x, n_wins = self.time_dependency_2(x, n_wins) + out = [mod(x, n_wins) for mod in self.pool_layers] + out = torch.cat(out, dim=1) + + return out + + + +class NISQA_DE(nn.Module): + ''' + NISQA: The main speech quality model for double-ended prediction. + The module loads the submodules for framewise modelling (e.g. CNN), + time-dependency modelling (e.g. Self-Attention or LSTM), time-alignment, + feature fusion and pooling (e.g. max-pooling or attention-pooling) + ''' + def __init__(self, + ms_seg_length=15, + ms_n_mels=48, + + cnn_model='adapt', + cnn_c_out_1=16, + cnn_c_out_2=32, + cnn_c_out_3=64, + cnn_kernel_size=3, + cnn_dropout=0.2, + cnn_pool_1=[24,7], + cnn_pool_2=[12,5], + cnn_pool_3=[6,3], + cnn_fc_out_h=None, + + td='self_att', + td_sa_d_model=64, + td_sa_nhead=1, + td_sa_pos_enc=None, + td_sa_num_layers=2, + td_sa_h=64, + td_sa_dropout=0.1, + td_lstm_h=128, + td_lstm_num_layers=1, + td_lstm_dropout=0, + td_lstm_bidirectional=True, + + td_2='skip', + td_2_sa_d_model=None, + td_2_sa_nhead=None, + td_2_sa_pos_enc=None, + td_2_sa_num_layers=None, + td_2_sa_h=None, + td_2_sa_dropout=None, + td_2_lstm_h=None, + td_2_lstm_num_layers=None, + td_2_lstm_dropout=None, + td_2_lstm_bidirectional=None, + + pool='att', + pool_att_h=128, + pool_att_dropout=0.1, + + de_align = 'dot', + de_align_apply = 'hard', + de_fuse_dim = None, + de_fuse = True, + + ): + + super().__init__() + + self.name = 'NISQA_DE' + + self.cnn = Framewise( + cnn_model, + ms_seg_length=ms_seg_length, + ms_n_mels=ms_n_mels, + c_out_1=cnn_c_out_1, + c_out_2=cnn_c_out_2, + c_out_3=cnn_c_out_3, + kernel_size=cnn_kernel_size, + dropout=cnn_dropout, + pool_1=cnn_pool_1, + pool_2=cnn_pool_2, + pool_3=cnn_pool_3, + fc_out_h=cnn_fc_out_h, + ) + + self.time_dependency = TimeDependency( + input_size=self.cnn.model.fan_out, + td=td, + sa_d_model=td_sa_d_model, + sa_nhead=td_sa_nhead, + sa_pos_enc=td_sa_pos_enc, + sa_num_layers=td_sa_num_layers, + sa_h=td_sa_h, + sa_dropout=td_sa_dropout, + lstm_h=td_lstm_h, + lstm_num_layers=td_lstm_num_layers, + lstm_dropout=td_lstm_dropout, + lstm_bidirectional=td_lstm_bidirectional + ) + + self.align = Alignment( + de_align, + de_align_apply, + q_dim=self.time_dependency.fan_out, + y_dim=self.time_dependency.fan_out, + ) + + self.fuse = Fusion( + in_feat=self.time_dependency.fan_out, + fuse_dim=de_fuse_dim, + fuse=de_fuse, + ) + + self.time_dependency_2 = TimeDependency( + input_size=self.fuse.fan_out, + td=td_2, + sa_d_model=td_2_sa_d_model, + sa_nhead=td_2_sa_nhead, + sa_pos_enc=td_2_sa_pos_enc, + sa_num_layers=td_2_sa_num_layers, + sa_h=td_2_sa_h, + sa_dropout=td_2_sa_dropout, + lstm_h=td_2_lstm_h, + lstm_num_layers=td_2_lstm_num_layers, + lstm_dropout=td_2_lstm_dropout, + lstm_bidirectional=td_2_lstm_bidirectional + ) + + self.pool = Pooling( + self.time_dependency_2.fan_out, + output_size=1, + pool=pool, + att_h=pool_att_h, + att_dropout=pool_att_dropout, + ) + + def _split_ref_deg(self, x, n_wins): + (x, y) = torch.chunk(x, 2, dim=2) + (n_wins_x, n_wins_y) = torch.chunk(n_wins, 2, dim=1) + n_wins_x = n_wins_x.view(-1) + n_wins_y = n_wins_y.view(-1) + return x, y, n_wins_x, n_wins_y + + def forward(self, x, n_wins): + + x, y, n_wins_x, n_wins_y = self._split_ref_deg(x, n_wins) + + x = self.cnn(x, n_wins_x) + y = self.cnn(y, n_wins_y) + + x, n_wins_x = self.time_dependency(x, n_wins_x) + y, n_wins_y = self.time_dependency(y, n_wins_y) + + y = self.align(x, y, n_wins_y) + + x = self.fuse(x, y) + + x, n_wins_x = self.time_dependency_2(x, n_wins_x) + + x = self.pool(x, n_wins_x) + + return x + + +#%% Framewise +class Framewise(nn.Module): + ''' + Framewise: The main framewise module. It loads either a CNN or feed-forward + network for framewise modelling of the Mel-spec segments. This module can + also be skipped by loading the SkipCNN module. There are two CNN modules + available. AdaptCNN with adaptive maxpooling and the StandardCNN module. + However, they could also be replaced with new modules, such as PyTorch + implementations of ResNet or Alexnet. + ''' + def __init__( + self, + cnn_model, + ms_seg_length=15, + ms_n_mels=48, + c_out_1=16, + c_out_2=32, + c_out_3=64, + kernel_size=3, + dropout=0.2, + pool_1=[24,7], + pool_2=[12,5], + pool_3=[6,3], + fc_out_h=None, + ): + super().__init__() + + if cnn_model=='adapt': + self.model = AdaptCNN( + input_channels=1, + c_out_1=c_out_1, + c_out_2=c_out_2, + c_out_3=c_out_3, + kernel_size=kernel_size, + dropout=dropout, + pool_1=pool_1, + pool_2=pool_2, + pool_3=pool_3, + fc_out_h=fc_out_h, + ) + elif cnn_model=='standard': + assert ms_n_mels == 48, "ms_n_mels is {} and should be 48, use adaptive model or change ms_n_mels".format(ms_n_mels) + assert ms_seg_length == 15, "ms_seg_len is {} should be 15, use adaptive model or change ms_seg_len".format(ms_seg_length) + assert ((kernel_size == 3) or (kernel_size == (3,3))), "cnn_kernel_size is {} should be 3, use adaptive model or change cnn_kernel_size".format(kernel_size) + self.model = StandardCNN( + input_channels=1, + c_out_1=c_out_1, + c_out_2=c_out_2, + c_out_3=c_out_3, + kernel_size=kernel_size, + dropout=dropout, + fc_out_h=fc_out_h, + ) + elif cnn_model=='dff': + self.model = DFF(ms_seg_length, ms_n_mels, dropout, fc_out_h) + elif (cnn_model is None) or (cnn_model=='skip'): + self.model = SkipCNN(ms_seg_length, ms_n_mels, fc_out_h) + else: + raise NotImplementedError('Framwise model not available') + + def forward(self, x, n_wins): + (bs, length, channels, height, width) = x.shape + x_packed = pack_padded_sequence( + x, + n_wins.cpu(), + batch_first=True, + enforce_sorted=False + ) + x = self.model(x_packed.data) + x = x_packed._replace(data=x) + x, _ = pad_packed_sequence( + x, + batch_first=True, + padding_value=0.0, + total_length=n_wins.max()) + return x + +class SkipCNN(nn.Module): + ''' + SkipCNN: Can be used to skip the framewise modelling stage and directly + apply an LSTM or Self-Attention network. + ''' + def __init__( + self, + cnn_seg_length, + ms_n_mels, + fc_out_h + ): + super().__init__() + + self.name = 'SkipCNN' + self.cnn_seg_length = cnn_seg_length + self.ms_n_mels = ms_n_mels + self.fan_in = cnn_seg_length*ms_n_mels + self.bn = nn.BatchNorm2d( 1 ) + + if fc_out_h is not None: + self.linear = nn.Linear(self.fan_in, fc_out_h) + self.fan_out = fc_out_h + else: + self.linear = nn.Identity() + self.fan_out = self.fan_in + + def forward(self, x): + x = self.bn(x) + x = x.view(-1, self.fan_in) + x = self.linear(x) + return x + +class DFF(nn.Module): + ''' + DFF: Deep Feed-Forward network that was used as baseline framwise model as + comparision to the CNN. + ''' + def __init__(self, + cnn_seg_length, + ms_n_mels, + dropout, + fc_out_h=4096, + ): + super().__init__() + self.name = 'DFF' + + self.dropout_rate = dropout + self.fc_out_h = fc_out_h + self.fan_out = fc_out_h + + self.cnn_seg_length = cnn_seg_length + self.ms_n_mels = ms_n_mels + self.fan_in = cnn_seg_length*ms_n_mels + + self.lin1 = nn.Linear(self.fan_in, self.fc_out_h) + self.lin2 = nn.Linear(self.fc_out_h, self.fc_out_h) + self.lin3 = nn.Linear(self.fc_out_h, self.fc_out_h) + self.lin4 = nn.Linear(self.fc_out_h, self.fc_out_h) + + self.bn1 = nn.BatchNorm2d(1) + self.bn2 = nn.BatchNorm1d( self.fc_out_h ) + self.bn3 = nn.BatchNorm1d( self.fc_out_h ) + self.bn4 = nn.BatchNorm1d( self.fc_out_h ) + self.bn5 = nn.BatchNorm1d( self.fc_out_h ) + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x): + + x = self.bn1(x) + x = x.view(-1, self.fan_in) + + x = F.relu( self.bn2( self.lin1(x) ) ) + x = self.dropout(x) + x = F.relu( self.bn3( self.lin2(x) ) ) + x = self.dropout(x) + x = F.relu( self.bn4( self.lin3(x) ) ) + x = self.dropout(x) + x = F.relu( self.bn5( self.lin4(x) ) ) + + return x + + +class AdaptCNN(nn.Module): + ''' + AdaptCNN: CNN with adaptive maxpooling that can be used as framewise model. + Overall, it has six convolutional layers. This CNN module is more flexible + than the StandardCNN that requires a fixed input dimension of 48x15. + ''' + def __init__(self, + input_channels, + c_out_1, + c_out_2, + c_out_3, + kernel_size, + dropout, + pool_1, + pool_2, + pool_3, + fc_out_h=20, + ): + super().__init__() + self.name = 'CNN_adapt' + + self.input_channels = input_channels + self.c_out_1 = c_out_1 + self.c_out_2 = c_out_2 + self.c_out_3 = c_out_3 + self.kernel_size = kernel_size + self.pool_1 = pool_1 + self.pool_2 = pool_2 + self.pool_3 = pool_3 + self.dropout_rate = dropout + self.fc_out_h = fc_out_h + + self.dropout = nn.Dropout2d(p=self.dropout_rate) + + if isinstance(self.kernel_size, int): + self.kernel_size = (self.kernel_size, self.kernel_size) + + # Set kernel width of last conv layer to last pool width to + # downsample width to one. + self.kernel_size_last = (self.kernel_size[0], self.pool_3[1]) + + # kernel_size[1]=1 can be used for seg_length=1 -> corresponds to + # 1D conv layer, no width padding needed. + if self.kernel_size[1] == 1: + self.cnn_pad = (1,0) + else: + self.cnn_pad = (1,1) + + self.conv1 = nn.Conv2d( + self.input_channels, + self.c_out_1, + self.kernel_size, + padding = self.cnn_pad) + + self.bn1 = nn.BatchNorm2d( self.conv1.out_channels ) + + self.conv2 = nn.Conv2d( + self.conv1.out_channels, + self.c_out_2, + self.kernel_size, + padding = self.cnn_pad) + + self.bn2 = nn.BatchNorm2d( self.conv2.out_channels ) + + self.conv3 = nn.Conv2d( + self.conv2.out_channels, + self.c_out_3, + self.kernel_size, + padding = self.cnn_pad) + + self.bn3 = nn.BatchNorm2d( self.conv3.out_channels ) + + self.conv4 = nn.Conv2d( + self.conv3.out_channels, + self.c_out_3, + self.kernel_size, + padding = self.cnn_pad) + + self.bn4 = nn.BatchNorm2d( self.conv4.out_channels ) + + self.conv5 = nn.Conv2d( + self.conv4.out_channels, + self.c_out_3, + self.kernel_size, + padding = self.cnn_pad) + + self.bn5 = nn.BatchNorm2d( self.conv5.out_channels ) + + self.conv6 = nn.Conv2d( + self.conv5.out_channels, + self.c_out_3, + self.kernel_size_last, + padding = (1,0)) + + self.bn6 = nn.BatchNorm2d( self.conv6.out_channels ) + + if self.fc_out_h: + self.fc = nn.Linear(self.conv6.out_channels * self.pool_3[0], self.fc_out_h) + self.fan_out = self.fc_out_h + else: + self.fan_out = (self.conv6.out_channels * self.pool_3[0]) + + def forward(self, x): + + x = F.relu( self.bn1( self.conv1(x) ) ) + x = F.adaptive_max_pool2d(x, output_size=(self.pool_1)) + + x = F.relu( self.bn2( self.conv2(x) ) ) + x = F.adaptive_max_pool2d(x, output_size=(self.pool_2)) + + x = self.dropout(x) + x = F.relu( self.bn3( self.conv3(x) ) ) + x = self.dropout(x) + x = F.relu( self.bn4( self.conv4(x) ) ) + x = F.adaptive_max_pool2d(x, output_size=(self.pool_3)) + + x = self.dropout(x) + x = F.relu( self.bn5( self.conv5(x) ) ) + x = self.dropout(x) + x = F.relu( self.bn6( self.conv6(x) ) ) + x = x.view(-1, self.conv6.out_channels * self.pool_3[0]) + + if self.fc_out_h: + x = self.fc( x ) + return x + +class StandardCNN(nn.Module): + ''' + StandardCNN: CNN with fixed maxpooling that can be used as framewise model. + Overall, it has six convolutional layers. This CNN module requires a fixed + input dimension of 48x15. + ''' + def __init__( + self, + input_channels, + c_out_1, + c_out_2, + c_out_3, + kernel_size, + dropout, + fc_out_h=None + ): + super().__init__() + + self.name = 'CNN_standard' + + self.input_channels = input_channels + self.c_out_1 = c_out_1 + self.c_out_2 = c_out_2 + self.c_out_3 = c_out_3 + self.kernel_size = kernel_size + self.pool_size = 2 + self.dropout_rate = dropout + self.fc_out_h = fc_out_h + + self.output_width = 2 # input width 15 pooled 3 times + self.output_height = 6 # input height 48 pooled 3 times + + self.dropout = nn.Dropout2d(p=self.dropout_rate) + + self.pool_first = nn.MaxPool2d( + self.pool_size, + stride = self.pool_size, + padding = (0,1)) + + self.pool = nn.MaxPool2d( + self.pool_size, + stride = self.pool_size, + padding = 0) + + self.conv1 = nn.Conv2d( + self.input_channels, + self.c_out_1, + self.kernel_size, + padding = 1) + + self.bn1 = nn.BatchNorm2d( self.conv1.out_channels ) + + self.conv2 = nn.Conv2d( + self.conv1.out_channels, + self.c_out_2, + self.kernel_size, + padding = 1) + + self.bn2 = nn.BatchNorm2d( self.conv2.out_channels ) + + + self.conv3 = nn.Conv2d( + self.conv2.out_channels, + self.c_out_3, + self.kernel_size, + padding = 1) + + self.bn3 = nn.BatchNorm2d( self.conv3.out_channels ) + + self.conv4 = nn.Conv2d( + self.conv3.out_channels, + self.c_out_3, + self.kernel_size, + padding = 1) + + self.bn4 = nn.BatchNorm2d( self.conv4.out_channels ) + + self.conv5 = nn.Conv2d( + self.conv4.out_channels, + self.c_out_3, + self.kernel_size, + padding = 1) + + self.bn5 = nn.BatchNorm2d( self.conv5.out_channels ) + + self.conv6 = nn.Conv2d( + self.conv5.out_channels, + self.c_out_3, + self.kernel_size, + padding = 1) + + self.bn6 = nn.BatchNorm2d( self.conv6.out_channels ) + + if self.fc_out_h: + self.fc_out = nn.Linear(self.conv6.out_channels * self.output_height * self.output_width, self.fc_out_h) + self.fan_out = self.fc_out_h + else: + self.fan_out = (self.conv6.out_channels * self.output_height * self.output_width) + + def forward(self, x): + + x = F.relu( self.bn1( self.conv1(x) ) ) + x = self.pool_first( x ) + + x = F.relu( self.bn2( self.conv2(x) ) ) + x = self.pool( x ) + + x = self.dropout(x) + x = F.relu( self.bn3( self.conv3(x) ) ) + x = self.dropout(x) + x = F.relu( self.bn4( self.conv4(x) ) ) + x = self.pool( x ) + + x = self.dropout(x) + x = F.relu( self.bn5( self.conv5(x) ) ) + x = self.dropout(x) + + x = F.relu( self.bn6( self.conv6(x) ) ) + + x = x.view(-1, self.conv6.out_channels * self.output_height * self.output_width) + + if self.fc_out_h: + x = self.fc_out( x ) + + return x + +#%% Time Dependency +class TimeDependency(nn.Module): + ''' + TimeDependency: The main time-dependency module. It loads either an LSTM + or self-attention network for time-dependency modelling of the framewise + features. This module can also be skipped. + ''' + def __init__(self, + input_size, + td='self_att', + sa_d_model=512, + sa_nhead=8, + sa_pos_enc=None, + sa_num_layers=6, + sa_h=2048, + sa_dropout=0.1, + lstm_h=128, + lstm_num_layers=1, + lstm_dropout=0, + lstm_bidirectional=True, + ): + super().__init__() + + if td=='self_att': + self.model = SelfAttention( + input_size=input_size, + d_model=sa_d_model, + nhead=sa_nhead, + pos_enc=sa_pos_enc, + num_layers=sa_num_layers, + sa_h=sa_h, + dropout=sa_dropout, + activation="relu" + ) + self.fan_out = sa_d_model + + elif td=='lstm': + self.model = LSTM( + input_size, + lstm_h=lstm_h, + num_layers=lstm_num_layers, + dropout=lstm_dropout, + bidirectional=lstm_bidirectional, + ) + self.fan_out = self.model.fan_out + + elif (td is None) or (td=='skip'): + self.model = self._skip + self.fan_out = input_size + else: + raise NotImplementedError('Time dependency option not available') + + def _skip(self, x, n_wins): + return x, n_wins + + def forward(self, x, n_wins): + x, n_wins = self.model(x, n_wins) + return x, n_wins + +class LSTM(nn.Module): + ''' + LSTM: The main LSTM module that can be used as a time-dependency model. + ''' + def __init__(self, + input_size, + lstm_h=128, + num_layers=1, + dropout=0.1, + bidirectional=True + ): + super().__init__() + + self.lstm = nn.LSTM( + input_size = input_size, + hidden_size = lstm_h, + num_layers = num_layers, + dropout = dropout, + batch_first = True, + bidirectional = bidirectional + ) + + if bidirectional: + num_directions = 2 + else: + num_directions = 1 + self.fan_out = num_directions*lstm_h + + def forward(self, x, n_wins): + + x = pack_padded_sequence( + x, + n_wins.cpu(), + batch_first=True, + enforce_sorted=False + ) + + self.lstm.flatten_parameters() + x = self.lstm(x)[0] + + x, _ = pad_packed_sequence( + x, + batch_first=True, + padding_value=0.0, + total_length=n_wins.max()) + + return x, n_wins + +class SelfAttention(nn.Module): + ''' + SelfAttention: The main SelfAttention module that can be used as a + time-dependency model. + ''' + def __init__(self, + input_size, + d_model=512, + nhead=8, + pool_size=3, + pos_enc=None, + num_layers=6, + sa_h=2048, + dropout=0.1, + activation="relu" + ): + super().__init__() + + encoder_layer = SelfAttentionLayer(d_model, nhead, pool_size, sa_h, dropout, activation) + self.norm1 = nn.LayerNorm(d_model) + + self.linear = nn.Linear(input_size, d_model) + + self.layers = self._get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.d_model = d_model + self.nhead = nhead + + if pos_enc: + self.pos_encoder = PositionalEncoding(d_model, dropout) + else: + self.pos_encoder = nn.Identity() + + self._reset_parameters() + + def _get_clones(self, module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, n_wins=None): + src = self.linear(src) + output = src.transpose(1,0) + output = self.norm1(output) + output = self.pos_encoder(output) + + for mod in self.layers: + output, n_wins = mod(output, n_wins=n_wins) + return output.transpose(1,0), n_wins + +class SelfAttentionLayer(nn.Module): + ''' + SelfAttentionLayer: The SelfAttentionLayer that is used by the + SelfAttention module. + ''' + def __init__(self, d_model, nhead, pool_size=1, sa_h=2048, dropout=0.1, activation="relu"): + super().__init__() + + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.linear1 = nn.Linear(d_model, sa_h) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(sa_h, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = self._get_activation_fn(activation) + + def _get_activation_fn(self, activation): + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + def forward(self, src, n_wins=None): + + if n_wins is not None: + mask = ~((torch.arange(src.shape[0])[None, :]).to(src.device) < n_wins[:, None].to(torch.long).to(src.device)) + else: + mask = None + + src2 = self.self_attn(src, src, src, key_padding_mask=mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + + src = self.norm2(src) + + return src, n_wins + +class PositionalEncoding(nn.Module): + ''' + PositionalEncoding: PositionalEncoding taken from the PyTorch Transformer + tutorial. Can be applied to the SelfAttention module. However, it did not + improve the results in previous experiments. + ''' + def __init__(self, d_model, dropout=0.1, max_len=3000): + super(PositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + + pe = torch.zeros(max_len, d_model) + position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0).transpose(0, 1) + self.register_buffer('pe', pe) + + def forward(self, x): + x = x + self.pe[:x.size(0), :] + return self.dropout(x) + +#%% Pooling +class Pooling(nn.Module): + ''' + Pooling: Main Pooling module. It can load either attention-pooling, average + pooling, maxpooling, or last-step pooling. In case of bidirectional LSTMs + last-step-bi pooling should be used instead of last-step pooling. + ''' + def __init__(self, + d_input, + output_size=1, + pool='att', + att_h=None, + att_dropout=0, + ): + super().__init__() + + if pool=='att': + if att_h is None: + self.model = PoolAtt(d_input, output_size) + else: + self.model = PoolAttFF(d_input, output_size, h=att_h, dropout=att_dropout) + elif pool=='last_step_bi': + self.model = PoolLastStepBi(d_input, output_size) + elif pool=='last_step': + self.model = PoolLastStep(d_input, output_size) + elif pool=='max': + self.model = PoolMax(d_input, output_size) + elif pool=='avg': + self.model = PoolAvg(d_input, output_size) + else: + raise NotImplementedError('Pool option not available') + + def forward(self, x, n_wins): + return self.model(x, n_wins) + +class PoolLastStepBi(nn.Module): + ''' + PoolLastStepBi: last step pooling for the case of bidirectional LSTM + ''' + def __init__(self, input_size, output_size): + super().__init__() + self.linear = nn.Linear(input_size, output_size) + + def forward(self, x, n_wins=None): + x = x.view(x.shape[0], n_wins.max(), 2, x.shape[-1]//2) + x = torch.cat( + (x[torch.arange(x.shape[0]), n_wins.type(torch.long)-1, 0, :], + x[:,0,1,:]), + dim=1 + ) + x = self.linear(x) + return x + +class PoolLastStep(nn.Module): + ''' + PoolLastStep: last step pooling can be applied to any one-directional + sequence. + ''' + def __init__(self, input_size, output_size): + super().__init__() + self.linear = nn.Linear(input_size, output_size) + + def forward(self, x, n_wins=None): + x = x[torch.arange(x.shape[0]), n_wins.type(torch.long)-1] + x = self.linear(x) + return x + +class PoolAtt(torch.nn.Module): + ''' + PoolAtt: Attention-Pooling module. + ''' + def __init__(self, d_input, output_size): + super().__init__() + + self.linear1 = nn.Linear(d_input, 1) + self.linear2 = nn.Linear(d_input, output_size) + + def forward(self, x, n_wins): + + att = self.linear1(x) + + att = att.transpose(2,1) + mask = torch.arange(att.shape[2])[None, :] < n_wins[:, None].to('cpu').to(torch.long) + att[~mask.unsqueeze(1)] = float("-Inf") + att = F.softmax(att, dim=2) + x = torch.bmm(att, x) + x = x.squeeze(1) + + x = self.linear2(x) + + return x + +class PoolAttFF(torch.nn.Module): + ''' + PoolAttFF: Attention-Pooling module with additonal feed-forward network. + ''' + def __init__(self, d_input, output_size, h, dropout=0.1): + super().__init__() + + self.linear1 = nn.Linear(d_input, h) + self.linear2 = nn.Linear(h, 1) + + self.linear3 = nn.Linear(d_input, output_size) + + self.activation = F.relu + self.dropout = nn.Dropout(dropout) + + def forward(self, x, n_wins): + + att = self.linear2(self.dropout(self.activation(self.linear1(x)))) + att = att.transpose(2,1) + mask = torch.arange(att.shape[2])[None, :] < n_wins[:, None].to('cpu').to(torch.long) + att[~mask.unsqueeze(1)] = float("-Inf") + att = F.softmax(att, dim=2) + x = torch.bmm(att, x) + x = x.squeeze(1) + + x = self.linear3(x) + + return x + +class PoolAvg(torch.nn.Module): + ''' + PoolAvg: Average pooling that consideres masked time-steps. + ''' + def __init__(self, d_input, output_size): + super().__init__() + + self.linear = nn.Linear(d_input, output_size) + + def forward(self, x, n_wins): + + mask = torch.arange(x.shape[1])[None, :] < n_wins[:, None].to('cpu').to(torch.long) + mask = ~mask.unsqueeze(2).to(x.device) + x.masked_fill_(mask, 0) + + x = torch.div(x.sum(1), n_wins.unsqueeze(1)) + + x = self.linear(x) + + return x + +class PoolMax(torch.nn.Module): + ''' + PoolMax: Max-pooling that consideres masked time-steps. + ''' + def __init__(self, d_input, output_size): + super().__init__() + + self.linear = nn.Linear(d_input, output_size) + + def forward(self, x, n_wins): + + mask = torch.arange(x.shape[1])[None, :] < n_wins[:, None].to('cpu').to(torch.long) + mask = ~mask.unsqueeze(2).to(x.device) + x.masked_fill_(mask, float("-Inf")) + + x = x.max(1)[0] + + x = self.linear(x) + + return x + +#%% Alignment +class Alignment(torch.nn.Module): + ''' + Alignment: Alignment module for the double-ended NISQA_DE model. It + supports five different alignment mechanisms. + ''' + def __init__(self, + att_method, + apply_att_method, + q_dim=None, + y_dim=None, + ): + super().__init__() + + # Attention method -------------------------------------------------------- + if att_method=='bahd': + self.att = AttBahdanau( + q_dim=q_dim, + y_dim=y_dim) + + elif att_method=='luong': + self.att = AttLuong( + q_dim=q_dim, + y_dim=y_dim) + + elif att_method=='dot': + self.att = AttDot() + + elif att_method=='cosine': + self.att = AttCosine() + + elif att_method=='distance': + self.att = AttDistance() + + elif (att_method=='none') or (att_method is None): + self.att = None + else: + raise NotImplementedError + + # Apply method ---------------------------------------------------------- + if apply_att_method=='soft': + self.apply_att = ApplySoftAttention() + elif apply_att_method=='hard': + self.apply_att = ApplyHardAttention() + else: + raise NotImplementedError + + def _mask_attention(self, att, y, n_wins): + mask = torch.arange(att.shape[2])[None, :] < n_wins[:, None].to('cpu').to(torch.long) + mask = mask.unsqueeze(1).expand_as(att) + att[~mask] = float("-Inf") + + def forward(self, query, y, n_wins_y): + if self.att is not None: + att_score, sim = self.att(query, y) + self._mask_attention(att_score, y, n_wins_y) + att_score = F.softmax(att_score, dim=2) + y = self.apply_att(y, att_score) + return y + +class AttDot(torch.nn.Module): + ''' + AttDot: Dot attention that can be used by the Alignment module. + ''' + def __init__(self): + super().__init__() + def forward(self, query, y): + att = torch.bmm(query, y.transpose(2,1)) + sim = att.max(2)[0].unsqueeze(1) + return att, sim + +class AttCosine(torch.nn.Module): + ''' + AttCosine: Cosine attention that can be used by the Alignment module. + ''' + def __init__(self): + super().__init__() + self.pdist = nn.CosineSimilarity(dim=3) + def forward(self, query, y): + att = self.pdist(query.unsqueeze(2), y.unsqueeze(1)) + sim = att.max(2)[0].unsqueeze(1) + return att, sim + +class AttDistance(torch.nn.Module): + ''' + AttDistance: Distance attention that can be used by the Alignment module. + ''' + def __init__(self, dist_norm=1, weight_norm=1): + super().__init__() + self.dist_norm = dist_norm + self.weight_norm = weight_norm + def forward(self, query, y): + att = (query.unsqueeze(1)-y.unsqueeze(2)).abs().pow(self.dist_norm) + att = att.mean(dim=3).pow(self.weight_norm) + att = - att.transpose(2,1) + sim = att.max(2)[0].unsqueeze(1) + return att, sim + +class AttBahdanau(torch.nn.Module): + ''' + AttBahdanau: Attention according to Bahdanau that can be used by the + Alignment module. + ''' + def __init__(self, q_dim, y_dim, att_dim=128): + super().__init__() + self.q_dim = q_dim + self.y_dim = y_dim + self.att_dim = att_dim + self.Wq = nn.Linear(self.q_dim, self.att_dim) + self.Wy = nn.Linear(self.y_dim, self.att_dim) + self.v = nn.Linear(self.att_dim, 1) + def forward(self, query, y): + att = torch.tanh( self.Wq(query).unsqueeze(1) + self.Wy(y).unsqueeze(2) ) + att = self.v(att).squeeze(3).transpose(2,1) + sim = att.max(2)[0].unsqueeze(1) + return att, sim + +class AttLuong(torch.nn.Module): + ''' + AttLuong: Attention according to Luong that can be used by the + Alignment module. + ''' + def __init__(self, q_dim, y_dim): + super().__init__() + self.q_dim = q_dim + self.y_dim = y_dim + self.W = nn.Linear(self.y_dim, self.q_dim) + def forward(self, query, y): + att = torch.bmm(query, self.W(y).transpose(2,1)) + sim = att.max(2)[0].unsqueeze(1) + return att, sim + +class ApplyHardAttention(torch.nn.Module): + ''' + ApplyHardAttention: Apply hard attention for the purpose of time-alignment. + ''' + def __init__(self): + super().__init__() + def forward(self, y, att): + self.idx = att.argmax(2) + y = y[torch.arange(y.shape[0]).unsqueeze(-1), self.idx] + return y + +class ApplySoftAttention(torch.nn.Module): + ''' + ApplySoftAttention: Apply soft attention for the purpose of time-alignment. + ''' + def __init__(self): + super().__init__() + def forward(self, y, att): + y = torch.bmm(att, y) + return y + +class Fusion(torch.nn.Module): + ''' + Fusion: Used by the double-ended NISQA_DE model and used to fuse the + degraded and reference features. + ''' + def __init__(self, fuse_dim=None, in_feat=None, fuse=None): + super().__init__() + self.fuse_dim = fuse_dim + self.fuse = fuse + + if self.fuse=='x/y/-': + self.fan_out = 3*in_feat + elif self.fuse=='+/-': + self.fan_out = 2*in_feat + elif self.fuse=='x/y': + self.fan_out = 2*in_feat + else: + raise NotImplementedError + + if self.fuse_dim: + self.lin_fusion = nn.Linear(self.fan_out, self.fuse_dim) + self.fan_out = fuse_dim + + def forward(self, x, y): + + if self.fuse=='x/y/-': + x = torch.cat((x, y, x-y), 2) + elif self.fuse=='+/-': + x = torch.cat((x+y, x-y), 2) + elif self.fuse=='x/y': + x = torch.cat((x, y), 2) + else: + raise NotImplementedError + + if self.fuse_dim: + x = self.lin_fusion(x) + + return x + +#%% Evaluation +def predict_mos(model, ds, bs, dev, num_workers=0): + ''' + predict_mos: predicts MOS of the given dataset with given model. Used for + NISQA and NISQA_DE model. + ''' + dl = DataLoader(ds, + batch_size=bs, + shuffle=False, + drop_last=False, + pin_memory=False, + num_workers=num_workers) + model.to(dev) + model.eval() + with torch.no_grad(): + y_hat_list = [ [model(xb.to(dev), n_wins.to(dev)).cpu().numpy(), yb.cpu().numpy()] for xb, yb, (idx, n_wins) in tqdm(dl)] + yy = np.concatenate( y_hat_list, axis=1 ) + y_hat = yy[0,:,0].reshape(-1,1) + y = yy[1,:,0].reshape(-1,1) + ds.df['mos_pred'] = y_hat.astype(dtype=float) + return y_hat, y + +def predict_dim(model, ds, bs, dev, num_workers=0): + ''' + predict_dim: predicts MOS and dimensions of the given dataset with given + model. Used for NISQA_DIM model. + ''' + dl = DataLoader(ds, + batch_size=bs, + shuffle=False, + drop_last=False, + pin_memory=False, + num_workers=num_workers) + model.to(dev) + model.eval() + with torch.no_grad(): + y_hat_list = [ [model(xb.to(dev), n_wins.to(dev)).cpu().numpy(), yb.cpu().numpy()] for xb, yb, (idx, n_wins) in tqdm(dl)] + yy = np.concatenate( y_hat_list, axis=1 ) + + y_hat = yy[0,:,:] + y = yy[1,:,:] + + ds.df['mos_pred'] = y_hat[:,0].reshape(-1,1) + ds.df['noi_pred'] = y_hat[:,1].reshape(-1,1) + ds.df['dis_pred'] = y_hat[:,2].reshape(-1,1) + ds.df['col_pred'] = y_hat[:,3].reshape(-1,1) + ds.df['loud_pred'] = y_hat[:,4].reshape(-1,1) + + return y_hat, y + +def is_const(x): + if np.linalg.norm(x - np.mean(x)) < 1e-13 * np.abs(np.mean(x)): + return True + elif np.all(x==x[0]): + return True + else: + return False + +def calc_eval_metrics(y, y_hat, y_hat_map=None, d=None, ci=None): + ''' + Calculate RMSE, mapped RMSE, mapped RMSE* and Pearson's correlation. + See ITU-T P.1401 for details on RMSE*. + ''' + r = { + 'r_p': np.nan, + 'rmse': np.nan, + 'rmse_map': np.nan, + 'rmse_star_map': np.nan, + } + if is_const(y_hat) or any(np.isnan(y)): + r['r_p'] = np.nan + else: + r['r_p'] = pearsonr(y, y_hat)[0] + r['rmse'] = calc_rmse(y, y_hat) + if y_hat_map is not None: + r['rmse_map'] = calc_rmse(y, y_hat_map, d=d) + if ci is not None: + r['rmse_star_map'] = calc_rmse_star(y, y_hat_map, ci, d)[0] + return r + +def calc_rmse(y_true, y_pred, d=0): + if d==0: + rmse = np.sqrt(np.mean(np.square(y_true-y_pred))) + else: + N = y_true.shape[0] + if (N-d)<1: + rmse = np.nan + else: + rmse = np.sqrt( 1/(N-d) * np.sum( np.square(y_true-y_pred) ) ) # Eq (7-29) P.1401 + return rmse + +def calc_rmse_star(mos_sub, mos_obj, ci, d): + N = mos_sub.shape[0] + error = mos_sub-mos_obj + + if np.isnan(ci).any(): + p_error = np.nan + rmse_star = np.nan + else: + p_error = (abs(error)-ci).clip(min=0) # Eq (7-27) P.1401 + if (N-d)<1: + rmse_star = np.nan + else: + rmse_star = np.sqrt( 1/(N-d) * sum(p_error**2) ) # Eq (7-29) P.1401 + + return rmse_star, p_error, error + +def calc_mapped(x, b): + N = x.shape[0] + order = b.shape[0]-1 + A = np.zeros([N,order+1]) + for i in range(order+1): + A[:,i] = x**(i) + return A @ b + +def fit_first_order(y_con, y_con_hat): + A = np.vstack([np.ones(len(y_con_hat)), y_con_hat]).T + b = np.linalg.lstsq(A, y_con, rcond=None)[0] + return b + +def fit_second_order(y_con, y_con_hat): + A = np.vstack([np.ones(len(y_con_hat)), y_con_hat, y_con_hat**2]).T + b = np.linalg.lstsq(A, y_con, rcond=None)[0] + return b + +def fit_third_order(y_con, y_con_hat): + A = np.vstack([np.ones(len(y_con_hat)), y_con_hat, y_con_hat**2, y_con_hat**3]).T + b = np.linalg.lstsq(A, y_con, rcond=None)[0] + + p = np.poly1d(np.flipud(b)) + p2 = np.polyder(p) + rr = np.roots(p2) + r = rr[np.imag(rr)==0] + monotonic = all( np.logical_or(r>max(y_con_hat),r bias update: min_r {:0.2f}, r_p {:0.2f}'.format(r, self.min_r)) + if r>self.min_r: + self.do_update = True + + if self.do_update: + if self.do_print: + print('--> bias updated') + for db_name in self.db.unique(): + + db_idx = (self.db==db_name).to_numpy().nonzero() + y_hat_db = y_hat[db_idx] + y_db = y[db_idx] + + if not np.isnan(y_db).any(): + if self.mapping=='first_order': + b_db = self._calc_bias_first_order(y_hat_db, y_db) + else: + raise NotImplementedError + if not db_name==self.anchor_db: + self.b[db_idx,:len(b_db)] = b_db + + def _calc_bias_first_order(self, y_hat, y): + A = np.vstack([np.ones(len(y_hat)), y_hat]).T + btmp = np.linalg.lstsq(A, y, rcond=None)[0] + b = np.zeros((4)) + b[0:2] = btmp + return b + + def _nan_mse(self, y, y_hat): + err = (y-y_hat).view(-1) + idx_not_nan = ~torch.isnan(err) + nan_err = err[idx_not_nan] + return torch.mean(nan_err**2) + +#%% Early stopping +class earlyStopper(object): + ''' + Early stopping class. + + Training is stopped if neither RMSE or Pearson's correlation + is improving after "patience" epochs. + ''' + def __init__(self, patience): + self.best_rmse = 1e10 + self.best_r_p = -1e10 + self.cnt = -1 + self.patience = patience + self.best = False + + def step(self, r): + self.best = False + if r['r_p_mean_file'] > self.best_r_p: + self.best_r_p = r['r_p_mean_file'] + self.cnt = -1 + if r['rmse_map_mean_file'] < self.best_rmse: + self.best_rmse = r['rmse_map_mean_file'] + self.cnt = -1 + self.best = True + self.cnt += 1 + + if self.cnt >= self.patience: + stop_early = True + return stop_early + else: + stop_early = False + return stop_early + +class earlyStopper_dim(object): + ''' + Early stopping class for dimension model. + + Training is stopped if neither RMSE or Pearson's correlation + is improving after "patience" epochs. + ''' + def __init__(self, patience): + + self.best_rmse = 1e10 + self.best_rmse_noi = 1e10 + self.best_rmse_col = 1e10 + self.best_rmse_dis = 1e10 + self.best_rmse_loud = 1e10 + + self.best_r_p = -1e10 + self.best_r_p_noi = -1e10 + self.best_r_p_col = -1e10 + self.best_r_p_dis = -1e10 + self.best_r_p_loud = -1e10 + + self.cnt = -1 + self.patience = patience + self.best = False + + def step(self, r): + + self.best = False + + if r['r_p_mean_file'] > self.best_r_p: + self.best_r_p = r['r_p_mean_file'] + self.cnt = -1 + if r['r_p_mean_file_noi'] > self.best_r_p_noi: + self.best_r_p_noi = r['r_p_mean_file_noi'] + self.cnt = -1 + if r['r_p_mean_file_col'] > self.best_r_p_col: + self.best_r_p_col = r['r_p_mean_file_col'] + self.cnt = -1 + if r['r_p_mean_file_dis'] > self.best_r_p_dis: + self.best_r_p_dis = r['r_p_mean_file_dis'] + self.cnt = -1 + if r['r_p_mean_file_loud'] > self.best_r_p_loud: + self.best_r_p_loud = r['r_p_mean_file_loud'] + self.cnt = -1 + + if r['rmse_map_mean_file'] < self.best_rmse: + self.best_rmse = r['rmse_map_mean_file'] + self.cnt = -1 + self.best = True + if r['rmse_map_mean_file_noi'] < self.best_rmse_noi: + self.best_rmse_noi = r['rmse_map_mean_file_noi'] + self.cnt = -1 + if r['rmse_map_mean_file_col'] < self.best_rmse_col: + self.best_rmse_col = r['rmse_map_mean_file_col'] + self.cnt = -1 + if r['rmse_map_mean_file_dis'] < self.best_rmse_dis: + self.best_rmse_dis = r['rmse_map_mean_file_dis'] + self.cnt = -1 + if r['rmse_map_mean_file_loud'] < self.best_rmse_loud: + self.best_rmse_loud = r['rmse_map_mean_file_loud'] + self.cnt = -1 + + self.cnt += 1 + + if self.cnt >= self.patience: + stop_early = True + return stop_early + else: + stop_early = False + return stop_early + +def get_lr(optimizer): + ''' + Get current learning rate from Pytorch optimizer. + ''' + for param_group in optimizer.param_groups: + return param_group['lr'] + +#%% Dataset +class SpeechQualityDataset(Dataset): + ''' + Dataset for Speech Quality Model. + ''' + def __init__( + self, + df, + df_con=None, + data_dir='', + folder_column='', + filename_column='filename', + mos_column='MOS', + seg_length=15, + max_length=None, + to_memory=False, + to_memory_workers=0, + transform=None, + seg_hop_length=1, + ms_n_fft = 1024, + ms_hop_length = 80, + ms_win_length = 170, + ms_n_mels=32, + ms_sr=48e3, + ms_fmax=16e3, + ms_channel=None, + double_ended=False, + filename_column_ref=None, + dim=False, + ): + + self.df = df + self.df_con = df_con + self.data_dir = data_dir + self.folder_column = folder_column + self.filename_column = filename_column + self.filename_column_ref = filename_column_ref + self.mos_column = mos_column + self.seg_length = seg_length + self.seg_hop_length = seg_hop_length + self.max_length = max_length + self.transform = transform + self.to_memory_workers = to_memory_workers + self.ms_n_fft = ms_n_fft + self.ms_hop_length = ms_hop_length + self.ms_win_length = ms_win_length + self.ms_n_mels = ms_n_mels + self.ms_sr = ms_sr + self.ms_fmax = ms_fmax + self.ms_channel = ms_channel + self.double_ended = double_ended + self.dim = dim + + # if True load all specs to memory + self.to_memory = False + if to_memory: + self._to_memory() + + def _to_memory_multi_helper(self, idx): + return [self._load_spec(i) for i in idx] + + def _to_memory(self): + if self.to_memory_workers==0: + self.mem_list = [self._load_spec(idx) for idx in tqdm(range(len(self)))] + else: + buffer_size = 128 + idx = np.arange(len(self)) + n_bufs = int(len(idx)/buffer_size) + idx = idx[:buffer_size*n_bufs].reshape(-1,buffer_size).tolist() + idx[buffer_size*n_bufs:].reshape(1,-1).tolist() + pool = multiprocessing.Pool(processes=self.to_memory_workers) + mem_list = [] + for out in tqdm(pool.imap(self._to_memory_multi_helper, idx), total=len(idx)): + mem_list = mem_list + out + self.mem_list = mem_list + pool.terminate() + pool.join() + self.to_memory=True + + def _load_spec(self, index): + + # Load spec + file_path = os.path.join(self.data_dir, self.df[self.filename_column].iloc[index]) + + if self.double_ended: + file_path_ref = os.path.join(self.data_dir, self.df[self.filename_column_ref].iloc[index]) + spec = get_librosa_melspec( + file_path, + sr = self.ms_sr, + n_fft=self.ms_n_fft, + hop_length=self.ms_hop_length, + win_length=self.ms_win_length, + n_mels=self.ms_n_mels, + fmax=self.ms_fmax, + ms_channel=self.ms_channel + ) + if self.double_ended: + spec_ref = get_librosa_melspec( + file_path_ref, + sr = self.ms_sr, + n_fft=self.ms_n_fft, + hop_length=self.ms_hop_length, + win_length=self.ms_win_length, + n_mels=self.ms_n_mels, + fmax=self.ms_fmax + ) + spec = (spec, spec_ref) + + return spec + + def __getitem__(self, index): + assert isinstance(index, int), 'index must be integer (no slice)' + + if self.to_memory: + spec = self.mem_list[index] + else: + spec = self._load_spec(index) + + if self.double_ended: + spec, spec_ref = spec + + # Apply transformation if given + if self.transform: + spec = self.transform(spec) + + # Segment specs + file_path = os.path.join(self.data_dir, self.df[self.filename_column].iloc[index]) + if self.seg_length is not None: + x_spec_seg, n_wins = segment_specs(file_path, + spec, + self.seg_length, + self.seg_hop_length, + self.max_length) + + if self.double_ended: + x_spec_seg_ref, n_wins_ref = segment_specs(file_path, + spec_ref, + self.seg_length, + self.seg_hop_length, + self.max_length) + else: + x_spec_seg = spec + n_wins = spec.shape[1] + if self.max_length is not None: + x_padded = np.zeros((x_spec_seg.shape[0], self.max_length)) + x_padded[:,:n_wins] = x_spec_seg + x_spec_seg = np.expand_dims(x_padded.transpose(1,0), axis=(1, 3)) + if not torch.is_tensor(x_spec_seg): + x_spec_seg = torch.tensor(x_spec_seg, dtype=torch.float) + + if self.double_ended: + x_spec_seg_ref = spec + n_wins_ref = spec.shape[1] + if self.max_length is not None: + x_padded = np.zeros((x_spec_seg_ref.shape[0], self.max_length)) + x_padded[:,:n_wins] = x_spec_seg_ref + x_spec_seg_ref = np.expand_dims(x_padded.transpose(1,0), axis=(1, 3)) + if not torch.is_tensor(x_spec_seg_ref): + x_spec_seg_ref = torch.tensor(x_spec_seg_ref, dtype=torch.float) + + if self.double_ended: + x_spec_seg = torch.cat((x_spec_seg, x_spec_seg_ref), dim=1) + n_wins = np.concatenate((n_wins.reshape(1), n_wins_ref.reshape(1)), axis=0) + + # Get MOS (apply NaN in case of prediction only mode) + if self.dim: + if self.mos_column=='predict_only': + y = np.full((5,1), np.nan).reshape(-1).astype('float32') + else: + y_mos = self.df['mos'].iloc[index].reshape(-1).astype('float32') + y_noi = self.df['noi'].iloc[index].reshape(-1).astype('float32') + y_dis = self.df['dis'].iloc[index].reshape(-1).astype('float32') + y_col = self.df['col'].iloc[index].reshape(-1).astype('float32') + y_loud = self.df['loud'].iloc[index].reshape(-1).astype('float32') + y = np.concatenate((y_mos, y_noi, y_dis, y_col, y_loud), axis=0) + else: + if self.mos_column=='predict_only': + y = np.full(1, np.nan).reshape(-1).astype('float32') + else: + y = self.df[self.mos_column].iloc[index].reshape(-1).astype('float32') + + return x_spec_seg, y, (index, n_wins) + + def __len__(self): + return len(self.df) + +#%% Spectrograms +def segment_specs(file_path, x, seg_length, seg_hop=1, max_length=None): + ''' + Segment a spectrogram into "seg_length" wide spectrogram segments. + Instead of using only the frequency bin of the current time step, + the neighboring bins are included as input to the CNN. For example + for a seg_length of 7, the previous 3 and the follwing 3 frequency + bins are included. + + A spectrogram with input size [H x W] will be segmented to: + [W-(seg_length-1) x C x H x seg_length], where W is the width of the + original mel-spec (corresponding to the length of the speech signal), + H is the height of the mel-spec (corresponding to the number of mel bands), + C is the number of CNN input Channels (always one in our case). + ''' + if seg_length % 2 == 0: + raise ValueError('seg_length must be odd! (seg_lenth={})'.format(seg_length)) + if not torch.is_tensor(x): + x = torch.tensor(x) + + n_wins = x.shape[1]-(seg_length-1) + if n_wins < 1: + raise ValueError( + f"Sample too short. Only {x.shape[1]} windows available but seg_length={seg_length}. " + f"Consider zero padding the audio sample. File: {file_path}" + ) + + # broadcast magic to segment melspec + idx1 = torch.arange(seg_length) + idx2 = torch.arange(n_wins) + idx3 = idx1.unsqueeze(0) + idx2.unsqueeze(1) + x = x.transpose(1,0)[idx3,:].unsqueeze(1).transpose(3,2) + + if seg_hop>1: + x = x[::seg_hop,:] + n_wins = int(np.ceil(n_wins/seg_hop)) + + if max_length is not None: + if max_length < n_wins: + raise ValueError('n_wins {} > max_length {} --- {}. Increase max window length ms_max_segments!'.format(n_wins, max_length, file_path)) + x_padded = torch.zeros((max_length, x.shape[1], x.shape[2], x.shape[3])) + x_padded[:n_wins,:] = x + x = x_padded + + return x, np.array(n_wins) + +def get_librosa_melspec( + file_path, + sr=48e3, + n_fft=1024, + hop_length=80, + win_length=170, + n_mels=32, + fmax=16e3, + ms_channel=None, + ): + ''' + Calculate mel-spectrograms with Librosa. + ''' + # Calc spec + try: + if ms_channel is not None: + y, sr = lb.load(file_path, sr=sr, mono=False) + if len(y.shape)>1: + y = y[ms_channel, :] + else: + y, sr = lb.load(file_path, sr=sr) + except: + raise ValueError('Could not load file {}'.format(file_path)) + + hop_length = int(sr * hop_length) + win_length = int(sr * win_length) + + S = lb.feature.melspectrogram( + y=y, + sr=sr, + S=None, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window='hann', + center=True, + pad_mode='reflect', + power=1.0, + + n_mels=n_mels, + fmin=0.0, + fmax=fmax, + htk=False, + norm='slaney', + ) + + spec = lb.core.amplitude_to_db(S, ref=1.0, amin=1e-4, top_db=80.0) + return spec + diff --git a/src/libs/NISQA/nisqa/NISQA_model.py b/src/libs/NISQA/nisqa/NISQA_model.py new file mode 100644 index 0000000..959c878 --- /dev/null +++ b/src/libs/NISQA/nisqa/NISQA_model.py @@ -0,0 +1,1116 @@ +# -*- coding: utf-8 -*- +""" +@author: Gabriel Mittag, TU-Berlin +""" +import time +import os +import datetime +from pathlib import Path + +import numpy as np +import pandas as pd; pd.options.mode.chained_assignment=None +from tqdm import tqdm +import yaml +import torch +import torch.nn as nn +from torch import optim +from torch.utils.data import DataLoader +from . import NISQA_lib as NL + +class nisqaModel(object): + ''' + nisqaModel: Main class that loads the model and the datasets. Contains + the training loop, prediction, and evaluation function. + ''' + def __init__(self, args): + self.args = args + + if 'mode' not in self.args: + self.args['mode'] = 'main' + + self.runinfos = {} + self._getDevice() + self._loadModel() + self._loadDatasets() + self.args['now'] = datetime.datetime.today() + + if self.args['mode']=='main': + print(yaml.dump(self.args, default_flow_style=None, sort_keys=False)) + + def train(self): + + if self.args['dim']==True: + self._train_dim() + else: + self._train_mos() + + def evaluate(self, mapping='first_order', do_print=True, do_plot=False): + if self.args['dim']==True: + self._evaluate_dim(mapping=mapping, do_print=do_print, do_plot=do_plot) + else: + self._evaluate_mos(mapping=mapping, do_print=do_print, do_plot=do_plot) + + def predict(self): + print('---> Predicting ...') + if self.args['tr_parallel']: + self.model = nn.DataParallel(self.model) + + if self.args['dim']==True: + y_val_hat, y_val = NL.predict_dim( + self.model, + self.ds_val, + self.args['tr_bs_val'], + self.dev, + num_workers=self.args['tr_num_workers']) + else: + y_val_hat, y_val = NL.predict_mos( + self.model, + self.ds_val, + self.args['tr_bs_val'], + self.dev, + num_workers=self.args['tr_num_workers']) + + if self.args['output_dir']: + self.ds_val.df['model'] = self.args['name'] + self.ds_val.df.to_csv( + os.path.join(self.args['output_dir'], 'NISQA_results.csv'), + index=False) + + return self.ds_val.df + + def _train_mos(self): + ''' + Trains speech quality model. + ''' + # Initialize ------------------------------------------------------------- + if self.args['tr_parallel']: + self.model = nn.DataParallel(self.model) + self.model.to(self.dev) + + # Runname and savepath --------------------------------------------------- + self.runname = self._makeRunnameAndWriteYAML() + + # Optimizer ------------------------------------------------------------- + opt = optim.Adam(self.model.parameters(), lr=self.args['tr_lr']) + scheduler = optim.lr_scheduler.ReduceLROnPlateau( + opt, + 'min', + verbose=True, + threshold=0.003, + patience=self.args['tr_lr_patience']) + earlyStp = NL.earlyStopper(self.args['tr_early_stop']) + + biasLoss = NL.biasLoss( + self.ds_train.df.db, + anchor_db=self.args['tr_bias_anchor_db'], + mapping=self.args['tr_bias_mapping'], + min_r=self.args['tr_bias_min_r'], + do_print=(self.args['tr_verbose']>0), + ) + + # Dataloader ----------------------------------------------------------- + dl_train = DataLoader( + self.ds_train, + batch_size=self.args['tr_bs'], + shuffle=True, + drop_last=False, + pin_memory=True, + num_workers=self.args['tr_num_workers']) + + # Start training loop --------------------------------------------------- + print('--> start training') + for epoch in range(self.args['tr_epochs']): + + tic_epoch = time.time() + batch_cnt = 0 + loss = 0.0 + y_train = self.ds_train.df[self.args['csv_mos_train']].to_numpy().reshape(-1) + y_train_hat = np.zeros((len(self.ds_train), 1)) + self.model.train() + + # Progress bar + if self.args['tr_verbose'] == 2: + pbar = tqdm(iterable=batch_cnt, total=len(dl_train), ascii=">—", + bar_format='{bar} {percentage:3.0f}%, {n_fmt}/{total_fmt}, {elapsed}<{remaining}{postfix}') + + for xb_spec, yb_mos, (idx, n_wins) in dl_train: + + # Estimate batch --------------------------------------------------- + xb_spec = xb_spec.to(self.dev) + yb_mos = yb_mos.to(self.dev) + n_wins = n_wins.to(self.dev) + + # Forward pass ---------------------------------------------------- + yb_mos_hat = self.model(xb_spec, n_wins) + y_train_hat[idx] = yb_mos_hat.detach().cpu().numpy() + + # Loss ------------------------------------------------------------ + lossb = biasLoss.get_loss(yb_mos, yb_mos_hat, idx) + + # Backprop ------------------------------------------------------- + lossb.backward() + opt.step() + opt.zero_grad() + + # Update total loss ----------------------------------------------- + loss += lossb.item() + batch_cnt += 1 + + if self.args['tr_verbose'] == 2: + pbar.set_postfix(loss=lossb.item()) + pbar.update() + + if self.args['tr_verbose'] == 2: + pbar.close() + + loss = loss/batch_cnt + + biasLoss.update_bias(y_train, y_train_hat) + + # Evaluate ----------------------------------------------------------- + if self.args['tr_verbose']>0: + print('\n<---- Training ---->') + self.ds_train.df['mos_pred'] = y_train_hat + db_results_train, r_train = NL.eval_results( + self.ds_train.df, + dcon=self.ds_train.df_con, + target_mos=self.args['csv_mos_train'], + target_ci=self.args['csv_mos_train'] + '_ci', + pred='mos_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + if self.args['tr_verbose']>0: + print('<---- Validation ---->') + NL.predict_mos(self.model, self.ds_val, self.args['tr_bs_val'], self.dev, num_workers=self.args['tr_num_workers']) + db_results, r_val = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos=self.args['csv_mos_val'], + target_ci=self.args['csv_mos_val'] + '_ci', + pred='mos_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + r = {'train_r_p_mean_file': r_train['r_p_mean_file'], + 'train_rmse_map_mean_file': r_train['rmse_map_mean_file'], + **r_val} + + # Scheduler update --------------------------------------------- + scheduler.step(loss) + earl_stp = earlyStp.step(r) + + # Print -------------------------------------------------------- + ep_runtime = time.time() - tic_epoch + print( + 'ep {} sec {:0.0f} es {} lr {:0.0e} loss {:0.4f} // ' + 'r_p_tr {:0.2f} rmse_map_tr {:0.2f} // r_p {:0.2f} rmse_map {:0.2f} // ' + 'best_r_p {:0.2f} best_rmse_map {:0.2f},' + .format(epoch+1, ep_runtime, earlyStp.cnt, NL.get_lr(opt), loss, + r['train_r_p_mean_file'], r['train_rmse_map_mean_file'], + r['r_p_mean_file'], r['rmse_map_mean_file'], + earlyStp.best_r_p, earlyStp.best_rmse)) + + # Save results and model ----------------------------------------- + self._saveResults(self.model, self.model_args, opt, epoch, loss, ep_runtime, r, db_results, earlyStp.best) + + # Early stopping ----------------------------------------------- + if earl_stp: + print('--> Early stopping. best_r_p {:0.2f} best_rmse {:0.2f}' + .format(earlyStp.best_r_p, earlyStp.best_rmse)) + return + + # Training done -------------------------------------------------------- + print('--> Training done. best_r_p {:0.2f} best_rmse_map {:0.2f}' + .format(earlyStp.best_r_p, earlyStp.best_rmse)) + return + + + + def _train_dim(self): + ''' + Trains multidimensional speech quality model. + ''' + # Initialize ------------------------------------------------------------- + if self.args['tr_parallel']: + self.model = nn.DataParallel(self.model) + self.model.to(self.dev) + + # Runname and savepath --------------------------------------------------- + self.runname = self._makeRunnameAndWriteYAML() + + # Optimizer ------------------------------------------------------------- + opt = optim.Adam(self.model.parameters(), lr=self.args['tr_lr']) + scheduler = optim.lr_scheduler.ReduceLROnPlateau( + opt, + 'min', + verbose=True, + threshold=0.003, + patience=self.args['tr_lr_patience']) + earlyStp = NL.earlyStopper_dim(self.args['tr_early_stop']) + + biasLoss_1 = NL.biasLoss( + self.ds_train.df.db, + anchor_db=self.args['tr_bias_anchor_db'], + mapping=self.args['tr_bias_mapping'], + min_r=self.args['tr_bias_min_r'], + do_print=(self.args['tr_verbose']>0), + ) + + biasLoss_2 = NL.biasLoss( + self.ds_train.df.db, + anchor_db=self.args['tr_bias_anchor_db'], + mapping=self.args['tr_bias_mapping'], + min_r=self.args['tr_bias_min_r'], + do_print=(self.args['tr_verbose']>0), + ) + + biasLoss_3 = NL.biasLoss( + self.ds_train.df.db, + anchor_db=self.args['tr_bias_anchor_db'], + mapping=self.args['tr_bias_mapping'], + min_r=self.args['tr_bias_min_r'], + do_print=(self.args['tr_verbose']>0), + ) + + biasLoss_4 = NL.biasLoss( + self.ds_train.df.db, + anchor_db=self.args['tr_bias_anchor_db'], + mapping=self.args['tr_bias_mapping'], + min_r=self.args['tr_bias_min_r'], + do_print=(self.args['tr_verbose']>0), + ) + + biasLoss_5 = NL.biasLoss( + self.ds_train.df.db, + anchor_db=self.args['tr_bias_anchor_db'], + mapping=self.args['tr_bias_mapping'], + min_r=self.args['tr_bias_min_r'], + do_print=(self.args['tr_verbose']>0), + ) + + # Dataloader ----------------------------------------------------------- + dl_train = DataLoader( + self.ds_train, + batch_size=self.args['tr_bs'], + shuffle=True, + drop_last=False, + pin_memory=True, + num_workers=self.args['tr_num_workers']) + + + # Start training loop --------------------------------------------------- + print('--> start training') + for epoch in range(self.args['tr_epochs']): + + tic_epoch = time.time() + batch_cnt = 0 + loss = 0.0 + y_mos = self.ds_train.df['mos'].to_numpy().reshape(-1,1) + y_noi = self.ds_train.df['noi'].to_numpy().reshape(-1,1) + y_dis = self.ds_train.df['dis'].to_numpy().reshape(-1,1) + y_col = self.ds_train.df['col'].to_numpy().reshape(-1,1) + y_loud = self.ds_train.df['loud'].to_numpy().reshape(-1,1) + y_train = np.concatenate((y_mos, y_noi, y_dis, y_col, y_loud), axis=1) + y_train_hat = np.zeros((len(self.ds_train), 5)) + + self.model.train() + + + # Progress bar + if self.args['tr_verbose'] == 2: + pbar = tqdm(iterable=batch_cnt, total=len(dl_train), ascii=">—", + bar_format='{bar} {percentage:3.0f}%, {n_fmt}/{total_fmt}, {elapsed}<{remaining}{postfix}') + + for xb_spec, yb_mos, (idx, n_wins) in dl_train: + + # Estimate batch --------------------------------------------------- + xb_spec = xb_spec.to(self.dev) + yb_mos = yb_mos.to(self.dev) + n_wins = n_wins.to(self.dev) + + # Forward pass ---------------------------------------------------- + yb_mos_hat = self.model(xb_spec, n_wins) + y_train_hat[idx,:] = yb_mos_hat.detach().cpu().numpy() + + # Loss ------------------------------------------------------------ + lossb_1 = biasLoss_1.get_loss(yb_mos[:,0].view(-1,1), yb_mos_hat[:,0].view(-1,1), idx) + lossb_2 = biasLoss_2.get_loss(yb_mos[:,1].view(-1,1), yb_mos_hat[:,1].view(-1,1), idx) + lossb_3 = biasLoss_3.get_loss(yb_mos[:,2].view(-1,1), yb_mos_hat[:,2].view(-1,1), idx) + lossb_4 = biasLoss_4.get_loss(yb_mos[:,3].view(-1,1), yb_mos_hat[:,3].view(-1,1), idx) + lossb_5 = biasLoss_5.get_loss(yb_mos[:,4].view(-1,1), yb_mos_hat[:,4].view(-1,1), idx) + + lossb = lossb_1 + lossb_2 + lossb_3 + lossb_4 + lossb_5 + + # Backprop ------------------------------------------------------- + lossb.backward() + opt.step() + opt.zero_grad() + + # Update total loss ----------------------------------------------- + loss += lossb.item() + batch_cnt += 1 + + if self.args['tr_verbose'] == 2: + pbar.set_postfix(loss=lossb.item()) + pbar.update() + + if self.args['tr_verbose'] == 2: + pbar.close() + + loss = loss/batch_cnt + + biasLoss_1.update_bias(y_train[:,0].reshape(-1,1), y_train_hat[:,0].reshape(-1,1)) + biasLoss_2.update_bias(y_train[:,1].reshape(-1,1), y_train_hat[:,1].reshape(-1,1)) + biasLoss_3.update_bias(y_train[:,2].reshape(-1,1), y_train_hat[:,2].reshape(-1,1)) + biasLoss_4.update_bias(y_train[:,3].reshape(-1,1), y_train_hat[:,3].reshape(-1,1)) + biasLoss_5.update_bias(y_train[:,4].reshape(-1,1), y_train_hat[:,4].reshape(-1,1)) + + # Evaluate ----------------------------------------------------------- + self.ds_train.df['mos_pred'] = y_train_hat[:,0].reshape(-1,1) + self.ds_train.df['noi_pred'] = y_train_hat[:,1].reshape(-1,1) + self.ds_train.df['dis_pred'] = y_train_hat[:,2].reshape(-1,1) + self.ds_train.df['col_pred'] = y_train_hat[:,3].reshape(-1,1) + self.ds_train.df['loud_pred'] = y_train_hat[:,4].reshape(-1,1) + + if self.args['tr_verbose']>0: + print('\n<---- Training ---->') + print('--> MOS:') + db_results_train_mos, r_train_mos = NL.eval_results( + self.ds_train.df, + dcon=self.ds_train.df_con, + target_mos='mos', + target_ci='mos_ci', + pred='mos_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + if self.args['tr_verbose']>0: + print('--> NOI:') + db_results_train_noi, r_train_noi = NL.eval_results( + self.ds_train.df, + dcon=self.ds_train.df_con, + target_mos='noi', + target_ci='noi_ci', + pred='noi_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + if self.args['tr_verbose']>0: + print('--> DIS:') + db_results_train_dis, r_train_dis = NL.eval_results( + self.ds_train.df, + dcon=self.ds_train.df_con, + target_mos='dis', + target_ci='dis_ci', + pred='dis_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + if self.args['tr_verbose']>0: + print('--> COL:') + db_results_train_col, r_train_col = NL.eval_results( + self.ds_train.df, + dcon=self.ds_train.df_con, + target_mos='col', + target_ci='col_ci', + pred='col_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + if self.args['tr_verbose']>0: + print('--> LOUD:') + db_results_train_loud, r_train_loud = NL.eval_results( + self.ds_train.df, + dcon=self.ds_train.df_con, + target_mos='loud', + target_ci='loud_ci', + pred='loud_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + NL.predict_dim(self.model, self.ds_val, self.args['tr_bs_val'], self.dev, num_workers=self.args['tr_num_workers']) + + if self.args['tr_verbose']>0: + print('<---- Validation ---->') + print('--> MOS:') + db_results_val_mos, r_val_mos = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='mos', + target_ci='mos_ci', + pred='mos_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + + if self.args['tr_verbose']>0: + print('--> NOI:') + db_results_val_noi, r_val_noi = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='noi', + target_ci='noi_ci', + pred='noi_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + r_val_noi = {k+'_noi': v for k, v in r_val_noi.items()} + + if self.args['tr_verbose']>0: + print('--> DIS:') + db_results_val_dis, r_val_dis = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='dis', + target_ci='dis_ci', + pred='dis_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + r_val_dis = {k+'_dis': v for k, v in r_val_dis.items()} + + if self.args['tr_verbose']>0: + print('--> COL:') + db_results_val_col, r_val_col = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='col', + target_ci='col_ci', + pred='col_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + r_val_col = {k+'_col': v for k, v in r_val_col.items()} + + if self.args['tr_verbose']>0: + print('--> LOUD:') + db_results_val_loud, r_val_loud = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='loud', + target_ci='loud_ci', + pred='loud_pred', + mapping = 'first_order', + do_print=(self.args['tr_verbose']>0) + ) + r_val_loud = {k+'_loud': v for k, v in r_val_loud.items()} + + r = { + 'train_r_p_mean_file': r_train_mos['r_p_mean_file'], + 'train_rmse_map_mean_file': r_train_mos['rmse_map_mean_file'], + + 'train_r_p_mean_file_noi': r_train_noi['r_p_mean_file'], + 'train_rmse_map_mean_file_noi': r_train_noi['rmse_map_mean_file'], + + 'train_r_p_mean_file_dis': r_train_dis['r_p_mean_file'], + 'train_rmse_map_mean_file_dis': r_train_dis['rmse_map_mean_file'], + + 'train_r_p_mean_file_col': r_train_col['r_p_mean_file'], + 'train_rmse_map_mean_file_col': r_train_col['rmse_map_mean_file'], + + 'train_r_p_mean_file_loud': r_train_loud['r_p_mean_file'], + 'train_rmse_map_mean_file_loud': r_train_loud['rmse_map_mean_file'], + + **r_val_mos, **r_val_noi, **r_val_dis, **r_val_col, **r_val_loud, } + + db_results = { + 'db_results_val_mos': db_results_val_mos, + 'db_results_val_noi': db_results_val_noi, + 'db_results_val_dis': db_results_val_dis, + 'db_results_val_col': db_results_val_col, + 'db_results_val_loud': db_results_val_loud + } + + # Scheduler update --------------------------------------------- + scheduler.step(loss) + earl_stp = earlyStp.step(r) + + # Print -------------------------------------------------------- + ep_runtime = time.time() - tic_epoch + + r_dim_mos_mean = 1/5 * (r['r_p_mean_file'] + + r['r_p_mean_file_noi'] + + r['r_p_mean_file_col'] + + r['r_p_mean_file_dis'] + + r['r_p_mean_file_loud']) + + print( + 'ep {} sec {:0.0f} es {} lr {:0.0e} loss {:0.4f} // ' + 'r_p_tr {:0.2f} rmse_map_tr {:0.2f} // r_dim_mos_mean {:0.2f}, r_p {:0.2f} rmse_map {:0.2f} // ' + 'best_r_p {:0.2f} best_rmse_map {:0.2f},' + .format(epoch+1, ep_runtime, earlyStp.cnt, NL.get_lr(opt), loss, + r['train_r_p_mean_file'], r['train_rmse_map_mean_file'], + r_dim_mos_mean, + r['r_p_mean_file'], r['rmse_map_mean_file'], + earlyStp.best_r_p, earlyStp.best_rmse)) + + # Save results and model ----------------------------------------- + self._saveResults(self.model, self.model_args, opt, epoch, loss, ep_runtime, r, db_results, earlyStp.best) + + # Early stopping ----------------------------------------------- + if earl_stp: + print('--> Early stopping. best_r_p {:0.2f} best_rmse {:0.2f}' + .format(earlyStp.best_r_p, earlyStp.best_rmse)) + return + + # Training done -------------------------------------------------------- + print('--> Training done. best_r_p {:0.2f} best_rmse {:0.2f}' + .format(earlyStp.best_r_p, earlyStp.best_rmse)) + return + + + def _evaluate_mos(self, mapping='first_order', do_print=True, do_plot=False): + ''' + Evaluates the model's predictions. + ''' + print('--> MOS:') + self.db_results, self.r = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='mos', + target_ci='mos_ci', + pred='mos_pred', + mapping=mapping, + do_print=do_print, + do_plot=do_plot + ) + if self.ds_val.df_con is None: + print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' + .format(self.r['r_p_mean_file'], self.r['rmse_mean_file']) + ) + else: + print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' + .format(self.r['r_p_mean_con'], self.r['rmse_mean_con'], self.r['rmse_star_map_mean_con']) + ) + + def _evaluate_dim(self, mapping='first_order', do_print=True, do_plot=False): + ''' + Evaluates the predictions of a multidimensional model. + ''' + print('--> MOS:') + self.db_results_val_mos, r_val_mos = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='mos', + target_ci='mos_ci', + pred='mos_pred', + mapping=mapping, + do_print=do_print, + do_plot=do_plot + ) + if self.ds_val.df_con is None: + print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' + .format(r_val_mos['r_p_mean_file'], r_val_mos['rmse_mean_file']) + ) + else: + print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' + .format(r_val_mos['r_p_mean_con'], r_val_mos['rmse_mean_con'], r_val_mos['rmse_star_map_mean_con']) + ) + + print('--> NOI:') + self.db_results_val_noi, r_val_noi = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='noi', + target_ci='noi_ci', + pred='noi_pred', + mapping=mapping, + do_print=do_print, + do_plot=do_plot + ) + if self.ds_val.df_con is None: + print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' + .format(r_val_noi['r_p_mean_file'], r_val_noi['rmse_mean_file']) + ) + else: + print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}' + .format(r_val_noi['r_p_mean_con'], r_val_noi['rmse_mean_con'], r_val_noi['rmse_star_map_mean_con']) + ) + r_val_noi = {k+'_noi': v for k, v in r_val_noi.items()} + + print('--> DIS:') + self.db_results_val_dis, r_val_dis = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='dis', + target_ci='dis_ci', + pred='dis_pred', + mapping=mapping, + do_print=do_print, + do_plot=do_plot + ) + if self.ds_val.df_con is None: + print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' + .format(r_val_dis['r_p_mean_file'], r_val_dis['rmse_mean_file']) + ) + else: + print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' + .format(r_val_dis['r_p_mean_con'], r_val_dis['rmse_mean_con'], r_val_dis['rmse_star_map_mean_con']) + ) + r_val_dis = {k+'_dis': v for k, v in r_val_dis.items()} + + print('--> COL:') + self.db_results_val_col, r_val_col = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='col', + target_ci='col_ci', + pred='col_pred', + mapping=mapping, + do_print=do_print, + do_plot=do_plot + ) + if self.ds_val.df_con is None: + print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' + .format(r_val_col['r_p_mean_file'], r_val_col['rmse_mean_file']) + ) + else: + print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' + .format(r_val_col['r_p_mean_con'], r_val_col['rmse_mean_con'], r_val_col['rmse_star_map_mean_con']) + ) + r_val_col = {k+'_col': v for k, v in r_val_col.items()} + + print('--> LOUD:') + self.db_results_val_loud, r_val_loud = NL.eval_results( + self.ds_val.df, + dcon=self.ds_val.df_con, + target_mos='loud', + target_ci='loud_ci', + pred='loud_pred', + mapping=mapping, + do_print=do_print, + do_plot=do_plot + ) + if self.ds_val.df_con is None: + print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' + .format(r_val_loud['r_p_mean_file'], r_val_loud['rmse_mean_file']) + ) + else: + print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' + .format(r_val_loud['r_p_mean_con'], r_val_loud['rmse_mean_con'], r_val_loud['rmse_star_map_mean_con']) + ) + r_val_loud = {k+'_loud': v for k, v in r_val_loud.items()} + + self.r = { + **r_val_mos, **r_val_noi, **r_val_dis, **r_val_col, **r_val_loud, } + + r_mean = 1/5 * (self.r['r_p_mean_con'] + + self.r['r_p_mean_con_noi'] + + self.r['r_p_mean_con_col'] + + self.r['r_p_mean_con_dis'] + + self.r['r_p_mean_con_loud']) + + print('\nAverage over MOS and dimensions: r_p={:0.3f}' + .format(r_mean) + ) + + + def _makeRunnameAndWriteYAML(self): + ''' + Creates individual run name. + ''' + runname = self.args['name'] + '_' + self.args['now'].strftime("%y%m%d_%H%M%S%f") + print('runname: ' + runname) + run_output_dir = os.path.join(self.args['output_dir'], runname) + Path(run_output_dir).mkdir(parents=True, exist_ok=True) + yaml_path = os.path.join(run_output_dir, runname+'.yaml') + with open(yaml_path, 'w') as file: + yaml.dump(self.args, file, default_flow_style=None, sort_keys=False) + + return runname + + def _loadDatasets(self): + if self.args['mode']=='predict_file': + self._loadDatasetsFile() + elif self.args['mode']=='predict_dir': + self._loadDatasetsFolder() + elif self.args['mode']=='predict_csv': + self._loadDatasetsCSVpredict() + elif self.args['mode']=='main': + self._loadDatasetsCSV() + else: + raise NotImplementedError('mode not available') + + + def _loadDatasetsFolder(self): + files = list(Path(self.args['data_dir']).rglob('*.wav')) + \ + list(Path(self.args['data_dir']).rglob('*.mp3')) + \ + list(Path(self.args['data_dir']).rglob('*.ogg')) + \ + list(Path(self.args['data_dir']).rglob('*.opus')) + \ + list(Path(self.args['data_dir']).rglob('*.fluc')) + + files = [os.path.basename(files) for files in files] + df_val = pd.DataFrame(files, columns=['deg']) + + print('# files: {}'.format( len(df_val) )) + if len(df_val)==0: + raise ValueError('No wav files found in data_dir') + + # creating Datasets --------------------------------------------------- + self.ds_val = NL.SpeechQualityDataset( + df_val, + df_con=None, + data_dir = self.args['data_dir'], + filename_column = 'deg', + mos_column = 'predict_only', + seg_length = self.args['ms_seg_length'], + max_length = self.args['ms_max_segments'], + to_memory = None, + to_memory_workers = None, + seg_hop_length = self.args['ms_seg_hop_length'], + transform = None, + ms_n_fft = self.args['ms_n_fft'], + ms_hop_length = self.args['ms_hop_length'], + ms_win_length = self.args['ms_win_length'], + ms_n_mels = self.args['ms_n_mels'], + ms_sr = self.args['ms_sr'], + ms_fmax = self.args['ms_fmax'], + ms_channel = self.args['ms_channel'], + double_ended = self.args['double_ended'], + dim = self.args['dim'], + filename_column_ref = None, + ) + + + def _loadDatasetsFile(self): + data_dir = os.path.dirname(self.args['deg']) + file_name = os.path.basename(self.args['deg']) + df_val = pd.DataFrame([file_name], columns=['deg']) + + # creating Datasets --------------------------------------------------- + self.ds_val = NL.SpeechQualityDataset( + df_val, + df_con=None, + data_dir = data_dir, + filename_column = 'deg', + mos_column = 'predict_only', + seg_length = self.args['ms_seg_length'], + max_length = self.args['ms_max_segments'], + to_memory = None, + to_memory_workers = None, + seg_hop_length = self.args['ms_seg_hop_length'], + transform = None, + ms_n_fft = self.args['ms_n_fft'], + ms_hop_length = self.args['ms_hop_length'], + ms_win_length = self.args['ms_win_length'], + ms_n_mels = self.args['ms_n_mels'], + ms_sr = self.args['ms_sr'], + ms_fmax = self.args['ms_fmax'], + ms_channel = self.args['ms_channel'], + double_ended = self.args['double_ended'], + dim = self.args['dim'], + filename_column_ref = None, + ) + + + def _loadDatasetsCSVpredict(self): + ''' + Loads validation dataset for prediction only. + ''' + csv_file_path = os.path.join(self.args['data_dir'], self.args['csv_file']) + dfile = pd.read_csv(csv_file_path) + if 'csv_con' in self.args: + csv_con_file_path = os.path.join(self.args['data_dir'], self.args['csv_con']) + dcon = pd.read_csv(csv_con_file_path) + else: + dcon = None + + + # creating Datasets --------------------------------------------------- + self.ds_val = NL.SpeechQualityDataset( + dfile, + df_con=dcon, + data_dir = self.args['data_dir'], + filename_column = self.args['csv_deg'], + mos_column = 'predict_only', + seg_length = self.args['ms_seg_length'], + max_length = self.args['ms_max_segments'], + to_memory = False, + to_memory_workers = None, + seg_hop_length = self.args['ms_seg_hop_length'], + transform = None, + ms_n_fft = self.args['ms_n_fft'], + ms_hop_length = self.args['ms_hop_length'], + ms_win_length = self.args['ms_win_length'], + ms_n_mels = self.args['ms_n_mels'], + ms_sr = self.args['ms_sr'], + ms_fmax = self.args['ms_fmax'], + ms_channel = self.args['ms_channel'], + double_ended = self.args['double_ended'], + dim = self.args['dim'], + filename_column_ref = self.args['csv_ref'], + ) + + + def _loadDatasetsCSV(self): + ''' + Loads training and validation dataset for training. + ''' + csv_file_path = os.path.join(self.args['data_dir'], self.args['csv_file']) + dfile = pd.read_csv(csv_file_path) + + if not set(self.args['csv_db_train'] + self.args['csv_db_val']).issubset(dfile.db.unique().tolist()): + missing_datasets = set(self.args['csv_db_train'] + self.args['csv_db_val']).difference(dfile.db.unique().tolist()) + raise ValueError('Not all dbs found in csv:', missing_datasets) + + df_train = dfile[dfile.db.isin(self.args['csv_db_train'])].reset_index() + df_val = dfile[dfile.db.isin(self.args['csv_db_val'])].reset_index() + + if self.args['csv_con'] is not None: + csv_con_path = os.path.join(self.args['data_dir'], self.args['csv_con']) + dcon = pd.read_csv(csv_con_path) + dcon_train = dcon[dcon.db.isin(self.args['csv_db_train'])].reset_index() + dcon_val = dcon[dcon.db.isin(self.args['csv_db_val'])].reset_index() + else: + dcon = None + dcon_train = None + dcon_val = None + + print('Training size: {}, Validation size: {}'.format(len(df_train), len(df_val))) + + # creating Datasets --------------------------------------------------- + self.ds_train = NL.SpeechQualityDataset( + df_train, + df_con=dcon_train, + data_dir = self.args['data_dir'], + filename_column = self.args['csv_deg'], + mos_column = self.args['csv_mos_train'], + seg_length = self.args['ms_seg_length'], + max_length = self.args['ms_max_segments'], + to_memory = self.args['tr_ds_to_memory'], + to_memory_workers = self.args['tr_ds_to_memory_workers'], + seg_hop_length = self.args['ms_seg_hop_length'], + transform = None, + ms_n_fft = self.args['ms_n_fft'], + ms_hop_length = self.args['ms_hop_length'], + ms_win_length = self.args['ms_win_length'], + ms_n_mels = self.args['ms_n_mels'], + ms_sr = self.args['ms_sr'], + ms_fmax = self.args['ms_fmax'], + ms_channel = self.args['ms_channel'], + double_ended = self.args['double_ended'], + dim = self.args['dim'], + filename_column_ref = self.args['csv_ref'], + ) + + self.ds_val = NL.SpeechQualityDataset( + df_val, + df_con=dcon_val, + data_dir = self.args['data_dir'], + filename_column = self.args['csv_deg'], + mos_column = self.args['csv_mos_val'], + seg_length = self.args['ms_seg_length'], + max_length = self.args['ms_max_segments'], + to_memory = self.args['tr_ds_to_memory'], + to_memory_workers = self.args['tr_ds_to_memory_workers'], + seg_hop_length = self.args['ms_seg_hop_length'], + transform = None, + ms_n_fft = self.args['ms_n_fft'], + ms_hop_length = self.args['ms_hop_length'], + ms_win_length = self.args['ms_win_length'], + ms_n_mels = self.args['ms_n_mels'], + ms_sr = self.args['ms_sr'], + ms_fmax = self.args['ms_fmax'], + ms_channel = self.args['ms_channel'], + double_ended = self.args['double_ended'], + dim = self.args['dim'], + filename_column_ref = self.args['csv_ref'], + ) + + self.runinfos['ds_train_len'] = len(self.ds_train) + self.runinfos['ds_val_len'] = len(self.ds_val) + + def _loadModel(self): + ''' + Loads the Pytorch models with given input arguments. + ''' + # if True overwrite input arguments from pretrained model + if self.args['pretrained_model']: + if os.path.isabs(self.args['pretrained_model']): + model_path = os.path.join(self.args['pretrained_model']) + else: + model_path = os.path.join(os.getcwd(), self.args['pretrained_model']) + checkpoint = torch.load(model_path, map_location=self.dev, weights_only=True) + + # update checkpoint arguments with new arguments + checkpoint['args'].update(self.args) + self.args = checkpoint['args'] + + if self.args['model']=='NISQA_DIM': + self.args['dim'] = True + self.args['csv_mos_train'] = None # column names hardcoded for dim models + self.args['csv_mos_val'] = None + else: + self.args['dim'] = False + + if self.args['model']=='NISQA_DE': + self.args['double_ended'] = True + else: + self.args['double_ended'] = False + self.args['csv_ref'] = None + + # Load Model + self.model_args = { + + 'ms_seg_length': self.args['ms_seg_length'], + 'ms_n_mels': self.args['ms_n_mels'], + + 'cnn_model': self.args['cnn_model'], + 'cnn_c_out_1': self.args['cnn_c_out_1'], + 'cnn_c_out_2': self.args['cnn_c_out_2'], + 'cnn_c_out_3': self.args['cnn_c_out_3'], + 'cnn_kernel_size': self.args['cnn_kernel_size'], + 'cnn_dropout': self.args['cnn_dropout'], + 'cnn_pool_1': self.args['cnn_pool_1'], + 'cnn_pool_2': self.args['cnn_pool_2'], + 'cnn_pool_3': self.args['cnn_pool_3'], + 'cnn_fc_out_h': self.args['cnn_fc_out_h'], + + 'td': self.args['td'], + 'td_sa_d_model': self.args['td_sa_d_model'], + 'td_sa_nhead': self.args['td_sa_nhead'], + 'td_sa_pos_enc': self.args['td_sa_pos_enc'], + 'td_sa_num_layers': self.args['td_sa_num_layers'], + 'td_sa_h': self.args['td_sa_h'], + 'td_sa_dropout': self.args['td_sa_dropout'], + 'td_lstm_h': self.args['td_lstm_h'], + 'td_lstm_num_layers': self.args['td_lstm_num_layers'], + 'td_lstm_dropout': self.args['td_lstm_dropout'], + 'td_lstm_bidirectional': self.args['td_lstm_bidirectional'], + + 'td_2': self.args['td_2'], + 'td_2_sa_d_model': self.args['td_2_sa_d_model'], + 'td_2_sa_nhead': self.args['td_2_sa_nhead'], + 'td_2_sa_pos_enc': self.args['td_2_sa_pos_enc'], + 'td_2_sa_num_layers': self.args['td_2_sa_num_layers'], + 'td_2_sa_h': self.args['td_2_sa_h'], + 'td_2_sa_dropout': self.args['td_2_sa_dropout'], + 'td_2_lstm_h': self.args['td_2_lstm_h'], + 'td_2_lstm_num_layers': self.args['td_2_lstm_num_layers'], + 'td_2_lstm_dropout': self.args['td_2_lstm_dropout'], + 'td_2_lstm_bidirectional': self.args['td_2_lstm_bidirectional'], + + 'pool': self.args['pool'], + 'pool_att_h': self.args['pool_att_h'], + 'pool_att_dropout': self.args['pool_att_dropout'], + } + + if self.args['double_ended']: + self.model_args.update({ + 'de_align': self.args['de_align'], + 'de_align_apply': self.args['de_align_apply'], + 'de_fuse_dim': self.args['de_fuse_dim'], + 'de_fuse': self.args['de_fuse'], + }) + + print('Model architecture: ' + self.args['model']) + if self.args['model']=='NISQA': + self.model = NL.NISQA(**self.model_args) + elif self.args['model']=='NISQA_DIM': + self.model = NL.NISQA_DIM(**self.model_args) + elif self.args['model']=='NISQA_DE': + self.model = NL.NISQA_DE(**self.model_args) + else: + raise NotImplementedError('Model not available') + + # Load weights if pretrained model is used ------------------------------------ + if self.args['pretrained_model']: + missing_keys, unexpected_keys = self.model.load_state_dict(checkpoint['model_state_dict'], strict=True) + print('Loaded pretrained model from ' + self.args['pretrained_model']) + if missing_keys: + print('missing_keys:') + print(missing_keys) + if unexpected_keys: + print('unexpected_keys:') + print(unexpected_keys) + + def _getDevice(self): + ''' + Train on GPU if available. + ''' + if torch.cuda.is_available(): + self.dev = torch.device("cuda") + else: + self.dev = torch.device("cpu") + + if "tr_device" in self.args: + if self.args['tr_device']=='cpu': + self.dev = torch.device("cpu") + elif self.args['tr_device']=='cuda': + self.dev = torch.device("cuda") + print('Device: {}'.format(self.dev)) + + if "tr_parallel" in self.args: + if (self.dev==torch.device("cpu")) and self.args['tr_parallel']==True: + self.args['tr_parallel']==False + print('Using CPU -> tr_parallel set to False') + + def _saveResults(self, model, model_args, opt, epoch, loss, ep_runtime, r, db_results, best): + ''' + Save model/results in dictionary and write results csv. + ''' + if (self.args['tr_checkpoint'] == 'best_only'): + filename = self.runname + '.tar' + else: + filename = self.runname + '__' + ('ep_{:03d}'.format(epoch+1)) + '.tar' + run_output_dir = os.path.join(self.args['output_dir'], self.runname) + model_path = os.path.join(run_output_dir, filename) + results_path = os.path.join(run_output_dir, self.runname+'__results.csv') + Path(run_output_dir).mkdir(parents=True, exist_ok=True) + + results = { + 'runname': self.runname, + 'epoch': '{:05d}'.format(epoch+1), + 'filename': filename, + 'loss': loss, + 'ep_runtime': '{:0.2f}'.format(ep_runtime), + **self.runinfos, + **r, + **self.args, + } + + for key in results: + results[key] = str(results[key]) + + if epoch==0: + self.results_hist = pd.DataFrame(results, index=[0]) + else: + self.results_hist.loc[epoch] = results + self.results_hist.to_csv(results_path, index=False) + + + if (self.args['tr_checkpoint'] == 'every_epoch') or (self.args['tr_checkpoint'] == 'best_only' and best): + + if hasattr(model, 'module'): + state_dict = model.module.state_dict() + model_name = model.module.name + else: + state_dict = model.state_dict() + model_name = model.name + + torch_dict = { + 'runname': self.runname, + 'epoch': epoch+1, + 'model_args': model_args, + 'args': self.args, + 'model_state_dict': state_dict, + 'optimizer_state_dict': opt.state_dict(), + 'db_results': db_results, + 'results': results, + 'model_name': model_name, + } + + torch.save(torch_dict, model_path) + + elif (self.args['tr_checkpoint']!='every_epoch') and (self.args['tr_checkpoint']!='best_only'): + raise ValueError('selected tr_checkpoint option not available') + + diff --git a/src/libs/NISQA/run_evaluate.py b/src/libs/NISQA/run_evaluate.py new file mode 100644 index 0000000..a81600e --- /dev/null +++ b/src/libs/NISQA/run_evaluate.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +@author: Gabriel Mittag, TU-Berlin + +This script evaluates a trained speech quality model on a given Dataset. + +If a 'csv_con' CSV file with per-condition results is provided, both the +per-file CSV and per-condition CSV need a column with the name 'con' that +contains the condition number. If 'csv_con' stays empty, only per-file results +are calculated. + +""" +from nisqa.NISQA_model import nisqaModel + +if __name__ == "__main__": + + args = { + 'mode': 'predict_csv', + 'pretrained_model': 'weights/nisqa.tar', + 'data_dir': r'C:\Users\Name\Downloads\NISQA_Corpus', + 'output_dir': r'C:\Users\Name\Downloads\NISQA_Corpus', + 'csv_file': 'NISQA_corpus_file.csv', + 'csv_con': 'NISQA_corpus_con.csv', + 'csv_deg': 'filepath_deg', + 'csv_mos_val': 'mos', + 'tr_num_workers': 6, + 'tr_bs_val': 40, + 'ms_channel': None, + } + + nisqa = nisqaModel(args) + nisqa.predict() + nisqa.evaluate( + mapping='first_order', + do_print=True, + do_plot=True + ) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/src/libs/NISQA/run_predict.py b/src/libs/NISQA/run_predict.py new file mode 100644 index 0000000..97263d6 --- /dev/null +++ b/src/libs/NISQA/run_predict.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +@author: Gabriel Mittag, TU-Berlin +""" +from .nisqa.NISQA_model import nisqaModel +import argparse + +def run_nisqa_with_config(config): + if 'mode' not in config: + raise ValueError('mode is required in config') + if 'pretrained_model' not in config: + raise ValueError('pretrained_model is required in config') + + if config['mode'] == 'predict_file': + if 'deg' not in config or config['deg'] is None: + raise ValueError('deg argument with path to input file needed') + elif config['mode'] == 'predict_dir': + if 'data_dir' not in config or config['data_dir'] is None: + raise ValueError('data_dir argument with folder with input files needed') + elif config['mode'] == 'predict_csv': + if 'csv_file' not in config or config['csv_file'] is None: + raise ValueError('csv_file argument with csv file name needed') + if 'csv_deg' not in config or config['csv_deg'] is None: + raise ValueError('csv_deg argument with csv column name of the filenames needed') + if 'data_dir' not in config or config['data_dir'] is None: + config['data_dir'] = '' + else: + raise NotImplementedError('mode given not available') + + config['tr_parallel'] = True + config['tr_bs_val'] = config.get('bs', 1) + config['tr_num_workers'] = config.get('num_workers', 0) + + nisqa = nisqaModel(config) + nisqa.predict() \ No newline at end of file diff --git a/src/libs/NISQA/run_train.py b/src/libs/NISQA/run_train.py new file mode 100644 index 0000000..40759af --- /dev/null +++ b/src/libs/NISQA/run_train.py @@ -0,0 +1,57 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 27 16:52:15 2020 + +@author: Gabriel +""" + +import yaml +from nisqa.NISQA_model import nisqaModel +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument('--yaml', required=True, type=str, help='YAML file with config') + +args = parser.parse_args() +args = vars(args) + +if __name__ == "__main__": + + with open(args['yaml'], "r") as ymlfile: + args_yaml = yaml.load(ymlfile, Loader=yaml.FullLoader) + args = {**args_yaml, **args} + + nisqa = nisqaModel(args) + nisqa.train() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/src/libs/NISQA/weights/LICENSE_model_weights b/src/libs/NISQA/weights/LICENSE_model_weights new file mode 100644 index 0000000..4fae8d5 --- /dev/null +++ b/src/libs/NISQA/weights/LICENSE_model_weights @@ -0,0 +1,437 @@ +Attribution-NonCommercial-ShareAlike 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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a/src/libs/smart_turn/inference.py b/src/libs/smart_turn/inference.py new file mode 100644 index 0000000..7349a84 --- /dev/null +++ b/src/libs/smart_turn/inference.py @@ -0,0 +1,87 @@ +import torch +from transformers import WhisperFeatureExtractor +import onnxruntime as ort + +# MODEL_PATH = "path/to/your/trained/model" +# MODEL_PATH = "pipecat-ai/smart-turn-v2" +ONNX_MODEL_PATH = "smart-turn-v3.0.onnx" + +# Load model and processor +def build_session(onnx_path): + so = ort.SessionOptions() + so.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + so.inter_op_num_threads = 1 + so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + return ort.InferenceSession(onnx_path, sess_options=so) + +feature_extractor = WhisperFeatureExtractor(chunk_length=8) +session = build_session(ONNX_MODEL_PATH) + +def truncate_audio_to_last_n_seconds(audio_array, n_seconds=8, sample_rate=16000): + """Truncate audio to last n seconds or pad with zeros to meet n seconds.""" + max_samples = n_seconds * sample_rate + if len(audio_array) > max_samples: + return audio_array[-max_samples:] + elif len(audio_array) < max_samples: + # Pad with zeros at the beginning + padding = max_samples - len(audio_array) + return np.pad(audio_array, (padding, 0), mode='constant', constant_values=0) + return audio_array + + +def predict_endpoint(audio_array): + """ + Predict whether an audio segment is complete (turn ended) or incomplete. + + Args: + audio_array: Numpy array containing audio samples at 16kHz + + Returns: + Dictionary containing prediction results: + - prediction: 1 for complete, 0 for incomplete + - probability: Probability of completion (sigmoid output) + """ + + # Truncate to 8 seconds (keeping the end) or pad to 8 seconds + audio_array = truncate_audio_to_last_n_seconds(audio_array, n_seconds=8) + + # Process audio using Whisper's feature extractor + inputs = feature_extractor( + audio_array, + sampling_rate=16000, + return_tensors="pt", + padding="max_length", + max_length=8 * 16000, + truncation=True, + do_normalize=True, + ) + + # Convert to numpy and ensure correct shape for ONNX + input_features = inputs.input_features.squeeze(0).numpy().astype(np.float32) + input_features = np.expand_dims(input_features, axis=0) # Add batch dimension + + # Run ONNX inference + outputs = session.run(None, {"input_features": input_features}) + + # Extract probability (ONNX model returns sigmoid probabilities) + probability = outputs[0][0].item() + + # Make prediction (1 for Complete, 0 for Incomplete) + prediction = 1 if probability > 0.5 else 0 + + return { + "prediction": prediction, + "probability": probability, + } + + +# Example usage +if __name__ == "__main__": + import numpy as np + + # Create a dummy audio array for testing (1 second of random audio) + dummy_audio = np.random.randn(16000).astype(np.float32) + + result = predict_endpoint(dummy_audio) + print(f"Prediction: {result['prediction']}") + print(f"Probability: {result['probability']:.4f}") \ No newline at end of file diff --git a/src/libs/smart_turn/model.py b/src/libs/smart_turn/model.py new file mode 100644 index 0000000..7e2d416 --- /dev/null +++ b/src/libs/smart_turn/model.py @@ -0,0 +1,98 @@ +import torch +from torch import nn +import torch.nn.functional as F +from transformers import Wav2Vec2PreTrainedModel, Wav2Vec2Model + + +class Wav2Vec2ForEndpointing(Wav2Vec2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.wav2vec2 = Wav2Vec2Model(config) + + self.pool_attention = nn.Sequential( + nn.Linear(config.hidden_size, 256), + nn.Tanh(), + nn.Linear(256, 1) + ) + + self.classifier = nn.Sequential( + nn.Linear(config.hidden_size, 256), + nn.LayerNorm(256), + nn.GELU(), + nn.Dropout(0.1), + nn.Linear(256, 64), + nn.GELU(), + nn.Linear(64, 1) + ) + + for module in self.classifier: + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.1) + if module.bias is not None: + module.bias.data.zero_() + + for module in self.pool_attention: + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.1) + if module.bias is not None: + module.bias.data.zero_() + + def attention_pool(self, hidden_states, attention_mask): + # Calculate attention weights + attention_weights = self.pool_attention(hidden_states) + + if attention_mask is None: + raise ValueError("attention_mask must be provided for attention pooling") + + attention_weights = attention_weights + ( + (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 + ) + + attention_weights = F.softmax(attention_weights, dim=1) + + # Apply attention to hidden states + weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) + + return weighted_sum + + def forward(self, input_values, attention_mask=None, labels=None): + outputs = self.wav2vec2(input_values, attention_mask=attention_mask) + hidden_states = outputs[0] + + # Create transformer padding mask + if attention_mask is not None: + input_length = attention_mask.size(1) + hidden_length = hidden_states.size(1) + ratio = input_length / hidden_length + indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() + attention_mask = attention_mask[:, indices] + attention_mask = attention_mask.bool() + else: + attention_mask = None + + pooled = self.attention_pool(hidden_states, attention_mask) + + logits = self.classifier(pooled) + + if torch.isnan(logits).any(): + raise ValueError("NaN values detected in logits") + + if labels is not None: + # Calculate positive sample weight based on batch statistics + pos_weight = ((labels == 0).sum() / (labels == 1).sum()).clamp(min=0.1, max=10.0) + loss_fct = nn.BCEWithLogitsLoss(pos_weight=pos_weight) + labels = labels.float() + loss = loss_fct(logits.view(-1), labels.view(-1)) + + # Add L2 regularization for classifier layers + l2_lambda = 0.01 + l2_reg = torch.tensor(0., device=logits.device) + for param in self.classifier.parameters(): + l2_reg += torch.norm(param) + loss += l2_lambda * l2_reg + + probs = torch.sigmoid(logits.detach()) + return {"loss": loss, "logits": probs} + + probs = torch.sigmoid(logits) + return {"logits": probs} \ No newline at end of file diff --git a/src/libs/smart_turn/offline_svad.py b/src/libs/smart_turn/offline_svad.py new file mode 100644 index 0000000..c5db2e4 --- /dev/null +++ b/src/libs/smart_turn/offline_svad.py @@ -0,0 +1,190 @@ +import torch +import os +from loguru import logger +import numpy as np +import onnxruntime as ort +import time +from transformers import WhisperFeatureExtractor + +class OfflineVAD: + def __init__( + self, + silero_vad_threshold: float = 0.4, + smart_vad_threshold: float = 0.4, + device: str = 'cuda:1', + resample_rate: int = 16_000, + max_speech_duration_s: float = 8, + smart_vad_path: str = "pipecat-ai/smart-turn-v3" + ): + + self.silero_vad_threshold = silero_vad_threshold + self.smart_vad_threshold = smart_vad_threshold + self.max_speech_duration_s= max_speech_duration_s + self.sample_rate = resample_rate + self.smart_vad_path = smart_vad_path + self.device = device + self.device_id = device.split(':')[1] if ':' in self.device else 0 + + # Initialize VAD models + self._init_silero_vad() + self._init_smart_vad() + + def _init_silero_vad(self): + logger.info('Initializing Silero VAD model...') + torch.hub.set_dir("./.torch_hub") + silero_vad_model, utils = torch.hub.load( + repo_or_dir="snakers4/silero-vad", + model="silero_vad", + force_reload=False, + onnx=True, + trust_repo=True + ) + logger.info('Silero VAD model successfully initialized.') + self.silero_vad_model = silero_vad_model + + (self.get_speech_timestamps, + _, + self.read_audio, + _, _) = utils + + + def _init_smart_vad(self): + logger.info('Initializing Smart VAD model...') + self.feature_extractor = WhisperFeatureExtractor(chunk_length=8) + + so = ort.SessionOptions() + so.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + so.inter_op_num_threads = 1 + so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + self.session = ort.InferenceSession( + self.smart_vad_path, sess_options=so, + providers=[ + ( + "CUDAExecutionProvider", + {"device_id": self.device_id} + ), + "CPUExecutionProvider", + ] + ) + logger.info('Smart VAD model successfully initialized.') + + def _load_and_validate_audio(self, audio_path: str) -> torch.Tensor: + if not os.path.exists(audio_path): + raise FileNotFoundError(f"File not found: {audio_path}") + + wav_tensor = self.read_audio(audio_path, sampling_rate=self.sample_rate) + return wav_tensor + + def predict_endpoint(self, audio_array): + """ + Predict whether an audio segment is complete (turn ended) or incomplete. + + Args: + audio_array: Numpy array containing audio samples at 16kHz + + Returns: + Dictionary containing prediction results: + - prediction: 1 for complete, 0 for incomplete + - probability: Probability of completion (sigmoid output) + """ + + # Truncate to 8 seconds (keeping the end) or pad to 8 seconds + audio_array = self.truncate_audio_to_last_n_seconds(audio_array, n_seconds=8) + + # Process audio using Whisper's feature extractor + inputs = self.feature_extractor( + audio_array, + sampling_rate=16000, + return_tensors="pt", + padding="max_length", + max_length=8 * 16000, + truncation=True, + do_normalize=True + ) + + # Convert to numpy and ensure correct shape for ONNX + input_features = inputs.input_features.squeeze(0).numpy().astype(np.float32) + input_features = np.expand_dims(input_features, axis=0) # Add batch dimension + + # Run ONNX inference + outputs = self.session.run(None, {"input_features": input_features}) + + # Extract probability (ONNX model returns sigmoid probabilities) + probability = outputs[0][0].item() + + # Make prediction (1 for Complete, 0 for Incomplete) + prediction = 1 if probability > self.smart_vad_threshold else 0 + + return { + "prediction": prediction, + "probability": probability, + } + + def truncate_audio_to_last_n_seconds(self, audio_array, n_seconds=8, sample_rate=16000): + """Truncate audio to last n seconds or pad with zeros to meet n seconds.""" + max_samples = n_seconds * sample_rate + if len(audio_array) > max_samples: + return audio_array[-max_samples:] + elif len(audio_array) < max_samples: + # Pad with zeros at the beginning + padding = max_samples - len(audio_array) + return np.pad(audio_array, (padding, 0), mode='constant', constant_values=0) + return audio_array + + def process_file(self, audio_path: str) -> list: + audio_tensor = self._load_and_validate_audio(audio_path) + + speech_timestamps = self.get_speech_timestamps( + audio_tensor, + self.silero_vad_model, + sampling_rate = self.sample_rate, + threshold = self.silero_vad_threshold, + max_speech_duration_s = self.max_speech_duration_s, + min_silence_duration_ms = 200 + ) + + if not speech_timestamps: + logger.debug("No speech found in audio.") + os.remove(audio_path) + return [] + + results = [] + for segment in speech_timestamps: + start_sample = segment['start'] + end_sample = segment['end'] + + start_time = round(start_sample / self.sample_rate, 2) + end_time = round(end_sample / self.sample_rate, 2) + + segment_audio_tensor = audio_tensor[start_sample:end_sample] + + # Ensure the segment_audio_tensor is on CPU for .numpy() if device is 'cuda' + segment_audio_np = segment_audio_tensor.cpu().numpy() + + prediction_result = self.predict_endpoint(segment_audio_np) # Call instance method + + final_result = { + "start_time": start_time, + "end_time": end_time, + "prediction": prediction_result['prediction'], + "probability": round(prediction_result['probability'], 4) + } + results.append(final_result) + + return results + +if __name__ == "__main__": + from pathlib import Path + TEST_WAV_FILES = list(Path("/home/nikita/balalaika/6271311").glob('*.mp3')) + print(len(TEST_WAV_FILES)) + + vad_processor = OfflineVAD( + smart_vad_path='/home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx', + device='cuda:2' + ) + + start = time.time() + for path in TEST_WAV_FILES: + result = vad_processor.process_file(path) + print(result) + logger.info(time.time() - start) \ No newline at end of file From 7f4596478b88b3c4951a4f368662e6da37a0d671 Mon Sep 17 00:00:00 2001 From: Nikita Date: Mon, 6 Oct 2025 18:46:26 +0000 Subject: [PATCH 07/43] feat: auto-download models on startup and add batching to music detector --- .gitignore | 2 +- base.sh | 4 +- configs/config.yaml | 64 +++---- requirements_dev.txt | Bin 1474 -> 1426 bytes src/libs/smart_turn/offline_svad.py | 22 ++- src/preprocess/preprocess.py | 22 +-- src/preprocess/preprocess_yaml.sh | 2 +- src/separation/music_detect.py | 205 ++++++++++----------- src/separation/musicdetector.py | 230 ++++++++++++++++++++++++ src/separation/separation_yaml.sh | 4 +- src/transcription/transcription_yaml.sh | 2 +- src/transcription/transcripton_base.py | 37 ++++ 12 files changed, 426 insertions(+), 168 deletions(-) create mode 100644 src/separation/musicdetector.py diff --git a/.gitignore b/.gitignore index bf54f1a..83a68ad 100644 --- a/.gitignore +++ b/.gitignore @@ -9,7 +9,7 @@ cl_*.py *.ipynb *.bin temp/* - +models/* .env __pycache__/ *.py[cod] diff --git a/base.sh b/base.sh index 4e59096..7ab09fc 100644 --- a/base.sh +++ b/base.sh @@ -23,8 +23,8 @@ SCRIPTS=( # "./src/preprocess/preprocess_yaml.sh" # "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" - "./src/punctuation/punctuation_yaml.sh" - "./src/accents/accents_yaml.sh" + # "./src/punctuation/punctuation_yaml.sh" + # "./src/accents/accents_yaml.sh" # "./src/phonemizer/phonemizer_yaml.sh" # "./src/collate_yamls.sh" ) diff --git a/configs/config.yaml b/configs/config.yaml index 4fede07..2ac4c3c 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,64 +1,68 @@ download: - podcasts_path: /mnt/disk1tb/radio-v4 + podcasts_path: "" episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 1 - podcasts_urls_file: '/home/nikita/yapoddataset/pickls/single.pkl' # pkl with urls + podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the podcasts are located + podcasts_path: "" # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) - num_workers: 2 + num_workers: 4 vad_args: - smart_vad_model: /home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx + smart_vad_model: ./models/smart-turn-v3.0.onnx silero_vad_threshold: 0.4 smart_vad_threshold: 0.4 separation: - podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located - # diar parms - num_workers_diarization: 4 - one_speaker: True # When this option is enabled, only single-speaker audio recordings are downloaded. - # nisqa parms - bs: 32 - num_workers_nisqa: 12 - # audio detect - num_workers_detect: 2 - checkpoint_path: /home/nikita/audio_detect/wavlm-endpointing-model/checkpoint-3000/music_detection.safetensors # /home/nikita/yapoddataset/models/audio_detect/model.safetensors - threshold: 0.2442 + podcasts_path: "" # full path to the folder where the chopped podcasts are located + + diarization: + num_workers: 4 # per gpu + one_speaker: True # When this option is enabled, only single-speaker audio recordings are downloaded. + + nisqa: + bs: 32 + num_workers: 12 + + music_detect: + bs: 32 + num_workers: 1 # per gpu + music_detect_model: ./models/music_detection.safetensors + threshold: 0.2442 transcription: - podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located + podcasts_path: "" # full path to the folder where the chopped podcasts are located with_timestamps: True - model_names: ['giga_ctc_lm', 'vosk', 'tone', 'giga_ctc', 'giga_rnnt'] # 'tone' ['vosk','tone'] + model_names: ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] # 'tone' ['vosk','tone'] giga: - num_workers: 2 # per one gpu + num_workers: 4 # per one gpu batch_size: 16 # if supported - lm_path: '/home/nikita/yapoddataset/kenlm.bin' # path to lm.bin for giga ctc model + lm_path: './models/kenlm.bin' # path to lm.bin for giga ctc model vosk: - vosk_path: /home/nikita/yapoddataset/models/vosk-model-ru - num_workers: 2 # per one gpu - batch_size: 12 # if supported + vosk_path: ./models/vosk-model-ru + num_workers: 4 # per one gpu + batch_size: 16 # if supported beam_size: 4 lm_scale: 0.2 ngram_lm_scale: -0.1 tone: - num_workers: 2 # per one gpu + num_workers: 4 # per one gpu batch_size: 16 # if supported punctuation: - podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located + podcasts_path: "" # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name - num_workers: 2 # per one gpu + num_workers: 1 # per one gpu accent: - podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the chopped podcasts are located - num_workers: 2 # per one gpu + podcasts_path: "" # full path to the folder where the chopped podcasts are located + num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /mnt/disk1tb/radio-v4 # full path to the folder where the text files are located - num_workers: 2 # per one gpu + podcasts_path: "" # full path to the folder where the text files are located + num_workers: 1 # per one gpu diff --git a/requirements_dev.txt b/requirements_dev.txt index 66d57f6eb088900a8d1967cc48f52ffcdf44570d..8e68b8b2372a58708339d061d63f25bc9f5c283b 100644 GIT binary patch delta 148 zcmX@aJ&Bv?|G$j}pBSYJ7%CYO8S;QIpP_^yl|c{4D`iMw$YjW$EXx#Zoe!3)U?>8L z<^fe?GUNhP@iK4$)q`}Q%jhzsGZX;T0#$52&$OCx@+}q-X%v0{b-AeqRJ4J3;u2Qu4kzQk0 torch.Tensor: wav_tensor = self.read_audio(audio_path, sampling_rate=self.sample_rate) return wav_tensor + def _load_from_hf(self): + return hf_hub_download( + repo_id="pipecat-ai/smart-turn-v3", + filename="smart-turn-v3.0.onnx", + local_dir="./models", + local_dir_use_symlinks=False + ) + + def predict_endpoint(self, audio_array): """ Predict whether an audio segment is complete (turn ended) or incomplete. @@ -179,7 +193,7 @@ def process_file(self, audio_path: str) -> list: print(len(TEST_WAV_FILES)) vad_processor = OfflineVAD( - smart_vad_path='/home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx', + smart_vad_model='/home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx', device='cuda:2' ) diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index c9fb7c9..66f15a9 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -184,9 +184,7 @@ def init_vad_process(gpu_id: int, vad_args: Dict[str, Any]): logger.warning(f"No GPU available, using CPU for process") smart_vad = OfflineVAD( - silero_vad_threshold=vad_args['silero_vad_threshold'], - smart_vad_threshold=vad_args['smart_vad_threshold'], - smart_vad_path=vad_args['smart_vad_path'], + **vad_args, device=device ) logger.info(f"VAD initialized on {device}") @@ -198,15 +196,14 @@ def process_audio_file(path_audio: str, duration: float): episode_id = os.path.splitext(os.path.basename(path_audio))[0] episode_folder = os.path.join(os.path.dirname(path_audio), episode_id) - try: - - # TODO: don't forget to remove the code - audio, sr = torchaudio.load(path_audio) - if audio.shape[-1] / sr <= 5: - logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") - os.remove(path_audio) - # # TODO: don't forget to remove the code + # TODO: don't forget to remove the code + audio, sr = torchaudio.load(path_audio) + if audio.shape[-1] / sr <= 3: + logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") + os.remove(path_audio) + # # TODO: don't forget to remove the code + try: if audio.shape[-1] / sr <= duration: return except Exception as e: @@ -277,7 +274,6 @@ def main(args): logger.info (f""" Running parallel processing: The path to Podcasts: {podcasts_path} - Smart VAD model: {smart_vad_model} Segment duration: {duration} seconds Total number of workers: {total_workers} Files to process: {len(audio_paths)} @@ -292,7 +288,7 @@ def main(args): futures = [executor.submit(process_audio_file, path, duration) for path in audio_paths] - for future in tqdm(as_completed(futures), total=len(audio_paths), desc="Обработка подкастов"): + for future in tqdm(as_completed(futures), total=len(audio_paths), desc="Podcast Processing"): try: future.result() except Exception as e: diff --git a/src/preprocess/preprocess_yaml.sh b/src/preprocess/preprocess_yaml.sh index 651c91c..acaf32d 100644 --- a/src/preprocess/preprocess_yaml.sh +++ b/src/preprocess/preprocess_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -taskset -c 0-24 python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" \ No newline at end of file +python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/separation/music_detect.py b/src/separation/music_detect.py index 1d4597f..e83f5eb 100644 --- a/src/separation/music_detect.py +++ b/src/separation/music_detect.py @@ -6,148 +6,125 @@ from typing import List import torch +import yaml from loguru import logger -from safetensors import safe_open from tqdm import tqdm +from safetensors import safe_open -from src.separation.model import WavLMForMusicDetection +from src.separation.musicdetector import WavLMForMusicDetection from src.utils import get_audio_paths, load_config -def process_files_on_device( - audio_paths: List[Path], - checkpoint_path: str, - device: str, +def process_and_delete_chunk( + files: List[Path], + device_str: str, + model_path: str, threshold: float, batch_size: int ) -> int: """ - Processes a list of audio files on a single device (GPU). - - This function initializes the model, gets predictions for all files, - and deletes those that exceed the threshold. - - Returns: - The number of deleted files. + Initializes a model on a specific device, processes a chunk of files in batches, + and deletes those with music. Designed to run in a separate process. """ - logger.info(f"Starting process on device {device} for {len(audio_paths)} files...") deleted_count = 0 - try: - # 1. Initialize the model, specifying the device and batch size - model = WavLMForMusicDetection(batch_size=batch_size, device=device) - - # 2. Load the weights - with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - state_dict = {k: f.get_tensor(k) for k in f.keys()} - model.load_state_dict(state_dict) - - # 3. Get probabilities for all files in a single call - # Convert Path objects to strings, as the model expects - string_paths = [str(p) for p in audio_paths] - probs = model.predict_proba(string_paths) - - # 4. Check and delete files - for path, prob in zip(audio_paths, probs): - score = prob.item() - if score > threshold: - logger.info(f"Deleting file: {path} (score: {score:.4f})") - os.remove(path) - deleted_count += 1 - - logger.info(f"Process on {device} finished. Deleted {deleted_count} files.") - return deleted_count + model = WavLMForMusicDetection(device=device_str, batch_size=batch_size) + + with safe_open(model_path, framework="pt") as f: + model.load_state_dict({k: f.get_tensor(k) for k in f.keys()}) + logger.info(f"Worker (PID: {os.getpid()}) initialized on {device_str} for {len(files)} files.") except Exception as e: - logger.error(f"Error in process on device {device}: {e}") - return 0 # Return 0 deleted files in case of an error + logger.error(f"Failed to initialize worker on {device_str} with model {model_path}: {e}") + return 0 + + for i in tqdm(range(0, len(files), batch_size)): + batch_paths = files[i:i + batch_size] + if not batch_paths: + continue + + try: + audio_paths_str = [str(p) for p in batch_paths] + probabilities = model.predict_proba(audio_paths_str) + + for path, prob in zip(batch_paths, probabilities): + prob_item = prob.item() + if prob_item > threshold: + try: + os.remove(path) + deleted_count += 1 + except OSError as e: + logger.error(f"Error deleting file {path} on {device_str}: {e}") + + except Exception as e: + batch_start = batch_paths[0].name if batch_paths else "N/A" + logger.error(f"Error processing batch starting with {batch_start} on {device_str}: {e}") + return deleted_count def main(args): """ - Main function to orchestrate the multiprocessing of audio files. + Main function to distribute audio processing tasks across multiple GPUs and workers. """ config = load_config(args.config_path, 'separation') - - # Get parameters from the config - batch_size = config.get('batch_size', 32) - checkpoint_path = config.get('checkpoint_path', '/path/to/your/model.safetensors') - audio_data_path = config.get('podcasts_path', '/path/to/your/audio') - threshold = config.get('threshold', 0.5) - - all_audio_files = get_audio_paths(audio_data_path) - if not all_audio_files: - logger.warning(f"Audio files not found in {audio_data_path}") + music_detect_config = config.get('music_detect', {}) + + src_path = config.get('podcasts_path') + n_workers_per_gpu = music_detect_config.get('num_workers', 1) + threshold = music_detect_config.get('threshold', 0.5) + model_path = music_detect_config.get('music_detect_model') + batch_size = music_detect_config.get('bs', 16) + + if not all([src_path, model_path]): + logger.error("Config missing required parameters: 'podcasts_path' or 'music_detect_model'.") return - if not torch.cuda.is_available(): - logger.error("No available GPUs. Exiting.") + available_gpu_ids = list(range(torch.cuda.device_count())) + if not available_gpu_ids: + logger.error("No CUDA-enabled GPUs found. Exiting.") return - available_gpu_ids = list(range(torch.cuda.device_count())) - num_gpus = len(available_gpu_ids) - - # We now use 1 worker per GPU - total_workers = num_gpus - - logger.info( - f""" - Starting audio processing: - - Data path: {audio_data_path} - - Checkpoint: {checkpoint_path} - - Number of GPUs: {num_gpus} (IDs: {available_gpu_ids}) - - Total workers: {total_workers} - - Total files to process: {len(all_audio_files)} - - Deletion threshold: {threshold} - - Batch size per GPU: {batch_size} - """ - ) - - # Distribute the files among the available GPUs - files_per_gpu = {gpu_id: [] for gpu_id in available_gpu_ids} - for i, path in enumerate(all_audio_files): - gpu_id = available_gpu_ids[i % num_gpus] - files_per_gpu[gpu_id].append(path) - - futures = [] - # Create a single process pool for all GPUs - with ProcessPoolExecutor(max_workers=total_workers) as executor: - for gpu_id, files_for_this_gpu in files_per_gpu.items(): - if not files_for_this_gpu: - continue - - device_str = f'cuda:{gpu_id}' - logger.info(f"Submitting task for {device_str} with {len(files_for_this_gpu)} files.") - - # Submit one large task for each GPU - future = executor.submit( - process_files_on_device, - files_for_this_gpu, - checkpoint_path, - device_str, - threshold, - batch_size - ) - futures.append(future) - - logger.info(f"All {len(futures)} tasks have been submitted for processing. Awaiting completion...") + all_audio_paths = get_audio_paths(src_path) + if not all_audio_paths: + logger.warning(f"No audio files found in '{src_path}'.") + return + logger.info(f"Found {len(all_audio_paths)} audio files to process.") + + num_devices = len(available_gpu_ids) + total_workers = n_workers_per_gpu * num_devices + logger.info(f"Found {num_devices} GPUs. Launching {n_workers_per_gpu} workers per GPU for a total of {total_workers} workers.") + + files_for_each_worker = [[] for _ in range(total_workers)] + for i, path in enumerate(all_audio_paths): + files_for_each_worker[i % total_workers].append(path) + + tasks = [] + for i in range(total_workers): + worker_files = files_for_each_worker[i] + if not worker_files: + continue + + device_str = f'cuda:{available_gpu_ids[i % num_devices]}' + + tasks.append( + (worker_files, device_str, model_path, threshold, batch_size) + ) total_deleted_count = 0 - # The progress bar will show the completion of tasks per GPU - with tqdm(total=len(futures), desc="Processing on GPUs") as pbar: + with ProcessPoolExecutor(max_workers=total_workers, mp_context=multiprocessing.get_context('spawn')) as executor: + futures = [executor.submit(process_and_delete_chunk, *task) for task in tasks] + for future in as_completed(futures): - # Collect the number of deleted files from each process - total_deleted_count += future.result() - pbar.update(1) - - logger.info(f"Processing complete. Total files deleted: {total_deleted_count}.") + try: + total_deleted_count += future.result() + except Exception as e: + logger.error(f"A worker process task failed: {e}") + logger.success(f"Total files deleted: {total_deleted_count}") if __name__ == "__main__": - # It's recommended to set the start method to 'spawn' for CUDA compatibility - multiprocessing.set_start_method("spawn", force=True) - - parser = argparse.ArgumentParser(description="Music detection and removal script.") - parser.add_argument("--config_path", type=str, required=True, help="Path to the config file.") - args = parser.parse_args() - main(args) \ No newline at end of file + multiprocessing.set_start_method('spawn', force=True) + parser = argparse.ArgumentParser(description="Parallel music detection and deletion based on a config file.") + parser.add_argument("--config_path", type=str, required=True, help="Path to the YAML configuration file.") + parsed_args = parser.parse_args() + main(parsed_args) \ No newline at end of file diff --git a/src/separation/musicdetector.py b/src/separation/musicdetector.py new file mode 100644 index 0000000..5565380 --- /dev/null +++ b/src/separation/musicdetector.py @@ -0,0 +1,230 @@ +import os +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import AutoModel, AutoConfig, AutoFeatureExtractor +import torchaudio +from safetensors import safe_open +from typing import List, Dict +import time + +from huggingface_hub import hf_hub_download + + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + +class WavLMForMusicDetection(nn.Module): + """ + Music detection model based on WavLM. + Uses attention pooling + classification head. + Outputs probability that input audio contains music. + Supports batched inference with automatic batching and preprocessing. + EER - 2.5-3 % + """ + def __init__( + self, + base_model_name: str = 'microsoft/wavlm-base-plus', + batch_size: int = 32, + device: str = 'cuda' + ) -> None: + super().__init__() + self.config = AutoConfig.from_pretrained(base_model_name) + self.wavlm = AutoModel.from_pretrained(base_model_name, config=self.config) + self.processor = AutoFeatureExtractor.from_pretrained(base_model_name) + + self.batch_size = batch_size + self.device = torch.device(device if torch.cuda.is_available() else 'cpu') + + self.target_sample_rate = self.processor.sampling_rate + + if not os.path.exists('./models/music_detection.safetensors'): + self._load_from_hf() + + # Attention-based pooling head + self.pool_attention = nn.Sequential( + nn.Linear(self.config.hidden_size, 256), + nn.Tanh(), + nn.Linear(256, 1) + ) + + # Classification head + self.classifier = nn.Sequential( + nn.Linear(self.config.hidden_size, 256), + nn.LayerNorm(256), + nn.GELU(), + nn.Dropout(0.1), + nn.Linear(256, 64), + nn.LayerNorm(64), + nn.GELU(), + nn.Linear(64, 1) + ) + + # to device + self.to(self.device) + + def _attention_pool( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor + ) -> torch.Tensor: + """ + Apply attention-based pooling over time dimension. + + Args: + hidden_states (torch.Tensor): [batch_size, seq_len, hidden_size] + attention_mask (torch.Tensor): [batch_size, seq_len] — mask to ignore padding + + Returns: + torch.Tensor: [batch_size, hidden_size] — context vector + """ + + attention_weights = self.pool_attention(hidden_states) # [B, T, 1] + # Mask out padded positions + attention_weights = attention_weights + ( + (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 + ) + + attention_weights = F.softmax(attention_weights, dim=1) # [B, T, 1] + + # Weighted sum over time + weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) # [B, D] + return weighted_sum + + def forward( + self, + input_values: torch.Tensor, + attention_mask: torch.Tensor + ) -> torch.Tensor: + """ + Forward pass for inference. + + Args: + input_values (torch.Tensor): [batch_size, audio_seq_len] — raw audio waveform + attention_mask (torch.Tensor): [batch_size, audio_seq_len] — input mask (1 = real, 0 = pad) + + Returns: + torch.Tensor: [batch_size, 1] — probability that audio contains music + """ + assert isinstance(input_values, torch.Tensor), f"Expected torch.Tensor, got {type(input_values)}" + assert isinstance(attention_mask, torch.Tensor), f"Expected torch.Tensor, got {type(attention_mask)}" + + outputs = self.wavlm(input_values.to(self.device), attention_mask=attention_mask.to(self.device)) + hidden_states = outputs.last_hidden_state # [B, T', D] + + # Align attention mask with downsampled hidden states + input_length = attention_mask.size(1) + hidden_length = hidden_states.size(1) + ratio = input_length / hidden_length + indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() + attention_mask = attention_mask[:, indices] # [B, T'] + attention_mask = attention_mask.bool() + + pooled = self._attention_pool(hidden_states, attention_mask) + logits = self.classifier(pooled) # [B, 1] + + probs = torch.sigmoid(logits) # [B, 1] → probability of MUSIC + return probs + + def _prepare_batches(self, audio_paths: List[str]) -> List[List[str]]: + """ + Split list of audio paths into batches of size `self.batch_size`. + + Args: + audio_paths (List[str]): List of paths to audio files. + + Returns: + List[List[str]]: List of batches, each batch is a list of paths. + """ + batches = [] + current_batch = [] + counter = 0 + + while counter < len(audio_paths): + if len(current_batch) == self.batch_size: + batches.append(current_batch) + current_batch = [] + current_batch.append(audio_paths[counter]) + counter += 1 + + if current_batch: + batches.append(current_batch) + + return batches + + def _preprocess_audio_batch(self, audio_paths: List[str]) -> Dict[str, torch.Tensor]: + """ + Load and preprocess a batch of audio files. + + Args: + audio_paths (List[str]): List of file paths. + + Returns: + Dict with keys: + "input_values": tensor [B, T] + "attention_mask": tensor [B, T] + """ + waveforms = [] + + for audio_path in audio_paths: + waveform, sample_rate = torchaudio.load(audio_path) + + # Resample if needed + if sample_rate != self.target_sample_rate: + resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.target_sample_rate) + waveform = resampler(waveform) + + # Convert to mono + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + + waveforms.append(waveform.squeeze()) + + # Extract features + inputs = self.processor( + [w.numpy() for w in waveforms], + sampling_rate=self.target_sample_rate, + return_tensors="pt", + padding=True, + truncation=False + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + return inputs + + def predict_proba(self, audio_paths: List[str]) -> torch.Tensor: + """ + Predict music probability for a list of audio files. + + Args: + audio_paths (List[str]): List of audio file paths. + + Returns: + torch.Tensor: [N] — probabilities for each audio file. + """ + + all_probs = [] + + batches = self._prepare_batches(audio_paths) + + for batch in batches: + inputs = self._preprocess_audio_batch(batch) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + with torch.no_grad(): + probs = self.forward(**inputs).squeeze(-1) # [B] + all_probs.append(probs) + + return torch.cat(all_probs, dim=0) + + def _load_from_hf(self): + return hf_hub_download( + repo_id="MTUCI/MusicDetection", + filename="music_detection.safetensors", + local_dir="./models", + local_dir_use_symlinks=False + ) + \ No newline at end of file diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index 749169d..be68452 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -21,6 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -# taskset -c 0-24 python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" +python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" taskset -c 0-24 python3 -m src.separation.nisqa --config_path "$CONFIG_PATH" -taskset -c 0-24 python3 -m src.separation.diarization --config_path "$CONFIG_PATH" \ No newline at end of file +python3 -m src.separation.diarization --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/transcription/transcription_yaml.sh b/src/transcription/transcription_yaml.sh index 0f0e217..0b9eb35 100644 --- a/src/transcription/transcription_yaml.sh +++ b/src/transcription/transcription_yaml.sh @@ -21,4 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -taskset -c 25-48 python3 -m src.transcription.transcription --config_path "$CONFIG_PATH" \ No newline at end of file +python3 -m src.transcription.transcription --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py index b31450d..e71356c 100644 --- a/src/transcription/transcripton_base.py +++ b/src/transcription/transcripton_base.py @@ -1,3 +1,4 @@ +import os import math import random from abc import ABC, abstractmethod @@ -21,6 +22,7 @@ from torch.nn.utils.rnn import pad_sequence from tone import StreamingCTCPipeline, TextPhrase from tqdm import tqdm +from huggingface_hub import snapshot_download, hf_hub_download from src.utils import read_file_content, get_audio_paths from src.utils_asr import (AttributeDict, LmScorer, NgramLm, @@ -87,6 +89,9 @@ def __init__(self, model_id: str, device: str, **kwargs): self.target_sr = 16_000 self.decoder = None self.sec_per_frame = self.GIGA_AM_FRAME_SIZE_MS / 1000.0 + + if self.use_lm and not os.path.exists(lm_path): + self._downlaod_lm() if self.use_lm: if self.model_type != 'ctc': @@ -171,6 +176,17 @@ def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int] end_time = end_frame * self.sec_per_frame output_lines.append(f"{word} {start_time:.3f} {end_time:.3f}") return "\n".join(output_lines) + + def _downlaod_lm(self): + return hf_hub_download( + repo_id="NikiPshg/lm", + filename="ru.lm.bin", + local_dir="./models", + local_dir_use_symlinks=False + ) + + + class ToneWrapper(ASRWrapper): @@ -241,6 +257,10 @@ def __init__(self, model_id: str, device: str, **kwargs): super().__init__(model_id, device) logger.info(f"Initializing VOSK CUDA model '{self.model_id}' on device {self.device}") vosk_path = Path(kwargs['vosk_path']) + + if not os.path.exists(str(vosk_path)): + self._load_from_hf() + self.nn_model_filename = str(vosk_path / 'am' / 'jit_script.pt') self.ngram_path = str(vosk_path / 'lm' / '2gram.fst.txt') self.lm_exp_dir_vosk = str(vosk_path / 'lm') @@ -328,12 +348,22 @@ def _read_audio(self, path_to_file: str) -> torch.Tensor: audio = torch.mean(audio, dim=0, keepdim=True) return audio.squeeze(0) + def _load_from_hf(self): + return snapshot_download( + repo_id="alphacep/vosk-model-ru", + local_dir="./models/vosk-model-ru", + local_dir_use_symlinks=False + ) class VoskSherpaOnnxWrapper(ASRWrapper): """Wrapper for Vosk-style models using sherpa-onnx.""" def __init__(self, model_id: str, device: str = 'cpu', **kwargs): logger.info(f"Initializing sherpa-onnx based ASR model from '{model_id}' on {device}") device = 'cpu' + + if not os.path.exists(model_id): + self._load_from_hf() + model_path = Path(model_id) encoder_path = str(model_path / "am-onnx" / "encoder.onnx") decoder_path = str(model_path / "am-onnx" / "decoder.onnx") @@ -410,6 +440,13 @@ def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List results_timestamps.append(result_timestamps) return result_texts, results_timestamps + + def _load_from_hf(self): + return snapshot_download( + repo_id="alphacep/vosk-model-ru", + local_dir="./models/vosk-model-ru", + local_dir_use_symlinks=False + ) class ROVERWrapper: From 06652966384b0e8245672077b1945b44349b4a7c Mon Sep 17 00:00:00 2001 From: Nikita Date: Mon, 6 Oct 2025 18:54:44 +0000 Subject: [PATCH 08/43] feat(docs): minimum instructions for use --- docs/guide.md | 64 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 docs/guide.md diff --git a/docs/guide.md b/docs/guide.md new file mode 100644 index 0000000..7da0cf2 --- /dev/null +++ b/docs/guide.md @@ -0,0 +1,64 @@ +# build vosk +```cmd +python -m pip install git+https://github.com/lhotse-speech/lhotse +``` +```cmd +python -m pip install pip install https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.4.dev20250807+cuda12.9.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl +``` +```cmd +python -m pip install kaldifeat==1.25.5.dev20250203+cuda12.4.torch2.5.1 -f https://csukuangfj.github.io +/kaldifeat/cuda.html +``` +``` +python3 -m pip install git+https://github.com/k2-fsa/icefall +``` + +# balalaika on your data +if the raw data +``` +dataset/ +└── {album_id}/ + └── {episode_id}/ + ├── {start_time}_{end_time}_{album_id}_{episode_id}.mp3 + ├── {start_time}_{end_time}_{album_id}_{episode_id}_giga.txt + ├── {start_time}_{end_time}_{album_id}_{episode_id}_punct.txt + ├── {start_time}_{end_time}_{album_id}_{episode_id}_accent.txt + └── {start_time}_{end_time}_{album_id}_{episode_id}_giga_phonemes.txt +``` + +if the raw data +``` +dataset/ +└── {album_id}/ + └── {episode_id}/ + └── {start_time}_{end_time}_{album_id}_{episode_id}.mp3 + ... +``` + +if the audio is already cropped + +``` +dataset/ +└── {album_id}/ + └── {episode_id}/ + ├─ audio_1.mp3 + ├─ audio_2.wav + ├─ audio_3.opus + ... +``` + +# if only transcriptions are needed +`base.sh + +``` +SCRIPTS=( + # "./src/download/download_yaml.sh" + # "./src/preprocess/preprocess_yaml.sh" + # "./src/separation/separation_yaml.sh" + "./src/transcription/transcription_yaml.sh" + # "./src/punctuation/punctuation_yaml.sh" + # "./src/accents/accents_yaml.sh" + # "./src/phonemizer/phonemizer_yaml.sh" + # "./src/collate_yamls.sh" +) +``` \ No newline at end of file From 7e7a57ec75e01956b8a351a6a6557ed2615a6d4c Mon Sep 17 00:00:00 2001 From: Nikita Date: Mon, 6 Oct 2025 18:55:42 +0000 Subject: [PATCH 09/43] docs: minimum instructions for use --- docs/guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guide.md b/docs/guide.md index 7da0cf2..e497e95 100644 --- a/docs/guide.md +++ b/docs/guide.md @@ -48,7 +48,7 @@ dataset/ ``` # if only transcriptions are needed -`base.sh +`base.sh` ``` SCRIPTS=( From 549a4e9ea239f14d7a5b9cdd33c0e99d4526b3bb Mon Sep 17 00:00:00 2001 From: Nikita Date: Wed, 8 Oct 2025 21:22:03 +0000 Subject: [PATCH 10/43] fix: correct LM download --- src/transcription/transcripton_base.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py index e71356c..d94aadb 100644 --- a/src/transcription/transcripton_base.py +++ b/src/transcription/transcripton_base.py @@ -90,7 +90,7 @@ def __init__(self, model_id: str, device: str, **kwargs): self.decoder = None self.sec_per_frame = self.GIGA_AM_FRAME_SIZE_MS / 1000.0 - if self.use_lm and not os.path.exists(lm_path): + if self.use_lm and not os.path.exists(kwargs['lm_path']): self._downlaod_lm() if self.use_lm: @@ -180,8 +180,9 @@ def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int] def _downlaod_lm(self): return hf_hub_download( repo_id="NikiPshg/lm", - filename="ru.lm.bin", - local_dir="./models", + filename="kenlm.bin", + local_dir="./models", + repo_type='dataset', local_dir_use_symlinks=False ) From f3f370fd4cba4fde4039cd351468ba426e368b0d Mon Sep 17 00:00:00 2001 From: Nikita Date: Wed, 8 Oct 2025 23:19:04 +0000 Subject: [PATCH 11/43] fix(diarization): switching to speaker-diarization-community-1 --- src/separation/diarization.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/src/separation/diarization.py b/src/separation/diarization.py index 6fe5c5b..bdf534f 100644 --- a/src/separation/diarization.py +++ b/src/separation/diarization.py @@ -32,7 +32,7 @@ def init_worker(hf_token: str, device_str: str): torch.cuda.set_device(device_str) diarization_pipeline = Pipeline.from_pretrained( "pyannote/speaker-diarization-community-1", - use_auth_token=hf_token + token=hf_token ).to(torch.device(device_str)) logger.info(f"Worker initialized successfully for diarization on {device_str}.") except Exception as e: @@ -61,13 +61,13 @@ def process_file(path: Path, one_speaker: bool) -> Dict: "waveform": audio, "sample_rate": sr }) - + speaker_diarization = diarization.speaker_diarization # 3. Save RTTM and check speaker count base_path_str = str(path.with_suffix('')) rttm_path = base_path_str + '.rttm' - _save_rttm_file(diarization=diarization, rttm_path=rttm_path) + _save_rttm_file(diarization=speaker_diarization, rttm_path=rttm_path) - num_speakers = len({speaker for _, _, speaker in diarization.itertracks(yield_label=True)}) + num_speakers = len(set([speaker for _, speaker in speaker_diarization])) is_single_speaker = num_speakers == 1 # 4. Handle multi-speaker files if one_speaker mode is enabled @@ -124,11 +124,13 @@ def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: str) -> Lis def main(args): load_dotenv() hf_token = os.getenv("HF_TOKEN") - config = load_config(args.config_path, 'separation') + config = load_config(args.config_path, 'separation') podcasts_path = config.get('podcasts_path', './data') - one_speaker = config.get('one_speaker', False) - num_workers_per_gpu = config.get('num_workers_diarization', 1) + + diarization_config = config.get('diarization') + one_speaker = diarization_config.get('one_speaker', False) + num_workers_per_gpu = diarization_config.get('num_workers', 1) result_csv_path = Path(podcasts_path) / 'balalaika.csv' available_gpu_ids = list(range(torch.cuda.device_count())) From f6c817fef0d2c43c9c6b2adec10ae11d345a3d75 Mon Sep 17 00:00:00 2001 From: Nikita Date: Thu, 9 Oct 2025 14:31:38 +0000 Subject: [PATCH 12/43] fix(accent): fixed cuda support --- src/accents/accents.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/accents/accents.py b/src/accents/accents.py index 3be466c..d6cccce 100644 --- a/src/accents/accents.py +++ b/src/accents/accents.py @@ -26,6 +26,7 @@ def init_process( ) -> None: global accentizer + device = 'CUDA' if ('cuda' in device) # RUAccent is waiting for CUDA without the cuda ID accentizer = RUAccent() accentizer.load( omograph_model_size=model_name, From 61eddd1da7c5988a6d9819a2ccb762862473b388 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Fri, 24 Oct 2025 11:44:53 +0000 Subject: [PATCH 13/43] feat: addded ability to run collate without preprocess block --- src/collate.py | 33 +++++++++++++++------------------ 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/src/collate.py b/src/collate.py index 63ce5ac..59be13d 100644 --- a/src/collate.py +++ b/src/collate.py @@ -6,7 +6,7 @@ import concurrent.futures from loguru import logger -from src.utils import load_config, read_file_content +from src.utils import load_config, read_file_content, get_audio_paths def process_audio_file(audio_path_str: str, base_path: Path) -> Dict[str, Optional[str]]: @@ -30,20 +30,23 @@ def process_audio_file(audio_path_str: str, base_path: Path) -> Dict[str, Option def main(args): - - base_path = Path( - load_config(args.config_path, 'download').get('podcasts_path', '../../balalaika') - if args.config_path else args.podcasts_path - ) - - - df = pd.read_csv(base_path / "balalaika.csv") - df.drop_duplicates(subset='filepath', inplace=True) + config = load_config(args.config_path, 'download') + base_path = Path(config.get('podcasts_path', '../../balalaika')) + num_workers = config.get('num_workers', 32) + + df_path = Path(base_path) / "balalaika.csv" + if df_path.exists(): + logger.info(f"Loading existing dataframe from {df_path}") + df = pd.read_csv(df_path) + df.drop_duplicates(subset='filepath', inplace=True) + else: + logger.info(f"No existing dataframe found. Creating new one from audio paths.") + audio_paths = [str(path) for path in get_audio_paths(base_path)] + df = pd.DataFrame({'filepath': audio_paths}) audio_paths = df['filepath'].tolist() results = [] - num_workers = 32 logger.info(f"Starting processing with {num_workers} workers") with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor: @@ -78,12 +81,6 @@ def main(args): type=str, help="Path to config file" ) - parser.add_argument( - "--podcasts_path", - type=str, - default='../../balalaika', - help="Path to dataset directory" - ) - + args = parser.parse_args() main(args) \ No newline at end of file From f8c111f918a70aa42b6a92c4db5e10746b133919 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Fri, 24 Oct 2025 12:53:34 +0000 Subject: [PATCH 14/43] feat: replaced external links with mtuci-related ones --- requirements_dev.txt | Bin 1426 -> 1430 bytes src/accents/accents.py | 2 +- src/transcription/transcripton_base.py | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements_dev.txt b/requirements_dev.txt index 8e68b8b2372a58708339d061d63f25bc9f5c283b..3980984a52cd3431ba12cae8cd3422d408321eea 100644 GIT binary patch delta 47 qcmbQlJ&k*V2Mcd5LkUAELo!1qLlHyi None: global accentizer - device = 'CUDA' if ('cuda' in device) # RUAccent is waiting for CUDA without the cuda ID + device = 'CUDA' if ('cuda' in device) else 'CPU' # RUAccent is waiting for CUDA without the cuda ID accentizer = RUAccent() accentizer.load( omograph_model_size=model_name, diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py index d94aadb..929b344 100644 --- a/src/transcription/transcripton_base.py +++ b/src/transcription/transcripton_base.py @@ -179,7 +179,7 @@ def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int] def _downlaod_lm(self): return hf_hub_download( - repo_id="NikiPshg/lm", + repo_id="MTUCI/lm", filename="kenlm.bin", local_dir="./models", repo_type='dataset', From 36f78c8bd30a39f5ca4b5d2c73c3045e60150b69 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Thu, 20 Nov 2025 21:30:45 +0000 Subject: [PATCH 15/43] feat: update transcribation, separation for optimization --- .gitignore | 3 +- base.sh | 12 +- configs/config.yaml | 33 +- configs/nisqa_b.yaml | 85 ++++ docs/preparing.md | 0 requirements_dev.txt | Bin 1430 -> 1616 bytes src/preprocess/preprocess.py | 6 +- src/separation/diarization.py | 16 +- src/separation/model.py | 216 ---------- src/separation/music_detect.py | 202 +++++---- src/separation/musicdetector.py | 230 ---------- src/separation/nisqa.py | 130 ------ src/separation/nisqa_process.py | 228 ++++++++++ src/separation/separation_yaml.sh | 4 +- src/transcription/transcription.py | 302 +++++++------ src/transcription/transcripton_base.py | 371 +++++----------- src/transcription/transcripton_dataset.py | 180 ++++++++ src/utils/utils.py | 109 +++++ src/utils/vosk_utils.py | 490 ++++++++++++++++++++++ 19 files changed, 1509 insertions(+), 1108 deletions(-) create mode 100644 configs/nisqa_b.yaml create mode 100644 docs/preparing.md delete mode 100644 src/separation/model.py delete mode 100644 src/separation/musicdetector.py delete mode 100644 src/separation/nisqa.py create mode 100644 src/separation/nisqa_process.py create mode 100644 src/transcription/transcripton_dataset.py create mode 100644 src/utils/utils.py create mode 100644 src/utils/vosk_utils.py diff --git a/.gitignore b/.gitignore index 83a68ad..7bda6e9 100644 --- a/.gitignore +++ b/.gitignore @@ -72,4 +72,5 @@ ipython_config.py .pdm-build/ __pypackages__/ -venv/* \ No newline at end of file +venv/* +cache \ No newline at end of file diff --git a/base.sh b/base.sh index 7ab09fc..f424d48 100644 --- a/base.sh +++ b/base.sh @@ -21,12 +21,12 @@ CONFIG_PATH=$(realpath "$1") SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" - # "./src/separation/separation_yaml.sh" + "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" - # "./src/punctuation/punctuation_yaml.sh" - # "./src/accents/accents_yaml.sh" - # "./src/phonemizer/phonemizer_yaml.sh" - # "./src/collate_yamls.sh" + "./src/punctuation/punctuation_yaml.sh" + "./src/accents/accents_yaml.sh" + "./src/phonemizer/phonemizer_yaml.sh" + "./src/collate_yamls.sh" ) activate_venv ".dev_venv" @@ -45,4 +45,4 @@ for script in "${SCRIPTS[@]}"; do } done -echo -e "\n\033[1;32mAll scripts executed successfully!\033[0m" \ No newline at end of file +echo -e "\n\033[1;32mAll scripts executed successfully!\033[0m" diff --git a/configs/config.yaml b/configs/config.yaml index 2ac4c3c..d726eb6 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,15 @@ +cache_path: ./cache + download: - podcasts_path: "" + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k episodes_limit: 100 # limit on downloading episodes from one playlist - num_workers: 1 + num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: "" # full path to the folder where the podcasts are located + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) - num_workers: 4 + num_workers: 2 vad_args: smart_vad_model: ./models/smart-turn-v3.0.onnx silero_vad_threshold: 0.4 @@ -15,54 +17,55 @@ preprocess: separation: - podcasts_path: "" # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located diarization: - num_workers: 4 # per gpu + num_workers: 1 # per gpu one_speaker: True # When this option is enabled, only single-speaker audio recordings are downloaded. nisqa: bs: 32 num_workers: 12 + nisqa_config_path: ./configs/nisqa_b.yaml music_detect: bs: 32 num_workers: 1 # per gpu music_detect_model: ./models/music_detection.safetensors - threshold: 0.2442 + threshold: 0.6 transcription: - podcasts_path: "" # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located with_timestamps: True - model_names: ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] # 'tone' ['vosk','tone'] + model_names: ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] giga: - num_workers: 4 # per one gpu + num_workers: 1 batch_size: 16 # if supported lm_path: './models/kenlm.bin' # path to lm.bin for giga ctc model vosk: vosk_path: ./models/vosk-model-ru - num_workers: 4 # per one gpu + num_workers: 1 batch_size: 16 # if supported beam_size: 4 lm_scale: 0.2 ngram_lm_scale: -0.1 tone: - num_workers: 4 # per one gpu + num_workers: 1 batch_size: 16 # if supported punctuation: - podcasts_path: "" # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: "" # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: "" # full path to the folder where the text files are located + podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/configs/nisqa_b.yaml b/configs/nisqa_b.yaml new file mode 100644 index 0000000..cee571e --- /dev/null +++ b/configs/nisqa_b.yaml @@ -0,0 +1,85 @@ +# Runname and paths +name: nisqa_s_np # name of current training run +data_dir: /data/Data/NISQA_Corpus # main input dir with dataset samples and csv files +output_dir: ./weights # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights +pretrained_model: false + +# Dataset options +csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings +csv_con: null # csv-file with per-condition MOS used for evaluation (optional) +csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' +csv_ref: filepath_ref # csv column name of filepath to reference speech sample, path must be relative to 'data_dir' +csv_mos_train: mos # csv column name of target training value (usually MOS) +csv_mos_val: mos # csv column name of target validation value (usually MOS) +csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file + - NISQA_TRAIN_SIM + - NISQA_TRAIN_LIVE +csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file + - NISQA_VAL_SIM + - NISQA_VAL_LIVE + +# Training options +tr_epochs: 500 # number of max training epochs +tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs +tr_bs: 120 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_bs_val: 120 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) +tr_lr: 0.001 # learning rate of ADAM optimiser +tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs +tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) +tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs +tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) +tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) +tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. +tr_checkpoint: best_only # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights +tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar + +# Bias loss options (optional) +tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' +tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied +tr_bias_anchor_db: null # name of anchor dataset (optional) + +# Mel-Specs options +ms_sr: null # resample speech signal to 'ms_sr' +ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples +ms_n_fft: 960 # fft size +ms_hop_length: 480 # hop length of fft windowing +ms_win_length: 960 # fft window length, will be padded with zeros to match 'ms_n_fft' +ms_n_mels: 48 # number of Mel bands +ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) +ms_seg_hop_length: 3 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. +ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used +ms_max_length: 1300 # spec length for training only (in bins). if samples of different duration are used in dataloader they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. change if you want to train on different samples + + +# CNN parameters +cnn_c_out_1: 16 # number of output channels of first convolutional layer +cnn_c_out_2: 32 # number of output channels of the second convolutional layer +cnn_c_out_3: 64 # number of output channels of the last four convolutional layer +cnn_kernel_size: !!python/tuple [3,3] +cnn_dropout: 0.2 +cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted +cnn_pool_1: [24,7] # output dimensions of first adaptive pooling ('adaptive' CNN only) +cnn_pool_2: [12,5] # output dimensions of second adaptive pooling ('adaptive' CNN only) +cnn_pool_3: [6,3] # output dimensions of third adaptive pooling ('adaptive' CNN only) + +# LSTM parameters +td_lstm_h: 128 # number of LSTM hidden units +td_lstm_num_layers: 1 # LSTM depth +td_lstm_dropout: 0 +td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 + +# Arguments for inference +ckp: /home/nikita/yapoddataset/src/libs/NISQA/weights/nisqa.tar # checkpoint that will be used for inference +sample: src/sample/gt.wav # path to file to run on +inf_device: cuda # device for inference runs +warmup: False # warmup run before inference; usually is not needed on CPU runs + +#Microphone inference specifics +frame: 2 # framesize for file/mic capture in seconds +updates: null # if null, metrics will be calculated over whole available frame (every [frame] seconds); if int - metrics will be calculated every n bins (which equivalent to ms_n_fft / sr * n seconds, for updates=1, n_fft=960 and sr=48000 it will be 960/48000*1 = 20ms) +sd_device: null # check mic device ID in sounddevice or leave null to use default input device (most probably your system default mic) +sd_dump: null # set this to filename if you want to dump mic signal into file + + + + diff --git a/docs/preparing.md b/docs/preparing.md new file mode 100644 index 0000000..e69de29 diff --git a/requirements_dev.txt b/requirements_dev.txt index 3980984a52cd3431ba12cae8cd3422d408321eea..a6ee83e26bace08d98b81c97b0715e301171377c 100644 GIT binary patch delta 105 zcmbQneSv3#1?%KEW<7a7hD?TRAP!(CX2@VjXV7QxWAJ1MW(Z_(WYA?unjFY%ZHTER nm!TA>K9eDtA%!88p#+GNfn+8_K0_V@F9X+PMHcnRajX^quFe&v delta 11 Scmcb>GmU$L1?%KbtZ@Ju)C5xi diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index 66f15a9..99dcec9 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -144,14 +144,14 @@ def cut_audio( output_folder: str, album_id: str, episode_id: str, - format: str = 'mp3', + format: str = 'opus', duration: float = 15.0 ): try: os.makedirs(output_folder, exist_ok=True) segments_created = 0 for start_time, end_time in zip(start_timestamps, end_timestamps): - if end_time - start_time <= duration / 3: + if end_time - start_time <= duration / 2: continue start_sample = int(start_time * sr) @@ -198,7 +198,7 @@ def process_audio_file(path_audio: str, duration: float): # TODO: don't forget to remove the code audio, sr = torchaudio.load(path_audio) - if audio.shape[-1] / sr <= 3: + if audio.shape[-1] / sr <= 2: logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") os.remove(path_audio) # # TODO: don't forget to remove the code diff --git a/src/separation/diarization.py b/src/separation/diarization.py index bdf534f..87b134e 100644 --- a/src/separation/diarization.py +++ b/src/separation/diarization.py @@ -102,23 +102,17 @@ def process_file(path: Path, one_speaker: bool) -> Dict: def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: str) -> List[Path]: - all_audio_paths = get_audio_paths(podcasts_path) + all_audio_paths = set(get_audio_paths(podcasts_path)) processed_audio_paths = [] if result_csv_path.exists(): logger.info(f"Resuming from existing results file: {result_csv_path}") df = pd.read_csv(result_csv_path) - processed_audio_paths = df.set_index('filepath').to_dict('index') - - unprocessed_paths = [ - Path(path) for path in processed_audio_paths.keys() - if not isinstance( - processed_audio_paths[path].get('is_single_speaker'), - bool - ) - ] + processed_audio_paths = set(df[df[['is_single_speaker']].notna()].to_list()) + + unprocessed_paths = all_audio_paths - processed_audio_paths - return unprocessed_paths + return list(unprocessed_paths) def main(args): diff --git a/src/separation/model.py b/src/separation/model.py deleted file mode 100644 index 9669db5..0000000 --- a/src/separation/model.py +++ /dev/null @@ -1,216 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from transformers import AutoModel, AutoConfig, AutoFeatureExtractor -import torchaudio -from safetensors import safe_open -from typing import List, Dict -import time - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cuda.enable_flash_sdp(True) -torch.backends.cuda.enable_mem_efficient_sdp(True) -torch.backends.cuda.enable_math_sdp(False) - - -class WavLMForMusicDetection(nn.Module): - """ - Music detection model based on WavLM. - Uses attention pooling + classification head. - Outputs probability that input audio contains music. - Supports batched inference with automatic batching and preprocessing. - EER - 2.5-3 % - """ - def __init__( - self, - base_model_name: str = 'microsoft/wavlm-base-plus', - batch_size: int = 32, - device: str = 'cuda' - ) -> None: - super().__init__() - self.config = AutoConfig.from_pretrained(base_model_name) - self.wavlm = AutoModel.from_pretrained(base_model_name, config=self.config) - self.processor = AutoFeatureExtractor.from_pretrained(base_model_name) - - self.batch_size = batch_size - self.device = torch.device(device if torch.cuda.is_available() else 'cpu') - - self.target_sample_rate = self.processor.sampling_rate - - # Attention-based pooling head - self.pool_attention = nn.Sequential( - nn.Linear(self.config.hidden_size, 256), - nn.Tanh(), - nn.Linear(256, 1) - ) - - # Classification head - self.classifier = nn.Sequential( - nn.Linear(self.config.hidden_size, 256), - nn.LayerNorm(256), - nn.GELU(), - nn.Dropout(0.1), - nn.Linear(256, 64), - nn.LayerNorm(64), - nn.GELU(), - nn.Linear(64, 1) - ) - - # to device - self.to(self.device) - self.eval() - - def _attention_pool( - self, - hidden_states: torch.Tensor, - attention_mask: torch.Tensor - ) -> torch.Tensor: - """ - Apply attention-based pooling over time dimension. - - Args: - hidden_states (torch.Tensor): [batch_size, seq_len, hidden_size] - attention_mask (torch.Tensor): [batch_size, seq_len] — mask to ignore padding - - Returns: - torch.Tensor: [batch_size, hidden_size] — context vector - """ - - attention_weights = self.pool_attention(hidden_states) # [B, T, 1] - # Mask out padded positions - attention_weights = attention_weights + ( - (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 - ) - - attention_weights = F.softmax(attention_weights, dim=1) # [B, T, 1] - - # Weighted sum over time - weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) # [B, D] - return weighted_sum - - @torch.inference_mode() - def forward( - self, - input_values: torch.Tensor, - attention_mask: torch.Tensor - ) -> torch.Tensor: - """ - Forward pass for inference. - - Args: - input_values (torch.Tensor): [batch_size, audio_seq_len] — raw audio waveform - attention_mask (torch.Tensor): [batch_size, audio_seq_len] — input mask (1 = real, 0 = pad) - - Returns: - torch.Tensor: [batch_size, 1] — probability that audio contains music - """ - assert isinstance(input_values, torch.Tensor), f"Expected torch.Tensor, got {type(input_values)}" - assert isinstance(attention_mask, torch.Tensor), f"Expected torch.Tensor, got {type(attention_mask)}" - - outputs = self.wavlm(input_values.to(self.device), attention_mask=attention_mask.to(self.device)) - hidden_states = outputs.last_hidden_state # [B, T', D] - - # Align attention mask with downsampled hidden states - input_length = attention_mask.size(1) - hidden_length = hidden_states.size(1) - ratio = input_length / hidden_length - indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() - attention_mask = attention_mask[:, indices] # [B, T'] - attention_mask = attention_mask.bool() - - pooled = self._attention_pool(hidden_states, attention_mask) - logits = self.classifier(pooled) # [B, 1] - - probs = torch.sigmoid(logits) # [B, 1] → probability of MUSIC - return probs - - def _prepare_batches(self, audio_paths: List[str]) -> List[List[str]]: - """ - Split list of audio paths into batches of size `self.batch_size`. - - Args: - audio_paths (List[str]): List of paths to audio files. - - Returns: - List[List[str]]: List of batches, each batch is a list of paths. - """ - batches = [] - current_batch = [] - counter = 0 - - while counter < len(audio_paths): - if len(current_batch) == self.batch_size: - batches.append(current_batch) - current_batch = [] - current_batch.append(audio_paths[counter]) - counter += 1 - - if current_batch: - batches.append(current_batch) - - return batches - - def _preprocess_audio_batch(self, audio_paths: List[str]) -> Dict[str, torch.Tensor]: - """ - Load and preprocess a batch of audio files. - - Args: - audio_paths (List[str]): List of file paths. - - Returns: - Dict with keys: - "input_values": tensor [B, T] - "attention_mask": tensor [B, T] - """ - waveforms = [] - - for audio_path in audio_paths: - waveform, sample_rate = torchaudio.load(audio_path) - - # Resample if needed - if sample_rate != self.target_sample_rate: - resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.target_sample_rate) - waveform = resampler(waveform) - - # Convert to mono - if waveform.shape[0] > 1: - waveform = waveform.mean(dim=0, keepdim=True) - - waveforms.append(waveform.squeeze()) - - # Extract features - inputs = self.processor( - [w.numpy() for w in waveforms], - sampling_rate=self.target_sample_rate, - return_tensors="pt", - padding=True, - truncation=False - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - return inputs - - def predict_proba(self, audio_paths: List[str]) -> torch.Tensor: - """ - Predict music probability for a list of audio files. - - Args: - audio_paths (List[str]): List of audio file paths. - - Returns: - torch.Tensor: [N] — probabilities for each audio file. - """ - - all_probs = [] - - batches = self._prepare_batches(audio_paths) - - for batch in batches: - inputs = self._preprocess_audio_batch(batch) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - with torch.no_grad(): - probs = self.forward(**inputs).squeeze(-1) # [B] - all_probs.append(probs) - - return torch.cat(all_probs, dim=0) \ No newline at end of file diff --git a/src/separation/music_detect.py b/src/separation/music_detect.py index e83f5eb..2475173 100644 --- a/src/separation/music_detect.py +++ b/src/separation/music_detect.py @@ -1,130 +1,126 @@ import argparse -import multiprocessing import os -from concurrent.futures import ProcessPoolExecutor, as_completed +import torch +import torch.multiprocessing as mp from pathlib import Path from typing import List - -import torch -import yaml from loguru import logger from tqdm import tqdm from safetensors import safe_open +from transformers import AutoFeatureExtractor +from torch.utils.data import DataLoader -from src.separation.musicdetector import WavLMForMusicDetection +from musicdetection.audio_cache import create_audio_length_cache +from musicdetection.dataset import MusicDetectionDataset, AudioCollate +from musicdetection.core.model import WavLMForMusicDetection +from musicdetection.audio_sampler import LengthBasedBatchSampler from src.utils import get_audio_paths, load_config +# Optimizations +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) -def process_and_delete_chunk( - files: List[Path], - device_str: str, - model_path: str, - threshold: float, - batch_size: int -) -> int: - """ - Initializes a model on a specific device, processes a chunk of files in batches, - and deletes those with music. Designed to run in a separate process. - """ - deleted_count = 0 - try: - model = WavLMForMusicDetection(device=device_str, batch_size=batch_size) - - with safe_open(model_path, framework="pt") as f: - model.load_state_dict({k: f.get_tensor(k) for k in f.keys()}) - - logger.info(f"Worker (PID: {os.getpid()}) initialized on {device_str} for {len(files)} files.") - except Exception as e: - logger.error(f"Failed to initialize worker on {device_str} with model {model_path}: {e}") - return 0 - - for i in tqdm(range(0, len(files), batch_size)): - batch_paths = files[i:i + batch_size] - if not batch_paths: - continue +def create_loader(paths: List[str], model_name: str, batch_size: int, num_workers: int, cache_file: Path): + """Creates a DataLoader with caching of audio lengths.""" + print(cache_file) + audio_lengths = create_audio_length_cache(file_paths=paths, cache_file=str(cache_file)) + processor = AutoFeatureExtractor.from_pretrained(model_name) + + dataset = MusicDetectionDataset(file_paths=paths, target_sample_rate=processor.sampling_rate) + sampler = LengthBasedBatchSampler(paths, audio_lengths, batch_size=batch_size, shuffle=False) + + return DataLoader( + dataset, + batch_sampler=sampler, + collate_fn=AudioCollate(processor), + num_workers=num_workers, + pin_memory=True, + persistent_workers=num_workers > 0 + ) + +def load_model(model_path: str, base_model: str, device: torch.device): + """Loads the model and weights.""" + model = WavLMForMusicDetection(base_model_name=base_model) + with safe_open(model_path, framework="pt", device="cpu") as f: + model.load_state_dict({k: f.get_tensor(k) for k in f.keys()}) + return model.to(device).eval() + +def run_worker(rank: int, world_size: int, all_paths: List[str], config: dict): + my_paths = all_paths[rank::world_size] + if not my_paths: return + + device = torch.device(f"cuda:{rank}") + cfg = config.get('music_detect', {}) + + # Params + threshold = cfg.get('threshold', 0.5) + cache_dir = Path(cfg.get('cache_path', './cache')) / f'nisqa_temp_worker_{rank}' + # cache/nisqa_temp_worker_0/nisqa_temp/audio_lengths_cache.json + cache_dir.mkdir(parents=True, exist_ok=True) - try: - audio_paths_str = [str(p) for p in batch_paths] - probabilities = model.predict_proba(audio_paths_str) + logger.info(f"[{device}] Processing {len(my_paths)} files...") - for path, prob in zip(batch_paths, probabilities): - prob_item = prob.item() - if prob_item > threshold: - try: - os.remove(path) - deleted_count += 1 - except OSError as e: - logger.error(f"Error deleting file {path} on {device_str}: {e}") + try: + # 1. Setup Data & Model + dataloader = create_loader( + my_paths, + cfg.get('base_model', 'microsoft/wavlm-base-plus'), + cfg.get('bs', 32), + cfg.get('num_workers', 4), + cache_dir / 'audio_lengths.json' + ) + + model = load_model( + cfg.get('music_detect_model'), + cfg.get('base_model', 'microsoft/wavlm-base-plus'), + device + ) - except Exception as e: - batch_start = batch_paths[0].name if batch_paths else "N/A" - logger.error(f"Error processing batch starting with {batch_start} on {device_str}: {e}") + # 2. Inference Loop + deleted_count = 0 + with torch.inference_mode(): + for batch, paths in tqdm(dataloader, desc=f"Worker-{rank}", position=rank): + if batch is None: continue + + # Forward pass + probs = model(batch['input_values'].to(device)) + + # Zip paths with probabilities -> Tuple(path, prob)[[]] + predictions = zip(paths, probs.detach().flatten()) + + # Filter and Delete + for path, prob in predictions: + if prob > threshold: + try: + os.remove(path) + deleted_count += 1 + except OSError: + pass + + logger.success(f"[{device}] Done. Deleted {deleted_count} files.") - return deleted_count + except Exception as e: + logger.exception(f"Worker {rank} error: {e}") def main(args): - """ - Main function to distribute audio processing tasks across multiple GPUs and workers. - """ + mp.set_start_method('spawn', force=True) config = load_config(args.config_path, 'separation') - music_detect_config = config.get('music_detect', {}) - src_path = config.get('podcasts_path') - n_workers_per_gpu = music_detect_config.get('num_workers', 1) - threshold = music_detect_config.get('threshold', 0.5) - model_path = music_detect_config.get('music_detect_model') - batch_size = music_detect_config.get('bs', 16) - - if not all([src_path, model_path]): - logger.error("Config missing required parameters: 'podcasts_path' or 'music_detect_model'.") + if not (podcasts_path := config.get('podcasts_path')): + logger.error("No podcasts_path in config") return - available_gpu_ids = list(range(torch.cuda.device_count())) - if not available_gpu_ids: - logger.error("No CUDA-enabled GPUs found. Exiting.") + if not (all_paths := [str(p) for p in get_audio_paths(podcasts_path)]): + logger.warning("No audio files found.") return - all_audio_paths = get_audio_paths(src_path) - if not all_audio_paths: - logger.warning(f"No audio files found in '{src_path}'.") + if (n_gpus := torch.cuda.device_count()) == 0: + logger.error("No GPU found.") return - logger.info(f"Found {len(all_audio_paths)} audio files to process.") - - num_devices = len(available_gpu_ids) - total_workers = n_workers_per_gpu * num_devices - logger.info(f"Found {num_devices} GPUs. Launching {n_workers_per_gpu} workers per GPU for a total of {total_workers} workers.") - - files_for_each_worker = [[] for _ in range(total_workers)] - for i, path in enumerate(all_audio_paths): - files_for_each_worker[i % total_workers].append(path) - - tasks = [] - for i in range(total_workers): - worker_files = files_for_each_worker[i] - if not worker_files: - continue - - device_str = f'cuda:{available_gpu_ids[i % num_devices]}' - - tasks.append( - (worker_files, device_str, model_path, threshold, batch_size) - ) - - total_deleted_count = 0 - with ProcessPoolExecutor(max_workers=total_workers, mp_context=multiprocessing.get_context('spawn')) as executor: - futures = [executor.submit(process_and_delete_chunk, *task) for task in tasks] - - for future in as_completed(futures): - try: - total_deleted_count += future.result() - except Exception as e: - logger.error(f"A worker process task failed: {e}") - logger.success(f"Total files deleted: {total_deleted_count}") + mp.spawn(run_worker, args=(n_gpus, all_paths, config), nprocs=n_gpus, join=True) if __name__ == "__main__": - multiprocessing.set_start_method('spawn', force=True) - parser = argparse.ArgumentParser(description="Parallel music detection and deletion based on a config file.") - parser.add_argument("--config_path", type=str, required=True, help="Path to the YAML configuration file.") - parsed_args = parser.parse_args() - main(parsed_args) \ No newline at end of file + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, required=True) + main(parser.parse_args()) \ No newline at end of file diff --git a/src/separation/musicdetector.py b/src/separation/musicdetector.py deleted file mode 100644 index 5565380..0000000 --- a/src/separation/musicdetector.py +++ /dev/null @@ -1,230 +0,0 @@ -import os -import torch -import torch.nn as nn -import torch.nn.functional as F -from transformers import AutoModel, AutoConfig, AutoFeatureExtractor -import torchaudio -from safetensors import safe_open -from typing import List, Dict -import time - -from huggingface_hub import hf_hub_download - - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cuda.enable_flash_sdp(True) -torch.backends.cuda.enable_mem_efficient_sdp(True) -torch.backends.cuda.enable_math_sdp(False) - - -class WavLMForMusicDetection(nn.Module): - """ - Music detection model based on WavLM. - Uses attention pooling + classification head. - Outputs probability that input audio contains music. - Supports batched inference with automatic batching and preprocessing. - EER - 2.5-3 % - """ - def __init__( - self, - base_model_name: str = 'microsoft/wavlm-base-plus', - batch_size: int = 32, - device: str = 'cuda' - ) -> None: - super().__init__() - self.config = AutoConfig.from_pretrained(base_model_name) - self.wavlm = AutoModel.from_pretrained(base_model_name, config=self.config) - self.processor = AutoFeatureExtractor.from_pretrained(base_model_name) - - self.batch_size = batch_size - self.device = torch.device(device if torch.cuda.is_available() else 'cpu') - - self.target_sample_rate = self.processor.sampling_rate - - if not os.path.exists('./models/music_detection.safetensors'): - self._load_from_hf() - - # Attention-based pooling head - self.pool_attention = nn.Sequential( - nn.Linear(self.config.hidden_size, 256), - nn.Tanh(), - nn.Linear(256, 1) - ) - - # Classification head - self.classifier = nn.Sequential( - nn.Linear(self.config.hidden_size, 256), - nn.LayerNorm(256), - nn.GELU(), - nn.Dropout(0.1), - nn.Linear(256, 64), - nn.LayerNorm(64), - nn.GELU(), - nn.Linear(64, 1) - ) - - # to device - self.to(self.device) - - def _attention_pool( - self, - hidden_states: torch.Tensor, - attention_mask: torch.Tensor - ) -> torch.Tensor: - """ - Apply attention-based pooling over time dimension. - - Args: - hidden_states (torch.Tensor): [batch_size, seq_len, hidden_size] - attention_mask (torch.Tensor): [batch_size, seq_len] — mask to ignore padding - - Returns: - torch.Tensor: [batch_size, hidden_size] — context vector - """ - - attention_weights = self.pool_attention(hidden_states) # [B, T, 1] - # Mask out padded positions - attention_weights = attention_weights + ( - (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 - ) - - attention_weights = F.softmax(attention_weights, dim=1) # [B, T, 1] - - # Weighted sum over time - weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) # [B, D] - return weighted_sum - - def forward( - self, - input_values: torch.Tensor, - attention_mask: torch.Tensor - ) -> torch.Tensor: - """ - Forward pass for inference. - - Args: - input_values (torch.Tensor): [batch_size, audio_seq_len] — raw audio waveform - attention_mask (torch.Tensor): [batch_size, audio_seq_len] — input mask (1 = real, 0 = pad) - - Returns: - torch.Tensor: [batch_size, 1] — probability that audio contains music - """ - assert isinstance(input_values, torch.Tensor), f"Expected torch.Tensor, got {type(input_values)}" - assert isinstance(attention_mask, torch.Tensor), f"Expected torch.Tensor, got {type(attention_mask)}" - - outputs = self.wavlm(input_values.to(self.device), attention_mask=attention_mask.to(self.device)) - hidden_states = outputs.last_hidden_state # [B, T', D] - - # Align attention mask with downsampled hidden states - input_length = attention_mask.size(1) - hidden_length = hidden_states.size(1) - ratio = input_length / hidden_length - indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() - attention_mask = attention_mask[:, indices] # [B, T'] - attention_mask = attention_mask.bool() - - pooled = self._attention_pool(hidden_states, attention_mask) - logits = self.classifier(pooled) # [B, 1] - - probs = torch.sigmoid(logits) # [B, 1] → probability of MUSIC - return probs - - def _prepare_batches(self, audio_paths: List[str]) -> List[List[str]]: - """ - Split list of audio paths into batches of size `self.batch_size`. - - Args: - audio_paths (List[str]): List of paths to audio files. - - Returns: - List[List[str]]: List of batches, each batch is a list of paths. - """ - batches = [] - current_batch = [] - counter = 0 - - while counter < len(audio_paths): - if len(current_batch) == self.batch_size: - batches.append(current_batch) - current_batch = [] - current_batch.append(audio_paths[counter]) - counter += 1 - - if current_batch: - batches.append(current_batch) - - return batches - - def _preprocess_audio_batch(self, audio_paths: List[str]) -> Dict[str, torch.Tensor]: - """ - Load and preprocess a batch of audio files. - - Args: - audio_paths (List[str]): List of file paths. - - Returns: - Dict with keys: - "input_values": tensor [B, T] - "attention_mask": tensor [B, T] - """ - waveforms = [] - - for audio_path in audio_paths: - waveform, sample_rate = torchaudio.load(audio_path) - - # Resample if needed - if sample_rate != self.target_sample_rate: - resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.target_sample_rate) - waveform = resampler(waveform) - - # Convert to mono - if waveform.shape[0] > 1: - waveform = waveform.mean(dim=0, keepdim=True) - - waveforms.append(waveform.squeeze()) - - # Extract features - inputs = self.processor( - [w.numpy() for w in waveforms], - sampling_rate=self.target_sample_rate, - return_tensors="pt", - padding=True, - truncation=False - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - return inputs - - def predict_proba(self, audio_paths: List[str]) -> torch.Tensor: - """ - Predict music probability for a list of audio files. - - Args: - audio_paths (List[str]): List of audio file paths. - - Returns: - torch.Tensor: [N] — probabilities for each audio file. - """ - - all_probs = [] - - batches = self._prepare_batches(audio_paths) - - for batch in batches: - inputs = self._preprocess_audio_batch(batch) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - with torch.no_grad(): - probs = self.forward(**inputs).squeeze(-1) # [B] - all_probs.append(probs) - - return torch.cat(all_probs, dim=0) - - def _load_from_hf(self): - return hf_hub_download( - repo_id="MTUCI/MusicDetection", - filename="music_detection.safetensors", - local_dir="./models", - local_dir_use_symlinks=False - ) - \ No newline at end of file diff --git a/src/separation/nisqa.py b/src/separation/nisqa.py deleted file mode 100644 index cf48098..0000000 --- a/src/separation/nisqa.py +++ /dev/null @@ -1,130 +0,0 @@ -import argparse -import sys -from pathlib import Path -from typing import List - -import pandas as pd -import yaml -from loguru import logger - -from src.utils import get_audio_paths, load_config -from src.libs.NISQA.run_predict import run_nisqa_with_config - - -def _create_input_csv(audio_files: List[Path], temp_dir: Path) -> Path: - csv_file_path = temp_dir / 'input_audio_list.csv' - df = pd.DataFrame({'filepath': [p.resolve() for p in audio_files]}) - df.to_csv(csv_file_path, index=False) - logger.info(f"A temporary CSV file has been created: {csv_file_path}") - return csv_file_path - - -def _save_results(output_dir: Path, final_output_path: Path): - source_file = output_dir / 'NISQA_results.csv' - if not source_file.exists(): - logger.warning(f"The NISQA output file was not found: {source_file}") - return - - final_output_path.parent.mkdir(parents=True, exist_ok=True) - try: - new_df = pd.read_csv(source_file) - - if final_output_path.exists(): - existing_df = pd.read_csv(final_output_path) - new_files_df = new_df[~new_df['filepath'].isin(existing_df['filepath'])] - - if not new_files_df.empty: - result_df = pd.concat([existing_df, new_files_df], ignore_index=True) - logger.success(f"Added {len(new_files_df)} new NISQA results.") - else: - result_df = existing_df - logger.info("There are no new files to add from NISQA.") - else: - result_df = new_df - - if 'Unnamed: 0' in result_df.columns: - result_df = result_df.drop('Unnamed: 0', axis=1) - - result_df.to_csv(final_output_path, index=False) - logger.success(f"Results are saved in: {final_output_path}") - - except Exception as e: - logger.error(f"Could not process and save the results: {e}") - - -def get_unprocessed_audio_paths(podcasts_path: Path, result_csv_path: Path) -> List[Path]: - all_audio_paths = get_audio_paths(str(podcasts_path)) - - if not result_csv_path.exists(): - logger.info("The results file was not found. All found audio files are processed.") - return all_audio_paths - - logger.info(f"Checking an existing results file: {result_csv_path}") - df = pd.read_csv(result_csv_path) - processed_audio_paths = set(df['filepath'].astype(str).to_list()) - - unprocessed_paths = [ - path for path in all_audio_paths - if str(path.resolve()) not in processed_audio_paths - ] - - return unprocessed_paths - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--config_path", type=str, required=True) - args = parser.parse_args() - - config = load_config(args.config_path, 'separation') - - podcasts_path = Path(config.get('podcasts_path', '.')) - bs = config.get('bs', 16) - num_workers = config.get('num_workers_nisqa', 2) - - nisqa_dir = Path('./src/libs/NISQA').resolve() - pretrained_model = nisqa_dir / 'weights' / 'nisqa.tar' - - final_output_path = podcasts_path / 'balalaika.csv' - temp_dir = final_output_path.parent / 'nisqa_temp' - output_dir = temp_dir / 'nisqa_results' - - output_dir.mkdir(parents=True, exist_ok=True) - - unprocessed_paths = get_unprocessed_audio_paths( - podcasts_path=podcasts_path, - result_csv_path=final_output_path - ) - - if not unprocessed_paths: - logger.warning("Не найдено аудиофайлов для обработки. Выход.") - return - - logger.info(f' Found {len(unprocessed_paths)} files to process.') - csv_file_path = _create_input_csv(unprocessed_paths, temp_dir) - - nisqa_config = { - 'mode': 'predict_csv', - 'pretrained_model': str(pretrained_model), - 'csv_file': str(csv_file_path), - 'csv_deg': 'filepath', - 'output_dir': str(output_dir), - 'bs': bs, - 'num_workers': num_workers, - 'ms_channel': None - } - - try: - logger.info(f"Launching NISQA with configuration: {nisqa_config}") - - run_nisqa_with_config(nisqa_config) - - logger.info("NISQA processing is complete. Saving the results...") - _save_results(output_dir=output_dir, final_output_path=final_output_path) - - except Exception as e: - logger.error(f"An error occurred during the execution of NISQA: {e}", exc_info=True) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/separation/nisqa_process.py b/src/separation/nisqa_process.py new file mode 100644 index 0000000..a56b24e --- /dev/null +++ b/src/separation/nisqa_process.py @@ -0,0 +1,228 @@ +import argparse +import os +import torch +import torch.multiprocessing as mp +import pandas as pd +import yaml +from pathlib import Path +from typing import List, Dict +from loguru import logger +from multiprocessing import cpu_count +from torch.utils.data import DataLoader +from tqdm import tqdm + +from src.utils import get_audio_paths, load_config + +from nisqab.core.model_torch import model_init +from nisqab.utils.dataset import NISQADataset, collate_fn +from nisqab.utils.audio_cache import create_audio_length_cache +from nisqab.utils.audio_sampler import LengthBasedBatchSampler + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) + +def save_chunk(results: List[Dict], output_path: Path): + """Saves a chunk of results to CSV, appending if exists.""" + if not results: + return + + df = pd.DataFrame(results) + output_path.parent.mkdir(parents=True, exist_ok=True) + + header = not output_path.exists() + df.to_csv(output_path, mode='a', header=header, index=False) + + +def run_inference_worker(rank: int, world_size: int, file_paths: List[str], config: dict, final_output_path: Path): + """ + Worker function running on a dedicated GPU. + """ + my_files = file_paths[rank::world_size] + if not my_files: + logger.info(f"Worker {rank}: No files to process.") + return + + device_str = f"cuda:{rank}" + device = torch.device(device_str) + + # Читаем конфиг + bs = config.get('bs', 32) + num_workers = config.get('num_workers_nisqa', 4) + + cache_dir = Path(config.get('cache_path', './cache')) / f'nisqa_temp_worker_{rank}' + cache_dir.mkdir(parents=True, exist_ok=True) + cache_file = cache_dir / 'audio_lengths.json' + + worker_output_path = final_output_path.with_name(f"{final_output_path.stem}_part_{rank}.csv") + + logger.info(f"[{device_str}] Starting worker. Processing {len(my_files)} files.") + + try: + nisqa_config_path = Path(config.get('nisqa_config_path', 'configs/nisqa_b.yaml')) + with open(nisqa_config_path, "r") as ymlfile: + args_yaml = yaml.load(ymlfile, Loader=yaml.FullLoader) + + model = model_init(args_yaml) + model = model.to(device) + model.eval() + + + audio_lengths = create_audio_length_cache( + file_paths=my_files, + cache_file=str(cache_file), + num_workers=min(num_workers, cpu_count() // world_size), + force_rebuild=False + ) + + dataset = NISQADataset( + file_paths=my_files, + audio_lengths=audio_lengths, + seg_length=15, seg_hop=1, max_length=2000, + n_fft=4096, hop_length=0.01, win_length=0.02, n_mels=48, fmax=20000 + ) + + sampler = LengthBasedBatchSampler( + file_paths=my_files, + audio_lengths=audio_lengths, + batch_size=bs, + drop_last=False, + shuffle=True + ) + + dataloader = DataLoader( + dataset, + batch_sampler=sampler, + num_workers=num_workers, + collate_fn=collate_fn, + pin_memory=True, + persistent_workers=True if num_workers > 0 else False, + prefetch_factor=2 if num_workers > 0 else None + ) + + dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 + model = model.to(dtype) + + results_buffer = [] + save_every = 1000 + + with torch.inference_mode(): + for batch in tqdm(dataloader, desc=f"NISQA-{rank}", position=rank): + x_batch = batch['x_spec_seg'].to(device, dtype=dtype) + n_wins_batch = batch['n_wins'].to(device) + + with torch.amp.autocast(device_type="cuda", dtype=dtype): + outputs = model(x_batch, n_wins_batch) + + outputs_cpu = outputs.float().cpu() + + for i in range(len(batch['file_paths'])): + MOS, NOI, DISC, COL, LOUD = outputs_cpu[i].tolist() + results_buffer.append({ + 'filepath': batch['file_paths'][i], + 'MOS': MOS, 'NOI': NOI, 'DISC': DISC, 'COL': COL, 'LOUD': LOUD + }) + + # Инкрементальное сохранение + if len(results_buffer) >= save_every: + save_chunk(results_buffer, worker_output_path) + results_buffer = [] + + save_chunk(results_buffer, worker_output_path) + logger.success(f"[{device_str}] Finished. Saved to {worker_output_path}") + + except Exception as e: + logger.exception(f"Worker {rank} failed: {e}") + + +def combine_results(final_output_path: Path, num_parts: int): + """Combines partial CSVs into one.""" + logger.info("Combining partial results...") + dfs = [] + + if final_output_path.exists(): + dfs.append(pd.read_csv(final_output_path)) + + found_parts = False + for i in range(num_parts): + part_path = final_output_path.with_name(f"{final_output_path.stem}_part_{i}.csv") + if part_path.exists(): + try: + dfs.append(pd.read_csv(part_path)) + found_parts = True + os.remove(part_path) + except Exception as e: + logger.error(f"Error reading {part_path}: {e}") + + if not found_parts and not dfs: + logger.warning("No results found to combine.") + return + + if dfs: + full_df = pd.concat(dfs, ignore_index=True) + full_df = full_df.drop_duplicates(subset=['filepath']) + full_df.to_csv(final_output_path, index=False) + logger.success(f"Combined results saved to {final_output_path}. Total rows: {len(full_df)}") + + +def get_unprocessed_audio_paths(podcasts_path: Path, result_csv_path: Path) -> List[str]: + """Get list of paths that are not in the result CSV yet.""" + all_audio_paths = get_audio_paths(str(podcasts_path)) + all_paths_str = [str(p.resolve()) for p in all_audio_paths] + + if not result_csv_path.exists(): + return all_paths_str + + logger.info(f"Filtering existing results from: {result_csv_path}") + try: + df = pd.read_csv(result_csv_path) + if 'filepath' not in df.columns: + return all_paths_str + processed = set(df['filepath'].astype(str).to_list()) + + return [p for p in all_paths_str if p not in processed] + except Exception as e: + logger.warning(f"Could not read existing CSV ({e}), processing all files.") + return all_paths_str + + +def main(): + mp.set_start_method('spawn', force=True) + + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, required=True) + args = parser.parse_args() + + config = load_config(args.config_path, 'separation') + podcasts_path = Path(config.get('podcasts_path', '.')) + final_output_path = podcasts_path / 'balalaika.csv' + + available_gpus = torch.cuda.device_count() + if available_gpus == 0: + logger.error("No GPU detected. NISQA is too slow on CPU for large datasets.") + return + + logger.info(f"Detected {available_gpus} GPUs. Preparing pipeline...") + + unprocessed_files = get_unprocessed_audio_paths(podcasts_path, final_output_path) + + if not unprocessed_files: + logger.success("All files processed. Exiting.") + return + + logger.info(f"Total files to process: {len(unprocessed_files)}") + + try: + mp.spawn( + run_inference_worker, + args=(available_gpus, unprocessed_files, config, final_output_path), + nprocs=available_gpus, + join=True + ) + except Exception as e: + logger.critical(f"Multiprocessing failed: {e}") + + combine_results(final_output_path, available_gpus) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index be68452..d9c5e90 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -21,6 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" -taskset -c 0-24 python3 -m src.separation.nisqa --config_path "$CONFIG_PATH" +# python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" +taskset -c 0-32 python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" python3 -m src.separation.diarization --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index b480c89..6b8ffaa 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -1,92 +1,164 @@ import argparse -import multiprocessing -import os -from concurrent.futures import ProcessPoolExecutor, as_completed -from functools import partial -from pathlib import Path -from typing import List, Tuple - import torch -import torchaudio +import torch.multiprocessing as mp +from pathlib import Path +from typing import List from loguru import logger from tqdm import tqdm -from src.transcription.transcripton_base import * +from src.transcription.transcripton_base import ( + GigaAMWrapper, + ToneWrapper, + VOSKCUDAWrapper, + ROVERWrapper +) +from src.transcription.transcripton_dataset import ( + GigaAudioDataset, collate_giga, LengthGroupedSampler, + ToneAudioDataset, collate_tone, + VoskAudioDataset, collate_vosk +) from src.utils import get_audio_paths, load_config torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) -torch.backends.cuda.enable_math_sdp(False) -model = None SUPPORTED_TIME_STAMPS = ['giga_ctc_lm', 'tone'] -def init_worker(model_name: str, device_str: str, **kwargs): - """ - Initializes the appropriate ASR model wrapper for each worker process. - """ - global model - logger.info(f"Initializing worker for model '{model_name}' on {device_str}...") +def save_results(paths: List[str], texts: List[str], timestamps: List[str], model_suffix: str): + """Helper to save text and timestamp files.""" + for path_str, text, ts_content in zip(paths, texts, timestamps): + path = Path(path_str) + + txt_path = path.with_name(f"{path.stem}_{model_suffix}.txt") + try: + with open(txt_path, "w", encoding="utf-8") as f: + f.write(text) + except Exception as e: + logger.error(f"Failed to write TXT for {path.name}: {e}") + + if ts_content: + tst_path = path.with_name(f"{path.stem}_{model_suffix}.tst") + try: + with open(tst_path, "w", encoding="utf-8") as f: + f.write(ts_content) + except Exception as e: + logger.error(f"Failed to write TST for {path.name}: {e}") + + +def run_inference_on_device(cuda_id: int, world_size: int, model_name: str, all_file_paths: List[Path], config: dict): + if not all_file_paths: + return + + my_files = all_file_paths[cuda_id::world_size] - try: - if 'giga' in model_name: - model = GigaAMWrapper(model_id=model_name, device=device_str, **kwargs) - elif 'tone' in model_name: - model = ToneWrapper(model_id=model_name, device=device_str, **kwargs) - elif 'vosk' in model_name: - model = VOSKCUDAWrapper(model_id=kwargs.get('vosk_path'), device=device_str, **kwargs) - else: - raise ValueError(f"Unknown model type for '{model_name}'") - logger.info(f"Worker initialized successfully for model '{model_name}' on {device_str}.") - except Exception as e: - logger.error(f"Failed to initialize worker for model '{model_name}' on {device_str}: {e}") - model = None - - -def process_batch(paths: List[Path], model_name_for_output: str, with_timestamps: bool): - """ - Transcribes a batch of audio files using the globally initialized model. - Generates .txt and optionally .tst files. - """ - global model - if model is None: - logger.error(f"Model is not initialized in this worker. Skipping batch starting with {paths[0].name}.") + if not my_files: return - txt_paths = [path.with_name(f"{path.stem}_{model_name_for_output}.txt") for path in paths] - tst_paths = [path.with_name(f"{path.stem}_{model_name_for_output}.tst") for path in paths] - audio_paths_str = [str(p) for p in paths] + device_str = f"cuda:{cuda_id}" + logger.info(f"Worker {cuda_id}/{world_size} started for '{model_name}' on {device_str}. Processing {len(my_files)} files.") + + model_config = config.get('giga') if 'giga' in model_name else config.get(model_name, {}) + batch_size = model_config.get('batch_size', 8) + num_workers = model_config.get('num_workers', 4) + + lengths_cache_path = config.get('audio_lengths_cache', './cache/audio_lengths_cache.json') + + model_name_for_output = 'vosk' if 'vosk' in model_name else model_name + + with_timestamps = config.get('with_timestamps', False) + timestamps_supported = any(x in model_name for x in SUPPORTED_TIME_STAMPS) + process_timestamps = with_timestamps and timestamps_supported + + my_files_str = [str(p) for p in my_files] try: - if with_timestamps: - plain_texts, tst_contents = model.transcribe_batch_with_timestamps(audio_paths_str) - else: - plain_texts = model.transcribe_batch(audio_paths_str) + if 'giga' in model_name: + model = GigaAMWrapper(model_id=model_name, device=device_str, **model_config) + dataset = GigaAudioDataset(my_files_str, target_sr=16000) + sampler = LengthGroupedSampler(dataset, lengths_cache_path, batch_size) + + loader = torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + num_workers=num_workers, + collate_fn=collate_giga, + pin_memory=True, + persistent_workers=True if num_workers > 0 else False + ) + + for batch_wavs, batch_lengths, batch_paths in tqdm(loader, desc=f"Giga-{cuda_id}", position=cuda_id): + if batch_wavs is None: continue + + if process_timestamps: + texts, tstamps = model.transcribe_tensors_with_timestamps(batch_wavs, batch_lengths) + else: + texts = model.transcribe_tensors(batch_wavs, batch_lengths) + tstamps = [''] * len(texts) + + save_results(batch_paths, texts, tstamps, model_name_for_output) - # Transcribe - for txt_path, plain_text in zip(txt_paths, plain_texts): - with open(txt_path, "w", encoding="utf-8") as f: - f.write(plain_text) + elif 'tone' in model_name: + model = ToneWrapper(model_id=model_name, device=device_str, **model_config) + + dataset = ToneAudioDataset(my_files_str, target_sr=8000) + loader = torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + collate_fn=collate_tone, + pin_memory=False + ) + + for batch_audios, batch_paths in tqdm(loader, desc=f"Tone-{cuda_id}", position=cuda_id): + if not batch_audios: continue + + if process_timestamps: + texts, tstamps = model.transcribe_audio_data_with_timestamps(batch_audios) + else: + texts = model.transcribe_audio_data(batch_audios) + tstamps = [''] * len(texts) + + save_results(batch_paths, texts, tstamps, model_name_for_output) - # Timestamps - if with_timestamps and tst_contents: - for tst_path, tst_content in zip(tst_paths, tst_contents): - if tst_content: - with open(tst_path, "w", encoding="utf-8") as f: - f.write(tst_content) + elif 'vosk' in model_name: + model = VOSKCUDAWrapper(model_id=model_config.get('vosk_path'), device=device_str, **model_config) + + dataset = VoskAudioDataset(my_files_str, target_sr=16000) + sampler = LengthGroupedSampler(dataset, lengths_cache_path, batch_size) + + loader = torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + num_workers=num_workers, + collate_fn=collate_vosk, + pin_memory=True + ) + + for batch_waveforms, batch_paths in tqdm(loader, desc=f"Vosk-{cuda_id}", position=cuda_id): + if not batch_waveforms: continue + + texts = model.transcribe_batch_data(batch_waveforms) + tstamps = [''] * len(texts) + + save_results(batch_paths, texts, tstamps, model_name_for_output) + + else: + logger.error(f"Unknown model type: {model_name}") except Exception as e: - logger.error(f"Failed to process batch starting with {paths[0].name}: {e}") + logger.exception(f"Critical error in worker {cuda_id} for model {model_name}: {e}") def get_valid_audio_paths(src_path: str, model_name_for_output: str) -> List[Path]: - """ - Gets all audio paths and filters out those that have already been transcribed. - """ all_audio_paths = get_audio_paths(src_path) - + if not all_audio_paths: + return [] + valid_paths = [ p for p in all_audio_paths if not p.with_name(f"{p.stem}_{model_name_for_output}.txt").exists() @@ -99,88 +171,50 @@ def main(args): model_names = config.get('model_names', ['giga_rnnt']) src_path = config.get('podcasts_path', '.') - logger.info(f"Starting transcription run for models: {model_names}") - available_gpu_ids = list(range(torch.cuda.device_count())) if not available_gpu_ids: - logger.warning("No CUDA GPUs detected. Using CPU. This will be slow.") - raise + logger.error("No CUDA GPUs detected. This optimized script requires GPU.") + return - num_devices = len(available_gpu_ids) + num_gpus = len(available_gpu_ids) + logger.info(f"Detected {num_gpus} GPUs. Starting processing pipeline.") + for model_name in model_names: - logger.info(f"--- Processing model: {model_name} ---") - model_config = config.get('giga') if 'giga' in model_name else config.get(model_name) - num_workers_per_gpu = model_config.get('num_workers') - batch_size = model_config.get('batch_size') + logger.info(f"=== Processing model: {model_name} ===") + model_name_for_output = 'vosk' if 'vosk' in model_name else model_name - with_timestamps = config.get('with_timestamps') - timestamps_supported = model_name in SUPPORTED_TIME_STAMPS - current_with_timestamps = with_timestamps and timestamps_supported - if with_timestamps and not timestamps_supported: - logger.warning(f"Timestamps requested but not supported for model '{model_name}'. Disabling.") - - all_audio_paths = get_valid_audio_paths(src_path, model_name_for_output) + all_paths = get_valid_audio_paths(src_path, model_name_for_output) - if not all_audio_paths: - logger.info(f"No new audio files to process for model '{model_name}'. Skipping.") + if not all_paths: + logger.info(f"No new files for {model_name}. Skipping.") continue - - logger.info(f"Found {len(all_audio_paths)} new audio files to process for '{model_name}'.") - - files_for_each_device = [[] for _ in range(num_devices)] - for i, path in enumerate(all_audio_paths): - files_for_each_device[i % num_devices].append(path) - - all_futures = [] - executors = [] - - task_fn = partial(process_batch, model_name_for_output=model_name_for_output, with_timestamps=current_with_timestamps) - for i, device_id in enumerate(available_gpu_ids): - device_str = f'cuda:{device_id}' if device_id != 'cpu' else 'cpu' - files_for_this_device = files_for_each_device[i] - - if not files_for_this_device: - continue - - logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") - initializer_fn = partial(init_worker, model_name, device_str, **model_config) - - executor = ProcessPoolExecutor( - max_workers=num_workers_per_gpu, - initializer=initializer_fn, - mp_context=multiprocessing.get_context('spawn') - ) - executors.append(executor) - - for j in range(0, len(files_for_this_device), batch_size): - batch = files_for_this_device[j:j + batch_size] - if batch: - future = executor.submit(task_fn, batch) - all_futures.append(future) - - for future in tqdm(as_completed(all_futures), total=len(all_futures), desc=f"Transcribing ({model_name})"): - try: - future.result() - except Exception as e: - logger.error(f"A task encountered an error: {e}") - - for executor in executors: - executor.shutdown(wait=True) + logger.info(f"Total files to process: {len(all_paths)}") - logger.info(f"Finished processing model: {model_name}") - - logger.info(f"Starting ROVER processing") - rover_wrapper = ROVERWrapper(podcasts_path=src_path, model_names=model_names) - rover_wrapper.aggregate_and_save() - - logger.info(f"Finished processing ROVER") - + try: + mp.spawn( + run_inference_on_device, + args=(num_gpus, model_name, all_paths, config), + nprocs=num_gpus, + join=True + ) + except Exception as e: + logger.error(f"Multiprocessing error: {e}") + + logger.info("Starting ROVER processing...") + try: + rover_wrapper = ROVERWrapper(podcasts_path=src_path, model_names=model_names) + rover_wrapper.aggregate_and_save() + logger.info("ROVER processing finished.") + except Exception as e: + logger.error(f"ROVER failed: {e}") if __name__ == "__main__": - multiprocessing.set_start_method('spawn', force=True) - parser = argparse.ArgumentParser(description="Transcribe audio files in parallel using multiple GPUs/CPUs.") - parser.add_argument("--config_path", type=str, help="Path to the configuration YAML file.") + mp.set_start_method('spawn', force=True) + + parser = argparse.ArgumentParser(description="Optimized GPU Audio Transcription") + parser.add_argument("--config_path", type=str, required=True, help="Path to config.yaml") args = parser.parse_args() - main(args) + + main(args) \ No newline at end of file diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py index 929b344..71a74d0 100644 --- a/src/transcription/transcripton_base.py +++ b/src/transcription/transcripton_base.py @@ -1,22 +1,17 @@ import os import math -import random from abc import ABC, abstractmethod from collections import defaultdict from pathlib import Path from typing import List, Tuple -import time import gigaam import kaldifeat -import miniaudio import numpy as np import pandas as pd import pyctcdecode import sentencepiece as spm -import sherpa_onnx import torch -import torchaudio from crowdkit.aggregation import ROVER from loguru import logger from torch.nn.utils.rnn import pad_sequence @@ -45,38 +40,8 @@ def __init__(self, model_id: str, device: str, **kwargs): self.model_id = model_id self.device = device - @abstractmethod - def transcribe_batch(self, audio_paths: List[str]) -> List[str]: - """ - Transcribes a batch of audio files and returns the texts. - - Args: - audio_paths (List[str]): A list of paths to the audio files. - - Returns: - List[str]: A list of transcribed texts. - """ - pass - - @abstractmethod - def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: - """ - Transcribes a batch of audio files, returning texts and timestamps. - - Args: - audio_paths (List[str]): A list of paths to the audio files. - - Returns: - Tuple[List[str], List[str]]: A tuple of two lists: - - A list of transcribed texts. - - A list of strings with timestamps for each audio. - """ - pass - class GigaAMWrapper(ASRWrapper): - """Wrapper for GigaAM models (CTC and RNN-T).""" - GIGA_AM_FRAME_SIZE_MS = 40 # Duration of a single frame in ms for CTC models def __init__(self, model_id: str, device: str, **kwargs): @@ -85,26 +50,36 @@ def __init__(self, model_id: str, device: str, **kwargs): self.model_type = 'ctc' if 'ctc' in self.model_id else 'rnnt' self.use_lm = 'lm' in self.model_id - self.model = gigaam.load_model(self.model_type, device=self.device) + + self.model = gigaam.load_model( + model_name=self.model_type, + device=self.device, + fp16_encoder=True, + use_flash=False + ) + self.model.eval() + self.target_sr = 16_000 self.decoder = None self.sec_per_frame = self.GIGA_AM_FRAME_SIZE_MS / 1000.0 - if self.use_lm and not os.path.exists(kwargs['lm_path']): - self._downlaod_lm() - if self.use_lm: + if 'lm_path' in kwargs and not os.path.exists(kwargs['lm_path']): + try: + self._downlaod_lm() + except Exception as e: + logger.warning(f"Could not download LM: {e}") + if self.model_type != 'ctc': - logger.warning(f"LM decoding is only supported for CTC models, but got {self.model_type}. LM will be ignored.") + logger.warning(f"LM decoding is only supported for CTC models. LM ignored.") self.use_lm = False elif 'lm_path' not in kwargs: - logger.error("'lm_path' is required for GigaAM with LM but was not provided. LM will not be used.") + logger.error("'lm_path' required for LM usage. LM ignored.") self.use_lm = False else: self._init_lm(kwargs['lm_path']) def _init_lm(self, lm_path: str, alpha: float = 0.5, beta: float = 1.0): - """Initializes the CTC decoder with a language model.""" logger.info(f"Building CTC decoder with LM: {lm_path}") try: vocab = self.model.decoding.tokenizer.vocab @@ -120,56 +95,59 @@ def _init_lm(self, lm_path: str, alpha: float = 0.5, beta: float = 1.0): self.use_lm = False @torch.inference_mode() - def transcribe_batch(self, audio_paths: List[str]) -> List[str]: - """Transcribes a batch of audio files.""" - # If an LM is used, the transcription logic is in the method with timestamps + def transcribe_tensors(self, batch_wav: torch.Tensor, batch_lengths: torch.Tensor) -> List[str]: if self.use_lm and self.decoder: - texts, _ = self.transcribe_batch_with_timestamps(audio_paths) + texts, _ = self.transcribe_tensors_with_timestamps(batch_wav, batch_lengths) return texts - texts = [self.model.transcribe(audio_path) for audio_path in audio_paths] - return texts + batch_wav = batch_wav.to(self.device, non_blocking=True) + batch_lengths = batch_lengths.to(self.device, non_blocking=True) + + encoded, encoded_len = self.model.forward(batch_wav, batch_lengths) + transcriptions = self.model.decoding.decode(self.model.head, encoded, encoded_len) + + return [t if t else "" for t in transcriptions] @torch.inference_mode() - def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: - """Transcribes audio, returning text and timestamps (only for CTC with LM).""" - if self.model_type != 'ctc' or self.decoder is None: - logger.warning("Timestamps are only available for CTC models with a language model.") - texts = self.transcribe_batch(audio_paths) - # Return empty strings for timestamps - return texts, [''] * len(texts) + def transcribe_tensors_with_timestamps(self, batch_wav: torch.Tensor, batch_lengths: torch.Tensor) -> Tuple[List[str], List[str]]: + batch_wav = batch_wav.to(self.device, non_blocking=True) + batch_lengths = batch_lengths.to(self.device, non_blocking=True) result_texts = [] result_timestamps = [] - for audio_path in audio_paths: - audio = self._read_audio(audio_path).to(self.device) - length = torch.tensor([audio.shape[-1]], device=self.device) - - encoded, _ = self.model.forward(audio, length) - logits = self.model.head(encoded).squeeze(0).detach().cpu().numpy() - - beams = self.decoder.decode_beams(logits, beam_width=100) - best_beam = beams[0] - text, _, word_timestamps_raw, _, _ = best_beam - formatted_timestamps = self._to_simple_timestamps(word_timestamps_raw) + encoded, encoded_len = self.model.forward(batch_wav, batch_lengths) + + if self.model_type == 'ctc' and self.decoder: + logits_batch = self.model.head(encoded) + logits_batch = logits_batch.detach().cpu().numpy() + encoded_len_cpu = encoded_len.cpu().tolist() - result_texts.append(text) - result_timestamps.append(formatted_timestamps) + for i, valid_len in enumerate(encoded_len_cpu): + sample_logits = logits_batch[i, :valid_len, :] + try: + beams = self.decoder.decode_beams(sample_logits, beam_width=100) + if beams: + best_beam = beams[0] + text, _, word_timestamps_raw, _, _ = best_beam + formatted_timestamps = self._to_simple_timestamps(word_timestamps_raw) + result_texts.append(text) + result_timestamps.append(formatted_timestamps) + else: + result_texts.append("") + result_timestamps.append("") + except Exception as e: + logger.error(f"LM decoding error: {e}") + result_texts.append("") + result_timestamps.append("") + else: + transcriptions = self.model.decoding.decode(self.model.head, encoded, encoded_len) + result_texts = [t if t else "" for t in transcriptions] + result_timestamps = [""] * len(result_texts) return result_texts, result_timestamps - def _read_audio(self, path_to_file: str) -> torch.Tensor: - """Reads an audio file, resamples it, and converts it to mono.""" - audio, sr = torchaudio.load(path_to_file) - if sr != self.target_sr: - audio = torchaudio.functional.resample(audio, sr, self.target_sr) - if audio.dim() > 1 and audio.size(0) > 1: - audio = torch.mean(audio, dim=0, keepdim=True) - return audio - def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int]]]) -> str: - """Formats timestamps into a string.""" output_lines = [] for word, (start_frame, end_frame) in word_timestamps: start_time = start_frame * self.sec_per_frame @@ -187,11 +165,7 @@ def _downlaod_lm(self): ) - - - class ToneWrapper(ASRWrapper): - """Wrapper for the Tone streaming CTC model.""" def __init__(self, model_id: str, device: str, **kwargs): super().__init__(model_id, device) logger.info(f"Initializing Tone model '{self.model_id}' on device {self.device}") @@ -203,57 +177,56 @@ def __init__(self, model_id: str, device: str, **kwargs): logger.error(f"Invalid CUDA device format: {self.device}. Using ID 0.") self.device_id = 0 else: - self.device_id = -1 # ID for CPU + self.device_id = -1 self.tone_pipeline = StreamingCTCPipeline.from_hugging_face(device_id=self.device_id) self.target_sr = 8_000 - @torch.inference_mode() - def transcribe_batch(self, audio_paths: List[str]) -> List[str]: - """Transcribes a batch of audio files.""" + def transcribe_audio_data(self, audios: List[np.ndarray]) -> List[str]: result_texts = [] - for audio_path in audio_paths: - audio = self._read_audio(audio_path) - phrases = self.tone_pipeline.forward_offline(audio) - result_text = ' '.join([phrase.text for phrase in phrases]).strip() - result_texts.append(result_text) + + for audio_np in audios: + try: + phrases = self.tone_pipeline.forward_offline(audio_np) + result_text = ' '.join([phrase.text for phrase in phrases]).strip() + result_texts.append(result_text) + except Exception as e: + logger.error(f"Tone inference error: {e}") + result_texts.append("") + return result_texts - def _read_audio(self, path_to_file: str) -> np.ndarray: - audio = miniaudio.decode_file(str(path_to_file), nchannels=1, sample_rate=8000) - assert audio.sample_rate == 8000 - assert audio.nchannels == 1 - return np.asarray(audio.samples, dtype=np.int16).astype(np.int32) - - @torch.inference_mode() - def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: - """Transcribes audio, returning text and phrase-level timestamps.""" + def transcribe_audio_data_with_timestamps(self, audios: List[np.ndarray]) -> Tuple[List[str], List[str]]: result_texts = [] - results_timestamps = [] - for audio_path in audio_paths: - audio_tensor = self._read_audio(audio_path) - phrases: List[TextPhrase] = self.tone_pipeline.forward_offline(audio_tensor) - - full_text_parts = [] - timestamp_lines = [] + result_timestamps = [] + + for audio_np in audios: + try: + phrases: List[TextPhrase] = self.tone_pipeline.forward_offline(audio_np) + + full_text_parts = [] + timestamp_lines = [] - for phrase in phrases: - if phrase.text: - full_text_parts.append(phrase.text) - # TODO: add logic for word-level alignment if needed - timestamp_lines.append(f"{phrase.text} {phrase.start_time:.3f} {phrase.end_time:.3f}") + for phrase in phrases: + if phrase.text: + full_text_parts.append(phrase.text) + timestamp_lines.append(f"{phrase.text} {phrase.start_time:.3f} {phrase.end_time:.3f}") - result_text = " ".join(full_text_parts).strip() - result_timestamps = "\n".join(timestamp_lines) + result_text = " ".join(full_text_parts).strip() + result_timestamps_str = "\n".join(timestamp_lines) - result_texts.append(result_text) - results_timestamps.append(result_timestamps) + result_texts.append(result_text) + result_timestamps.append(result_timestamps_str) + + except Exception as e: + logger.error(f"Tone inference error (timestamps): {e}") + result_texts.append("") + result_timestamps.append("") - return result_texts, results_timestamps + return result_texts, result_timestamps class VOSKCUDAWrapper(ASRWrapper): - """Wrapper for a custom VOSK model adapted for CUDA.""" def __init__(self, model_id: str, device: str, **kwargs): super().__init__(model_id, device) logger.info(f"Initializing VOSK CUDA model '{self.model_id}' on device {self.device}") @@ -268,8 +241,9 @@ def __init__(self, model_id: str, device: str, **kwargs): self.bpe_model = str(vosk_path / 'lang' / 'bpe.model') self.target_sr = 16_000 - self.beam_size = kwargs['beam_size'] - self.lm_scale = kwargs['lm_scale'] + self.beam_size = kwargs.get('beam_size', 10) + self.lm_scale = kwargs.get('lm_scale', 0.5) + self.model = torch.jit.load(self.nn_model_filename).to(self.device).eval() self.sp = spm.SentencePieceProcessor() @@ -307,20 +281,23 @@ def __init__(self, model_id: str, device: str, **kwargs): backoff_id=500, is_binary=False, ) - self.ngram_lm_scale = kwargs['ngram_lm_scale'] + self.ngram_lm_scale = kwargs.get('ngram_lm_scale', 1.0) @torch.inference_mode() - def transcribe_batch(self, audio_paths: List[str]) -> List[str]: - """Transcribes a batch of audio files.""" - waves = [self._read_audio(path).to(self.device) for path in audio_paths] - features = self.fbank(waves) + def transcribe_batch_data(self, waveforms: List[torch.Tensor]) -> List[str]: + if not waveforms: + return [] + + waves_gpu = [w.to(self.device, non_blocking=True) for w in waveforms] + features = self.fbank(waves_gpu) feature_lengths = torch.tensor([f.size(0) for f in features], device=self.device) - features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10)) + features_padded = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10)) + encoder_out, encoder_out_lens = self.model.encoder( - features=features, + features=features_padded, feature_lengths=feature_lengths, ) - # start_time = time.time() + hyps = modified_beam_search_LODR( model=self.model, encoder_out=encoder_out, @@ -330,118 +307,11 @@ def transcribe_batch(self, audio_paths: List[str]) -> List[str]: LODR_lm_scale=self.ngram_lm_scale, LM=self.LM, ) - # print(time.time() - start_time, 'hyps') + result_texts = [self.sp.decode(hyp) for hyp in hyps] return result_texts - @torch.inference_mode() - def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: - """This model does not support timestamp generation.""" - texts = self.transcribe_batch(audio_paths) - return texts, [''] * len(audio_paths) - - def _read_audio(self, path_to_file: str) -> torch.Tensor: - """Reads an audio file, resamples it, and converts it to mono.""" - audio, sr = torchaudio.load(path_to_file) - if sr != self.target_sr: - audio = torchaudio.functional.resample(audio, sr, self.target_sr) - if audio.dim() > 1 and audio.size(0) > 1: - audio = torch.mean(audio, dim=0, keepdim=True) - return audio.squeeze(0) - - def _load_from_hf(self): - return snapshot_download( - repo_id="alphacep/vosk-model-ru", - local_dir="./models/vosk-model-ru", - local_dir_use_symlinks=False - ) - -class VoskSherpaOnnxWrapper(ASRWrapper): - """Wrapper for Vosk-style models using sherpa-onnx.""" - def __init__(self, model_id: str, device: str = 'cpu', **kwargs): - logger.info(f"Initializing sherpa-onnx based ASR model from '{model_id}' on {device}") - device = 'cpu' - if not os.path.exists(model_id): - self._load_from_hf() - - model_path = Path(model_id) - encoder_path = str(model_path / "am-onnx" / "encoder.onnx") - decoder_path = str(model_path / "am-onnx" / "decoder.onnx") - joiner_path = str(model_path / "am-onnx" / "joiner.onnx") - tokens_path = str(model_path / "lang" / "tokens.txt") - self.target_sr = 16000 - - if not all(Path(p).exists() for p in [encoder_path, decoder_path, joiner_path, tokens_path]): - raise FileNotFoundError(f"One or more required model files not found in {model_path}") - - self.recognizer = sherpa_onnx.OfflineRecognizer.from_transducer( - encoder=encoder_path, - decoder=decoder_path, - joiner=joiner_path, - tokens=tokens_path, - num_threads=4, - provider=device, - sample_rate=self.target_sr, - decoding_method="modified_beam_search" - ) - logger.info(f"sherpa-onnx recognizer initialized successfully on {device}.") - - @torch.inference_mode() - def transcribe_batch(self, audio_paths: List[str]) -> List[str]: - result_texts = [] - for audio_path in audio_paths: - audio = self._read_audio(audio_path) - s = self.recognizer.create_stream() - s.accept_waveform(self.target_sr, audio) - self.recognizer.decode_stream(s) - result_texts.append(s.result.text.strip()) - - return result_texts - - def _read_audio(self, path_to_file: str) -> Tuple[np.ndarray, int]: - """Reads audio and returns a float32 numpy array for sherpa-onnx.""" - audio, sr = torchaudio.load(path_to_file) - - if sr != self.target_sr: - audio = torchaudio.functional.resample(audio, sr, self.target_sr) - - if audio.dim() > 1 and audio.size(0) > 1: - audio = torch.mean(audio, dim=0, keepdim=True) - - audio_samples = audio.numpy().squeeze().astype(np.float32) - - if np.max(np.abs(audio_samples)) > 1.0: - audio_samples = audio_samples / 32768.0 - - return audio_samples - - @torch.inference_mode() - def transcribe_batch_with_timestamps(self, audio_paths: List[str]) -> Tuple[List[str], List[str]]: - """Transcribes an audio file and returns the text and word-level timestamps.""" - result_texts = [] - results_timestamps = [] - for audio_path in audio_paths: - # TODO: add logic for word alignment - audio = self._read_audio(audio_path) - s = self.recognizer.create_stream() - s.accept_waveform(self.target_sr, audio) - self.recognizer.decode_stream(s) - - result = s.result - full_text = result.text.strip() - - timestamp_lines = [] - for token, timestamp in zip(result.tokens, result.timestamps): - timestamp_lines.append(f"{token} {timestamp:.3f} {timestamp:.3f}") - - result_timestamps = "\n".join(timestamp_lines) - - result_texts.append(full_text) - results_timestamps.append(result_timestamps) - - return result_texts, results_timestamps - def _load_from_hf(self): return snapshot_download( repo_id="alphacep/vosk-model-ru", @@ -449,30 +319,15 @@ def _load_from_hf(self): local_dir_use_symlinks=False ) - class ROVERWrapper: - """Aggregates transcription results from multiple models using ROVER.""" - def __init__(self, podcasts_path: str, model_names: List[str]): - """ - Args: - podcasts_path (str): Path to the directory with transcription files. - model_names (List[str]): A list of model suffixes to include in the aggregation. - """ self.podcasts_path = Path(podcasts_path) self.model_names = model_names - # self.model_names = ['giga_ctc_lm', 'vosk', 'tone', 'giga_ctc', 'giga_rnnt'] self.tokenizer = lambda s: s.lower().split() self.detokenizer = lambda tokens: ' '.join(tokens) self.rover_aggregator = ROVER(self.tokenizer, self.detokenizer) def aggregate_and_save(self): - """ - Performs transcription aggregation and saves the results. - - This method now first finds all audio files and then collects the - corresponding transcription files for each model. - """ logger.info("Starting transcription aggregation based on audio files.") all_audio_paths = get_audio_paths(str(self.podcasts_path)) @@ -496,12 +351,14 @@ def aggregate_and_save(self): try: text = read_file_content(transcript_path) - if text: - records.append({ - 'task': str(audio_path), - 'worker': model_name, - 'text': text - }) + if not text: + continue + + records.append({ + 'task': str(audio_path), + 'worker': model_name, + 'text': text + }) except Exception as e: logger.error(f"Error reading file {transcript_path}: {e}") diff --git a/src/transcription/transcripton_dataset.py b/src/transcription/transcripton_dataset.py new file mode 100644 index 0000000..08fbbf2 --- /dev/null +++ b/src/transcription/transcripton_dataset.py @@ -0,0 +1,180 @@ +import os +import json +import torch +import torchaudio +import logging +from typing import List, Tuple, Optional +from torch.utils.data import Dataset, Sampler +from torch.nn.utils.rnn import pad_sequence +import numpy as np +from loguru import logger + +class GigaAudioDataset(Dataset): + def __init__(self, audio_paths: List[str], target_sr: int = 16000): + self.audio_paths = audio_paths + self.target_sr = target_sr + + def __len__(self): + return len(self.audio_paths) + + def __getitem__(self, idx): + path = self.audio_paths[idx] + try: + if not os.path.exists(path): + return None, path + + waveform, sr = torchaudio.load(path) + + if sr != self.target_sr: + waveform = torchaudio.functional.resample(waveform, sr, self.target_sr) + + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + + return waveform.squeeze(0), path + + except Exception as e: + logger.warning(f"Error loading {path}: {e}") + return None, path + + +class LengthGroupedSampler(Sampler): + def __init__(self, data_source, json_cache_path: str, batch_size: int): + self.data_source = data_source + self.batch_size = batch_size + self.json_cache_path = json_cache_path + self.indices = self._build_indices() + + def _build_indices(self): + length_cache = {} + if os.path.exists(self.json_cache_path): + try: + with open(self.json_cache_path, 'r') as f: + length_cache = json.load(f) + except Exception as e: + logger.warning(f"Ошибка чтения кеша длин {self.json_cache_path}: {e}") + + pairs = [] + missing_count = 0 + + for idx, path in enumerate(self.data_source.audio_paths): + length = length_cache.get(path) + + if length is None: + length = 5.0 + missing_count += 1 + + pairs.append((idx, length)) + + if missing_count > 0: + logger.info(f"For {missing_count} files the length was not found in the cache (default used).") + + pairs.sort(key=lambda x: x[1], reverse=True) + + return [p[0] for p in pairs] + + def __iter__(self): + return iter(self.indices) + + def __len__(self): + return len(self.data_source) + + +def collate_giga(batch: List[Tuple[Optional[torch.Tensor], str]]): + batch = [item for item in batch if item[0] is not None] + + if not batch: + return None, None, [] + + waveforms = [item[0] for item in batch] + paths = [item[1] for item in batch] + + lengths = torch.tensor([w.size(0) for w in waveforms], dtype=torch.long) + padded_wavs = pad_sequence(waveforms, batch_first=True, padding_value=0.0) + + return padded_wavs, lengths, paths + + +class ToneAudioDataset(Dataset): + def __init__(self, audio_paths: List[str], target_sr: int = 8000): + self.audio_paths = audio_paths + self.target_sr = target_sr + + def __len__(self): + return len(self.audio_paths) + + def __getitem__(self, idx): + path = self.audio_paths[idx] + try: + if not os.path.exists(path): + return None, path + + waveform, sr = torchaudio.load(path) + + if sr != self.target_sr: + waveform = torchaudio.functional.resample( + waveform, orig_freq=sr, new_freq=self.target_sr + ) + + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + + waveform = (waveform * 32768.0).clamp(-32768, 32767).to(torch.int32) + + audio_np = waveform.squeeze(0).numpy() + + return audio_np, path + + except Exception as e: + logger.warning(f"Error processing {path} for Tone: {e}") + return None, path + +def collate_tone(batch: List[Tuple[Optional[np.ndarray], str]]): + valid_batch = [item for item in batch if item[0] is not None] + + if not valid_batch: + return [], [] + + audios = [item[0] for item in valid_batch] + paths = [item[1] for item in valid_batch] + + return audios, paths + + +class VoskAudioDataset(Dataset): + def __init__(self, audio_paths: List[str], target_sr: int = 16000): + self.audio_paths = audio_paths + self.target_sr = target_sr + + def __len__(self): + return len(self.audio_paths) + + def __getitem__(self, idx): + path = self.audio_paths[idx] + try: + if not os.path.exists(path): + return None, path + + waveform, sr = torchaudio.load(path) + + if sr != self.target_sr: + waveform = torchaudio.functional.resample(waveform, sr, self.target_sr) + + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + + return waveform.squeeze(0), path + + except Exception as e: + logger.warning(f"Error loading {path}: {e}") + return None, path + +def collate_vosk(batch: List[Tuple[Optional[torch.Tensor], str]]): + batch = [item for item in batch if item[0] is not None] + if not batch: + return [], [] + + waveforms = [item[0] for item in batch] + paths = [item[1] for item in batch] + + return waveforms, paths \ No newline at end of file diff --git a/src/utils/utils.py b/src/utils/utils.py new file mode 100644 index 0000000..c15ce3e --- /dev/null +++ b/src/utils/utils.py @@ -0,0 +1,109 @@ +from pathlib import Path +from typing import List +import yaml +from loguru import logger + +def load_config(config_path: str, process_name: str): + config = {} + if config_path is None or process_name is None: + logger.info("Configuration not provided. Parameters will be taken from argparse.") + return config + try: + with open(config_path, 'r') as config_file: + config = yaml.safe_load(config_file) + config = config.get(process_name, {}) + logger.info('Loaded parameters from config') + except Exception as e: + logger.error(f"Configuration loading error: {e}") + + finally: + return config + +def get_txt_paths(podcast_path: str, postfix: str) -> List[Path]: + return list(Path(podcast_path).rglob(f"*{postfix}")) + +def read_file_content(file_path): + try: + with open(file_path, 'r', encoding='utf-8') as f: + return f.read().strip() + except FileNotFoundError: + return '' + +def get_audio_paths(podcast_path: str): + podcast_path=Path(podcast_path) + return ( + list(podcast_path.rglob("*.mp3")) + + list(podcast_path.rglob("*.wav")) + + list(podcast_path.rglob("*.flac")) + + list(podcast_path.rglob("*.ogg")) + + list(podcast_path.rglob("*.opus")) + ) + + +def process_token(token, label): + if label == "LOWER_O": + return token + if label == "LOWER_PERIOD": + return token + "." + if label == "LOWER_COMMA": + return token + "," + if label == "LOWER_QUESTION": + return token + "?" + if label == "LOWER_TIRE": + return token + "—" + if label == "LOWER_DVOETOCHIE": + return token + ":" + if label == "LOWER_VOSKL": + return token + "!" + if label == "LOWER_PERIODCOMMA": + return token + ";" + if label == "LOWER_DEFIS": + return token + "-" + if label == "LOWER_MNOGOTOCHIE": + return token + "..." + if label == "LOWER_QUESTIONVOSKL": + return token + "?!" + if label == "UPPER_O": + return token.capitalize() + if label == "UPPER_PERIOD": + return token.capitalize() + "." + if label == "UPPER_COMMA": + return token.capitalize() + "," + if label == "UPPER_QUESTION": + return token.capitalize() + "?" + if label == "UPPER_TIRE": + return token.capitalize() + " —" + if label == "UPPER_DVOETOCHIE": + return token.capitalize() + ":" + if label == "UPPER_VOSKL": + return token.capitalize() + "!" + if label == "UPPER_PERIODCOMMA": + return token.capitalize() + ";" + if label == "UPPER_DEFIS": + return token.capitalize() + "-" + if label == "UPPER_MNOGOTOCHIE": + return token.capitalize() + "..." + if label == "UPPER_QUESTIONVOSKL": + return token.capitalize() + "?!" + if label == "UPPER_TOTAL_O": + return token.upper() + if label == "UPPER_TOTAL_PERIOD": + return token.upper() + "." + if label == "UPPER_TOTAL_COMMA": + return token.upper() + "," + if label == "UPPER_TOTAL_QUESTION": + return token.upper() + "?" + if label == "UPPER_TOTAL_TIRE": + return token.upper() + " —" + if label == "UPPER_TOTAL_DVOETOCHIE": + return token.upper() + ":" + if label == "UPPER_TOTAL_VOSKL": + return token.upper() + "!" + if label == "UPPER_TOTAL_PERIODCOMMA": + return token.upper() + ";" + if label == "UPPER_TOTAL_DEFIS": + return token.upper() + "-" + if label == "UPPER_TOTAL_MNOGOTOCHIE": + return token.upper() + "..." + if label == "UPPER_TOTAL_QUESTIONVOSKL": + return token.upper() + "?!" \ No newline at end of file diff --git a/src/utils/vosk_utils.py b/src/utils/vosk_utils.py new file mode 100644 index 0000000..3dea75f --- /dev/null +++ b/src/utils/vosk_utils.py @@ -0,0 +1,490 @@ + +import argparse +import logging +import math +import random +import os +import sys +import warnings +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple, Union + +import kaldifeat +import sentencepiece as spm +import torch +import torchaudio +from torch.nn.utils.rnn import pad_sequence +from timeit import default_timer as timer + +from icefall import NgramLm, NgramLmStateCost +from icefall.decode import Nbest, one_best_decoding +from icefall.lm_wrapper import LmScorer +from icefall.rnn_lm.model import RnnLmModel +from icefall.transformer_lm.model import TransformerLM +from icefall.utils import AttributeDict +from icefall.lexicon import Lexicon + +import k2 + +def read_sound_files( + filenames: List[str], expected_sample_rate: float = 16000 +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + resampler = torchaudio.transforms.Resample(sample_rate, 16_000) + wav = resampler(wave[0]) + ans.append(wav) + return ans + + + +@dataclass +class Hypothesis: + # The predicted tokens so far. + # Newly predicted tokens are appended to `ys`. + ys: List[int] + + # The log prob of ys. + # It contains only one entry. + log_prob: torch.Tensor + + # timestamp[i] is the frame index after subsampling + # on which ys[i] is decoded + timestamp: List[int] = field(default_factory=list) + + # the lm score for next token given the current ys + lm_score: Optional[torch.Tensor] = None + + # the RNNLM states (h and c in LSTM) + state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None + + # N-gram LM state + state_cost: Optional[NgramLmStateCost] = None + + @property + def key(self) -> str: + """Return a string representation of self.ys""" + return "_".join(map(str, self.ys)) + + +class HypothesisList(object): + def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None: + """ + Args: + data: + A dict of Hypotheses. Its key is its `value.key`. + """ + if data is None: + self._data = {} + else: + self._data = data + + @property + def data(self) -> Dict[str, Hypothesis]: + return self._data + + def add(self, hyp: Hypothesis) -> None: + """Add a Hypothesis to `self`. + + If `hyp` already exists in `self`, its probability is updated using + `log-sum-exp` with the existed one. + + Args: + hyp: + The hypothesis to be added. + """ + key = hyp.key + if key in self: + old_hyp = self._data[key] # shallow copy + torch.logaddexp(old_hyp.log_prob, hyp.log_prob, out=old_hyp.log_prob) + else: + self._data[key] = hyp + + def get_most_probable(self, length_norm: bool = False) -> Hypothesis: + """Get the most probable hypothesis, i.e., the one with + the largest `log_prob`. + + Args: + length_norm: + If True, the `log_prob` of a hypothesis is normalized by the + number of tokens in it. + Returns: + Return the hypothesis that has the largest `log_prob`. + """ + if length_norm: + return max(self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys)) + else: + return max(self._data.values(), key=lambda hyp: hyp.log_prob) + + def remove(self, hyp: Hypothesis) -> None: + """Remove a given hypothesis. + + Caution: + `self` is modified **in-place**. + + Args: + hyp: + The hypothesis to be removed from `self`. + Note: It must be contained in `self`. Otherwise, + an exception is raised. + """ + key = hyp.key + assert key in self, f"{key} does not exist" + del self._data[key] + + def filter(self, threshold: torch.Tensor) -> "HypothesisList": + """Remove all Hypotheses whose log_prob is less than threshold. + + Caution: + `self` is not modified. Instead, a new HypothesisList is returned. + + Returns: + Return a new HypothesisList containing all hypotheses from `self` + with `log_prob` being greater than the given `threshold`. + """ + ans = HypothesisList() + for _, hyp in self._data.items(): + if hyp.log_prob > threshold: + ans.add(hyp) # shallow copy + return ans + + def topk(self, k: int, length_norm: bool = False) -> "HypothesisList": + """Return the top-k hypothesis. + + Args: + length_norm: + If True, the `log_prob` of a hypothesis is normalized by the + number of tokens in it. + """ + hyps = list(self._data.items()) + + if length_norm: + hyps = sorted( + hyps, key=lambda h: h[1].log_prob / len(h[1].ys), reverse=True + )[:k] + else: + hyps = sorted(hyps, key=lambda h: h[1].log_prob, reverse=True)[:k] + + ans = HypothesisList(dict(hyps)) + return ans + + def __contains__(self, key: str): + return key in self._data + + def __iter__(self): + return iter(self._data.values()) + + def __len__(self) -> int: + return len(self._data) + + def __str__(self) -> str: + s = [] + for key in self: + s.append(key) + return ", ".join(s) + + +def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape: + """Return a ragged shape with axes [utt][num_hyps]. + + Args: + hyps: + len(hyps) == batch_size. It contains the current hypothesis for + each utterance in the batch. + Returns: + Return a ragged shape with 2 axes [utt][num_hyps]. Note that + the shape is on CPU. + """ + num_hyps = [len(h) for h in hyps] + + # torch.cumsum() is inclusive sum, so we put a 0 at the beginning + # to get exclusive sum later. + num_hyps.insert(0, 0) + + num_hyps = torch.tensor(num_hyps) + row_splits = torch.cumsum(num_hyps, dim=0, dtype=torch.int32) + ans = k2.ragged.create_ragged_shape2( + row_splits=row_splits, cached_tot_size=row_splits[-1].item() + ) + return ans + + +def modified_beam_search_LODR( + model, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + LODR_lm: NgramLm, + LODR_lm_scale: float, + LM: LmScorer, + beam: int = 4, +) -> List[List[int]]: + """This function implements LODR (https://arxiv.org/abs/2203.16776) with + `modified_beam_search`. It uses a bi-gram language model as the estimate + of the internal language model and subtracts its score during shallow fusion + with an external language model. This implementation uses a RNNLM as the + external language model. + + Args: + model (Transducer): + The transducer model + encoder_out (torch.Tensor): + Encoder output in (N,T,C) + encoder_out_lens (torch.Tensor): + A 1-D tensor of shape (N,), containing the number of + valid frames in encoder_out before padding. + LODR_lm: + A low order n-gram LM, whose score will be subtracted during shallow fusion + LODR_lm_scale: + The scale of the LODR_lm + LM: + A neural net LM, e.g an RNNLM or transformer LM + beam (int, optional): + Beam size. Defaults to 4. + + Returns: + Return a list-of-list of token IDs. ans[i] is the decoding results + for the i-th utterance. + + """ + assert encoder_out.ndim == 3, encoder_out.shape + assert encoder_out.size(0) >= 1, encoder_out.size(0) + assert LM is not None + lm_scale = LM.lm_scale + + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) + + blank_id = model.decoder.blank_id + sos_id = getattr(LM, "sos_id", 1) + unk_id = getattr(model, "unk_id", blank_id) + context_size = model.decoder.context_size + device = next(model.parameters()).device + + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + # get initial lm score and lm state by scoring the "sos" token + sos_token = torch.tensor([[sos_id]]).to(torch.int64).to(device) + lens = torch.tensor([1]).to(device) + init_score, init_states = LM.score_token(sos_token, lens) + + B = [HypothesisList() for _ in range(N)] + for i in range(N): + B[i].add( + Hypothesis( + ys=([-1] * (context_size - 1) + [blank_id]), + log_prob=torch.zeros(1, dtype=torch.float32, device=device), + state=init_states, # state of the NN LM + lm_score=init_score.reshape(-1), + state_cost=NgramLmStateCost( + LODR_lm + ), # state of the source domain ngram + ) + ) + + encoder_out = model.joiner.encoder_proj(packed_encoder_out.data) + + offset = 0 + finalized_B = [] + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] # get batch + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) + # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim) + offset = end + + finalized_B = B[batch_size:] + finalized_B + B = B[:batch_size] + + hyps_shape = get_hyps_shape(B).to(device) + + A = [list(b) for b in B] + B = [HypothesisList() for _ in range(batch_size)] + + ys_log_probs = torch.cat( + [hyp.log_prob.reshape(1, 1) for hyps in A for hyp in hyps] + ) + + decoder_input = torch.tensor( + [hyp.ys[-context_size:] for hyps in A for hyp in hyps], + device=device, + dtype=torch.int64, + ) # (num_hyps, context_size) + + decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1) + decoder_out = model.joiner.decoder_proj(decoder_out) + + current_encoder_out = torch.index_select( + current_encoder_out, + dim=0, + index=hyps_shape.row_ids(1).to(torch.int64), + ) # (num_hyps, 1, 1, encoder_out_dim) + + logits = model.joiner( + current_encoder_out, + decoder_out, + project_input=False, + ) # (num_hyps, 1, 1, vocab_size) + + logits = logits.squeeze(1).squeeze(1) # (num_hyps, vocab_size) + + log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size) + + log_probs.add_(ys_log_probs) + + vocab_size = log_probs.size(-1) + + log_probs = log_probs.reshape(-1) + + row_splits = hyps_shape.row_splits(1) * vocab_size + log_probs_shape = k2.ragged.create_ragged_shape2( + row_splits=row_splits, cached_tot_size=log_probs.numel() + ) + ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs) + """ + for all hyps with a non-blank new token, score this token. + It is a little confusing here because this for-loop + looks very similar to the one below. Here, we go through all + top-k tokens and only add the non-blanks ones to the token_list. + LM will score those tokens given the LM states. Note that + the variable `scores` is the LM score after seeing the new + non-blank token. + """ + token_list = [] + hs = [] + cs = [] + for i in range(batch_size): + topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + topk_hyp_indexes = (topk_indexes // vocab_size).tolist() + topk_token_indexes = (topk_indexes % vocab_size).tolist() + for k in range(len(topk_hyp_indexes)): + hyp_idx = topk_hyp_indexes[k] + hyp = A[i][hyp_idx] + + new_token = topk_token_indexes[k] + if new_token not in (blank_id, unk_id): + if LM.lm_type == "rnn": + token_list.append([new_token]) + # store the LSTM states + hs.append(hyp.state[0]) + cs.append(hyp.state[1]) + else: + # for transformer LM + token_list.append( + [sos_id] + hyp.ys[context_size:] + [new_token] + ) + + # forward NN LM to get new states and scores + if len(token_list) != 0: + x_lens = torch.tensor([len(tokens) for tokens in token_list]).to(device) + if LM.lm_type == "rnn": + tokens_to_score = ( + torch.tensor(token_list).to(torch.int64).to(device).reshape(-1, 1) + ) + hs = torch.cat(hs, dim=1).to(device) + cs = torch.cat(cs, dim=1).to(device) + state = (hs, cs) + else: + # for transformer LM + tokens_list = [torch.tensor(tokens) for tokens in token_list] + tokens_to_score = ( + torch.nn.utils.rnn.pad_sequence( + tokens_list, batch_first=True, padding_value=0.0 + ) + .to(device) + .to(torch.int64) + ) + + state = None + + scores, lm_states = LM.score_token(tokens_to_score, x_lens, state) + + count = 0 # index, used to locate score and lm states + for i in range(batch_size): + topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + topk_hyp_indexes = (topk_indexes // vocab_size).tolist() + topk_token_indexes = (topk_indexes % vocab_size).tolist() + + for k in range(len(topk_hyp_indexes)): + hyp_idx = topk_hyp_indexes[k] + hyp = A[i][hyp_idx] + + ys = hyp.ys[:] + + # current score of hyp + lm_score = hyp.lm_score + state = hyp.state + + hyp_log_prob = topk_log_probs[k] # get score of current hyp + new_token = topk_token_indexes[k] + if new_token not in (blank_id, unk_id): + + ys.append(new_token) + state_cost = hyp.state_cost.forward_one_step(new_token) + + # calculate the score of the latest token + current_ngram_score = state_cost.lm_score - hyp.state_cost.lm_score + + assert current_ngram_score <= 0.0, ( + state_cost.lm_score, + hyp.state_cost.lm_score, + ) + # score = score + TDLM_score - LODR_score + # LODR_LM_scale should be a negative number here + hyp_log_prob += ( + lm_score[new_token] * lm_scale + + LODR_lm_scale * current_ngram_score + ) # add the lm score + + lm_score = scores[count] + if LM.lm_type == "rnn": + state = ( + lm_states[0][:, count, :].unsqueeze(1), + lm_states[1][:, count, :].unsqueeze(1), + ) + count += 1 + else: + state_cost = hyp.state_cost + + new_hyp = Hypothesis( + ys=ys, + log_prob=hyp_log_prob, + state=state, + lm_score=lm_score, + state_cost=state_cost, + ) + B[i].add(new_hyp) + + B = B + finalized_B + best_hyps = [b.get_most_probable(length_norm=True) for b in B] + + sorted_ans = [h.ys[context_size:] for h in best_hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) + + return ans \ No newline at end of file From ace522ec030301009549598ebae07a6bfe17bfdd Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Mon, 8 Dec 2025 21:54:55 +0000 Subject: [PATCH 16/43] feat: removing Nisqa from libs, update transcription(tone) --- src/libs/NISQA/.gitignore | 145 - src/libs/NISQA/LICENSE | 21 - src/libs/NISQA/README.md | 172 -- src/libs/NISQA/config/finetune_nisqa.yaml | 53 - .../finetune_nisqa_multidimensional.yaml | 51 - .../config/train_nisqa_cnn_lstm_avg.yaml | 103 - .../NISQA/config/train_nisqa_cnn_sa_ap.yaml | 104 - .../config/train_nisqa_double_ended.yaml | 109 - src/libs/NISQA/env.yml | 24 - src/libs/NISQA/nisqa/NISQA_lib.py | 2334 ----------------- src/libs/NISQA/nisqa/NISQA_model.py | 1116 -------- src/libs/NISQA/run_evaluate.py | 69 - src/libs/NISQA/run_predict.py | 35 - src/libs/NISQA/run_train.py | 57 - src/libs/NISQA/weights/LICENSE_model_weights | 437 --- src/libs/NISQA/weights/nisqa.tar | Bin 1051663 -> 0 bytes src/libs/NISQA/weights/nisqa_mos_only.tar | Bin 907215 -> 0 bytes src/libs/NISQA/weights/nisqa_tts.tar | Bin 1237159 -> 0 bytes src/separation/music_detect.py | 12 +- src/transcription/transcription.py | 1 + src/transcription/transcripton_dataset.py | 17 +- 21 files changed, 10 insertions(+), 4850 deletions(-) delete mode 100644 src/libs/NISQA/.gitignore delete mode 100644 src/libs/NISQA/LICENSE delete mode 100644 src/libs/NISQA/README.md delete mode 100644 src/libs/NISQA/config/finetune_nisqa.yaml delete mode 100644 src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml delete mode 100644 src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml delete mode 100644 src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml delete mode 100644 src/libs/NISQA/config/train_nisqa_double_ended.yaml delete mode 100644 src/libs/NISQA/env.yml delete mode 100644 src/libs/NISQA/nisqa/NISQA_lib.py delete mode 100644 src/libs/NISQA/nisqa/NISQA_model.py delete mode 100644 src/libs/NISQA/run_evaluate.py delete mode 100644 src/libs/NISQA/run_predict.py delete mode 100644 src/libs/NISQA/run_train.py delete mode 100644 src/libs/NISQA/weights/LICENSE_model_weights delete mode 100644 src/libs/NISQA/weights/nisqa.tar delete mode 100644 src/libs/NISQA/weights/nisqa_mos_only.tar delete mode 100644 src/libs/NISQA/weights/nisqa_tts.tar diff --git a/src/libs/NISQA/.gitignore b/src/libs/NISQA/.gitignore deleted file mode 100644 index 80b5816..0000000 --- a/src/libs/NISQA/.gitignore +++ /dev/null @@ -1,145 +0,0 @@ - -# Created by https://www.toptal.com/developers/gitignore/api/python -# Edit at https://www.toptal.com/developers/gitignore?templates=python - -### Python ### -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# End of https://www.toptal.com/developers/gitignore/api/python \ No newline at end of file diff --git a/src/libs/NISQA/LICENSE b/src/libs/NISQA/LICENSE deleted file mode 100644 index 73b3df7..0000000 --- a/src/libs/NISQA/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2021 Gabriel Mittag, Quality and Usability Lab - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/src/libs/NISQA/README.md b/src/libs/NISQA/README.md deleted file mode 100644 index 2336fd7..0000000 --- a/src/libs/NISQA/README.md +++ /dev/null @@ -1,172 +0,0 @@ -# NISQA: Speech Quality and Naturalness Assessment - -*+++ News: The NISQA model has recently been updated to NISQA v2.0. The new version offers multidimensional predictions with higher accuracy and allows for training and finetuning the model.* - -**Speech Quality Prediction:** -NISQA is a deep learning model/framework for speech quality prediction. The NISQA model weights can be used to predict the quality of a speech sample that has been sent through a communication system (e.g telephone or video call). Besides overall speech quality, NISQA also provides predictions for the quality dimensions *Noisiness*, *Coloration*, *Discontinuity*, and *Loudness* to give more insight into the cause of the quality degradation. - -**TTS Naturalness Prediction:** -The NISQA-TTS model weights can be used to estimate the *Naturalness* of synthetic speech generated by a Voice Conversion or Text-To-Speech system (Siri, Alexa, etc.). - -**Training/Finetuning:** -NISQA can be used to train new single-ended or double-ended speech quality prediction models with different deep learning architectures, such as CNN or DFF -> Self-Attention or LSTM -> Attention-Pooling or Max-Pooling. The provided model weights can also be applied to finetune the trained model towards new data or for transfer-learning to a different regression task (e.g. quality estimation of enhanced speech, speaker similarity estimation, or emotion recognition) . - -**Speech Quality Datasets:** -We provide a large corpus of more than 14,000 speech samples with subjective speech quality and speech quality dimension labels. - -## Table of Contents -- [Installation](#installation) -- [Using NISQA](#using-nisqa) - - [Prediction](#prediction) - - [Training](#training) - - [Finetuning / Transfer Learning](#finetuning--transfer-learning) - - [Training a new model](#training-a-new-model) - - [Evaluation](#evaluation) -- [NISQA Corpus](#nisqa-corpus) -- [Paper and License](#paper-and-license) - -More information about the deep learning model structure, the used training datasets, and the training options, see the [NISQA paper](https://arxiv.org/abs/2104.09494) and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/). - - -## Installation - -To install requirements install [Anaconda](https://www.anaconda.com/products/individual) and then use: - -```setup -conda env create -f env.yml -``` - -This will create a new environment with the name "nisqa". Activate this environment to go on: - -```setup2 -conda activate nisqa -``` - - - -## Using NISQA - -We provide examples for using NISQA to predict the quality of speech samples, to train a new speech quality model, and to evaluate the performance of a trained speech quality model. - -There are three different model weights available, the appropriate weights should be loaded depending on the domain: - -| Model | Prediction Output | Domain | Filename | -| --------------------- | --------------------------------------------------------------- | ------------------ | ------------------ | -| NISQA (v2.0) | Overall Quality, Noisiness, Coloration, Discontinuity, Loudness | Transmitted Speech | nisqa.tar | -| NISQA (v2.0) mos only | Overall Quality only (for finetuning/transfer learning) | Transmitted Speech | nisqa_mos_only.tar | -| NISQA-TTS (v1.0) | Naturalness | Synthesized Speech | nisqa_tts.tar | - -### Prediction - -There are three modes available to predict the quality of speech via command line arguments: -* Predict a single file -* Predict all files in a folder -* Predict all files in a CSV table - -**Important:** Select "*nisqa.tar*" to predict the quality of a transmitted speech sample and "*nisqa_tts.tar*" to predict the Naturalness of a synthesized speech sample. - -To predict the quality of a single .wav file use: - -``` -python run_predict.py --mode predict_file --pretrained_model weights/nisqa.tar --deg /path/to/wav/file.wav --output_dir /path/to/dir/with/results -``` -To predict the quality of all .wav files in a folder use: -``` -python run_predict.py --mode predict_dir --pretrained_model weights/nisqa.tar --data_dir /path/to/folder/with/wavs --num_workers 0 --bs 10 --output_dir /path/to/dir/with/results -``` - -To predict the quality of all .wav files listed in a csv table use: -``` -python run_predict.py --mode predict_csv --pretrained_model weights/nisqa.tar --csv_file files.csv --csv_deg column_name_of_filepaths --num_workers 0 --bs 10 --output_dir /path/to/dir/with/results -``` - -The results will be printed to the console and saved to a csv file in a given folder (optional with --output_dir). To speed up the prediction, the number of workers and batch size of the Pytorch Dataloader can be increased (optional with --num_workers and --bs). In case of stereo files --ms_channel can be used to select the audio channel. - -### Training - -#### Finetuning / Transfer Learning - -To use the model weights to finetune the model on a new dataset, only a CSV file with the filenames and labels is needed. The training configuration is controlled from a YAML file and can be started as follows: - -``` -python run_train.py --yaml config/finetune_nisqa.yaml -``` - -- If the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) is used, only two arguments need to updated in the YAML file and you are ready to go: The `data_dir` to the extracted NISQA_Corpus folder and the `output_dir`, where the results should be stored. - -- If you use your own dataset or want to load the NISQA-TTS model, some other updates are needed. - - Your CSV file needs to contain at least three columns with the following names - - - `db` with the individual dataset names for each file - - `filepath_deg` filepath to the degraded WAV file, either absolute paths or relative to the `data_dir` (CSV column name can be changed in YAML) - - `mos` with the target labels (CSV column name can be changed in YAML) - - The `finetune_nisqa.yaml` needs to be updated as follows: - - - `data_dir` path to the main folder, which contains the CSV file and the datasets - - `output_dir` path to output folder with saved model weights and results - - `pretrained_model` filename of the pretrained model, either `nisqa_mos_only.tar` for natural speech or `nisqa_tts.tar` for synthesized speech - - `csv_file` name of the CSV with filepaths and target labels - - `csv_deg` CSV column name that contains filepaths (e.g. `filepath_deg`) - - `csv_mos_train` and `csv_mos_val` CSV column names of the target value (e.g. `mos`) - - `csv_db_train` and `csv_db_val` names of the datasets you want to use for training and validation. Datasets names must be in the `db` column. - -See the comments in the YAML configuration file and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/) (not yet added) for more advanced training options. A good starting point would be to use the NISQA Corpus to get the training started with the standard configuration. - -#### Training a new model - -NISQA can also be used as a framework to train new speech quality models with different deep learning architectures. The general model structure is as follows: - -1. *Framewise model:* CNN or Feedforward network -2. *Time-Dependency* model: Self-Attention or LSTM -3. *Pooling:* Average, Max, Attention or Last-Step-Pooling - -The framewise and time-dependency models can be skipped, for example to train an LSTM model without CNN that uses the last-time step for prediction. Also a second time-dependency stage can be added, for example for LSTM-Self-Attention structure. The model structure can be easily controlled via the YAML configuration file. The training with the standard NISQA model configuration can be started with the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) as follows: - -``` -python run_train.py --yaml config/train_nisqa_cnn_sa_ap.yaml -``` - -If the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) is used, only the `data_dir` needs to be updated to the unzipped NISQA_Corpus folder and the `output_dir` in the YAML file. Otherwise, see the previous [finetuning section](#finetuning-transfer-learning) for updating the YAML file if a custom dataset is applied. - -It is also possible to train any other combination of neural networks, for example, to train a model with LSTM instead of Self-Attention, the `train_nisqa_cnn_lstm_avg.yaml` example configuration file is provided. - -To train a **double-ended** model for full-reference speech quality prediction, the `train_nisqa_double_ended.yaml` configuration file can be used as an example. See the comments in the YAML files and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/) (not yet added) for more details on different possible model structures and advanced training options. - -### Evaluation - -Trained models can be evaluated on a given dataset as follows (can also be used as a conformance test of the model installation): - -``` -python run_evaluate.py -``` - -Before running, the options and paths inside the Python script `run_evaluate.py` should be updated. If the [NISQA Corpus](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus) is used, only the `data_dir` and `output_dir` paths need to be adjusted. Besides Pearson's Correlation and RMSE, also an RMSE after first-order polynomial mapping is calculated. If a CSV file with per-condition labels is provided, the script will also output per-condition results and RMSE*. Optionally, correlation diagrams can be plotted. The script should return the same results as in the NISQA paper when it is run on the NISQA Corpus. - -## NISQA Corpus - -The NISQA Corpus includes more than 14,000 speech samples with simulated (e.g. codecs, packet-loss, background noise) and live (e.g. mobile phone, Zoom, Skype, WhatsApp) conditions. - -For the download link and more details on the datasets and used source speech samples see the [NISQA Corpus Wiki](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus). - -## Paper and License - -- If you use the **NISQA model** or the **NISQA Corpus** for your research, please cite following paper: - [G. Mittag, B. Naderi, A. Chehadi, and S. Möller “NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets,” in Proc. Interspeech 2021, 2021.](https://www.isca-speech.org/archive/pdfs/interspeech_2021/mittag21_interspeech.pdf) -- Please cite following paper if you use the **NISQA-TTS** model for Naturalness prediction of synthesized speech: - [G. Mittag and S. Moller, “Deep Learning Based Assessment of Synthetic Speech Naturalness,” in Proc. Interspeech 2020, 2020.](https://www.isca-speech.org/archive/Interspeech_2020/abstracts/2382.html) -- Please cite following paper if you use the **double-ended NISQA model**: - [G. Mittag and S. Möller. Full-reference speech quality estimation with attentional Siamese neural networks. In Proc. ICASSP 2020, 2020.](https://ieeexplore.ieee.org/document/9053951) -- The older NISQA (v0.42) model version is described in following paper: - [G. Mittag and S. Möller, “Non-intrusive speech quality assessment for super-wideband speech communication networks,” in Proc. ICASSP 2019, 2019](https://ieeexplore.ieee.org/document/8683770) - -The NISQA code is licensed under [MIT License](LICENSE). - -The model weights (nisqa.tar, nisqa_mos_only.tar, nisqa_tts.tar) are provided under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License](weights/LICENSE_model_weights) - -The NISQA Corpus is provided under the original terms of the used source speech and noise samples. More information can be found in the [NISQA Corpus Wiki](https://github.com/gabrielmittag/NISQA/wiki/NISQA-Corpus). - -Copyright © 2021 Gabriel Mittag -www.qu.tu-berlin.de - diff --git a/src/libs/NISQA/config/finetune_nisqa.yaml b/src/libs/NISQA/config/finetune_nisqa.yaml deleted file mode 100644 index ec811c1..0000000 --- a/src/libs/NISQA/config/finetune_nisqa.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# Config example for transfer-learning or finetuning of NISQA or NISQA-TTS: - -# Runname and paths -name: training_run_name # name of current training run -data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files -output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights -pretrained_model: weights/nisqa_mos_only.tar # absolute path to pretrained model | path to pretrained model relative to current folder - -# Dataset options -csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings -csv_con: null # csv-file with per-condition MOS used for evaluation (optional) -csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' -csv_mos_train: mos # csv column name of target training value (usually MOS) -csv_mos_val: mos # csv column name of target validation value (usually MOS) -csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file - - NISQA_TRAIN_SIM - - NISQA_TRAIN_LIVE -csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file - - NISQA_VAL_SIM - - NISQA_VAL_LIVE - -# Training options -tr_epochs: 500 # number of max training epochs -tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs -tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_lr: 0.001 # learning rate of ADAM optimiser -tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs -tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) -tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs -tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) -tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) -tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. -tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights -tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar -ms_max_segments: 1300 # if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used - -# Bias loss options (optional) -tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' -tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied -tr_bias_anchor_db: null # name of anchor dataset (optional) - - - - - - - - - - - diff --git a/src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml b/src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml deleted file mode 100644 index f4a503e..0000000 --- a/src/libs/NISQA/config/finetune_nisqa_multidimensional.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# Config example for transfer-learning or finetuning of NISQA with dimension prediction (it requires csv files with MOS, Noisiness, Coloration, Discontinuity, and Loudness labels): - -# Runname and paths -name: training_run_name # name of current training run -data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files -output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights -pretrained_model: weights/nisqa.tar # absolute path to pretrained model | path to pretrained model relative to current folder - -# Dataset options -csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings -csv_con: null # csv-file with per-condition MOS used for evaluation (optional) -csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' -csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file - - NISQA_TRAIN_SIM - - NISQA_TRAIN_LIVE -csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file - - NISQA_VAL_SIM - - NISQA_VAL_LIVE - -# Training options -tr_epochs: 500 # number of max training epochs -tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs -tr_bs: 40 # training dataset mini-batch size -tr_bs_val: 40 # validation dataset mini-batch size -tr_lr: 0.001 # learning rate of ADAM optimiser -tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs -tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) -tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs -tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) -tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) -tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. -tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights -tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar -ms_max_segments: 1300 # if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used - -# Bias loss options (optional) -tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' -tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied -tr_bias_anchor_db: null # name of anchor dataset (optional) - - - - - - - - - - - diff --git a/src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml b/src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml deleted file mode 100644 index bbeb868..0000000 --- a/src/libs/NISQA/config/train_nisqa_cnn_lstm_avg.yaml +++ /dev/null @@ -1,103 +0,0 @@ -# Config example for training single-ended model: CNN->LSTM->Avg-Pooling - -# Runname and paths -name: training_run_name # name of current training run -data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files -output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights -pretrained_model: false - -# Dataset options -csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings -csv_con: null # csv-file with per-condition MOS used for evaluation (optional) -csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' -csv_ref: filepath_ref # csv column name of filepath to reference speech sample, path must be relative to 'data_dir' -csv_mos_train: mos # csv column name of target training value (usually MOS) -csv_mos_val: mos # csv column name of target validation value (usually MOS) -csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file - - NISQA_TRAIN_SIM - - NISQA_TRAIN_LIVE -csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file - - NISQA_VAL_SIM - - NISQA_VAL_LIVE - -# Training options -tr_epochs: 500 # number of max training epochs -tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs -tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_lr: 0.001 # learning rate of ADAM optimiser -tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs -tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) -tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs -tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) -tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) -tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. -tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights -tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar - -# Bias loss options (optional) -tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' -tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied -tr_bias_anchor_db: null # name of anchor dataset (optional) - -# Mel-Specs options -ms_sr: null # resample speech signal to 'ms_sr', (usually not needed, because window length 'ms_win_length' is adjusted automatically for different sample frequencies) -ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples -ms_n_fft: 4096 # padded fft window length (in bins), 4096 still fits 40 ms window length, even for 96kHz sample rate -ms_hop_length: 0.01 # hop length of fft windowing (in seconds) -ms_win_length: 0.02 # fft window length (in seconds), will be padded with zeros to match 'ms_n_fft' -ms_n_mels: 48 # number of Mel bands -ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) -ms_seg_hop_length: 3 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. segment hop length in seconds: ms_hop_length*ms_seg_hop_length -ms_max_segments: 1300 # max segment length (in bins). if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used - -# Main model -model: NISQA # 'NISQA' single-ended | 'NISQA_DIM' single-ended with multidimension prediction | 'NISQA_DE' double-ended - -# Framewise options (usually CNN) -cnn_model: standard # framewise model, either 'adapt' for CNN with adaptive maxpooling | 'standard' for standard CNN | 'dff' for deep feed-forward network | 'skip' to skip framewise modelling -cnn_c_out_1: 16 # number of output channels of first convolutional layer -cnn_c_out_2: 32 # number of output channels of the second convolutional layer -cnn_c_out_3: 64 # number of output channels of the last four convolutional layer -cnn_kernel_size: !!python/tuple [3,3] -cnn_dropout: 0.2 -cnn_fc_out_h: 20 # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted -cnn_pool_1: null # outpuf dimensions of first adaptive pooling ('adaptive' CNN only) -cnn_pool_2: null # outpuf dimensions of second adaptive pooling ('adaptive' CNN only) -cnn_pool_3: null # outpuf dimensions of third adaptive pooling ('adaptive' CNN only) - -# Time-Depedency options -td: lstm # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) -td_sa_d_model: null # attention network dimension -td_sa_nhead: null # number of heads -td_sa_pos_enc: null # apply positional encoding (no improvement in previous experiments) -td_sa_num_layers: null # self-attention depth -td_sa_h: null # hidden units of self-attention feedforward network -td_sa_dropout: null # self-attention dropout -td_lstm_h: 128 # number of LSTM hidden units -td_lstm_num_layers: 1 # LSTM depth -td_lstm_dropout: 0 -td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 - -# Second Time-Depedency options (optional, for example for LSTM->Self-Attention network) -td_2: skip # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) -td_2_sa_d_model: null # attention network dimension -td_2_sa_nhead: null # number of heads -td_2_sa_pos_enc: null # apply positional encoding (no improvement in previous experiments) -td_2_sa_num_layers: null -td_2_sa_h: null # hidden units of self-attention feedforward network -td_2_sa_dropout: null # self-attention dropout -td_2_lstm_h: null # number of LSTM hidden units -td_2_lstm_num_layers: null # LSTM depth -td_2_lstm_dropout: null -td_2_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 - -# Pooling options -pool: avg # 'att' for Attention-Pooling | 'avg' for average-pooling | 'max' max-pooling | 'last_step' last-step pooling | 'last_step_bi' last-step pooling with BiLSTMs -pool_att_h: null # number of hidden units of attention-pooling feedforward network -pool_att_dropout: null # attention pooling dropout - - - - diff --git a/src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml b/src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml deleted file mode 100644 index 9171779..0000000 --- a/src/libs/NISQA/config/train_nisqa_cnn_sa_ap.yaml +++ /dev/null @@ -1,104 +0,0 @@ -# Config example for training a new model with standard NISQA settings - -# Runname and paths -name: training_run_name # name of current training run -data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files -output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights -pretrained_model: false - -# Dataset options -csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings -csv_con: null # csv-file with per-condition MOS used for evaluation (optional) -csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' -csv_mos_train: mos # csv column name of target training value (usually MOS) -csv_mos_val: mos # csv column name of target validation value (usually MOS) -csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file - - NISQA_TRAIN_SIM - - NISQA_TRAIN_LIVE -csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file - - NISQA_VAL_SIM - - NISQA_VAL_LIVE - -# Training options -tr_epochs: 500 # number of max training epochs -tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs -tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_lr: 0.001 # learning rate of ADAM optimiser -tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs -tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) -tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs -tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) -tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) -tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. -tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights -tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar - -# Bias loss options (optional) -tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' -tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied -tr_bias_anchor_db: null # name of anchor dataset (optional) - -# Mel-Specs options -ms_sr: null # resample speech signal to 'ms_sr', (usually not needed, because window length 'ms_win_length' is adjusted automatically for different sample frequencies) -ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples -ms_n_fft: 4096 # padded fft window length (in bins), 4096 still fits 40 ms window length, even for 96kHz sample rate -ms_hop_length: 0.01 # hop length of fft windowing (in seconds) -ms_win_length: 0.02 # fft window length (in seconds), will be padded with zeros to match 'ms_n_fft' -ms_n_mels: 48 # number of Mel bands -ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) -ms_seg_hop_length: 4 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. segment hop length in seconds: ms_hop_length*ms_seg_hop_length -ms_max_segments: 1300 # max segment length (in bins). if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used - -# Main model -model: NISQA # 'NISQA' single-ended | 'NISQA_DIM' single-ended with multidimension prediction | 'NISQA_DE' double-ended - -# Framewise options (usually CNN) -cnn_model: adapt # framewise model, either 'adapt' for CNN with adaptive maxpooling | 'standard' for standard CNN | 'dff' for deep feed-forward network | 'skip' to skip framewise modelling -cnn_c_out_1: 16 # number of output channels of first convolutional layer -cnn_c_out_2: 32 # number of output channels of the second convolutional layer -cnn_c_out_3: 64 # number of output channels of the last four convolutional layer -cnn_kernel_size: !!python/tuple [3,3] -cnn_dropout: 0.2 -cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted -cnn_pool_1: [24,7] # outpuf dimensions of first adaptive pooling ('adaptive' CNN only) -cnn_pool_2: [12,5] # outpuf dimensions of second adaptive pooling ('adaptive' CNN only) -cnn_pool_3: [6,3] # outpuf dimensions of third adaptive pooling ('adaptive' CNN only) - -# Time-Depedency options -td: self_att # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) -td_sa_d_model: 64 # attention network dimension -td_sa_nhead: 1 # number of heads -td_sa_pos_enc: False # apply positional encoding (no improvement in previous experiments) -td_sa_num_layers: 2 # self-attention depth -td_sa_h: 64 # hidden units of self-attention feedforward network -td_sa_dropout: 0.1 # self-attention dropout -td_lstm_h: null # number of LSTM hidden units -td_lstm_num_layers: null # LSTM depth -td_lstm_dropout: null -td_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 - -# Second Time-Depedency options (optional, for example for LSTM-Self-Attention network) -td_2: skip -td_2_sa_d_model: null -td_2_sa_nhead: null -td_2_sa_pos_enc: null -td_2_sa_num_layers: null -td_2_sa_h: null -td_2_sa_dropout: null -td_2_lstm_h: null -td_2_lstm_num_layers: null -td_2_lstm_dropout: null -td_2_lstm_bidirectional: null - -# Pooling options -pool: att # 'att' for Attention-Pooling | 'avg' for average-pooling | 'max' max-pooling | 'last_step' last-step pooling | 'last_step_bi' last-step pooling with BiLSTMs -pool_att_h: 128 # number of hidden units of attention-pooling feedforward network -pool_att_dropout: 0 # attention pooling dropout - - - - - - diff --git a/src/libs/NISQA/config/train_nisqa_double_ended.yaml b/src/libs/NISQA/config/train_nisqa_double_ended.yaml deleted file mode 100644 index c49a376..0000000 --- a/src/libs/NISQA/config/train_nisqa_double_ended.yaml +++ /dev/null @@ -1,109 +0,0 @@ -# Config example for training double-ended model: CNN-SA-Align-Fuse-SA-AP (SA: Self-Attention, AP: Attention-Pooling) - -# Runname and paths -name: training_run_name # name of current training run -data_dir: C:/Users/Name/Downloads/NISQA_Corpus # main input dir with dataset samples and csv files -output_dir: C:/Users/Name/Downloads/trained_models # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights -pretrained_model: false - -# Dataset options -csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings -csv_con: null # csv-file with per-condition MOS used for evaluation (optional) -csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' -csv_ref: filepath_ref # csv column name of filepath to reference speech sample, path must be relative to 'data_dir' -csv_mos_train: mos # csv column name of target training value (usually MOS) -csv_mos_val: mos # csv column name of target validation value (usually MOS) -csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file - - NISQA_TRAIN_SIM - - NISQA_TRAIN_LIVE -csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file - - NISQA_VAL_SIM - - NISQA_VAL_LIVE - -# Training options -tr_epochs: 500 # number of max training epochs -tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs -tr_bs: 40 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_bs_val: 40 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_lr: 0.001 # learning rate of ADAM optimiser -tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs -tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) -tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs -tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) -tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) -tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. -tr_checkpoint: every_epoch # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights -tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar - -# Bias loss options (optional) -tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' -tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied -tr_bias_anchor_db: null # name of anchor dataset (optional) - -# Mel-Specs options -ms_sr: null # resample speech signal to 'ms_sr', (usually not needed, because window length 'ms_win_length' is adjusted automatically for different sample frequencies) -ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples -ms_n_fft: 4096 # padded fft window length (in bins), 4096 still fits 40 ms window length, even for 96kHz sample rate -ms_hop_length: 0.01 # hop length of fft windowing (in seconds) -ms_win_length: 0.02 # fft window length (in seconds), will be padded with zeros to match 'ms_n_fft' -ms_n_mels: 48 # number of Mel bands -ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) -ms_seg_hop_length: 4 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. segment hop length in seconds: ms_hop_length*ms_seg_hop_length -ms_max_segments: 1300 # max segment length (in bins). if samples of different duration are used they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. increase if you apply the model to longer samples -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used - -# Main model -model: NISQA_DE # 'NISQA' single-ended | 'NISQA_DIM' single-ended with multidimension prediction | 'NISQA_DE' double-ended - -# Framewise options (usually CNN) -cnn_model: adapt # framewise model, either 'adapt' for CNN with adaptive maxpooling | 'standard' for standard CNN | 'dff' for deep feed-forward network | 'skip' to skip framewise modelling -cnn_c_out_1: 16 # number of output channels of first convolutional layer -cnn_c_out_2: 32 # number of output channels of the second convolutional layer -cnn_c_out_3: 64 # number of output channels of the last four convolutional layer -cnn_kernel_size: !!python/tuple [3,3] -cnn_dropout: 0.2 -cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted -cnn_pool_1: [24,7] # outpuf dimensions of first adaptive pooling ('adaptive' CNN only) -cnn_pool_2: [12,5] # outpuf dimensions of second adaptive pooling ('adaptive' CNN only) -cnn_pool_3: [6,3] # outpuf dimensions of third adaptive pooling ('adaptive' CNN only) - -# Time-Depedency options -td: self_att # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) -td_sa_d_model: 64 # attention network dimension -td_sa_nhead: 1 # number of heads -td_sa_pos_enc: False # apply positional encoding (no improvement in previous experiments) -td_sa_num_layers: 2 # self-attention depth -td_sa_h: 64 # hidden units of self-attention feedforward network -td_sa_dropout: 0.1 # self-attention dropout -td_lstm_h: null # number of LSTM hidden units -td_lstm_num_layers: null # LSTM depth -td_lstm_dropout: null -td_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 - -# Second Time-Depedency options (optional, for example for LSTM-Self-Attention network) -td_2: self_att # time-depdency model, either 'self_att' for Transfomer based Self-Attention network (td_lstm_.. options will be ignored) | 'lstm' (td_sa_.. options will be ignored) -td_2_sa_d_model: 64 # attention network dimension -td_2_sa_nhead: 1 # number of heads -td_2_sa_pos_enc: False # apply positional encoding (no improvement in previous experiments) -td_2_sa_num_layers: 2 -td_2_sa_h: 64 # hidden units of self-attention feedforward network -td_2_sa_dropout: 0.1 # self-attention dropout -td_2_lstm_h: null # number of LSTM hidden units -td_2_lstm_num_layers: null # LSTM depth -td_2_lstm_dropout: null -td_2_lstm_bidirectional: null # use bidirectional LSTM -> hidden units x 2 - -# Pooling options -pool: att # 'att' for Attention-Pooling | 'avg' for average-pooling | 'max' max-pooling | 'last_step' last-step pooling | 'last_step_bi' last-step pooling with BiLSTMs -pool_att_h: 128 # number of hidden units of attention-pooling feedforward network -pool_att_dropout: 0 # attention pooling dropout - -# Double-Ended options -de_align: cosine # attention mechanism for alignment, 'bahd' | 'luong' | 'dot' | 'cosine' | 'distance' | 'none' -de_align_apply: hard # attention application method for alignment, 'soft' | 'hard' -de_fuse: x/y/- # fusing mechanism, 'x/y/-' | '+/-' | 'x/y' -de_fuse_dim: null # apply fully connected to change fuse output dimension, if 'null' fully conneted is skipped - - - - diff --git a/src/libs/NISQA/env.yml b/src/libs/NISQA/env.yml deleted file mode 100644 index 1e70ef4..0000000 --- a/src/libs/NISQA/env.yml +++ /dev/null @@ -1,24 +0,0 @@ -name: nisqa -channels: - - pytorch - - conda-forge - - defaults -dependencies: - - cudatoolkit=10.2.89 - - libsndfile=1.0.31 - - pillow=8.4.0 - - pip=21.3.1 - - python=3.9.9 - - pytorch=1.10.1 - - scipy=1.7.3 - - torchvision=0.11.2 - - pip: - - librosa==0.8.1 - - matplotlib==3.5.1 - - numba==0.54.1 - - numpy==1.20.3 - - pandas==1.3.5 - - pyyaml==6.0 - - scikit-learn==1.0.2 - - seaborn==0.11.2 - - tqdm==4.62.3 diff --git a/src/libs/NISQA/nisqa/NISQA_lib.py b/src/libs/NISQA/nisqa/NISQA_lib.py deleted file mode 100644 index 59bc2c6..0000000 --- a/src/libs/NISQA/nisqa/NISQA_lib.py +++ /dev/null @@ -1,2334 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@author: Gabriel Mittag, TU-Berlin -""" -import os -import multiprocessing -import copy -import math -import time - -import librosa as lb -import numpy as np -import pandas as pd; pd.options.mode.chained_assignment = None -import matplotlib.pyplot as plt - -from tqdm import tqdm -from scipy.stats import pearsonr -from scipy.optimize import minimize - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils.rnn import pad_packed_sequence -from torch.nn.utils.rnn import pack_padded_sequence -from torch.utils.data import DataLoader -from torch.utils.data import Dataset - -# import torchaudio.transforms as T -import torchaudio - - -#%% Models -class NISQA(nn.Module): - ''' - NISQA: The main speech quality model without speech quality dimension - estimation (MOS only). The module loads the submodules for framewise - modelling (e.g. CNN), time-dependency modelling (e.g. Self-Attention - or LSTM), and pooling (e.g. max-pooling or attention-pooling) - ''' - def __init__(self, - ms_seg_length=15, - ms_n_mels=48, - - cnn_model='adapt', - cnn_c_out_1=16, - cnn_c_out_2=32, - cnn_c_out_3=64, - cnn_kernel_size=3, - cnn_dropout=0.2, - cnn_pool_1=[24,7], - cnn_pool_2=[12,5], - cnn_pool_3=[6,3], - cnn_fc_out_h=None, - - td='self_att', - td_sa_d_model=64, - td_sa_nhead=1, - td_sa_pos_enc=None, - td_sa_num_layers=2, - td_sa_h=64, - td_sa_dropout=0.1, - td_lstm_h=128, - td_lstm_num_layers=1, - td_lstm_dropout=0, - td_lstm_bidirectional=True, - - td_2='skip', - td_2_sa_d_model=None, - td_2_sa_nhead=None, - td_2_sa_pos_enc=None, - td_2_sa_num_layers=None, - td_2_sa_h=None, - td_2_sa_dropout=None, - td_2_lstm_h=None, - td_2_lstm_num_layers=None, - td_2_lstm_dropout=None, - td_2_lstm_bidirectional=None, - - pool='att', - pool_att_h=128, - pool_att_dropout=0.1, - - ): - super().__init__() - - self.name = 'NISQA' - - self.cnn = Framewise( - cnn_model, - ms_seg_length=ms_seg_length, - ms_n_mels=ms_n_mels, - c_out_1=cnn_c_out_1, - c_out_2=cnn_c_out_2, - c_out_3=cnn_c_out_3, - kernel_size=cnn_kernel_size, - dropout=cnn_dropout, - pool_1=cnn_pool_1, - pool_2=cnn_pool_2, - pool_3=cnn_pool_3, - fc_out_h=cnn_fc_out_h, - ) - - self.time_dependency = TimeDependency( - input_size=self.cnn.model.fan_out, - td=td, - sa_d_model=td_sa_d_model, - sa_nhead=td_sa_nhead, - sa_pos_enc=td_sa_pos_enc, - sa_num_layers=td_sa_num_layers, - sa_h=td_sa_h, - sa_dropout=td_sa_dropout, - lstm_h=td_lstm_h, - lstm_num_layers=td_lstm_num_layers, - lstm_dropout=td_lstm_dropout, - lstm_bidirectional=td_lstm_bidirectional - ) - - self.time_dependency_2 = TimeDependency( - input_size=self.time_dependency.fan_out, - td=td_2, - sa_d_model=td_2_sa_d_model, - sa_nhead=td_2_sa_nhead, - sa_pos_enc=td_2_sa_pos_enc, - sa_num_layers=td_2_sa_num_layers, - sa_h=td_2_sa_h, - sa_dropout=td_2_sa_dropout, - lstm_h=td_2_lstm_h, - lstm_num_layers=td_2_lstm_num_layers, - lstm_dropout=td_2_lstm_dropout, - lstm_bidirectional=td_2_lstm_bidirectional - ) - - self.pool = Pooling( - self.time_dependency_2.fan_out, - output_size=1, - pool=pool, - att_h=pool_att_h, - att_dropout=pool_att_dropout, - ) - - def forward(self, x, n_wins): - x = self.cnn(x, n_wins) - x, n_wins = self.time_dependency(x, n_wins) - x, n_wins = self.time_dependency_2(x, n_wins) - x = self.pool(x, n_wins) - return x - - - -class NISQA_DIM(nn.Module): - ''' - NISQA_DIM: The main speech quality model with speech quality dimension - estimation (MOS, Noisiness, Coloration, Discontinuity, and Loudness). - The module loads the submodules for framewise modelling (e.g. CNN), - time-dependency modelling (e.g. Self-Attention or LSTM), and pooling - (e.g. max-pooling or attention-pooling) - ''' - def __init__(self, - ms_seg_length=15, - ms_n_mels=48, - - cnn_model='adapt', - cnn_c_out_1=16, - cnn_c_out_2=32, - cnn_c_out_3=64, - cnn_kernel_size=3, - cnn_dropout=0.2, - cnn_pool_1=[24,7], - cnn_pool_2=[12,5], - cnn_pool_3=[6,3], - cnn_fc_out_h=None, - - td='self_att', - td_sa_d_model=64, - td_sa_nhead=1, - td_sa_pos_enc=None, - td_sa_num_layers=2, - td_sa_h=64, - td_sa_dropout=0.1, - td_lstm_h=128, - td_lstm_num_layers=1, - td_lstm_dropout=0, - td_lstm_bidirectional=True, - - td_2='skip', - td_2_sa_d_model=None, - td_2_sa_nhead=None, - td_2_sa_pos_enc=None, - td_2_sa_num_layers=None, - td_2_sa_h=None, - td_2_sa_dropout=None, - td_2_lstm_h=None, - td_2_lstm_num_layers=None, - td_2_lstm_dropout=None, - td_2_lstm_bidirectional=None, - - pool='att', - pool_att_h=128, - pool_att_dropout=0.1, - - ): - super().__init__() - - self.name = 'NISQA_DIM' - - self.cnn = Framewise( - cnn_model, - ms_seg_length=ms_seg_length, - ms_n_mels=ms_n_mels, - c_out_1=cnn_c_out_1, - c_out_2=cnn_c_out_2, - c_out_3=cnn_c_out_3, - kernel_size=cnn_kernel_size, - dropout=cnn_dropout, - pool_1=cnn_pool_1, - pool_2=cnn_pool_2, - pool_3=cnn_pool_3, - fc_out_h=cnn_fc_out_h, - ) - - self.time_dependency = TimeDependency( - input_size=self.cnn.model.fan_out, - td=td, - sa_d_model=td_sa_d_model, - sa_nhead=td_sa_nhead, - sa_pos_enc=td_sa_pos_enc, - sa_num_layers=td_sa_num_layers, - sa_h=td_sa_h, - sa_dropout=td_sa_dropout, - lstm_h=td_lstm_h, - lstm_num_layers=td_lstm_num_layers, - lstm_dropout=td_lstm_dropout, - lstm_bidirectional=td_lstm_bidirectional - ) - - self.time_dependency_2 = TimeDependency( - input_size=self.time_dependency.fan_out, - td=td_2, - sa_d_model=td_2_sa_d_model, - sa_nhead=td_2_sa_nhead, - sa_pos_enc=td_2_sa_pos_enc, - sa_num_layers=td_2_sa_num_layers, - sa_h=td_2_sa_h, - sa_dropout=td_2_sa_dropout, - lstm_h=td_2_lstm_h, - lstm_num_layers=td_2_lstm_num_layers, - lstm_dropout=td_2_lstm_dropout, - lstm_bidirectional=td_2_lstm_bidirectional - ) - - pool = Pooling( - self.time_dependency.fan_out, - output_size=1, - pool=pool, - att_h=pool_att_h, - att_dropout=pool_att_dropout, - ) - - self.pool_layers = self._get_clones(pool, 5) - - def _get_clones(self, module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - def forward(self, x, n_wins): - - x = self.cnn(x, n_wins) - x, n_wins = self.time_dependency(x, n_wins) - x, n_wins = self.time_dependency_2(x, n_wins) - out = [mod(x, n_wins) for mod in self.pool_layers] - out = torch.cat(out, dim=1) - - return out - - - -class NISQA_DE(nn.Module): - ''' - NISQA: The main speech quality model for double-ended prediction. - The module loads the submodules for framewise modelling (e.g. CNN), - time-dependency modelling (e.g. Self-Attention or LSTM), time-alignment, - feature fusion and pooling (e.g. max-pooling or attention-pooling) - ''' - def __init__(self, - ms_seg_length=15, - ms_n_mels=48, - - cnn_model='adapt', - cnn_c_out_1=16, - cnn_c_out_2=32, - cnn_c_out_3=64, - cnn_kernel_size=3, - cnn_dropout=0.2, - cnn_pool_1=[24,7], - cnn_pool_2=[12,5], - cnn_pool_3=[6,3], - cnn_fc_out_h=None, - - td='self_att', - td_sa_d_model=64, - td_sa_nhead=1, - td_sa_pos_enc=None, - td_sa_num_layers=2, - td_sa_h=64, - td_sa_dropout=0.1, - td_lstm_h=128, - td_lstm_num_layers=1, - td_lstm_dropout=0, - td_lstm_bidirectional=True, - - td_2='skip', - td_2_sa_d_model=None, - td_2_sa_nhead=None, - td_2_sa_pos_enc=None, - td_2_sa_num_layers=None, - td_2_sa_h=None, - td_2_sa_dropout=None, - td_2_lstm_h=None, - td_2_lstm_num_layers=None, - td_2_lstm_dropout=None, - td_2_lstm_bidirectional=None, - - pool='att', - pool_att_h=128, - pool_att_dropout=0.1, - - de_align = 'dot', - de_align_apply = 'hard', - de_fuse_dim = None, - de_fuse = True, - - ): - - super().__init__() - - self.name = 'NISQA_DE' - - self.cnn = Framewise( - cnn_model, - ms_seg_length=ms_seg_length, - ms_n_mels=ms_n_mels, - c_out_1=cnn_c_out_1, - c_out_2=cnn_c_out_2, - c_out_3=cnn_c_out_3, - kernel_size=cnn_kernel_size, - dropout=cnn_dropout, - pool_1=cnn_pool_1, - pool_2=cnn_pool_2, - pool_3=cnn_pool_3, - fc_out_h=cnn_fc_out_h, - ) - - self.time_dependency = TimeDependency( - input_size=self.cnn.model.fan_out, - td=td, - sa_d_model=td_sa_d_model, - sa_nhead=td_sa_nhead, - sa_pos_enc=td_sa_pos_enc, - sa_num_layers=td_sa_num_layers, - sa_h=td_sa_h, - sa_dropout=td_sa_dropout, - lstm_h=td_lstm_h, - lstm_num_layers=td_lstm_num_layers, - lstm_dropout=td_lstm_dropout, - lstm_bidirectional=td_lstm_bidirectional - ) - - self.align = Alignment( - de_align, - de_align_apply, - q_dim=self.time_dependency.fan_out, - y_dim=self.time_dependency.fan_out, - ) - - self.fuse = Fusion( - in_feat=self.time_dependency.fan_out, - fuse_dim=de_fuse_dim, - fuse=de_fuse, - ) - - self.time_dependency_2 = TimeDependency( - input_size=self.fuse.fan_out, - td=td_2, - sa_d_model=td_2_sa_d_model, - sa_nhead=td_2_sa_nhead, - sa_pos_enc=td_2_sa_pos_enc, - sa_num_layers=td_2_sa_num_layers, - sa_h=td_2_sa_h, - sa_dropout=td_2_sa_dropout, - lstm_h=td_2_lstm_h, - lstm_num_layers=td_2_lstm_num_layers, - lstm_dropout=td_2_lstm_dropout, - lstm_bidirectional=td_2_lstm_bidirectional - ) - - self.pool = Pooling( - self.time_dependency_2.fan_out, - output_size=1, - pool=pool, - att_h=pool_att_h, - att_dropout=pool_att_dropout, - ) - - def _split_ref_deg(self, x, n_wins): - (x, y) = torch.chunk(x, 2, dim=2) - (n_wins_x, n_wins_y) = torch.chunk(n_wins, 2, dim=1) - n_wins_x = n_wins_x.view(-1) - n_wins_y = n_wins_y.view(-1) - return x, y, n_wins_x, n_wins_y - - def forward(self, x, n_wins): - - x, y, n_wins_x, n_wins_y = self._split_ref_deg(x, n_wins) - - x = self.cnn(x, n_wins_x) - y = self.cnn(y, n_wins_y) - - x, n_wins_x = self.time_dependency(x, n_wins_x) - y, n_wins_y = self.time_dependency(y, n_wins_y) - - y = self.align(x, y, n_wins_y) - - x = self.fuse(x, y) - - x, n_wins_x = self.time_dependency_2(x, n_wins_x) - - x = self.pool(x, n_wins_x) - - return x - - -#%% Framewise -class Framewise(nn.Module): - ''' - Framewise: The main framewise module. It loads either a CNN or feed-forward - network for framewise modelling of the Mel-spec segments. This module can - also be skipped by loading the SkipCNN module. There are two CNN modules - available. AdaptCNN with adaptive maxpooling and the StandardCNN module. - However, they could also be replaced with new modules, such as PyTorch - implementations of ResNet or Alexnet. - ''' - def __init__( - self, - cnn_model, - ms_seg_length=15, - ms_n_mels=48, - c_out_1=16, - c_out_2=32, - c_out_3=64, - kernel_size=3, - dropout=0.2, - pool_1=[24,7], - pool_2=[12,5], - pool_3=[6,3], - fc_out_h=None, - ): - super().__init__() - - if cnn_model=='adapt': - self.model = AdaptCNN( - input_channels=1, - c_out_1=c_out_1, - c_out_2=c_out_2, - c_out_3=c_out_3, - kernel_size=kernel_size, - dropout=dropout, - pool_1=pool_1, - pool_2=pool_2, - pool_3=pool_3, - fc_out_h=fc_out_h, - ) - elif cnn_model=='standard': - assert ms_n_mels == 48, "ms_n_mels is {} and should be 48, use adaptive model or change ms_n_mels".format(ms_n_mels) - assert ms_seg_length == 15, "ms_seg_len is {} should be 15, use adaptive model or change ms_seg_len".format(ms_seg_length) - assert ((kernel_size == 3) or (kernel_size == (3,3))), "cnn_kernel_size is {} should be 3, use adaptive model or change cnn_kernel_size".format(kernel_size) - self.model = StandardCNN( - input_channels=1, - c_out_1=c_out_1, - c_out_2=c_out_2, - c_out_3=c_out_3, - kernel_size=kernel_size, - dropout=dropout, - fc_out_h=fc_out_h, - ) - elif cnn_model=='dff': - self.model = DFF(ms_seg_length, ms_n_mels, dropout, fc_out_h) - elif (cnn_model is None) or (cnn_model=='skip'): - self.model = SkipCNN(ms_seg_length, ms_n_mels, fc_out_h) - else: - raise NotImplementedError('Framwise model not available') - - def forward(self, x, n_wins): - (bs, length, channels, height, width) = x.shape - x_packed = pack_padded_sequence( - x, - n_wins.cpu(), - batch_first=True, - enforce_sorted=False - ) - x = self.model(x_packed.data) - x = x_packed._replace(data=x) - x, _ = pad_packed_sequence( - x, - batch_first=True, - padding_value=0.0, - total_length=n_wins.max()) - return x - -class SkipCNN(nn.Module): - ''' - SkipCNN: Can be used to skip the framewise modelling stage and directly - apply an LSTM or Self-Attention network. - ''' - def __init__( - self, - cnn_seg_length, - ms_n_mels, - fc_out_h - ): - super().__init__() - - self.name = 'SkipCNN' - self.cnn_seg_length = cnn_seg_length - self.ms_n_mels = ms_n_mels - self.fan_in = cnn_seg_length*ms_n_mels - self.bn = nn.BatchNorm2d( 1 ) - - if fc_out_h is not None: - self.linear = nn.Linear(self.fan_in, fc_out_h) - self.fan_out = fc_out_h - else: - self.linear = nn.Identity() - self.fan_out = self.fan_in - - def forward(self, x): - x = self.bn(x) - x = x.view(-1, self.fan_in) - x = self.linear(x) - return x - -class DFF(nn.Module): - ''' - DFF: Deep Feed-Forward network that was used as baseline framwise model as - comparision to the CNN. - ''' - def __init__(self, - cnn_seg_length, - ms_n_mels, - dropout, - fc_out_h=4096, - ): - super().__init__() - self.name = 'DFF' - - self.dropout_rate = dropout - self.fc_out_h = fc_out_h - self.fan_out = fc_out_h - - self.cnn_seg_length = cnn_seg_length - self.ms_n_mels = ms_n_mels - self.fan_in = cnn_seg_length*ms_n_mels - - self.lin1 = nn.Linear(self.fan_in, self.fc_out_h) - self.lin2 = nn.Linear(self.fc_out_h, self.fc_out_h) - self.lin3 = nn.Linear(self.fc_out_h, self.fc_out_h) - self.lin4 = nn.Linear(self.fc_out_h, self.fc_out_h) - - self.bn1 = nn.BatchNorm2d(1) - self.bn2 = nn.BatchNorm1d( self.fc_out_h ) - self.bn3 = nn.BatchNorm1d( self.fc_out_h ) - self.bn4 = nn.BatchNorm1d( self.fc_out_h ) - self.bn5 = nn.BatchNorm1d( self.fc_out_h ) - self.dropout = nn.Dropout(p=dropout) - - def forward(self, x): - - x = self.bn1(x) - x = x.view(-1, self.fan_in) - - x = F.relu( self.bn2( self.lin1(x) ) ) - x = self.dropout(x) - x = F.relu( self.bn3( self.lin2(x) ) ) - x = self.dropout(x) - x = F.relu( self.bn4( self.lin3(x) ) ) - x = self.dropout(x) - x = F.relu( self.bn5( self.lin4(x) ) ) - - return x - - -class AdaptCNN(nn.Module): - ''' - AdaptCNN: CNN with adaptive maxpooling that can be used as framewise model. - Overall, it has six convolutional layers. This CNN module is more flexible - than the StandardCNN that requires a fixed input dimension of 48x15. - ''' - def __init__(self, - input_channels, - c_out_1, - c_out_2, - c_out_3, - kernel_size, - dropout, - pool_1, - pool_2, - pool_3, - fc_out_h=20, - ): - super().__init__() - self.name = 'CNN_adapt' - - self.input_channels = input_channels - self.c_out_1 = c_out_1 - self.c_out_2 = c_out_2 - self.c_out_3 = c_out_3 - self.kernel_size = kernel_size - self.pool_1 = pool_1 - self.pool_2 = pool_2 - self.pool_3 = pool_3 - self.dropout_rate = dropout - self.fc_out_h = fc_out_h - - self.dropout = nn.Dropout2d(p=self.dropout_rate) - - if isinstance(self.kernel_size, int): - self.kernel_size = (self.kernel_size, self.kernel_size) - - # Set kernel width of last conv layer to last pool width to - # downsample width to one. - self.kernel_size_last = (self.kernel_size[0], self.pool_3[1]) - - # kernel_size[1]=1 can be used for seg_length=1 -> corresponds to - # 1D conv layer, no width padding needed. - if self.kernel_size[1] == 1: - self.cnn_pad = (1,0) - else: - self.cnn_pad = (1,1) - - self.conv1 = nn.Conv2d( - self.input_channels, - self.c_out_1, - self.kernel_size, - padding = self.cnn_pad) - - self.bn1 = nn.BatchNorm2d( self.conv1.out_channels ) - - self.conv2 = nn.Conv2d( - self.conv1.out_channels, - self.c_out_2, - self.kernel_size, - padding = self.cnn_pad) - - self.bn2 = nn.BatchNorm2d( self.conv2.out_channels ) - - self.conv3 = nn.Conv2d( - self.conv2.out_channels, - self.c_out_3, - self.kernel_size, - padding = self.cnn_pad) - - self.bn3 = nn.BatchNorm2d( self.conv3.out_channels ) - - self.conv4 = nn.Conv2d( - self.conv3.out_channels, - self.c_out_3, - self.kernel_size, - padding = self.cnn_pad) - - self.bn4 = nn.BatchNorm2d( self.conv4.out_channels ) - - self.conv5 = nn.Conv2d( - self.conv4.out_channels, - self.c_out_3, - self.kernel_size, - padding = self.cnn_pad) - - self.bn5 = nn.BatchNorm2d( self.conv5.out_channels ) - - self.conv6 = nn.Conv2d( - self.conv5.out_channels, - self.c_out_3, - self.kernel_size_last, - padding = (1,0)) - - self.bn6 = nn.BatchNorm2d( self.conv6.out_channels ) - - if self.fc_out_h: - self.fc = nn.Linear(self.conv6.out_channels * self.pool_3[0], self.fc_out_h) - self.fan_out = self.fc_out_h - else: - self.fan_out = (self.conv6.out_channels * self.pool_3[0]) - - def forward(self, x): - - x = F.relu( self.bn1( self.conv1(x) ) ) - x = F.adaptive_max_pool2d(x, output_size=(self.pool_1)) - - x = F.relu( self.bn2( self.conv2(x) ) ) - x = F.adaptive_max_pool2d(x, output_size=(self.pool_2)) - - x = self.dropout(x) - x = F.relu( self.bn3( self.conv3(x) ) ) - x = self.dropout(x) - x = F.relu( self.bn4( self.conv4(x) ) ) - x = F.adaptive_max_pool2d(x, output_size=(self.pool_3)) - - x = self.dropout(x) - x = F.relu( self.bn5( self.conv5(x) ) ) - x = self.dropout(x) - x = F.relu( self.bn6( self.conv6(x) ) ) - x = x.view(-1, self.conv6.out_channels * self.pool_3[0]) - - if self.fc_out_h: - x = self.fc( x ) - return x - -class StandardCNN(nn.Module): - ''' - StandardCNN: CNN with fixed maxpooling that can be used as framewise model. - Overall, it has six convolutional layers. This CNN module requires a fixed - input dimension of 48x15. - ''' - def __init__( - self, - input_channels, - c_out_1, - c_out_2, - c_out_3, - kernel_size, - dropout, - fc_out_h=None - ): - super().__init__() - - self.name = 'CNN_standard' - - self.input_channels = input_channels - self.c_out_1 = c_out_1 - self.c_out_2 = c_out_2 - self.c_out_3 = c_out_3 - self.kernel_size = kernel_size - self.pool_size = 2 - self.dropout_rate = dropout - self.fc_out_h = fc_out_h - - self.output_width = 2 # input width 15 pooled 3 times - self.output_height = 6 # input height 48 pooled 3 times - - self.dropout = nn.Dropout2d(p=self.dropout_rate) - - self.pool_first = nn.MaxPool2d( - self.pool_size, - stride = self.pool_size, - padding = (0,1)) - - self.pool = nn.MaxPool2d( - self.pool_size, - stride = self.pool_size, - padding = 0) - - self.conv1 = nn.Conv2d( - self.input_channels, - self.c_out_1, - self.kernel_size, - padding = 1) - - self.bn1 = nn.BatchNorm2d( self.conv1.out_channels ) - - self.conv2 = nn.Conv2d( - self.conv1.out_channels, - self.c_out_2, - self.kernel_size, - padding = 1) - - self.bn2 = nn.BatchNorm2d( self.conv2.out_channels ) - - - self.conv3 = nn.Conv2d( - self.conv2.out_channels, - self.c_out_3, - self.kernel_size, - padding = 1) - - self.bn3 = nn.BatchNorm2d( self.conv3.out_channels ) - - self.conv4 = nn.Conv2d( - self.conv3.out_channels, - self.c_out_3, - self.kernel_size, - padding = 1) - - self.bn4 = nn.BatchNorm2d( self.conv4.out_channels ) - - self.conv5 = nn.Conv2d( - self.conv4.out_channels, - self.c_out_3, - self.kernel_size, - padding = 1) - - self.bn5 = nn.BatchNorm2d( self.conv5.out_channels ) - - self.conv6 = nn.Conv2d( - self.conv5.out_channels, - self.c_out_3, - self.kernel_size, - padding = 1) - - self.bn6 = nn.BatchNorm2d( self.conv6.out_channels ) - - if self.fc_out_h: - self.fc_out = nn.Linear(self.conv6.out_channels * self.output_height * self.output_width, self.fc_out_h) - self.fan_out = self.fc_out_h - else: - self.fan_out = (self.conv6.out_channels * self.output_height * self.output_width) - - def forward(self, x): - - x = F.relu( self.bn1( self.conv1(x) ) ) - x = self.pool_first( x ) - - x = F.relu( self.bn2( self.conv2(x) ) ) - x = self.pool( x ) - - x = self.dropout(x) - x = F.relu( self.bn3( self.conv3(x) ) ) - x = self.dropout(x) - x = F.relu( self.bn4( self.conv4(x) ) ) - x = self.pool( x ) - - x = self.dropout(x) - x = F.relu( self.bn5( self.conv5(x) ) ) - x = self.dropout(x) - - x = F.relu( self.bn6( self.conv6(x) ) ) - - x = x.view(-1, self.conv6.out_channels * self.output_height * self.output_width) - - if self.fc_out_h: - x = self.fc_out( x ) - - return x - -#%% Time Dependency -class TimeDependency(nn.Module): - ''' - TimeDependency: The main time-dependency module. It loads either an LSTM - or self-attention network for time-dependency modelling of the framewise - features. This module can also be skipped. - ''' - def __init__(self, - input_size, - td='self_att', - sa_d_model=512, - sa_nhead=8, - sa_pos_enc=None, - sa_num_layers=6, - sa_h=2048, - sa_dropout=0.1, - lstm_h=128, - lstm_num_layers=1, - lstm_dropout=0, - lstm_bidirectional=True, - ): - super().__init__() - - if td=='self_att': - self.model = SelfAttention( - input_size=input_size, - d_model=sa_d_model, - nhead=sa_nhead, - pos_enc=sa_pos_enc, - num_layers=sa_num_layers, - sa_h=sa_h, - dropout=sa_dropout, - activation="relu" - ) - self.fan_out = sa_d_model - - elif td=='lstm': - self.model = LSTM( - input_size, - lstm_h=lstm_h, - num_layers=lstm_num_layers, - dropout=lstm_dropout, - bidirectional=lstm_bidirectional, - ) - self.fan_out = self.model.fan_out - - elif (td is None) or (td=='skip'): - self.model = self._skip - self.fan_out = input_size - else: - raise NotImplementedError('Time dependency option not available') - - def _skip(self, x, n_wins): - return x, n_wins - - def forward(self, x, n_wins): - x, n_wins = self.model(x, n_wins) - return x, n_wins - -class LSTM(nn.Module): - ''' - LSTM: The main LSTM module that can be used as a time-dependency model. - ''' - def __init__(self, - input_size, - lstm_h=128, - num_layers=1, - dropout=0.1, - bidirectional=True - ): - super().__init__() - - self.lstm = nn.LSTM( - input_size = input_size, - hidden_size = lstm_h, - num_layers = num_layers, - dropout = dropout, - batch_first = True, - bidirectional = bidirectional - ) - - if bidirectional: - num_directions = 2 - else: - num_directions = 1 - self.fan_out = num_directions*lstm_h - - def forward(self, x, n_wins): - - x = pack_padded_sequence( - x, - n_wins.cpu(), - batch_first=True, - enforce_sorted=False - ) - - self.lstm.flatten_parameters() - x = self.lstm(x)[0] - - x, _ = pad_packed_sequence( - x, - batch_first=True, - padding_value=0.0, - total_length=n_wins.max()) - - return x, n_wins - -class SelfAttention(nn.Module): - ''' - SelfAttention: The main SelfAttention module that can be used as a - time-dependency model. - ''' - def __init__(self, - input_size, - d_model=512, - nhead=8, - pool_size=3, - pos_enc=None, - num_layers=6, - sa_h=2048, - dropout=0.1, - activation="relu" - ): - super().__init__() - - encoder_layer = SelfAttentionLayer(d_model, nhead, pool_size, sa_h, dropout, activation) - self.norm1 = nn.LayerNorm(d_model) - - self.linear = nn.Linear(input_size, d_model) - - self.layers = self._get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.d_model = d_model - self.nhead = nhead - - if pos_enc: - self.pos_encoder = PositionalEncoding(d_model, dropout) - else: - self.pos_encoder = nn.Identity() - - self._reset_parameters() - - def _get_clones(self, module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward(self, src, n_wins=None): - src = self.linear(src) - output = src.transpose(1,0) - output = self.norm1(output) - output = self.pos_encoder(output) - - for mod in self.layers: - output, n_wins = mod(output, n_wins=n_wins) - return output.transpose(1,0), n_wins - -class SelfAttentionLayer(nn.Module): - ''' - SelfAttentionLayer: The SelfAttentionLayer that is used by the - SelfAttention module. - ''' - def __init__(self, d_model, nhead, pool_size=1, sa_h=2048, dropout=0.1, activation="relu"): - super().__init__() - - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - - self.linear1 = nn.Linear(d_model, sa_h) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(sa_h, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = self._get_activation_fn(activation) - - def _get_activation_fn(self, activation): - if activation == "relu": - return F.relu - elif activation == "gelu": - return F.gelu - - def forward(self, src, n_wins=None): - - if n_wins is not None: - mask = ~((torch.arange(src.shape[0])[None, :]).to(src.device) < n_wins[:, None].to(torch.long).to(src.device)) - else: - mask = None - - src2 = self.self_attn(src, src, src, key_padding_mask=mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - - src = self.norm2(src) - - return src, n_wins - -class PositionalEncoding(nn.Module): - ''' - PositionalEncoding: PositionalEncoding taken from the PyTorch Transformer - tutorial. Can be applied to the SelfAttention module. However, it did not - improve the results in previous experiments. - ''' - def __init__(self, d_model, dropout=0.1, max_len=3000): - super(PositionalEncoding, self).__init__() - self.dropout = nn.Dropout(p=dropout) - - pe = torch.zeros(max_len, d_model) - position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0).transpose(0, 1) - self.register_buffer('pe', pe) - - def forward(self, x): - x = x + self.pe[:x.size(0), :] - return self.dropout(x) - -#%% Pooling -class Pooling(nn.Module): - ''' - Pooling: Main Pooling module. It can load either attention-pooling, average - pooling, maxpooling, or last-step pooling. In case of bidirectional LSTMs - last-step-bi pooling should be used instead of last-step pooling. - ''' - def __init__(self, - d_input, - output_size=1, - pool='att', - att_h=None, - att_dropout=0, - ): - super().__init__() - - if pool=='att': - if att_h is None: - self.model = PoolAtt(d_input, output_size) - else: - self.model = PoolAttFF(d_input, output_size, h=att_h, dropout=att_dropout) - elif pool=='last_step_bi': - self.model = PoolLastStepBi(d_input, output_size) - elif pool=='last_step': - self.model = PoolLastStep(d_input, output_size) - elif pool=='max': - self.model = PoolMax(d_input, output_size) - elif pool=='avg': - self.model = PoolAvg(d_input, output_size) - else: - raise NotImplementedError('Pool option not available') - - def forward(self, x, n_wins): - return self.model(x, n_wins) - -class PoolLastStepBi(nn.Module): - ''' - PoolLastStepBi: last step pooling for the case of bidirectional LSTM - ''' - def __init__(self, input_size, output_size): - super().__init__() - self.linear = nn.Linear(input_size, output_size) - - def forward(self, x, n_wins=None): - x = x.view(x.shape[0], n_wins.max(), 2, x.shape[-1]//2) - x = torch.cat( - (x[torch.arange(x.shape[0]), n_wins.type(torch.long)-1, 0, :], - x[:,0,1,:]), - dim=1 - ) - x = self.linear(x) - return x - -class PoolLastStep(nn.Module): - ''' - PoolLastStep: last step pooling can be applied to any one-directional - sequence. - ''' - def __init__(self, input_size, output_size): - super().__init__() - self.linear = nn.Linear(input_size, output_size) - - def forward(self, x, n_wins=None): - x = x[torch.arange(x.shape[0]), n_wins.type(torch.long)-1] - x = self.linear(x) - return x - -class PoolAtt(torch.nn.Module): - ''' - PoolAtt: Attention-Pooling module. - ''' - def __init__(self, d_input, output_size): - super().__init__() - - self.linear1 = nn.Linear(d_input, 1) - self.linear2 = nn.Linear(d_input, output_size) - - def forward(self, x, n_wins): - - att = self.linear1(x) - - att = att.transpose(2,1) - mask = torch.arange(att.shape[2])[None, :] < n_wins[:, None].to('cpu').to(torch.long) - att[~mask.unsqueeze(1)] = float("-Inf") - att = F.softmax(att, dim=2) - x = torch.bmm(att, x) - x = x.squeeze(1) - - x = self.linear2(x) - - return x - -class PoolAttFF(torch.nn.Module): - ''' - PoolAttFF: Attention-Pooling module with additonal feed-forward network. - ''' - def __init__(self, d_input, output_size, h, dropout=0.1): - super().__init__() - - self.linear1 = nn.Linear(d_input, h) - self.linear2 = nn.Linear(h, 1) - - self.linear3 = nn.Linear(d_input, output_size) - - self.activation = F.relu - self.dropout = nn.Dropout(dropout) - - def forward(self, x, n_wins): - - att = self.linear2(self.dropout(self.activation(self.linear1(x)))) - att = att.transpose(2,1) - mask = torch.arange(att.shape[2])[None, :] < n_wins[:, None].to('cpu').to(torch.long) - att[~mask.unsqueeze(1)] = float("-Inf") - att = F.softmax(att, dim=2) - x = torch.bmm(att, x) - x = x.squeeze(1) - - x = self.linear3(x) - - return x - -class PoolAvg(torch.nn.Module): - ''' - PoolAvg: Average pooling that consideres masked time-steps. - ''' - def __init__(self, d_input, output_size): - super().__init__() - - self.linear = nn.Linear(d_input, output_size) - - def forward(self, x, n_wins): - - mask = torch.arange(x.shape[1])[None, :] < n_wins[:, None].to('cpu').to(torch.long) - mask = ~mask.unsqueeze(2).to(x.device) - x.masked_fill_(mask, 0) - - x = torch.div(x.sum(1), n_wins.unsqueeze(1)) - - x = self.linear(x) - - return x - -class PoolMax(torch.nn.Module): - ''' - PoolMax: Max-pooling that consideres masked time-steps. - ''' - def __init__(self, d_input, output_size): - super().__init__() - - self.linear = nn.Linear(d_input, output_size) - - def forward(self, x, n_wins): - - mask = torch.arange(x.shape[1])[None, :] < n_wins[:, None].to('cpu').to(torch.long) - mask = ~mask.unsqueeze(2).to(x.device) - x.masked_fill_(mask, float("-Inf")) - - x = x.max(1)[0] - - x = self.linear(x) - - return x - -#%% Alignment -class Alignment(torch.nn.Module): - ''' - Alignment: Alignment module for the double-ended NISQA_DE model. It - supports five different alignment mechanisms. - ''' - def __init__(self, - att_method, - apply_att_method, - q_dim=None, - y_dim=None, - ): - super().__init__() - - # Attention method -------------------------------------------------------- - if att_method=='bahd': - self.att = AttBahdanau( - q_dim=q_dim, - y_dim=y_dim) - - elif att_method=='luong': - self.att = AttLuong( - q_dim=q_dim, - y_dim=y_dim) - - elif att_method=='dot': - self.att = AttDot() - - elif att_method=='cosine': - self.att = AttCosine() - - elif att_method=='distance': - self.att = AttDistance() - - elif (att_method=='none') or (att_method is None): - self.att = None - else: - raise NotImplementedError - - # Apply method ---------------------------------------------------------- - if apply_att_method=='soft': - self.apply_att = ApplySoftAttention() - elif apply_att_method=='hard': - self.apply_att = ApplyHardAttention() - else: - raise NotImplementedError - - def _mask_attention(self, att, y, n_wins): - mask = torch.arange(att.shape[2])[None, :] < n_wins[:, None].to('cpu').to(torch.long) - mask = mask.unsqueeze(1).expand_as(att) - att[~mask] = float("-Inf") - - def forward(self, query, y, n_wins_y): - if self.att is not None: - att_score, sim = self.att(query, y) - self._mask_attention(att_score, y, n_wins_y) - att_score = F.softmax(att_score, dim=2) - y = self.apply_att(y, att_score) - return y - -class AttDot(torch.nn.Module): - ''' - AttDot: Dot attention that can be used by the Alignment module. - ''' - def __init__(self): - super().__init__() - def forward(self, query, y): - att = torch.bmm(query, y.transpose(2,1)) - sim = att.max(2)[0].unsqueeze(1) - return att, sim - -class AttCosine(torch.nn.Module): - ''' - AttCosine: Cosine attention that can be used by the Alignment module. - ''' - def __init__(self): - super().__init__() - self.pdist = nn.CosineSimilarity(dim=3) - def forward(self, query, y): - att = self.pdist(query.unsqueeze(2), y.unsqueeze(1)) - sim = att.max(2)[0].unsqueeze(1) - return att, sim - -class AttDistance(torch.nn.Module): - ''' - AttDistance: Distance attention that can be used by the Alignment module. - ''' - def __init__(self, dist_norm=1, weight_norm=1): - super().__init__() - self.dist_norm = dist_norm - self.weight_norm = weight_norm - def forward(self, query, y): - att = (query.unsqueeze(1)-y.unsqueeze(2)).abs().pow(self.dist_norm) - att = att.mean(dim=3).pow(self.weight_norm) - att = - att.transpose(2,1) - sim = att.max(2)[0].unsqueeze(1) - return att, sim - -class AttBahdanau(torch.nn.Module): - ''' - AttBahdanau: Attention according to Bahdanau that can be used by the - Alignment module. - ''' - def __init__(self, q_dim, y_dim, att_dim=128): - super().__init__() - self.q_dim = q_dim - self.y_dim = y_dim - self.att_dim = att_dim - self.Wq = nn.Linear(self.q_dim, self.att_dim) - self.Wy = nn.Linear(self.y_dim, self.att_dim) - self.v = nn.Linear(self.att_dim, 1) - def forward(self, query, y): - att = torch.tanh( self.Wq(query).unsqueeze(1) + self.Wy(y).unsqueeze(2) ) - att = self.v(att).squeeze(3).transpose(2,1) - sim = att.max(2)[0].unsqueeze(1) - return att, sim - -class AttLuong(torch.nn.Module): - ''' - AttLuong: Attention according to Luong that can be used by the - Alignment module. - ''' - def __init__(self, q_dim, y_dim): - super().__init__() - self.q_dim = q_dim - self.y_dim = y_dim - self.W = nn.Linear(self.y_dim, self.q_dim) - def forward(self, query, y): - att = torch.bmm(query, self.W(y).transpose(2,1)) - sim = att.max(2)[0].unsqueeze(1) - return att, sim - -class ApplyHardAttention(torch.nn.Module): - ''' - ApplyHardAttention: Apply hard attention for the purpose of time-alignment. - ''' - def __init__(self): - super().__init__() - def forward(self, y, att): - self.idx = att.argmax(2) - y = y[torch.arange(y.shape[0]).unsqueeze(-1), self.idx] - return y - -class ApplySoftAttention(torch.nn.Module): - ''' - ApplySoftAttention: Apply soft attention for the purpose of time-alignment. - ''' - def __init__(self): - super().__init__() - def forward(self, y, att): - y = torch.bmm(att, y) - return y - -class Fusion(torch.nn.Module): - ''' - Fusion: Used by the double-ended NISQA_DE model and used to fuse the - degraded and reference features. - ''' - def __init__(self, fuse_dim=None, in_feat=None, fuse=None): - super().__init__() - self.fuse_dim = fuse_dim - self.fuse = fuse - - if self.fuse=='x/y/-': - self.fan_out = 3*in_feat - elif self.fuse=='+/-': - self.fan_out = 2*in_feat - elif self.fuse=='x/y': - self.fan_out = 2*in_feat - else: - raise NotImplementedError - - if self.fuse_dim: - self.lin_fusion = nn.Linear(self.fan_out, self.fuse_dim) - self.fan_out = fuse_dim - - def forward(self, x, y): - - if self.fuse=='x/y/-': - x = torch.cat((x, y, x-y), 2) - elif self.fuse=='+/-': - x = torch.cat((x+y, x-y), 2) - elif self.fuse=='x/y': - x = torch.cat((x, y), 2) - else: - raise NotImplementedError - - if self.fuse_dim: - x = self.lin_fusion(x) - - return x - -#%% Evaluation -def predict_mos(model, ds, bs, dev, num_workers=0): - ''' - predict_mos: predicts MOS of the given dataset with given model. Used for - NISQA and NISQA_DE model. - ''' - dl = DataLoader(ds, - batch_size=bs, - shuffle=False, - drop_last=False, - pin_memory=False, - num_workers=num_workers) - model.to(dev) - model.eval() - with torch.no_grad(): - y_hat_list = [ [model(xb.to(dev), n_wins.to(dev)).cpu().numpy(), yb.cpu().numpy()] for xb, yb, (idx, n_wins) in tqdm(dl)] - yy = np.concatenate( y_hat_list, axis=1 ) - y_hat = yy[0,:,0].reshape(-1,1) - y = yy[1,:,0].reshape(-1,1) - ds.df['mos_pred'] = y_hat.astype(dtype=float) - return y_hat, y - -def predict_dim(model, ds, bs, dev, num_workers=0): - ''' - predict_dim: predicts MOS and dimensions of the given dataset with given - model. Used for NISQA_DIM model. - ''' - dl = DataLoader(ds, - batch_size=bs, - shuffle=False, - drop_last=False, - pin_memory=False, - num_workers=num_workers) - model.to(dev) - model.eval() - with torch.no_grad(): - y_hat_list = [ [model(xb.to(dev), n_wins.to(dev)).cpu().numpy(), yb.cpu().numpy()] for xb, yb, (idx, n_wins) in tqdm(dl)] - yy = np.concatenate( y_hat_list, axis=1 ) - - y_hat = yy[0,:,:] - y = yy[1,:,:] - - ds.df['mos_pred'] = y_hat[:,0].reshape(-1,1) - ds.df['noi_pred'] = y_hat[:,1].reshape(-1,1) - ds.df['dis_pred'] = y_hat[:,2].reshape(-1,1) - ds.df['col_pred'] = y_hat[:,3].reshape(-1,1) - ds.df['loud_pred'] = y_hat[:,4].reshape(-1,1) - - return y_hat, y - -def is_const(x): - if np.linalg.norm(x - np.mean(x)) < 1e-13 * np.abs(np.mean(x)): - return True - elif np.all(x==x[0]): - return True - else: - return False - -def calc_eval_metrics(y, y_hat, y_hat_map=None, d=None, ci=None): - ''' - Calculate RMSE, mapped RMSE, mapped RMSE* and Pearson's correlation. - See ITU-T P.1401 for details on RMSE*. - ''' - r = { - 'r_p': np.nan, - 'rmse': np.nan, - 'rmse_map': np.nan, - 'rmse_star_map': np.nan, - } - if is_const(y_hat) or any(np.isnan(y)): - r['r_p'] = np.nan - else: - r['r_p'] = pearsonr(y, y_hat)[0] - r['rmse'] = calc_rmse(y, y_hat) - if y_hat_map is not None: - r['rmse_map'] = calc_rmse(y, y_hat_map, d=d) - if ci is not None: - r['rmse_star_map'] = calc_rmse_star(y, y_hat_map, ci, d)[0] - return r - -def calc_rmse(y_true, y_pred, d=0): - if d==0: - rmse = np.sqrt(np.mean(np.square(y_true-y_pred))) - else: - N = y_true.shape[0] - if (N-d)<1: - rmse = np.nan - else: - rmse = np.sqrt( 1/(N-d) * np.sum( np.square(y_true-y_pred) ) ) # Eq (7-29) P.1401 - return rmse - -def calc_rmse_star(mos_sub, mos_obj, ci, d): - N = mos_sub.shape[0] - error = mos_sub-mos_obj - - if np.isnan(ci).any(): - p_error = np.nan - rmse_star = np.nan - else: - p_error = (abs(error)-ci).clip(min=0) # Eq (7-27) P.1401 - if (N-d)<1: - rmse_star = np.nan - else: - rmse_star = np.sqrt( 1/(N-d) * sum(p_error**2) ) # Eq (7-29) P.1401 - - return rmse_star, p_error, error - -def calc_mapped(x, b): - N = x.shape[0] - order = b.shape[0]-1 - A = np.zeros([N,order+1]) - for i in range(order+1): - A[:,i] = x**(i) - return A @ b - -def fit_first_order(y_con, y_con_hat): - A = np.vstack([np.ones(len(y_con_hat)), y_con_hat]).T - b = np.linalg.lstsq(A, y_con, rcond=None)[0] - return b - -def fit_second_order(y_con, y_con_hat): - A = np.vstack([np.ones(len(y_con_hat)), y_con_hat, y_con_hat**2]).T - b = np.linalg.lstsq(A, y_con, rcond=None)[0] - return b - -def fit_third_order(y_con, y_con_hat): - A = np.vstack([np.ones(len(y_con_hat)), y_con_hat, y_con_hat**2, y_con_hat**3]).T - b = np.linalg.lstsq(A, y_con, rcond=None)[0] - - p = np.poly1d(np.flipud(b)) - p2 = np.polyder(p) - rr = np.roots(p2) - r = rr[np.imag(rr)==0] - monotonic = all( np.logical_or(r>max(y_con_hat),r bias update: min_r {:0.2f}, r_p {:0.2f}'.format(r, self.min_r)) - if r>self.min_r: - self.do_update = True - - if self.do_update: - if self.do_print: - print('--> bias updated') - for db_name in self.db.unique(): - - db_idx = (self.db==db_name).to_numpy().nonzero() - y_hat_db = y_hat[db_idx] - y_db = y[db_idx] - - if not np.isnan(y_db).any(): - if self.mapping=='first_order': - b_db = self._calc_bias_first_order(y_hat_db, y_db) - else: - raise NotImplementedError - if not db_name==self.anchor_db: - self.b[db_idx,:len(b_db)] = b_db - - def _calc_bias_first_order(self, y_hat, y): - A = np.vstack([np.ones(len(y_hat)), y_hat]).T - btmp = np.linalg.lstsq(A, y, rcond=None)[0] - b = np.zeros((4)) - b[0:2] = btmp - return b - - def _nan_mse(self, y, y_hat): - err = (y-y_hat).view(-1) - idx_not_nan = ~torch.isnan(err) - nan_err = err[idx_not_nan] - return torch.mean(nan_err**2) - -#%% Early stopping -class earlyStopper(object): - ''' - Early stopping class. - - Training is stopped if neither RMSE or Pearson's correlation - is improving after "patience" epochs. - ''' - def __init__(self, patience): - self.best_rmse = 1e10 - self.best_r_p = -1e10 - self.cnt = -1 - self.patience = patience - self.best = False - - def step(self, r): - self.best = False - if r['r_p_mean_file'] > self.best_r_p: - self.best_r_p = r['r_p_mean_file'] - self.cnt = -1 - if r['rmse_map_mean_file'] < self.best_rmse: - self.best_rmse = r['rmse_map_mean_file'] - self.cnt = -1 - self.best = True - self.cnt += 1 - - if self.cnt >= self.patience: - stop_early = True - return stop_early - else: - stop_early = False - return stop_early - -class earlyStopper_dim(object): - ''' - Early stopping class for dimension model. - - Training is stopped if neither RMSE or Pearson's correlation - is improving after "patience" epochs. - ''' - def __init__(self, patience): - - self.best_rmse = 1e10 - self.best_rmse_noi = 1e10 - self.best_rmse_col = 1e10 - self.best_rmse_dis = 1e10 - self.best_rmse_loud = 1e10 - - self.best_r_p = -1e10 - self.best_r_p_noi = -1e10 - self.best_r_p_col = -1e10 - self.best_r_p_dis = -1e10 - self.best_r_p_loud = -1e10 - - self.cnt = -1 - self.patience = patience - self.best = False - - def step(self, r): - - self.best = False - - if r['r_p_mean_file'] > self.best_r_p: - self.best_r_p = r['r_p_mean_file'] - self.cnt = -1 - if r['r_p_mean_file_noi'] > self.best_r_p_noi: - self.best_r_p_noi = r['r_p_mean_file_noi'] - self.cnt = -1 - if r['r_p_mean_file_col'] > self.best_r_p_col: - self.best_r_p_col = r['r_p_mean_file_col'] - self.cnt = -1 - if r['r_p_mean_file_dis'] > self.best_r_p_dis: - self.best_r_p_dis = r['r_p_mean_file_dis'] - self.cnt = -1 - if r['r_p_mean_file_loud'] > self.best_r_p_loud: - self.best_r_p_loud = r['r_p_mean_file_loud'] - self.cnt = -1 - - if r['rmse_map_mean_file'] < self.best_rmse: - self.best_rmse = r['rmse_map_mean_file'] - self.cnt = -1 - self.best = True - if r['rmse_map_mean_file_noi'] < self.best_rmse_noi: - self.best_rmse_noi = r['rmse_map_mean_file_noi'] - self.cnt = -1 - if r['rmse_map_mean_file_col'] < self.best_rmse_col: - self.best_rmse_col = r['rmse_map_mean_file_col'] - self.cnt = -1 - if r['rmse_map_mean_file_dis'] < self.best_rmse_dis: - self.best_rmse_dis = r['rmse_map_mean_file_dis'] - self.cnt = -1 - if r['rmse_map_mean_file_loud'] < self.best_rmse_loud: - self.best_rmse_loud = r['rmse_map_mean_file_loud'] - self.cnt = -1 - - self.cnt += 1 - - if self.cnt >= self.patience: - stop_early = True - return stop_early - else: - stop_early = False - return stop_early - -def get_lr(optimizer): - ''' - Get current learning rate from Pytorch optimizer. - ''' - for param_group in optimizer.param_groups: - return param_group['lr'] - -#%% Dataset -class SpeechQualityDataset(Dataset): - ''' - Dataset for Speech Quality Model. - ''' - def __init__( - self, - df, - df_con=None, - data_dir='', - folder_column='', - filename_column='filename', - mos_column='MOS', - seg_length=15, - max_length=None, - to_memory=False, - to_memory_workers=0, - transform=None, - seg_hop_length=1, - ms_n_fft = 1024, - ms_hop_length = 80, - ms_win_length = 170, - ms_n_mels=32, - ms_sr=48e3, - ms_fmax=16e3, - ms_channel=None, - double_ended=False, - filename_column_ref=None, - dim=False, - ): - - self.df = df - self.df_con = df_con - self.data_dir = data_dir - self.folder_column = folder_column - self.filename_column = filename_column - self.filename_column_ref = filename_column_ref - self.mos_column = mos_column - self.seg_length = seg_length - self.seg_hop_length = seg_hop_length - self.max_length = max_length - self.transform = transform - self.to_memory_workers = to_memory_workers - self.ms_n_fft = ms_n_fft - self.ms_hop_length = ms_hop_length - self.ms_win_length = ms_win_length - self.ms_n_mels = ms_n_mels - self.ms_sr = ms_sr - self.ms_fmax = ms_fmax - self.ms_channel = ms_channel - self.double_ended = double_ended - self.dim = dim - - # if True load all specs to memory - self.to_memory = False - if to_memory: - self._to_memory() - - def _to_memory_multi_helper(self, idx): - return [self._load_spec(i) for i in idx] - - def _to_memory(self): - if self.to_memory_workers==0: - self.mem_list = [self._load_spec(idx) for idx in tqdm(range(len(self)))] - else: - buffer_size = 128 - idx = np.arange(len(self)) - n_bufs = int(len(idx)/buffer_size) - idx = idx[:buffer_size*n_bufs].reshape(-1,buffer_size).tolist() + idx[buffer_size*n_bufs:].reshape(1,-1).tolist() - pool = multiprocessing.Pool(processes=self.to_memory_workers) - mem_list = [] - for out in tqdm(pool.imap(self._to_memory_multi_helper, idx), total=len(idx)): - mem_list = mem_list + out - self.mem_list = mem_list - pool.terminate() - pool.join() - self.to_memory=True - - def _load_spec(self, index): - - # Load spec - file_path = os.path.join(self.data_dir, self.df[self.filename_column].iloc[index]) - - if self.double_ended: - file_path_ref = os.path.join(self.data_dir, self.df[self.filename_column_ref].iloc[index]) - spec = get_librosa_melspec( - file_path, - sr = self.ms_sr, - n_fft=self.ms_n_fft, - hop_length=self.ms_hop_length, - win_length=self.ms_win_length, - n_mels=self.ms_n_mels, - fmax=self.ms_fmax, - ms_channel=self.ms_channel - ) - if self.double_ended: - spec_ref = get_librosa_melspec( - file_path_ref, - sr = self.ms_sr, - n_fft=self.ms_n_fft, - hop_length=self.ms_hop_length, - win_length=self.ms_win_length, - n_mels=self.ms_n_mels, - fmax=self.ms_fmax - ) - spec = (spec, spec_ref) - - return spec - - def __getitem__(self, index): - assert isinstance(index, int), 'index must be integer (no slice)' - - if self.to_memory: - spec = self.mem_list[index] - else: - spec = self._load_spec(index) - - if self.double_ended: - spec, spec_ref = spec - - # Apply transformation if given - if self.transform: - spec = self.transform(spec) - - # Segment specs - file_path = os.path.join(self.data_dir, self.df[self.filename_column].iloc[index]) - if self.seg_length is not None: - x_spec_seg, n_wins = segment_specs(file_path, - spec, - self.seg_length, - self.seg_hop_length, - self.max_length) - - if self.double_ended: - x_spec_seg_ref, n_wins_ref = segment_specs(file_path, - spec_ref, - self.seg_length, - self.seg_hop_length, - self.max_length) - else: - x_spec_seg = spec - n_wins = spec.shape[1] - if self.max_length is not None: - x_padded = np.zeros((x_spec_seg.shape[0], self.max_length)) - x_padded[:,:n_wins] = x_spec_seg - x_spec_seg = np.expand_dims(x_padded.transpose(1,0), axis=(1, 3)) - if not torch.is_tensor(x_spec_seg): - x_spec_seg = torch.tensor(x_spec_seg, dtype=torch.float) - - if self.double_ended: - x_spec_seg_ref = spec - n_wins_ref = spec.shape[1] - if self.max_length is not None: - x_padded = np.zeros((x_spec_seg_ref.shape[0], self.max_length)) - x_padded[:,:n_wins] = x_spec_seg_ref - x_spec_seg_ref = np.expand_dims(x_padded.transpose(1,0), axis=(1, 3)) - if not torch.is_tensor(x_spec_seg_ref): - x_spec_seg_ref = torch.tensor(x_spec_seg_ref, dtype=torch.float) - - if self.double_ended: - x_spec_seg = torch.cat((x_spec_seg, x_spec_seg_ref), dim=1) - n_wins = np.concatenate((n_wins.reshape(1), n_wins_ref.reshape(1)), axis=0) - - # Get MOS (apply NaN in case of prediction only mode) - if self.dim: - if self.mos_column=='predict_only': - y = np.full((5,1), np.nan).reshape(-1).astype('float32') - else: - y_mos = self.df['mos'].iloc[index].reshape(-1).astype('float32') - y_noi = self.df['noi'].iloc[index].reshape(-1).astype('float32') - y_dis = self.df['dis'].iloc[index].reshape(-1).astype('float32') - y_col = self.df['col'].iloc[index].reshape(-1).astype('float32') - y_loud = self.df['loud'].iloc[index].reshape(-1).astype('float32') - y = np.concatenate((y_mos, y_noi, y_dis, y_col, y_loud), axis=0) - else: - if self.mos_column=='predict_only': - y = np.full(1, np.nan).reshape(-1).astype('float32') - else: - y = self.df[self.mos_column].iloc[index].reshape(-1).astype('float32') - - return x_spec_seg, y, (index, n_wins) - - def __len__(self): - return len(self.df) - -#%% Spectrograms -def segment_specs(file_path, x, seg_length, seg_hop=1, max_length=None): - ''' - Segment a spectrogram into "seg_length" wide spectrogram segments. - Instead of using only the frequency bin of the current time step, - the neighboring bins are included as input to the CNN. For example - for a seg_length of 7, the previous 3 and the follwing 3 frequency - bins are included. - - A spectrogram with input size [H x W] will be segmented to: - [W-(seg_length-1) x C x H x seg_length], where W is the width of the - original mel-spec (corresponding to the length of the speech signal), - H is the height of the mel-spec (corresponding to the number of mel bands), - C is the number of CNN input Channels (always one in our case). - ''' - if seg_length % 2 == 0: - raise ValueError('seg_length must be odd! (seg_lenth={})'.format(seg_length)) - if not torch.is_tensor(x): - x = torch.tensor(x) - - n_wins = x.shape[1]-(seg_length-1) - if n_wins < 1: - raise ValueError( - f"Sample too short. Only {x.shape[1]} windows available but seg_length={seg_length}. " - f"Consider zero padding the audio sample. File: {file_path}" - ) - - # broadcast magic to segment melspec - idx1 = torch.arange(seg_length) - idx2 = torch.arange(n_wins) - idx3 = idx1.unsqueeze(0) + idx2.unsqueeze(1) - x = x.transpose(1,0)[idx3,:].unsqueeze(1).transpose(3,2) - - if seg_hop>1: - x = x[::seg_hop,:] - n_wins = int(np.ceil(n_wins/seg_hop)) - - if max_length is not None: - if max_length < n_wins: - raise ValueError('n_wins {} > max_length {} --- {}. Increase max window length ms_max_segments!'.format(n_wins, max_length, file_path)) - x_padded = torch.zeros((max_length, x.shape[1], x.shape[2], x.shape[3])) - x_padded[:n_wins,:] = x - x = x_padded - - return x, np.array(n_wins) - -def get_librosa_melspec( - file_path, - sr=48e3, - n_fft=1024, - hop_length=80, - win_length=170, - n_mels=32, - fmax=16e3, - ms_channel=None, - ): - ''' - Calculate mel-spectrograms with Librosa. - ''' - # Calc spec - try: - if ms_channel is not None: - y, sr = lb.load(file_path, sr=sr, mono=False) - if len(y.shape)>1: - y = y[ms_channel, :] - else: - y, sr = lb.load(file_path, sr=sr) - except: - raise ValueError('Could not load file {}'.format(file_path)) - - hop_length = int(sr * hop_length) - win_length = int(sr * win_length) - - S = lb.feature.melspectrogram( - y=y, - sr=sr, - S=None, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window='hann', - center=True, - pad_mode='reflect', - power=1.0, - - n_mels=n_mels, - fmin=0.0, - fmax=fmax, - htk=False, - norm='slaney', - ) - - spec = lb.core.amplitude_to_db(S, ref=1.0, amin=1e-4, top_db=80.0) - return spec - diff --git a/src/libs/NISQA/nisqa/NISQA_model.py b/src/libs/NISQA/nisqa/NISQA_model.py deleted file mode 100644 index 959c878..0000000 --- a/src/libs/NISQA/nisqa/NISQA_model.py +++ /dev/null @@ -1,1116 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@author: Gabriel Mittag, TU-Berlin -""" -import time -import os -import datetime -from pathlib import Path - -import numpy as np -import pandas as pd; pd.options.mode.chained_assignment=None -from tqdm import tqdm -import yaml -import torch -import torch.nn as nn -from torch import optim -from torch.utils.data import DataLoader -from . import NISQA_lib as NL - -class nisqaModel(object): - ''' - nisqaModel: Main class that loads the model and the datasets. Contains - the training loop, prediction, and evaluation function. - ''' - def __init__(self, args): - self.args = args - - if 'mode' not in self.args: - self.args['mode'] = 'main' - - self.runinfos = {} - self._getDevice() - self._loadModel() - self._loadDatasets() - self.args['now'] = datetime.datetime.today() - - if self.args['mode']=='main': - print(yaml.dump(self.args, default_flow_style=None, sort_keys=False)) - - def train(self): - - if self.args['dim']==True: - self._train_dim() - else: - self._train_mos() - - def evaluate(self, mapping='first_order', do_print=True, do_plot=False): - if self.args['dim']==True: - self._evaluate_dim(mapping=mapping, do_print=do_print, do_plot=do_plot) - else: - self._evaluate_mos(mapping=mapping, do_print=do_print, do_plot=do_plot) - - def predict(self): - print('---> Predicting ...') - if self.args['tr_parallel']: - self.model = nn.DataParallel(self.model) - - if self.args['dim']==True: - y_val_hat, y_val = NL.predict_dim( - self.model, - self.ds_val, - self.args['tr_bs_val'], - self.dev, - num_workers=self.args['tr_num_workers']) - else: - y_val_hat, y_val = NL.predict_mos( - self.model, - self.ds_val, - self.args['tr_bs_val'], - self.dev, - num_workers=self.args['tr_num_workers']) - - if self.args['output_dir']: - self.ds_val.df['model'] = self.args['name'] - self.ds_val.df.to_csv( - os.path.join(self.args['output_dir'], 'NISQA_results.csv'), - index=False) - - return self.ds_val.df - - def _train_mos(self): - ''' - Trains speech quality model. - ''' - # Initialize ------------------------------------------------------------- - if self.args['tr_parallel']: - self.model = nn.DataParallel(self.model) - self.model.to(self.dev) - - # Runname and savepath --------------------------------------------------- - self.runname = self._makeRunnameAndWriteYAML() - - # Optimizer ------------------------------------------------------------- - opt = optim.Adam(self.model.parameters(), lr=self.args['tr_lr']) - scheduler = optim.lr_scheduler.ReduceLROnPlateau( - opt, - 'min', - verbose=True, - threshold=0.003, - patience=self.args['tr_lr_patience']) - earlyStp = NL.earlyStopper(self.args['tr_early_stop']) - - biasLoss = NL.biasLoss( - self.ds_train.df.db, - anchor_db=self.args['tr_bias_anchor_db'], - mapping=self.args['tr_bias_mapping'], - min_r=self.args['tr_bias_min_r'], - do_print=(self.args['tr_verbose']>0), - ) - - # Dataloader ----------------------------------------------------------- - dl_train = DataLoader( - self.ds_train, - batch_size=self.args['tr_bs'], - shuffle=True, - drop_last=False, - pin_memory=True, - num_workers=self.args['tr_num_workers']) - - # Start training loop --------------------------------------------------- - print('--> start training') - for epoch in range(self.args['tr_epochs']): - - tic_epoch = time.time() - batch_cnt = 0 - loss = 0.0 - y_train = self.ds_train.df[self.args['csv_mos_train']].to_numpy().reshape(-1) - y_train_hat = np.zeros((len(self.ds_train), 1)) - self.model.train() - - # Progress bar - if self.args['tr_verbose'] == 2: - pbar = tqdm(iterable=batch_cnt, total=len(dl_train), ascii=">—", - bar_format='{bar} {percentage:3.0f}%, {n_fmt}/{total_fmt}, {elapsed}<{remaining}{postfix}') - - for xb_spec, yb_mos, (idx, n_wins) in dl_train: - - # Estimate batch --------------------------------------------------- - xb_spec = xb_spec.to(self.dev) - yb_mos = yb_mos.to(self.dev) - n_wins = n_wins.to(self.dev) - - # Forward pass ---------------------------------------------------- - yb_mos_hat = self.model(xb_spec, n_wins) - y_train_hat[idx] = yb_mos_hat.detach().cpu().numpy() - - # Loss ------------------------------------------------------------ - lossb = biasLoss.get_loss(yb_mos, yb_mos_hat, idx) - - # Backprop ------------------------------------------------------- - lossb.backward() - opt.step() - opt.zero_grad() - - # Update total loss ----------------------------------------------- - loss += lossb.item() - batch_cnt += 1 - - if self.args['tr_verbose'] == 2: - pbar.set_postfix(loss=lossb.item()) - pbar.update() - - if self.args['tr_verbose'] == 2: - pbar.close() - - loss = loss/batch_cnt - - biasLoss.update_bias(y_train, y_train_hat) - - # Evaluate ----------------------------------------------------------- - if self.args['tr_verbose']>0: - print('\n<---- Training ---->') - self.ds_train.df['mos_pred'] = y_train_hat - db_results_train, r_train = NL.eval_results( - self.ds_train.df, - dcon=self.ds_train.df_con, - target_mos=self.args['csv_mos_train'], - target_ci=self.args['csv_mos_train'] + '_ci', - pred='mos_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - if self.args['tr_verbose']>0: - print('<---- Validation ---->') - NL.predict_mos(self.model, self.ds_val, self.args['tr_bs_val'], self.dev, num_workers=self.args['tr_num_workers']) - db_results, r_val = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos=self.args['csv_mos_val'], - target_ci=self.args['csv_mos_val'] + '_ci', - pred='mos_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - r = {'train_r_p_mean_file': r_train['r_p_mean_file'], - 'train_rmse_map_mean_file': r_train['rmse_map_mean_file'], - **r_val} - - # Scheduler update --------------------------------------------- - scheduler.step(loss) - earl_stp = earlyStp.step(r) - - # Print -------------------------------------------------------- - ep_runtime = time.time() - tic_epoch - print( - 'ep {} sec {:0.0f} es {} lr {:0.0e} loss {:0.4f} // ' - 'r_p_tr {:0.2f} rmse_map_tr {:0.2f} // r_p {:0.2f} rmse_map {:0.2f} // ' - 'best_r_p {:0.2f} best_rmse_map {:0.2f},' - .format(epoch+1, ep_runtime, earlyStp.cnt, NL.get_lr(opt), loss, - r['train_r_p_mean_file'], r['train_rmse_map_mean_file'], - r['r_p_mean_file'], r['rmse_map_mean_file'], - earlyStp.best_r_p, earlyStp.best_rmse)) - - # Save results and model ----------------------------------------- - self._saveResults(self.model, self.model_args, opt, epoch, loss, ep_runtime, r, db_results, earlyStp.best) - - # Early stopping ----------------------------------------------- - if earl_stp: - print('--> Early stopping. best_r_p {:0.2f} best_rmse {:0.2f}' - .format(earlyStp.best_r_p, earlyStp.best_rmse)) - return - - # Training done -------------------------------------------------------- - print('--> Training done. best_r_p {:0.2f} best_rmse_map {:0.2f}' - .format(earlyStp.best_r_p, earlyStp.best_rmse)) - return - - - - def _train_dim(self): - ''' - Trains multidimensional speech quality model. - ''' - # Initialize ------------------------------------------------------------- - if self.args['tr_parallel']: - self.model = nn.DataParallel(self.model) - self.model.to(self.dev) - - # Runname and savepath --------------------------------------------------- - self.runname = self._makeRunnameAndWriteYAML() - - # Optimizer ------------------------------------------------------------- - opt = optim.Adam(self.model.parameters(), lr=self.args['tr_lr']) - scheduler = optim.lr_scheduler.ReduceLROnPlateau( - opt, - 'min', - verbose=True, - threshold=0.003, - patience=self.args['tr_lr_patience']) - earlyStp = NL.earlyStopper_dim(self.args['tr_early_stop']) - - biasLoss_1 = NL.biasLoss( - self.ds_train.df.db, - anchor_db=self.args['tr_bias_anchor_db'], - mapping=self.args['tr_bias_mapping'], - min_r=self.args['tr_bias_min_r'], - do_print=(self.args['tr_verbose']>0), - ) - - biasLoss_2 = NL.biasLoss( - self.ds_train.df.db, - anchor_db=self.args['tr_bias_anchor_db'], - mapping=self.args['tr_bias_mapping'], - min_r=self.args['tr_bias_min_r'], - do_print=(self.args['tr_verbose']>0), - ) - - biasLoss_3 = NL.biasLoss( - self.ds_train.df.db, - anchor_db=self.args['tr_bias_anchor_db'], - mapping=self.args['tr_bias_mapping'], - min_r=self.args['tr_bias_min_r'], - do_print=(self.args['tr_verbose']>0), - ) - - biasLoss_4 = NL.biasLoss( - self.ds_train.df.db, - anchor_db=self.args['tr_bias_anchor_db'], - mapping=self.args['tr_bias_mapping'], - min_r=self.args['tr_bias_min_r'], - do_print=(self.args['tr_verbose']>0), - ) - - biasLoss_5 = NL.biasLoss( - self.ds_train.df.db, - anchor_db=self.args['tr_bias_anchor_db'], - mapping=self.args['tr_bias_mapping'], - min_r=self.args['tr_bias_min_r'], - do_print=(self.args['tr_verbose']>0), - ) - - # Dataloader ----------------------------------------------------------- - dl_train = DataLoader( - self.ds_train, - batch_size=self.args['tr_bs'], - shuffle=True, - drop_last=False, - pin_memory=True, - num_workers=self.args['tr_num_workers']) - - - # Start training loop --------------------------------------------------- - print('--> start training') - for epoch in range(self.args['tr_epochs']): - - tic_epoch = time.time() - batch_cnt = 0 - loss = 0.0 - y_mos = self.ds_train.df['mos'].to_numpy().reshape(-1,1) - y_noi = self.ds_train.df['noi'].to_numpy().reshape(-1,1) - y_dis = self.ds_train.df['dis'].to_numpy().reshape(-1,1) - y_col = self.ds_train.df['col'].to_numpy().reshape(-1,1) - y_loud = self.ds_train.df['loud'].to_numpy().reshape(-1,1) - y_train = np.concatenate((y_mos, y_noi, y_dis, y_col, y_loud), axis=1) - y_train_hat = np.zeros((len(self.ds_train), 5)) - - self.model.train() - - - # Progress bar - if self.args['tr_verbose'] == 2: - pbar = tqdm(iterable=batch_cnt, total=len(dl_train), ascii=">—", - bar_format='{bar} {percentage:3.0f}%, {n_fmt}/{total_fmt}, {elapsed}<{remaining}{postfix}') - - for xb_spec, yb_mos, (idx, n_wins) in dl_train: - - # Estimate batch --------------------------------------------------- - xb_spec = xb_spec.to(self.dev) - yb_mos = yb_mos.to(self.dev) - n_wins = n_wins.to(self.dev) - - # Forward pass ---------------------------------------------------- - yb_mos_hat = self.model(xb_spec, n_wins) - y_train_hat[idx,:] = yb_mos_hat.detach().cpu().numpy() - - # Loss ------------------------------------------------------------ - lossb_1 = biasLoss_1.get_loss(yb_mos[:,0].view(-1,1), yb_mos_hat[:,0].view(-1,1), idx) - lossb_2 = biasLoss_2.get_loss(yb_mos[:,1].view(-1,1), yb_mos_hat[:,1].view(-1,1), idx) - lossb_3 = biasLoss_3.get_loss(yb_mos[:,2].view(-1,1), yb_mos_hat[:,2].view(-1,1), idx) - lossb_4 = biasLoss_4.get_loss(yb_mos[:,3].view(-1,1), yb_mos_hat[:,3].view(-1,1), idx) - lossb_5 = biasLoss_5.get_loss(yb_mos[:,4].view(-1,1), yb_mos_hat[:,4].view(-1,1), idx) - - lossb = lossb_1 + lossb_2 + lossb_3 + lossb_4 + lossb_5 - - # Backprop ------------------------------------------------------- - lossb.backward() - opt.step() - opt.zero_grad() - - # Update total loss ----------------------------------------------- - loss += lossb.item() - batch_cnt += 1 - - if self.args['tr_verbose'] == 2: - pbar.set_postfix(loss=lossb.item()) - pbar.update() - - if self.args['tr_verbose'] == 2: - pbar.close() - - loss = loss/batch_cnt - - biasLoss_1.update_bias(y_train[:,0].reshape(-1,1), y_train_hat[:,0].reshape(-1,1)) - biasLoss_2.update_bias(y_train[:,1].reshape(-1,1), y_train_hat[:,1].reshape(-1,1)) - biasLoss_3.update_bias(y_train[:,2].reshape(-1,1), y_train_hat[:,2].reshape(-1,1)) - biasLoss_4.update_bias(y_train[:,3].reshape(-1,1), y_train_hat[:,3].reshape(-1,1)) - biasLoss_5.update_bias(y_train[:,4].reshape(-1,1), y_train_hat[:,4].reshape(-1,1)) - - # Evaluate ----------------------------------------------------------- - self.ds_train.df['mos_pred'] = y_train_hat[:,0].reshape(-1,1) - self.ds_train.df['noi_pred'] = y_train_hat[:,1].reshape(-1,1) - self.ds_train.df['dis_pred'] = y_train_hat[:,2].reshape(-1,1) - self.ds_train.df['col_pred'] = y_train_hat[:,3].reshape(-1,1) - self.ds_train.df['loud_pred'] = y_train_hat[:,4].reshape(-1,1) - - if self.args['tr_verbose']>0: - print('\n<---- Training ---->') - print('--> MOS:') - db_results_train_mos, r_train_mos = NL.eval_results( - self.ds_train.df, - dcon=self.ds_train.df_con, - target_mos='mos', - target_ci='mos_ci', - pred='mos_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - if self.args['tr_verbose']>0: - print('--> NOI:') - db_results_train_noi, r_train_noi = NL.eval_results( - self.ds_train.df, - dcon=self.ds_train.df_con, - target_mos='noi', - target_ci='noi_ci', - pred='noi_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - if self.args['tr_verbose']>0: - print('--> DIS:') - db_results_train_dis, r_train_dis = NL.eval_results( - self.ds_train.df, - dcon=self.ds_train.df_con, - target_mos='dis', - target_ci='dis_ci', - pred='dis_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - if self.args['tr_verbose']>0: - print('--> COL:') - db_results_train_col, r_train_col = NL.eval_results( - self.ds_train.df, - dcon=self.ds_train.df_con, - target_mos='col', - target_ci='col_ci', - pred='col_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - if self.args['tr_verbose']>0: - print('--> LOUD:') - db_results_train_loud, r_train_loud = NL.eval_results( - self.ds_train.df, - dcon=self.ds_train.df_con, - target_mos='loud', - target_ci='loud_ci', - pred='loud_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - NL.predict_dim(self.model, self.ds_val, self.args['tr_bs_val'], self.dev, num_workers=self.args['tr_num_workers']) - - if self.args['tr_verbose']>0: - print('<---- Validation ---->') - print('--> MOS:') - db_results_val_mos, r_val_mos = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='mos', - target_ci='mos_ci', - pred='mos_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - - if self.args['tr_verbose']>0: - print('--> NOI:') - db_results_val_noi, r_val_noi = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='noi', - target_ci='noi_ci', - pred='noi_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - r_val_noi = {k+'_noi': v for k, v in r_val_noi.items()} - - if self.args['tr_verbose']>0: - print('--> DIS:') - db_results_val_dis, r_val_dis = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='dis', - target_ci='dis_ci', - pred='dis_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - r_val_dis = {k+'_dis': v for k, v in r_val_dis.items()} - - if self.args['tr_verbose']>0: - print('--> COL:') - db_results_val_col, r_val_col = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='col', - target_ci='col_ci', - pred='col_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - r_val_col = {k+'_col': v for k, v in r_val_col.items()} - - if self.args['tr_verbose']>0: - print('--> LOUD:') - db_results_val_loud, r_val_loud = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='loud', - target_ci='loud_ci', - pred='loud_pred', - mapping = 'first_order', - do_print=(self.args['tr_verbose']>0) - ) - r_val_loud = {k+'_loud': v for k, v in r_val_loud.items()} - - r = { - 'train_r_p_mean_file': r_train_mos['r_p_mean_file'], - 'train_rmse_map_mean_file': r_train_mos['rmse_map_mean_file'], - - 'train_r_p_mean_file_noi': r_train_noi['r_p_mean_file'], - 'train_rmse_map_mean_file_noi': r_train_noi['rmse_map_mean_file'], - - 'train_r_p_mean_file_dis': r_train_dis['r_p_mean_file'], - 'train_rmse_map_mean_file_dis': r_train_dis['rmse_map_mean_file'], - - 'train_r_p_mean_file_col': r_train_col['r_p_mean_file'], - 'train_rmse_map_mean_file_col': r_train_col['rmse_map_mean_file'], - - 'train_r_p_mean_file_loud': r_train_loud['r_p_mean_file'], - 'train_rmse_map_mean_file_loud': r_train_loud['rmse_map_mean_file'], - - **r_val_mos, **r_val_noi, **r_val_dis, **r_val_col, **r_val_loud, } - - db_results = { - 'db_results_val_mos': db_results_val_mos, - 'db_results_val_noi': db_results_val_noi, - 'db_results_val_dis': db_results_val_dis, - 'db_results_val_col': db_results_val_col, - 'db_results_val_loud': db_results_val_loud - } - - # Scheduler update --------------------------------------------- - scheduler.step(loss) - earl_stp = earlyStp.step(r) - - # Print -------------------------------------------------------- - ep_runtime = time.time() - tic_epoch - - r_dim_mos_mean = 1/5 * (r['r_p_mean_file'] + - r['r_p_mean_file_noi'] + - r['r_p_mean_file_col'] + - r['r_p_mean_file_dis'] + - r['r_p_mean_file_loud']) - - print( - 'ep {} sec {:0.0f} es {} lr {:0.0e} loss {:0.4f} // ' - 'r_p_tr {:0.2f} rmse_map_tr {:0.2f} // r_dim_mos_mean {:0.2f}, r_p {:0.2f} rmse_map {:0.2f} // ' - 'best_r_p {:0.2f} best_rmse_map {:0.2f},' - .format(epoch+1, ep_runtime, earlyStp.cnt, NL.get_lr(opt), loss, - r['train_r_p_mean_file'], r['train_rmse_map_mean_file'], - r_dim_mos_mean, - r['r_p_mean_file'], r['rmse_map_mean_file'], - earlyStp.best_r_p, earlyStp.best_rmse)) - - # Save results and model ----------------------------------------- - self._saveResults(self.model, self.model_args, opt, epoch, loss, ep_runtime, r, db_results, earlyStp.best) - - # Early stopping ----------------------------------------------- - if earl_stp: - print('--> Early stopping. best_r_p {:0.2f} best_rmse {:0.2f}' - .format(earlyStp.best_r_p, earlyStp.best_rmse)) - return - - # Training done -------------------------------------------------------- - print('--> Training done. best_r_p {:0.2f} best_rmse {:0.2f}' - .format(earlyStp.best_r_p, earlyStp.best_rmse)) - return - - - def _evaluate_mos(self, mapping='first_order', do_print=True, do_plot=False): - ''' - Evaluates the model's predictions. - ''' - print('--> MOS:') - self.db_results, self.r = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='mos', - target_ci='mos_ci', - pred='mos_pred', - mapping=mapping, - do_print=do_print, - do_plot=do_plot - ) - if self.ds_val.df_con is None: - print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' - .format(self.r['r_p_mean_file'], self.r['rmse_mean_file']) - ) - else: - print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' - .format(self.r['r_p_mean_con'], self.r['rmse_mean_con'], self.r['rmse_star_map_mean_con']) - ) - - def _evaluate_dim(self, mapping='first_order', do_print=True, do_plot=False): - ''' - Evaluates the predictions of a multidimensional model. - ''' - print('--> MOS:') - self.db_results_val_mos, r_val_mos = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='mos', - target_ci='mos_ci', - pred='mos_pred', - mapping=mapping, - do_print=do_print, - do_plot=do_plot - ) - if self.ds_val.df_con is None: - print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' - .format(r_val_mos['r_p_mean_file'], r_val_mos['rmse_mean_file']) - ) - else: - print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' - .format(r_val_mos['r_p_mean_con'], r_val_mos['rmse_mean_con'], r_val_mos['rmse_star_map_mean_con']) - ) - - print('--> NOI:') - self.db_results_val_noi, r_val_noi = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='noi', - target_ci='noi_ci', - pred='noi_pred', - mapping=mapping, - do_print=do_print, - do_plot=do_plot - ) - if self.ds_val.df_con is None: - print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' - .format(r_val_noi['r_p_mean_file'], r_val_noi['rmse_mean_file']) - ) - else: - print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}' - .format(r_val_noi['r_p_mean_con'], r_val_noi['rmse_mean_con'], r_val_noi['rmse_star_map_mean_con']) - ) - r_val_noi = {k+'_noi': v for k, v in r_val_noi.items()} - - print('--> DIS:') - self.db_results_val_dis, r_val_dis = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='dis', - target_ci='dis_ci', - pred='dis_pred', - mapping=mapping, - do_print=do_print, - do_plot=do_plot - ) - if self.ds_val.df_con is None: - print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' - .format(r_val_dis['r_p_mean_file'], r_val_dis['rmse_mean_file']) - ) - else: - print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' - .format(r_val_dis['r_p_mean_con'], r_val_dis['rmse_mean_con'], r_val_dis['rmse_star_map_mean_con']) - ) - r_val_dis = {k+'_dis': v for k, v in r_val_dis.items()} - - print('--> COL:') - self.db_results_val_col, r_val_col = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='col', - target_ci='col_ci', - pred='col_pred', - mapping=mapping, - do_print=do_print, - do_plot=do_plot - ) - if self.ds_val.df_con is None: - print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' - .format(r_val_col['r_p_mean_file'], r_val_col['rmse_mean_file']) - ) - else: - print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' - .format(r_val_col['r_p_mean_con'], r_val_col['rmse_mean_con'], r_val_col['rmse_star_map_mean_con']) - ) - r_val_col = {k+'_col': v for k, v in r_val_col.items()} - - print('--> LOUD:') - self.db_results_val_loud, r_val_loud = NL.eval_results( - self.ds_val.df, - dcon=self.ds_val.df_con, - target_mos='loud', - target_ci='loud_ci', - pred='loud_pred', - mapping=mapping, - do_print=do_print, - do_plot=do_plot - ) - if self.ds_val.df_con is None: - print('r_p_mean_file: {:0.2f}, rmse_mean_file: {:0.2f}' - .format(r_val_loud['r_p_mean_file'], r_val_loud['rmse_mean_file']) - ) - else: - print('r_p_mean_con: {:0.2f}, rmse_mean_con: {:0.2f}, rmse_star_map_mean_con: {:0.2f}' - .format(r_val_loud['r_p_mean_con'], r_val_loud['rmse_mean_con'], r_val_loud['rmse_star_map_mean_con']) - ) - r_val_loud = {k+'_loud': v for k, v in r_val_loud.items()} - - self.r = { - **r_val_mos, **r_val_noi, **r_val_dis, **r_val_col, **r_val_loud, } - - r_mean = 1/5 * (self.r['r_p_mean_con'] + - self.r['r_p_mean_con_noi'] + - self.r['r_p_mean_con_col'] + - self.r['r_p_mean_con_dis'] + - self.r['r_p_mean_con_loud']) - - print('\nAverage over MOS and dimensions: r_p={:0.3f}' - .format(r_mean) - ) - - - def _makeRunnameAndWriteYAML(self): - ''' - Creates individual run name. - ''' - runname = self.args['name'] + '_' + self.args['now'].strftime("%y%m%d_%H%M%S%f") - print('runname: ' + runname) - run_output_dir = os.path.join(self.args['output_dir'], runname) - Path(run_output_dir).mkdir(parents=True, exist_ok=True) - yaml_path = os.path.join(run_output_dir, runname+'.yaml') - with open(yaml_path, 'w') as file: - yaml.dump(self.args, file, default_flow_style=None, sort_keys=False) - - return runname - - def _loadDatasets(self): - if self.args['mode']=='predict_file': - self._loadDatasetsFile() - elif self.args['mode']=='predict_dir': - self._loadDatasetsFolder() - elif self.args['mode']=='predict_csv': - self._loadDatasetsCSVpredict() - elif self.args['mode']=='main': - self._loadDatasetsCSV() - else: - raise NotImplementedError('mode not available') - - - def _loadDatasetsFolder(self): - files = list(Path(self.args['data_dir']).rglob('*.wav')) + \ - list(Path(self.args['data_dir']).rglob('*.mp3')) + \ - list(Path(self.args['data_dir']).rglob('*.ogg')) + \ - list(Path(self.args['data_dir']).rglob('*.opus')) + \ - list(Path(self.args['data_dir']).rglob('*.fluc')) - - files = [os.path.basename(files) for files in files] - df_val = pd.DataFrame(files, columns=['deg']) - - print('# files: {}'.format( len(df_val) )) - if len(df_val)==0: - raise ValueError('No wav files found in data_dir') - - # creating Datasets --------------------------------------------------- - self.ds_val = NL.SpeechQualityDataset( - df_val, - df_con=None, - data_dir = self.args['data_dir'], - filename_column = 'deg', - mos_column = 'predict_only', - seg_length = self.args['ms_seg_length'], - max_length = self.args['ms_max_segments'], - to_memory = None, - to_memory_workers = None, - seg_hop_length = self.args['ms_seg_hop_length'], - transform = None, - ms_n_fft = self.args['ms_n_fft'], - ms_hop_length = self.args['ms_hop_length'], - ms_win_length = self.args['ms_win_length'], - ms_n_mels = self.args['ms_n_mels'], - ms_sr = self.args['ms_sr'], - ms_fmax = self.args['ms_fmax'], - ms_channel = self.args['ms_channel'], - double_ended = self.args['double_ended'], - dim = self.args['dim'], - filename_column_ref = None, - ) - - - def _loadDatasetsFile(self): - data_dir = os.path.dirname(self.args['deg']) - file_name = os.path.basename(self.args['deg']) - df_val = pd.DataFrame([file_name], columns=['deg']) - - # creating Datasets --------------------------------------------------- - self.ds_val = NL.SpeechQualityDataset( - df_val, - df_con=None, - data_dir = data_dir, - filename_column = 'deg', - mos_column = 'predict_only', - seg_length = self.args['ms_seg_length'], - max_length = self.args['ms_max_segments'], - to_memory = None, - to_memory_workers = None, - seg_hop_length = self.args['ms_seg_hop_length'], - transform = None, - ms_n_fft = self.args['ms_n_fft'], - ms_hop_length = self.args['ms_hop_length'], - ms_win_length = self.args['ms_win_length'], - ms_n_mels = self.args['ms_n_mels'], - ms_sr = self.args['ms_sr'], - ms_fmax = self.args['ms_fmax'], - ms_channel = self.args['ms_channel'], - double_ended = self.args['double_ended'], - dim = self.args['dim'], - filename_column_ref = None, - ) - - - def _loadDatasetsCSVpredict(self): - ''' - Loads validation dataset for prediction only. - ''' - csv_file_path = os.path.join(self.args['data_dir'], self.args['csv_file']) - dfile = pd.read_csv(csv_file_path) - if 'csv_con' in self.args: - csv_con_file_path = os.path.join(self.args['data_dir'], self.args['csv_con']) - dcon = pd.read_csv(csv_con_file_path) - else: - dcon = None - - - # creating Datasets --------------------------------------------------- - self.ds_val = NL.SpeechQualityDataset( - dfile, - df_con=dcon, - data_dir = self.args['data_dir'], - filename_column = self.args['csv_deg'], - mos_column = 'predict_only', - seg_length = self.args['ms_seg_length'], - max_length = self.args['ms_max_segments'], - to_memory = False, - to_memory_workers = None, - seg_hop_length = self.args['ms_seg_hop_length'], - transform = None, - ms_n_fft = self.args['ms_n_fft'], - ms_hop_length = self.args['ms_hop_length'], - ms_win_length = self.args['ms_win_length'], - ms_n_mels = self.args['ms_n_mels'], - ms_sr = self.args['ms_sr'], - ms_fmax = self.args['ms_fmax'], - ms_channel = self.args['ms_channel'], - double_ended = self.args['double_ended'], - dim = self.args['dim'], - filename_column_ref = self.args['csv_ref'], - ) - - - def _loadDatasetsCSV(self): - ''' - Loads training and validation dataset for training. - ''' - csv_file_path = os.path.join(self.args['data_dir'], self.args['csv_file']) - dfile = pd.read_csv(csv_file_path) - - if not set(self.args['csv_db_train'] + self.args['csv_db_val']).issubset(dfile.db.unique().tolist()): - missing_datasets = set(self.args['csv_db_train'] + self.args['csv_db_val']).difference(dfile.db.unique().tolist()) - raise ValueError('Not all dbs found in csv:', missing_datasets) - - df_train = dfile[dfile.db.isin(self.args['csv_db_train'])].reset_index() - df_val = dfile[dfile.db.isin(self.args['csv_db_val'])].reset_index() - - if self.args['csv_con'] is not None: - csv_con_path = os.path.join(self.args['data_dir'], self.args['csv_con']) - dcon = pd.read_csv(csv_con_path) - dcon_train = dcon[dcon.db.isin(self.args['csv_db_train'])].reset_index() - dcon_val = dcon[dcon.db.isin(self.args['csv_db_val'])].reset_index() - else: - dcon = None - dcon_train = None - dcon_val = None - - print('Training size: {}, Validation size: {}'.format(len(df_train), len(df_val))) - - # creating Datasets --------------------------------------------------- - self.ds_train = NL.SpeechQualityDataset( - df_train, - df_con=dcon_train, - data_dir = self.args['data_dir'], - filename_column = self.args['csv_deg'], - mos_column = self.args['csv_mos_train'], - seg_length = self.args['ms_seg_length'], - max_length = self.args['ms_max_segments'], - to_memory = self.args['tr_ds_to_memory'], - to_memory_workers = self.args['tr_ds_to_memory_workers'], - seg_hop_length = self.args['ms_seg_hop_length'], - transform = None, - ms_n_fft = self.args['ms_n_fft'], - ms_hop_length = self.args['ms_hop_length'], - ms_win_length = self.args['ms_win_length'], - ms_n_mels = self.args['ms_n_mels'], - ms_sr = self.args['ms_sr'], - ms_fmax = self.args['ms_fmax'], - ms_channel = self.args['ms_channel'], - double_ended = self.args['double_ended'], - dim = self.args['dim'], - filename_column_ref = self.args['csv_ref'], - ) - - self.ds_val = NL.SpeechQualityDataset( - df_val, - df_con=dcon_val, - data_dir = self.args['data_dir'], - filename_column = self.args['csv_deg'], - mos_column = self.args['csv_mos_val'], - seg_length = self.args['ms_seg_length'], - max_length = self.args['ms_max_segments'], - to_memory = self.args['tr_ds_to_memory'], - to_memory_workers = self.args['tr_ds_to_memory_workers'], - seg_hop_length = self.args['ms_seg_hop_length'], - transform = None, - ms_n_fft = self.args['ms_n_fft'], - ms_hop_length = self.args['ms_hop_length'], - ms_win_length = self.args['ms_win_length'], - ms_n_mels = self.args['ms_n_mels'], - ms_sr = self.args['ms_sr'], - ms_fmax = self.args['ms_fmax'], - ms_channel = self.args['ms_channel'], - double_ended = self.args['double_ended'], - dim = self.args['dim'], - filename_column_ref = self.args['csv_ref'], - ) - - self.runinfos['ds_train_len'] = len(self.ds_train) - self.runinfos['ds_val_len'] = len(self.ds_val) - - def _loadModel(self): - ''' - Loads the Pytorch models with given input arguments. - ''' - # if True overwrite input arguments from pretrained model - if self.args['pretrained_model']: - if os.path.isabs(self.args['pretrained_model']): - model_path = os.path.join(self.args['pretrained_model']) - else: - model_path = os.path.join(os.getcwd(), self.args['pretrained_model']) - checkpoint = torch.load(model_path, map_location=self.dev, weights_only=True) - - # update checkpoint arguments with new arguments - checkpoint['args'].update(self.args) - self.args = checkpoint['args'] - - if self.args['model']=='NISQA_DIM': - self.args['dim'] = True - self.args['csv_mos_train'] = None # column names hardcoded for dim models - self.args['csv_mos_val'] = None - else: - self.args['dim'] = False - - if self.args['model']=='NISQA_DE': - self.args['double_ended'] = True - else: - self.args['double_ended'] = False - self.args['csv_ref'] = None - - # Load Model - self.model_args = { - - 'ms_seg_length': self.args['ms_seg_length'], - 'ms_n_mels': self.args['ms_n_mels'], - - 'cnn_model': self.args['cnn_model'], - 'cnn_c_out_1': self.args['cnn_c_out_1'], - 'cnn_c_out_2': self.args['cnn_c_out_2'], - 'cnn_c_out_3': self.args['cnn_c_out_3'], - 'cnn_kernel_size': self.args['cnn_kernel_size'], - 'cnn_dropout': self.args['cnn_dropout'], - 'cnn_pool_1': self.args['cnn_pool_1'], - 'cnn_pool_2': self.args['cnn_pool_2'], - 'cnn_pool_3': self.args['cnn_pool_3'], - 'cnn_fc_out_h': self.args['cnn_fc_out_h'], - - 'td': self.args['td'], - 'td_sa_d_model': self.args['td_sa_d_model'], - 'td_sa_nhead': self.args['td_sa_nhead'], - 'td_sa_pos_enc': self.args['td_sa_pos_enc'], - 'td_sa_num_layers': self.args['td_sa_num_layers'], - 'td_sa_h': self.args['td_sa_h'], - 'td_sa_dropout': self.args['td_sa_dropout'], - 'td_lstm_h': self.args['td_lstm_h'], - 'td_lstm_num_layers': self.args['td_lstm_num_layers'], - 'td_lstm_dropout': self.args['td_lstm_dropout'], - 'td_lstm_bidirectional': self.args['td_lstm_bidirectional'], - - 'td_2': self.args['td_2'], - 'td_2_sa_d_model': self.args['td_2_sa_d_model'], - 'td_2_sa_nhead': self.args['td_2_sa_nhead'], - 'td_2_sa_pos_enc': self.args['td_2_sa_pos_enc'], - 'td_2_sa_num_layers': self.args['td_2_sa_num_layers'], - 'td_2_sa_h': self.args['td_2_sa_h'], - 'td_2_sa_dropout': self.args['td_2_sa_dropout'], - 'td_2_lstm_h': self.args['td_2_lstm_h'], - 'td_2_lstm_num_layers': self.args['td_2_lstm_num_layers'], - 'td_2_lstm_dropout': self.args['td_2_lstm_dropout'], - 'td_2_lstm_bidirectional': self.args['td_2_lstm_bidirectional'], - - 'pool': self.args['pool'], - 'pool_att_h': self.args['pool_att_h'], - 'pool_att_dropout': self.args['pool_att_dropout'], - } - - if self.args['double_ended']: - self.model_args.update({ - 'de_align': self.args['de_align'], - 'de_align_apply': self.args['de_align_apply'], - 'de_fuse_dim': self.args['de_fuse_dim'], - 'de_fuse': self.args['de_fuse'], - }) - - print('Model architecture: ' + self.args['model']) - if self.args['model']=='NISQA': - self.model = NL.NISQA(**self.model_args) - elif self.args['model']=='NISQA_DIM': - self.model = NL.NISQA_DIM(**self.model_args) - elif self.args['model']=='NISQA_DE': - self.model = NL.NISQA_DE(**self.model_args) - else: - raise NotImplementedError('Model not available') - - # Load weights if pretrained model is used ------------------------------------ - if self.args['pretrained_model']: - missing_keys, unexpected_keys = self.model.load_state_dict(checkpoint['model_state_dict'], strict=True) - print('Loaded pretrained model from ' + self.args['pretrained_model']) - if missing_keys: - print('missing_keys:') - print(missing_keys) - if unexpected_keys: - print('unexpected_keys:') - print(unexpected_keys) - - def _getDevice(self): - ''' - Train on GPU if available. - ''' - if torch.cuda.is_available(): - self.dev = torch.device("cuda") - else: - self.dev = torch.device("cpu") - - if "tr_device" in self.args: - if self.args['tr_device']=='cpu': - self.dev = torch.device("cpu") - elif self.args['tr_device']=='cuda': - self.dev = torch.device("cuda") - print('Device: {}'.format(self.dev)) - - if "tr_parallel" in self.args: - if (self.dev==torch.device("cpu")) and self.args['tr_parallel']==True: - self.args['tr_parallel']==False - print('Using CPU -> tr_parallel set to False') - - def _saveResults(self, model, model_args, opt, epoch, loss, ep_runtime, r, db_results, best): - ''' - Save model/results in dictionary and write results csv. - ''' - if (self.args['tr_checkpoint'] == 'best_only'): - filename = self.runname + '.tar' - else: - filename = self.runname + '__' + ('ep_{:03d}'.format(epoch+1)) + '.tar' - run_output_dir = os.path.join(self.args['output_dir'], self.runname) - model_path = os.path.join(run_output_dir, filename) - results_path = os.path.join(run_output_dir, self.runname+'__results.csv') - Path(run_output_dir).mkdir(parents=True, exist_ok=True) - - results = { - 'runname': self.runname, - 'epoch': '{:05d}'.format(epoch+1), - 'filename': filename, - 'loss': loss, - 'ep_runtime': '{:0.2f}'.format(ep_runtime), - **self.runinfos, - **r, - **self.args, - } - - for key in results: - results[key] = str(results[key]) - - if epoch==0: - self.results_hist = pd.DataFrame(results, index=[0]) - else: - self.results_hist.loc[epoch] = results - self.results_hist.to_csv(results_path, index=False) - - - if (self.args['tr_checkpoint'] == 'every_epoch') or (self.args['tr_checkpoint'] == 'best_only' and best): - - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - model_name = model.module.name - else: - state_dict = model.state_dict() - model_name = model.name - - torch_dict = { - 'runname': self.runname, - 'epoch': epoch+1, - 'model_args': model_args, - 'args': self.args, - 'model_state_dict': state_dict, - 'optimizer_state_dict': opt.state_dict(), - 'db_results': db_results, - 'results': results, - 'model_name': model_name, - } - - torch.save(torch_dict, model_path) - - elif (self.args['tr_checkpoint']!='every_epoch') and (self.args['tr_checkpoint']!='best_only'): - raise ValueError('selected tr_checkpoint option not available') - - diff --git a/src/libs/NISQA/run_evaluate.py b/src/libs/NISQA/run_evaluate.py deleted file mode 100644 index a81600e..0000000 --- a/src/libs/NISQA/run_evaluate.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@author: Gabriel Mittag, TU-Berlin - -This script evaluates a trained speech quality model on a given Dataset. - -If a 'csv_con' CSV file with per-condition results is provided, both the -per-file CSV and per-condition CSV need a column with the name 'con' that -contains the condition number. If 'csv_con' stays empty, only per-file results -are calculated. - -""" -from nisqa.NISQA_model import nisqaModel - -if __name__ == "__main__": - - args = { - 'mode': 'predict_csv', - 'pretrained_model': 'weights/nisqa.tar', - 'data_dir': r'C:\Users\Name\Downloads\NISQA_Corpus', - 'output_dir': r'C:\Users\Name\Downloads\NISQA_Corpus', - 'csv_file': 'NISQA_corpus_file.csv', - 'csv_con': 'NISQA_corpus_con.csv', - 'csv_deg': 'filepath_deg', - 'csv_mos_val': 'mos', - 'tr_num_workers': 6, - 'tr_bs_val': 40, - 'ms_channel': None, - } - - nisqa = nisqaModel(args) - nisqa.predict() - nisqa.evaluate( - mapping='first_order', - do_print=True, - do_plot=True - ) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/src/libs/NISQA/run_predict.py b/src/libs/NISQA/run_predict.py deleted file mode 100644 index 97263d6..0000000 --- a/src/libs/NISQA/run_predict.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@author: Gabriel Mittag, TU-Berlin -""" -from .nisqa.NISQA_model import nisqaModel -import argparse - -def run_nisqa_with_config(config): - if 'mode' not in config: - raise ValueError('mode is required in config') - if 'pretrained_model' not in config: - raise ValueError('pretrained_model is required in config') - - if config['mode'] == 'predict_file': - if 'deg' not in config or config['deg'] is None: - raise ValueError('deg argument with path to input file needed') - elif config['mode'] == 'predict_dir': - if 'data_dir' not in config or config['data_dir'] is None: - raise ValueError('data_dir argument with folder with input files needed') - elif config['mode'] == 'predict_csv': - if 'csv_file' not in config or config['csv_file'] is None: - raise ValueError('csv_file argument with csv file name needed') - if 'csv_deg' not in config or config['csv_deg'] is None: - raise ValueError('csv_deg argument with csv column name of the filenames needed') - if 'data_dir' not in config or config['data_dir'] is None: - config['data_dir'] = '' - else: - raise NotImplementedError('mode given not available') - - config['tr_parallel'] = True - config['tr_bs_val'] = config.get('bs', 1) - config['tr_num_workers'] = config.get('num_workers', 0) - - nisqa = nisqaModel(config) - nisqa.predict() \ No newline at end of file diff --git a/src/libs/NISQA/run_train.py b/src/libs/NISQA/run_train.py deleted file mode 100644 index 40759af..0000000 --- a/src/libs/NISQA/run_train.py +++ /dev/null @@ -1,57 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Apr 27 16:52:15 2020 - -@author: Gabriel -""" - -import yaml -from nisqa.NISQA_model import nisqaModel -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument('--yaml', required=True, type=str, help='YAML file with config') - -args = parser.parse_args() -args = vars(args) - -if __name__ == "__main__": - - with open(args['yaml'], "r") as ymlfile: - args_yaml = yaml.load(ymlfile, Loader=yaml.FullLoader) - args = {**args_yaml, **args} - - nisqa = nisqaModel(args) - nisqa.train() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/src/libs/NISQA/weights/LICENSE_model_weights b/src/libs/NISQA/weights/LICENSE_model_weights deleted file mode 100644 index 4fae8d5..0000000 --- a/src/libs/NISQA/weights/LICENSE_model_weights +++ /dev/null @@ -1,437 +0,0 @@ -Attribution-NonCommercial-ShareAlike 4.0 International - -======================================================================= - -Creative Commons Corporation ("Creative Commons") is not a law firm and -does not provide legal services or legal advice. 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AutoFeatureExtractor.from_pretrained(model_name) @@ -34,8 +34,7 @@ def create_loader(paths: List[str], model_name: str, batch_size: int, num_worker batch_sampler=sampler, collate_fn=AudioCollate(processor), num_workers=num_workers, - pin_memory=True, - persistent_workers=num_workers > 0 + pin_memory=True ) def load_model(model_path: str, base_model: str, device: torch.device): @@ -79,11 +78,12 @@ def run_worker(rank: int, world_size: int, all_paths: List[str], config: dict): # 2. Inference Loop deleted_count = 0 with torch.inference_mode(): - for batch, paths in tqdm(dataloader, desc=f"Worker-{rank}", position=rank): - if batch is None: continue + for batch in tqdm(dataloader, desc=f"Worker-{rank}", position=rank): + if batch is None: + continue # Forward pass - probs = model(batch['input_values'].to(device)) + probs, paths = model.predict_proba(batch) # Zip paths with probabilities -> Tuple(path, prob)[[]] predictions = zip(paths, probs.detach().flatten()) diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 6b8ffaa..146e8dd 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -117,6 +117,7 @@ def run_inference_on_device(cuda_id: int, world_size: int, model_name: str, all_ if not batch_audios: continue if process_timestamps: + # tone model does not support batch processing texts, tstamps = model.transcribe_audio_data_with_timestamps(batch_audios) else: texts = model.transcribe_audio_data(batch_audios) diff --git a/src/transcription/transcripton_dataset.py b/src/transcription/transcripton_dataset.py index 08fbbf2..d9c221c 100644 --- a/src/transcription/transcripton_dataset.py +++ b/src/transcription/transcripton_dataset.py @@ -2,8 +2,8 @@ import json import torch import torchaudio -import logging from typing import List, Tuple, Optional +import miniaudio from torch.utils.data import Dataset, Sampler from torch.nn.utils.rnn import pad_sequence import numpy as np @@ -109,19 +109,8 @@ def __getitem__(self, idx): if not os.path.exists(path): return None, path - waveform, sr = torchaudio.load(path) - - if sr != self.target_sr: - waveform = torchaudio.functional.resample( - waveform, orig_freq=sr, new_freq=self.target_sr - ) - - if waveform.shape[0] > 1: - waveform = waveform.mean(dim=0, keepdim=True) - - waveform = (waveform * 32768.0).clamp(-32768, 32767).to(torch.int32) - - audio_np = waveform.squeeze(0).numpy() + audio = miniaudio.decode_file(str(path), nchannels=1, sample_rate=self.target_sr) + audio_np = np.asarray(audio.samples, dtype=np.int16).astype(np.int32) return audio_np, path From f7853e5e57918d42c0ecf1d9268978895064b695 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Mon, 8 Dec 2025 22:50:45 +0000 Subject: [PATCH 17/43] feat: upgrade to gigaamv3 --- requirements_dev.txt | Bin 1616 -> 1534 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/requirements_dev.txt b/requirements_dev.txt index a6ee83e26bace08d98b81c97b0715e301171377c..d10a3f5afa0f638617f8d82287cbacc4eb2fa039 100644 GIT binary patch delta 20 ccmcb>^N)MO5$4HJ%tn(pun29AV)@1h09s53-~a#s delta 102 zcmeyzeSv4g5#~S#1|xM~1> Date: Thu, 11 Dec 2025 21:45:13 +0000 Subject: [PATCH 18/43] feat: dinamic consensus_num --- configs/config.yaml | 19 ++++++++++--------- src/utils/utils.py | 9 ++++++++- 2 files changed, 18 insertions(+), 10 deletions(-) diff --git a/configs/config.yaml b/configs/config.yaml index d726eb6..901a0c6 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,13 @@ cache_path: ./cache download: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k + podcasts_path: /home/nikita/balalaika_pipe_datasets/test episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the podcasts are located + podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 2 vad_args: @@ -17,11 +17,11 @@ preprocess: separation: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located diarization: num_workers: 1 # per gpu - one_speaker: True # When this option is enabled, only single-speaker audio recordings are downloaded. + one_speaker: False # When this option is enabled, only single-speaker audio recordings are downloaded. nisqa: bs: 32 @@ -35,9 +35,10 @@ separation: threshold: 0.6 transcription: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located + consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True - model_names: ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] + model_names: ['giga_ctc', 'giga_rnnt', 'giga_ctc_lm', 'vosk', 'tone',] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] giga: num_workers: 1 @@ -57,15 +58,15 @@ transcription: batch_size: 16 # if supported punctuation: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /mnt/drive2_8tb/yt-subs-rus-6k # full path to the folder where the text files are located + podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/src/utils/utils.py b/src/utils/utils.py index c15ce3e..25e538a 100644 --- a/src/utils/utils.py +++ b/src/utils/utils.py @@ -2,6 +2,7 @@ from typing import List import yaml from loguru import logger +import re def load_config(config_path: str, process_name: str): config = {} @@ -106,4 +107,10 @@ def process_token(token, label): if label == "UPPER_TOTAL_MNOGOTOCHIE": return token.upper() + "..." if label == "UPPER_TOTAL_QUESTIONVOSKL": - return token.upper() + "?!" \ No newline at end of file + return token.upper() + "?!" + +def normalize_text(text: str) -> str: + text = text.lower().strip() + text = re.sub(r'[^\w\s]', '', text) + text = re.sub(r'\s+', ' ', text) + return text \ No newline at end of file From 894dbe6546e53ec4c680f41f52966fad81853521 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Mon, 15 Dec 2025 01:23:58 +0000 Subject: [PATCH 19/43] feat: update imports, fix transcription block --- base.sh | 4 +- configs/config.yaml | 14 +- configs/nisqa_b.yaml | 2 +- docs/guide.md | 6 +- src/accents/accents.py | 2 +- src/collate.py | 4 +- src/download/download.py | 2 +- src/preprocess/preprocess.py | 4 +- src/punctuation/punctuation.py | 2 +- src/separation/diarization.py | 16 +- src/separation/music_detect.py | 2 +- src/separation/nisqa_process.py | 2 +- src/separation/separation_yaml.sh | 7 +- src/transcription/transcription.py | 3 +- src/transcription/transcripton_base.py | 8 +- src/utils.py | 109 ------ src/utils_asr.py | 490 ------------------------- 17 files changed, 41 insertions(+), 636 deletions(-) delete mode 100644 src/utils.py delete mode 100644 src/utils_asr.py diff --git a/base.sh b/base.sh index f424d48..1423f68 100644 --- a/base.sh +++ b/base.sh @@ -17,11 +17,11 @@ if [ -z "${1:-}" ]; then fi CONFIG_PATH=$(realpath "$1") - +echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" - "./src/separation/separation_yaml.sh" + # "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" "./src/punctuation/punctuation_yaml.sh" "./src/accents/accents_yaml.sh" diff --git a/configs/config.yaml b/configs/config.yaml index 901a0c6..bded615 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,13 @@ cache_path: ./cache download: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test + podcasts_path: /mnt/drive2_8tb/ruls/train/audio episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the podcasts are located + podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 2 vad_args: @@ -17,7 +17,7 @@ preprocess: separation: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located diarization: num_workers: 1 # per gpu @@ -35,7 +35,7 @@ separation: threshold: 0.6 transcription: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True model_names: ['giga_ctc', 'giga_rnnt', 'giga_ctc_lm', 'vosk', 'tone',] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] @@ -58,15 +58,15 @@ transcription: batch_size: 16 # if supported punctuation: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /home/nikita/balalaika_pipe_datasets/test # full path to the folder where the text files are located + podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/configs/nisqa_b.yaml b/configs/nisqa_b.yaml index cee571e..1e17354 100644 --- a/configs/nisqa_b.yaml +++ b/configs/nisqa_b.yaml @@ -69,7 +69,7 @@ td_lstm_dropout: 0 td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 # Arguments for inference -ckp: /home/nikita/yapoddataset/src/libs/NISQA/weights/nisqa.tar # checkpoint that will be used for inference +ckp: /home/nikita/yapoddataset/models/nisqa.tar # checkpoint that will be used for inference sample: src/sample/gt.wav # path to file to run on inf_device: cuda # device for inference runs warmup: False # warmup run before inference; usually is not needed on CPU runs diff --git a/docs/guide.md b/docs/guide.md index e497e95..1f96a7e 100644 --- a/docs/guide.md +++ b/docs/guide.md @@ -3,11 +3,9 @@ python -m pip install git+https://github.com/lhotse-speech/lhotse ``` ```cmd -python -m pip install pip install https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.4.dev20250807+cuda12.9.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl -``` +python -m pip install https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.4.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl ```cmd -python -m pip install kaldifeat==1.25.5.dev20250203+cuda12.4.torch2.5.1 -f https://csukuangfj.github.io -/kaldifeat/cuda.html +python -m pip install https://huggingface.co/csukuangfj/kaldifeat/resolve/main/cuda/1.25.5.dev20241029/linux/kaldifeat-1.25.5.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl ``` ``` python3 -m pip install git+https://github.com/k2-fsa/icefall diff --git a/src/accents/accents.py b/src/accents/accents.py index 46d2b67..d4a9d6c 100644 --- a/src/accents/accents.py +++ b/src/accents/accents.py @@ -10,7 +10,7 @@ from ruaccent import RUAccent from tqdm import tqdm -from src.utils import load_config, get_txt_paths, read_file_content +from src.utils.utils import load_config, get_txt_paths, read_file_content torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) diff --git a/src/collate.py b/src/collate.py index 59be13d..aeadce9 100644 --- a/src/collate.py +++ b/src/collate.py @@ -6,7 +6,7 @@ import concurrent.futures from loguru import logger -from src.utils import load_config, read_file_content, get_audio_paths +from src.utils.utils import load_config, read_file_content, get_audio_paths def process_audio_file(audio_path_str: str, base_path: Path) -> Dict[str, Optional[str]]: @@ -18,7 +18,7 @@ def process_audio_file(audio_path_str: str, base_path: Path) -> Dict[str, Option 'accent': '_accent.txt', 'rover': '_rover.txt', 'punct': '_punct.txt', - 'phonemes': '_rover_phonemes.txt' + 'phonemes': '_rover_phonemes.txt', } results = {'filepath': audio_path_str} diff --git a/src/download/download.py b/src/download/download.py index 7a7a939..0baef2b 100644 --- a/src/download/download.py +++ b/src/download/download.py @@ -11,7 +11,7 @@ from loguru import logger from yandex_music import Client -from src.utils import load_config +from src.utils.utils import load_config def init_client(client_key): try: diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index 99dcec9..fffbddc 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -12,7 +12,7 @@ from huggingface_hub import login -from src.utils import load_config, get_audio_paths +from src.utils.utils import load_config, get_audio_paths from src.libs.smart_turn.offline_svad import OfflineVAD from typing import List, Dict, Any, Tuple @@ -198,7 +198,7 @@ def process_audio_file(path_audio: str, duration: float): # TODO: don't forget to remove the code audio, sr = torchaudio.load(path_audio) - if audio.shape[-1] / sr <= 2: + if (audio.shape[-1] / sr) <= 2 or(audio.shape[-1] / sr >= 12): logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") os.remove(path_audio) # # TODO: don't forget to remove the code diff --git a/src/punctuation/punctuation.py b/src/punctuation/punctuation.py index 023d883..766a9e0 100644 --- a/src/punctuation/punctuation.py +++ b/src/punctuation/punctuation.py @@ -10,7 +10,7 @@ from tqdm import tqdm from transformers import pipeline, AutoTokenizer -from src.utils import load_config, get_audio_paths, process_token, read_file_content +from src.utils.utils import load_config, get_audio_paths, process_token, read_file_content torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) diff --git a/src/separation/diarization.py b/src/separation/diarization.py index 87b134e..cf20cfa 100644 --- a/src/separation/diarization.py +++ b/src/separation/diarization.py @@ -14,7 +14,7 @@ from pyannote.audio import Pipeline from tqdm import tqdm -from src.utils import get_audio_paths, load_config +from src.utils.utils import get_audio_paths, load_config torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) @@ -108,17 +108,23 @@ def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: str) -> Lis if result_csv_path.exists(): logger.info(f"Resuming from existing results file: {result_csv_path}") df = pd.read_csv(result_csv_path) - processed_audio_paths = set(df[df[['is_single_speaker']].notna()].to_list()) - - unprocessed_paths = all_audio_paths - processed_audio_paths + if 'is_single_speaker' not in df.columns: + logger.warning("Column 'is_single_speaker' not found in results file. Treating all entries as unprocessed.") + processed_audio_paths = set() + else: + processed_mask = df['is_single_speaker'].notna() + processed_audio_paths = set(df.loc[processed_mask, 'filepath'].astype(str).tolist()) + else: + return all_audio_paths + unprocessed_paths = all_audio_paths - processed_audio_paths return list(unprocessed_paths) def main(args): load_dotenv() hf_token = os.getenv("HF_TOKEN") - + print(args.config_path) config = load_config(args.config_path, 'separation') podcasts_path = config.get('podcasts_path', './data') diff --git a/src/separation/music_detect.py b/src/separation/music_detect.py index 8324b21..7c79a45 100644 --- a/src/separation/music_detect.py +++ b/src/separation/music_detect.py @@ -14,7 +14,7 @@ from musicdetection.dataset import MusicDetectionDataset, AudioCollate from musicdetection.core.model import WavLMForMusicDetection from musicdetection.audio_sampler import LengthBasedBatchSampler -from src.utils import get_audio_paths, load_config +from src.utils.utils import get_audio_paths, load_config # Optimizations torch.backends.cuda.matmul.allow_tf32 = True diff --git a/src/separation/nisqa_process.py b/src/separation/nisqa_process.py index a56b24e..a384756 100644 --- a/src/separation/nisqa_process.py +++ b/src/separation/nisqa_process.py @@ -11,7 +11,7 @@ from torch.utils.data import DataLoader from tqdm import tqdm -from src.utils import get_audio_paths, load_config +from src.utils.utils import get_audio_paths, load_config from nisqab.core.model_torch import model_init from nisqab.utils.dataset import NISQADataset, collate_fn diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index d9c5e90..3305f24 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -16,11 +16,10 @@ if [ -z "${1:-}" ]; then fi CONFIG_PATH=$(realpath "$1") - activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -# python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" -taskset -c 0-32 python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" -python3 -m src.separation.diarization --config_path "$CONFIG_PATH" \ No newline at end of file +python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" +python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" +python3 -m src.separation.diarization --config_path "$CONFIG_PATH" diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 146e8dd..943cdbc 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -17,7 +17,7 @@ ToneAudioDataset, collate_tone, VoskAudioDataset, collate_vosk ) -from src.utils import get_audio_paths, load_config +from src.utils.utils import get_audio_paths, load_config torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) @@ -117,7 +117,6 @@ def run_inference_on_device(cuda_id: int, world_size: int, model_name: str, all_ if not batch_audios: continue if process_timestamps: - # tone model does not support batch processing texts, tstamps = model.transcribe_audio_data_with_timestamps(batch_audios) else: texts = model.transcribe_audio_data(batch_audios) diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py index 71a74d0..f4be06b 100644 --- a/src/transcription/transcripton_base.py +++ b/src/transcription/transcripton_base.py @@ -19,9 +19,11 @@ from tqdm import tqdm from huggingface_hub import snapshot_download, hf_hub_download -from src.utils import read_file_content, get_audio_paths -from src.utils_asr import (AttributeDict, LmScorer, NgramLm, - modified_beam_search_LODR) +from src.utils.utils import read_file_content, get_audio_paths +from src.utils.vosk_utils import ( + AttributeDict, LmScorer, NgramLm, + modified_beam_search_LODR + ) class ASRWrapper(ABC): diff --git a/src/utils.py b/src/utils.py deleted file mode 100644 index c15ce3e..0000000 --- a/src/utils.py +++ /dev/null @@ -1,109 +0,0 @@ -from pathlib import Path -from typing import List -import yaml -from loguru import logger - -def load_config(config_path: str, process_name: str): - config = {} - if config_path is None or process_name is None: - logger.info("Configuration not provided. Parameters will be taken from argparse.") - return config - try: - with open(config_path, 'r') as config_file: - config = yaml.safe_load(config_file) - config = config.get(process_name, {}) - logger.info('Loaded parameters from config') - except Exception as e: - logger.error(f"Configuration loading error: {e}") - - finally: - return config - -def get_txt_paths(podcast_path: str, postfix: str) -> List[Path]: - return list(Path(podcast_path).rglob(f"*{postfix}")) - -def read_file_content(file_path): - try: - with open(file_path, 'r', encoding='utf-8') as f: - return f.read().strip() - except FileNotFoundError: - return '' - -def get_audio_paths(podcast_path: str): - podcast_path=Path(podcast_path) - return ( - list(podcast_path.rglob("*.mp3")) + - list(podcast_path.rglob("*.wav")) + - list(podcast_path.rglob("*.flac")) + - list(podcast_path.rglob("*.ogg")) + - list(podcast_path.rglob("*.opus")) - ) - - -def process_token(token, label): - if label == "LOWER_O": - return token - if label == "LOWER_PERIOD": - return token + "." - if label == "LOWER_COMMA": - return token + "," - if label == "LOWER_QUESTION": - return token + "?" - if label == "LOWER_TIRE": - return token + "—" - if label == "LOWER_DVOETOCHIE": - return token + ":" - if label == "LOWER_VOSKL": - return token + "!" - if label == "LOWER_PERIODCOMMA": - return token + ";" - if label == "LOWER_DEFIS": - return token + "-" - if label == "LOWER_MNOGOTOCHIE": - return token + "..." - if label == "LOWER_QUESTIONVOSKL": - return token + "?!" - if label == "UPPER_O": - return token.capitalize() - if label == "UPPER_PERIOD": - return token.capitalize() + "." - if label == "UPPER_COMMA": - return token.capitalize() + "," - if label == "UPPER_QUESTION": - return token.capitalize() + "?" - if label == "UPPER_TIRE": - return token.capitalize() + " —" - if label == "UPPER_DVOETOCHIE": - return token.capitalize() + ":" - if label == "UPPER_VOSKL": - return token.capitalize() + "!" - if label == "UPPER_PERIODCOMMA": - return token.capitalize() + ";" - if label == "UPPER_DEFIS": - return token.capitalize() + "-" - if label == "UPPER_MNOGOTOCHIE": - return token.capitalize() + "..." - if label == "UPPER_QUESTIONVOSKL": - return token.capitalize() + "?!" - if label == "UPPER_TOTAL_O": - return token.upper() - if label == "UPPER_TOTAL_PERIOD": - return token.upper() + "." - if label == "UPPER_TOTAL_COMMA": - return token.upper() + "," - if label == "UPPER_TOTAL_QUESTION": - return token.upper() + "?" - if label == "UPPER_TOTAL_TIRE": - return token.upper() + " —" - if label == "UPPER_TOTAL_DVOETOCHIE": - return token.upper() + ":" - if label == "UPPER_TOTAL_VOSKL": - return token.upper() + "!" - if label == "UPPER_TOTAL_PERIODCOMMA": - return token.upper() + ";" - if label == "UPPER_TOTAL_DEFIS": - return token.upper() + "-" - if label == "UPPER_TOTAL_MNOGOTOCHIE": - return token.upper() + "..." - if label == "UPPER_TOTAL_QUESTIONVOSKL": - return token.upper() + "?!" \ No newline at end of file diff --git a/src/utils_asr.py b/src/utils_asr.py deleted file mode 100644 index 3dea75f..0000000 --- a/src/utils_asr.py +++ /dev/null @@ -1,490 +0,0 @@ - -import argparse -import logging -import math -import random -import os -import sys -import warnings -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple, Union - -import kaldifeat -import sentencepiece as spm -import torch -import torchaudio -from torch.nn.utils.rnn import pad_sequence -from timeit import default_timer as timer - -from icefall import NgramLm, NgramLmStateCost -from icefall.decode import Nbest, one_best_decoding -from icefall.lm_wrapper import LmScorer -from icefall.rnn_lm.model import RnnLmModel -from icefall.transformer_lm.model import TransformerLM -from icefall.utils import AttributeDict -from icefall.lexicon import Lexicon - -import k2 - -def read_sound_files( - filenames: List[str], expected_sample_rate: float = 16000 -) -> List[torch.Tensor]: - """Read a list of sound files into a list 1-D float32 torch tensors. - Args: - filenames: - A list of sound filenames. - expected_sample_rate: - The expected sample rate of the sound files. - Returns: - Return a list of 1-D float32 torch tensors. - """ - ans = [] - for f in filenames: - wave, sample_rate = torchaudio.load(f) - resampler = torchaudio.transforms.Resample(sample_rate, 16_000) - wav = resampler(wave[0]) - ans.append(wav) - return ans - - - -@dataclass -class Hypothesis: - # The predicted tokens so far. - # Newly predicted tokens are appended to `ys`. - ys: List[int] - - # The log prob of ys. - # It contains only one entry. - log_prob: torch.Tensor - - # timestamp[i] is the frame index after subsampling - # on which ys[i] is decoded - timestamp: List[int] = field(default_factory=list) - - # the lm score for next token given the current ys - lm_score: Optional[torch.Tensor] = None - - # the RNNLM states (h and c in LSTM) - state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None - - # N-gram LM state - state_cost: Optional[NgramLmStateCost] = None - - @property - def key(self) -> str: - """Return a string representation of self.ys""" - return "_".join(map(str, self.ys)) - - -class HypothesisList(object): - def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None: - """ - Args: - data: - A dict of Hypotheses. Its key is its `value.key`. - """ - if data is None: - self._data = {} - else: - self._data = data - - @property - def data(self) -> Dict[str, Hypothesis]: - return self._data - - def add(self, hyp: Hypothesis) -> None: - """Add a Hypothesis to `self`. - - If `hyp` already exists in `self`, its probability is updated using - `log-sum-exp` with the existed one. - - Args: - hyp: - The hypothesis to be added. - """ - key = hyp.key - if key in self: - old_hyp = self._data[key] # shallow copy - torch.logaddexp(old_hyp.log_prob, hyp.log_prob, out=old_hyp.log_prob) - else: - self._data[key] = hyp - - def get_most_probable(self, length_norm: bool = False) -> Hypothesis: - """Get the most probable hypothesis, i.e., the one with - the largest `log_prob`. - - Args: - length_norm: - If True, the `log_prob` of a hypothesis is normalized by the - number of tokens in it. - Returns: - Return the hypothesis that has the largest `log_prob`. - """ - if length_norm: - return max(self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys)) - else: - return max(self._data.values(), key=lambda hyp: hyp.log_prob) - - def remove(self, hyp: Hypothesis) -> None: - """Remove a given hypothesis. - - Caution: - `self` is modified **in-place**. - - Args: - hyp: - The hypothesis to be removed from `self`. - Note: It must be contained in `self`. Otherwise, - an exception is raised. - """ - key = hyp.key - assert key in self, f"{key} does not exist" - del self._data[key] - - def filter(self, threshold: torch.Tensor) -> "HypothesisList": - """Remove all Hypotheses whose log_prob is less than threshold. - - Caution: - `self` is not modified. Instead, a new HypothesisList is returned. - - Returns: - Return a new HypothesisList containing all hypotheses from `self` - with `log_prob` being greater than the given `threshold`. - """ - ans = HypothesisList() - for _, hyp in self._data.items(): - if hyp.log_prob > threshold: - ans.add(hyp) # shallow copy - return ans - - def topk(self, k: int, length_norm: bool = False) -> "HypothesisList": - """Return the top-k hypothesis. - - Args: - length_norm: - If True, the `log_prob` of a hypothesis is normalized by the - number of tokens in it. - """ - hyps = list(self._data.items()) - - if length_norm: - hyps = sorted( - hyps, key=lambda h: h[1].log_prob / len(h[1].ys), reverse=True - )[:k] - else: - hyps = sorted(hyps, key=lambda h: h[1].log_prob, reverse=True)[:k] - - ans = HypothesisList(dict(hyps)) - return ans - - def __contains__(self, key: str): - return key in self._data - - def __iter__(self): - return iter(self._data.values()) - - def __len__(self) -> int: - return len(self._data) - - def __str__(self) -> str: - s = [] - for key in self: - s.append(key) - return ", ".join(s) - - -def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape: - """Return a ragged shape with axes [utt][num_hyps]. - - Args: - hyps: - len(hyps) == batch_size. It contains the current hypothesis for - each utterance in the batch. - Returns: - Return a ragged shape with 2 axes [utt][num_hyps]. Note that - the shape is on CPU. - """ - num_hyps = [len(h) for h in hyps] - - # torch.cumsum() is inclusive sum, so we put a 0 at the beginning - # to get exclusive sum later. - num_hyps.insert(0, 0) - - num_hyps = torch.tensor(num_hyps) - row_splits = torch.cumsum(num_hyps, dim=0, dtype=torch.int32) - ans = k2.ragged.create_ragged_shape2( - row_splits=row_splits, cached_tot_size=row_splits[-1].item() - ) - return ans - - -def modified_beam_search_LODR( - model, - encoder_out: torch.Tensor, - encoder_out_lens: torch.Tensor, - LODR_lm: NgramLm, - LODR_lm_scale: float, - LM: LmScorer, - beam: int = 4, -) -> List[List[int]]: - """This function implements LODR (https://arxiv.org/abs/2203.16776) with - `modified_beam_search`. It uses a bi-gram language model as the estimate - of the internal language model and subtracts its score during shallow fusion - with an external language model. This implementation uses a RNNLM as the - external language model. - - Args: - model (Transducer): - The transducer model - encoder_out (torch.Tensor): - Encoder output in (N,T,C) - encoder_out_lens (torch.Tensor): - A 1-D tensor of shape (N,), containing the number of - valid frames in encoder_out before padding. - LODR_lm: - A low order n-gram LM, whose score will be subtracted during shallow fusion - LODR_lm_scale: - The scale of the LODR_lm - LM: - A neural net LM, e.g an RNNLM or transformer LM - beam (int, optional): - Beam size. Defaults to 4. - - Returns: - Return a list-of-list of token IDs. ans[i] is the decoding results - for the i-th utterance. - - """ - assert encoder_out.ndim == 3, encoder_out.shape - assert encoder_out.size(0) >= 1, encoder_out.size(0) - assert LM is not None - lm_scale = LM.lm_scale - - packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( - input=encoder_out, - lengths=encoder_out_lens.cpu(), - batch_first=True, - enforce_sorted=False, - ) - - blank_id = model.decoder.blank_id - sos_id = getattr(LM, "sos_id", 1) - unk_id = getattr(model, "unk_id", blank_id) - context_size = model.decoder.context_size - device = next(model.parameters()).device - - batch_size_list = packed_encoder_out.batch_sizes.tolist() - N = encoder_out.size(0) - assert torch.all(encoder_out_lens > 0), encoder_out_lens - assert N == batch_size_list[0], (N, batch_size_list) - - # get initial lm score and lm state by scoring the "sos" token - sos_token = torch.tensor([[sos_id]]).to(torch.int64).to(device) - lens = torch.tensor([1]).to(device) - init_score, init_states = LM.score_token(sos_token, lens) - - B = [HypothesisList() for _ in range(N)] - for i in range(N): - B[i].add( - Hypothesis( - ys=([-1] * (context_size - 1) + [blank_id]), - log_prob=torch.zeros(1, dtype=torch.float32, device=device), - state=init_states, # state of the NN LM - lm_score=init_score.reshape(-1), - state_cost=NgramLmStateCost( - LODR_lm - ), # state of the source domain ngram - ) - ) - - encoder_out = model.joiner.encoder_proj(packed_encoder_out.data) - - offset = 0 - finalized_B = [] - for batch_size in batch_size_list: - start = offset - end = offset + batch_size - current_encoder_out = encoder_out.data[start:end] # get batch - current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) - # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim) - offset = end - - finalized_B = B[batch_size:] + finalized_B - B = B[:batch_size] - - hyps_shape = get_hyps_shape(B).to(device) - - A = [list(b) for b in B] - B = [HypothesisList() for _ in range(batch_size)] - - ys_log_probs = torch.cat( - [hyp.log_prob.reshape(1, 1) for hyps in A for hyp in hyps] - ) - - decoder_input = torch.tensor( - [hyp.ys[-context_size:] for hyps in A for hyp in hyps], - device=device, - dtype=torch.int64, - ) # (num_hyps, context_size) - - decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1) - decoder_out = model.joiner.decoder_proj(decoder_out) - - current_encoder_out = torch.index_select( - current_encoder_out, - dim=0, - index=hyps_shape.row_ids(1).to(torch.int64), - ) # (num_hyps, 1, 1, encoder_out_dim) - - logits = model.joiner( - current_encoder_out, - decoder_out, - project_input=False, - ) # (num_hyps, 1, 1, vocab_size) - - logits = logits.squeeze(1).squeeze(1) # (num_hyps, vocab_size) - - log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size) - - log_probs.add_(ys_log_probs) - - vocab_size = log_probs.size(-1) - - log_probs = log_probs.reshape(-1) - - row_splits = hyps_shape.row_splits(1) * vocab_size - log_probs_shape = k2.ragged.create_ragged_shape2( - row_splits=row_splits, cached_tot_size=log_probs.numel() - ) - ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs) - """ - for all hyps with a non-blank new token, score this token. - It is a little confusing here because this for-loop - looks very similar to the one below. Here, we go through all - top-k tokens and only add the non-blanks ones to the token_list. - LM will score those tokens given the LM states. Note that - the variable `scores` is the LM score after seeing the new - non-blank token. - """ - token_list = [] - hs = [] - cs = [] - for i in range(batch_size): - topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) - - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - topk_hyp_indexes = (topk_indexes // vocab_size).tolist() - topk_token_indexes = (topk_indexes % vocab_size).tolist() - for k in range(len(topk_hyp_indexes)): - hyp_idx = topk_hyp_indexes[k] - hyp = A[i][hyp_idx] - - new_token = topk_token_indexes[k] - if new_token not in (blank_id, unk_id): - if LM.lm_type == "rnn": - token_list.append([new_token]) - # store the LSTM states - hs.append(hyp.state[0]) - cs.append(hyp.state[1]) - else: - # for transformer LM - token_list.append( - [sos_id] + hyp.ys[context_size:] + [new_token] - ) - - # forward NN LM to get new states and scores - if len(token_list) != 0: - x_lens = torch.tensor([len(tokens) for tokens in token_list]).to(device) - if LM.lm_type == "rnn": - tokens_to_score = ( - torch.tensor(token_list).to(torch.int64).to(device).reshape(-1, 1) - ) - hs = torch.cat(hs, dim=1).to(device) - cs = torch.cat(cs, dim=1).to(device) - state = (hs, cs) - else: - # for transformer LM - tokens_list = [torch.tensor(tokens) for tokens in token_list] - tokens_to_score = ( - torch.nn.utils.rnn.pad_sequence( - tokens_list, batch_first=True, padding_value=0.0 - ) - .to(device) - .to(torch.int64) - ) - - state = None - - scores, lm_states = LM.score_token(tokens_to_score, x_lens, state) - - count = 0 # index, used to locate score and lm states - for i in range(batch_size): - topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) - - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - topk_hyp_indexes = (topk_indexes // vocab_size).tolist() - topk_token_indexes = (topk_indexes % vocab_size).tolist() - - for k in range(len(topk_hyp_indexes)): - hyp_idx = topk_hyp_indexes[k] - hyp = A[i][hyp_idx] - - ys = hyp.ys[:] - - # current score of hyp - lm_score = hyp.lm_score - state = hyp.state - - hyp_log_prob = topk_log_probs[k] # get score of current hyp - new_token = topk_token_indexes[k] - if new_token not in (blank_id, unk_id): - - ys.append(new_token) - state_cost = hyp.state_cost.forward_one_step(new_token) - - # calculate the score of the latest token - current_ngram_score = state_cost.lm_score - hyp.state_cost.lm_score - - assert current_ngram_score <= 0.0, ( - state_cost.lm_score, - hyp.state_cost.lm_score, - ) - # score = score + TDLM_score - LODR_score - # LODR_LM_scale should be a negative number here - hyp_log_prob += ( - lm_score[new_token] * lm_scale - + LODR_lm_scale * current_ngram_score - ) # add the lm score - - lm_score = scores[count] - if LM.lm_type == "rnn": - state = ( - lm_states[0][:, count, :].unsqueeze(1), - lm_states[1][:, count, :].unsqueeze(1), - ) - count += 1 - else: - state_cost = hyp.state_cost - - new_hyp = Hypothesis( - ys=ys, - log_prob=hyp_log_prob, - state=state, - lm_score=lm_score, - state_cost=state_cost, - ) - B[i].add(new_hyp) - - B = B + finalized_B - best_hyps = [b.get_most_probable(length_norm=True) for b in B] - - sorted_ans = [h.ys[context_size:] for h in best_hyps] - ans = [] - unsorted_indices = packed_encoder_out.unsorted_indices.tolist() - for i in range(N): - ans.append(sorted_ans[unsorted_indices[i]]) - - return ans \ No newline at end of file From 1dff6bb90204dcacbcb228c9dd670e816c2d5901 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Fri, 26 Dec 2025 23:42:16 +0000 Subject: [PATCH 20/43] feat: add volume normalization and deletion of files that do not go through the crest factor --- base.sh | 4 +- configs/config.yaml | 17 +- src/phonemizer/phonemizer.py | 2 +- src/preprocess/crest_factor_remover.py | 191 +++++++++++++++++++++++ src/preprocess/preprocess.py | 2 +- src/preprocess/preprocess_audio.py | 206 +++++++++++++++++++++++++ src/preprocess/preprocess_yaml.sh | 2 + src/separation/separation_yaml.sh | 4 +- src/transcription/transcription.py | 59 ++++++- 9 files changed, 469 insertions(+), 18 deletions(-) create mode 100644 src/preprocess/crest_factor_remover.py create mode 100644 src/preprocess/preprocess_audio.py diff --git a/base.sh b/base.sh index 1423f68..027e892 100644 --- a/base.sh +++ b/base.sh @@ -21,12 +21,12 @@ echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" - # "./src/separation/separation_yaml.sh" + "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" "./src/punctuation/punctuation_yaml.sh" "./src/accents/accents_yaml.sh" "./src/phonemizer/phonemizer_yaml.sh" - "./src/collate_yamls.sh" + # "./src/collate_yamls.sh" ) activate_venv ".dev_venv" diff --git a/configs/config.yaml b/configs/config.yaml index bded615..40b7619 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,15 +1,16 @@ cache_path: ./cache download: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio + podcasts_path: /mnt/drive2_8tb/biggest-ru-book episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the podcasts are located + podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) - num_workers: 2 + num_workers: 1 + crest_treshold: 10 # in dB vad_args: smart_vad_model: ./models/smart-turn-v3.0.onnx silero_vad_threshold: 0.4 @@ -17,7 +18,7 @@ preprocess: separation: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located diarization: num_workers: 1 # per gpu @@ -35,7 +36,7 @@ separation: threshold: 0.6 transcription: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True model_names: ['giga_ctc', 'giga_rnnt', 'giga_ctc_lm', 'vosk', 'tone',] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] @@ -58,15 +59,15 @@ transcription: batch_size: 16 # if supported punctuation: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /mnt/drive2_8tb/ruls/train/audio # full path to the folder where the text files are located + podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/src/phonemizer/phonemizer.py b/src/phonemizer/phonemizer.py index 5b22c43..df30411 100644 --- a/src/phonemizer/phonemizer.py +++ b/src/phonemizer/phonemizer.py @@ -9,7 +9,7 @@ from tryiparu import G2PModel from tqdm import tqdm -from src.utils import get_txt_paths, load_config, read_file_content +from src.utils.utils import get_txt_paths, load_config, read_file_content torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) diff --git a/src/preprocess/crest_factor_remover.py b/src/preprocess/crest_factor_remover.py new file mode 100644 index 0000000..ec5043e --- /dev/null +++ b/src/preprocess/crest_factor_remover.py @@ -0,0 +1,191 @@ +import argparse +import os +import torch +import torch.multiprocessing as mp +import torchaudio +from pathlib import Path +from typing import List + +import numpy as np +from loguru import logger +from tqdm import tqdm + +from src.utils.utils import load_config, get_audio_paths + + +def calculate_crest_factor(audio: np.ndarray) -> float: + """ + Calculate crest factor (peak / rms) for audio signal. + + Args: + audio: audio data array (1D numpy array) + + Returns: + crest factor value (peak / rms) + """ + # Calculate peak (maximum absolute value) + peak = np.max(np.abs(audio)) + + # Calculate RMS (root mean square) + rms = np.sqrt(np.mean(audio ** 2)) + + # Avoid division by zero + if rms == 0: + return float('inf') + + # Crest factor = peak / rms + crest_factor = peak / rms + + return crest_factor + + +def process_audio_file( + audio_path: str, + crest_threshold: float +) -> bool: + """ + Process a single audio file: check crest factor and delete if it exceeds threshold. + + Args: + audio_path: Path to the audio file to process + crest_threshold: Maximum allowed crest factor (peak / rms) + + Returns: + True if file was deleted, False otherwise + """ + try: + # Read audio file using torchaudio + audio_tensor, sample_rate = torchaudio.load(audio_path) + + # Convert to numpy array (handle mono/stereo) + if audio_tensor.shape[0] > 1: + # For stereo, use the channel with higher energy or average + audio = audio_tensor.mean(dim=0).numpy() + else: + audio = audio_tensor.squeeze(0).numpy() + + # Calculate crest factor + crest_factor = calculate_crest_factor(audio) + + # Check if crest factor exceeds threshold + if crest_factor > crest_threshold: + # Delete the file + os.remove(audio_path) + logger.debug(f"Deleted {audio_path} (crest factor: {crest_factor:.2f} > {crest_threshold})") + return True + else: + logger.debug(f"Kept {audio_path} (crest factor: {crest_factor:.2f} <= {crest_threshold})") + return False + + except Exception as e: + logger.error(f"Error processing {audio_path}: {e}") + return False + finally: + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def run_worker( + rank: int, + world_size: int, + all_file_paths: List[Path], + crest_threshold: float +): + """ + Worker function for processing files on a specific process. + + Args: + rank: Process rank (0 to world_size-1) + world_size: Total number of processes + all_file_paths: List of all audio file paths to process + crest_threshold: Maximum allowed crest factor + """ + if not all_file_paths: + return + + # Distribute files across workers + my_files = all_file_paths[rank::world_size] + + if not my_files: + return + + deleted_count = 0 + logger.info(f"Worker {rank}/{world_size} processing {len(my_files)} files") + + for file_path in tqdm(my_files, desc=f"Worker-{rank}", position=rank): + if process_audio_file(str(file_path), crest_threshold): + deleted_count += 1 + + logger.info(f"Worker {rank}/{world_size} deleted {deleted_count} files") + + +def main(args): + """ + Main function to remove audio files that exceed crest factor threshold. + """ + config = load_config(args.config_path, 'preprocess') + + podcasts_path = config.get('podcasts_path') + if not podcasts_path: + podcasts_path = config.get('podcasts_path', '../../../podcasts') + logger.warning("Using default podcasts_path") + + # Crest factor threshold parameter + crest_threshold = config.get('crest_treshold', 10.0) # Default 10.0 if not specified + num_workers = config.get('num_workers', 4) + + # Use CPU workers + num_processes = num_workers + + logger.info(f""" + Running crest factor removal: + Podcasts path: {podcasts_path} + Crest factor threshold: {crest_threshold} + Files with crest factor (peak/rms) > {crest_threshold} will be deleted + Number of processes: {num_processes} + """) + + # Get all audio files + audio_paths = get_audio_paths(podcasts_path) + if not audio_paths: + logger.info("No audio files found for processing.") + return + + logger.info(f"Found {len(audio_paths)} audio files to check") + + # Process files using torch multiprocessing + if num_processes > 1: + mp.spawn( + run_worker, + args=(num_processes, audio_paths, crest_threshold), + nprocs=num_processes, + join=True + ) + else: + # Single process mode + deleted_count = 0 + for file_path in tqdm(audio_paths, desc="Checking crest factor"): + if process_audio_file(str(file_path), crest_threshold): + deleted_count += 1 + + logger.info(f"Deleted {deleted_count} files that exceeded crest factor threshold") + + logger.info("Crest factor check completed.") + + +if __name__ == "__main__": + torchaudio.set_audio_backend('soundfile') + mp.set_start_method('spawn', force=True) + + parser = argparse.ArgumentParser( + description="Remove audio files that exceed crest factor threshold (peak/rms > threshold)." + ) + parser.add_argument( + "--config_path", + type=str, + required=True, + help="Path to YAML configuration file" + ) + args = parser.parse_args() + + main(args) diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index fffbddc..cdba8ba 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -198,7 +198,7 @@ def process_audio_file(path_audio: str, duration: float): # TODO: don't forget to remove the code audio, sr = torchaudio.load(path_audio) - if (audio.shape[-1] / sr) <= 2 or(audio.shape[-1] / sr >= 12): + if (audio.shape[-1] / sr) <= 1 or audio.shape[-1] / sr > 15: logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") os.remove(path_audio) # # TODO: don't forget to remove the code diff --git a/src/preprocess/preprocess_audio.py b/src/preprocess/preprocess_audio.py new file mode 100644 index 0000000..72daf88 --- /dev/null +++ b/src/preprocess/preprocess_audio.py @@ -0,0 +1,206 @@ +import argparse +import os +import torch +import torch.multiprocessing as mp +import torchaudio +from pathlib import Path +from typing import List + +import numpy as np +import pyloudnorm as pyln +from loguru import logger +from tqdm import tqdm + +from src.utils.utils import load_config, get_audio_paths + +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + +def normalize_audio_loudness( + audio: np.ndarray, + rate: int, + peak: float = -1.0, + loudness: float = -23.0, + block_size: float = 0.400 +) -> np.ndarray: + """ + Perform loudness normalization (ITU-R BS.1770-4) on audio data. + + Args: + audio: audio data array + rate: sample rate + peak: peak normalize audio to N dB. Defaults to -1.0. + loudness: loudness normalize audio to N dB LUFS. Defaults to -23.0. + block_size: block size for loudness measurement in seconds. Defaults to 0.400 (400 ms). + + Returns: + loudness normalized audio array + """ + # Peak normalize audio to [peak] dB + audio = pyln.normalize.peak(audio, peak) + + # Measure the loudness first + meter = pyln.Meter(rate, block_size=block_size) # create BS.1770 meter + _loudness = meter.integrated_loudness(audio) + + return pyln.normalize.loudness(audio, _loudness, loudness) + + +def process_audio_file( + audio_path: str, + peak: float, + loudness: float, + block_size: float +): + """ + Process a single audio file: normalize loudness and overwrite the original file. + + Args: + audio_path: Path to the audio file to process + peak: Peak normalization level in dB + loudness: Target loudness level in LUFS + block_size: Block size for loudness measurement in seconds + """ + try: + # Read audio file using torchaudio + audio, sample_rate = torchaudio.load(audio_path) + + # Normalize loudness (pyloudnorm supports both mono and stereo) + normalized_audio = normalize_audio_loudness( + audio.numpy(), + sample_rate, + peak=peak, + loudness=loudness, + block_size=block_size + ) + + + # Overwrite the original file (preserves original format and channels) + torchaudio.save(audio_path, torch.from_numpy(normalized_audio), sample_rate) + + logger.debug(f"Normalized: {audio_path}") + + except Exception as e: + logger.error(f"Error processing {audio_path}: {e}") + finally: + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def run_worker( + rank: int, + world_size: int, + all_file_paths: List[Path], + peak: float, + loudness: float, + block_size: float +): + """ + Worker function for processing files on a specific GPU/process. + + Args: + rank: Process rank (0 to world_size-1) + world_size: Total number of processes + all_file_paths: List of all audio file paths to process + peak: Peak normalization level in dB + loudness: Target loudness level in LUFS + block_size: Block size for loudness measurement in seconds + """ + if not all_file_paths: + return + + # Distribute files across workers + my_files = all_file_paths[rank::world_size] + + if not my_files: + return + + logger.info(f"Worker {rank}/{world_size} processing {len(my_files)} files") + + for file_path in tqdm(my_files, desc=f"Worker-{rank}", position=rank): + process_audio_file( + str(file_path), + peak, + loudness, + block_size + ) + + +def main(args): + """ + Main function to normalize audio loudness for all audio files in the specified directory. + """ + config = load_config(args.config_path, 'preprocess') + + podcasts_path = config.get('podcasts_path') + if not podcasts_path: + podcasts_path = config.get('podcasts_path', '../../../podcasts') + logger.warning("Using default podcasts_path") + + # Loudness normalization parameters + peak = config.get('peak', -1.0) + loudness = config.get('loudness', -23.0) + block_size = config.get('block_size', 0.400) + num_workers = config.get('num_workers', 4) + + # Use CPU workers (loudness normalization doesn't require GPU) + # But we can use multiple CPU cores + num_processes = num_workers + + logger.info(f""" + Running loudness normalization: + Podcasts path: {podcasts_path} + Peak normalization: {peak} dB + Target loudness: {loudness} LUFS + Block size: {block_size} seconds + Number of processes: {num_processes} + """) + + # Get all audio files + audio_paths = get_audio_paths(podcasts_path) + if not audio_paths: + logger.info("No audio files found for processing.") + return + + logger.info(f"Found {len(audio_paths)} audio files to process") + + # Process files using torch multiprocessing + if num_processes > 1: + mp.spawn( + run_worker, + args=(num_processes, audio_paths, peak, loudness, block_size), + nprocs=num_processes, + join=True + ) + else: + # Single process mode + for file_path in tqdm(audio_paths, desc="Normalizing loudness"): + process_audio_file( + str(file_path), + peak, + loudness, + block_size + ) + + logger.info("All files have been processed and normalized.") + + +if __name__ == "__main__": + torchaudio.set_audio_backend('soundfile') + mp.set_start_method('spawn', force=True) + + parser = argparse.ArgumentParser( + description="Normalize audio loudness (ITU-R BS.1770-4) for all audio files in the dataset." + ) + parser.add_argument( + "--config_path", + type=str, + required=True, + help="Path to YAML configuration file" + ) + args = parser.parse_args() + + main(args) diff --git a/src/preprocess/preprocess_yaml.sh b/src/preprocess/preprocess_yaml.sh index acaf32d..7e53ecb 100644 --- a/src/preprocess/preprocess_yaml.sh +++ b/src/preprocess/preprocess_yaml.sh @@ -21,4 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") +python3 -m src.preprocess.crest_factor_remover --config_path "$CONFIG_PATH" +python3 -m src.preprocess.preprocess_audio --config_path "$CONFIG_PATH" python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index 3305f24..906ffe5 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -20,6 +20,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") -python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" -python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" +# python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" +# python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" python3 -m src.separation.diarization --config_path "$CONFIG_PATH" diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 943cdbc..70494aa 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -154,15 +154,60 @@ def run_inference_on_device(cuda_id: int, world_size: int, model_name: str, all_ logger.exception(f"Critical error in worker {cuda_id} for model {model_name}: {e}") -def get_valid_audio_paths(src_path: str, model_name_for_output: str) -> List[Path]: +def check_consensus_reached(audio_path: Path, model_names: List[str], consensus_num: int) -> bool: + """Check if consensus_num models have produced the same transcription for this audio file.""" + from collections import Counter + from src.utils.utils import read_file_content + + transcriptions = [] + for model_name in model_names: + model_name_for_file = 'vosk' if 'vosk' in model_name else model_name + transcript_path = audio_path.with_name(f"{audio_path.stem}_{model_name_for_file}.txt") + + if transcript_path.exists(): + try: + text = read_file_content(transcript_path) + if text: + # Normalize text for comparison (lowercase, strip) + normalized = text.lower().strip() + transcriptions.append(normalized) + except Exception: + pass + + if len(transcriptions) < consensus_num: + return False + + # Count occurrences of each transcription + counter = Counter(transcriptions) + # Check if any transcription appears at least consensus_num times + return max(counter.values()) >= consensus_num + + +def get_valid_audio_paths(src_path: str, model_name_for_output: str, processed_models: List[str], consensus_num: int) -> List[Path]: all_audio_paths = get_audio_paths(src_path) if not all_audio_paths: return [] + # Filter out files that already have transcription for this model valid_paths = [ p for p in all_audio_paths if not p.with_name(f"{p.stem}_{model_name_for_output}.txt").exists() ] + + # Filter out files that already reached consensus + if consensus_num > 0 and len(processed_models) >= consensus_num: + consensus_count = 0 + filtered_paths = [] + for p in valid_paths: + if check_consensus_reached(p, processed_models, consensus_num): + consensus_count += 1 + else: + filtered_paths.append(p) + + if consensus_count > 0: + logger.info(f"Skipping {consensus_count} files that already reached consensus ({consensus_num} models agree).") + valid_paths = filtered_paths + return valid_paths @@ -170,6 +215,7 @@ def main(args): config = load_config(args.config_path, 'transcription') model_names = config.get('model_names', ['giga_rnnt']) src_path = config.get('podcasts_path', '.') + consensus_num = config.get('consensus_num', 0) # 0 means process all models available_gpu_ids = list(range(torch.cuda.device_count())) if not available_gpu_ids: @@ -178,13 +224,18 @@ def main(args): num_gpus = len(available_gpu_ids) logger.info(f"Detected {num_gpus} GPUs. Starting processing pipeline.") + if consensus_num > 0: + logger.info(f"Consensus mode enabled: will skip files where {consensus_num} models agree.") - for model_name in model_names: - logger.info(f"=== Processing model: {model_name} ===") + for model_idx, model_name in enumerate(model_names): + logger.info(f"=== Processing model {model_idx + 1}/{len(model_names)}: {model_name} ===") model_name_for_output = 'vosk' if 'vosk' in model_name else model_name - all_paths = get_valid_audio_paths(src_path, model_name_for_output) + # Get models processed so far (before current model) + processed_models = model_names[:model_idx] if consensus_num > 0 else [] + + all_paths = get_valid_audio_paths(src_path, model_name_for_output, processed_models, consensus_num) if not all_paths: logger.info(f"No new files for {model_name}. Skipping.") From ea2ed34bcb14b817bdf0cdf4d08682a1b6695f68 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Fri, 26 Dec 2025 23:42:51 +0000 Subject: [PATCH 21/43] docs: update readme --- README.md | 285 +++++++++------------------------------ docs/guide.md | 329 ++++++++++++++++++++++++++++++++++++++++------ docs/preparing.md | 282 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 633 insertions(+), 263 deletions(-) diff --git a/README.md b/README.md index 9e26831..8c422b4 100644 --- a/README.md +++ b/README.md @@ -1,278 +1,119 @@ -# Balalaika Pipeline +# YapodDataset Pipeline -A complete production-ready pipeline for processing podcast audio data, from download to feature extraction. +A complete production-ready pipeline for processing audio data, from download to feature extraction. This pipeline handles audio preprocessing, speaker diarization, transcription, punctuation restoration, accent restoration, and phonemization. ---- - -## Table of Contents - -1. [Prerequisites](#prerequisites) -2. [Installation](#installation) -3. [Data Preparation](#data-preparation) - - [Quick Setup (Default Parameters)](#quick-setup) - - [Custom Metadata Download](#custom-metadata-download) -4. [Running the Pipeline](#running-the-pipeline) - - [Basic Scenario (Local Processing)](#basic-scenario-local-processing) -5. [Configuration](#configuration) -6. [Environment Variables](#environment-variables) -7. [Models](#models) -8. [Citation](#citation) -9. [Acknowledgments](#acknowledgments) +The pipeline is designed to work with podcasts from Yandex Music, but **can also process your own audio datasets** by organizing them in the expected format (see [Preparing Your Dataset](docs/preparing.md)). --- -## Prerequisites +## Quick Start -Ensure you have the following tools installed on your system: +### Prerequisites ```bash sudo apt update && sudo apt install -y ffmpeg wget -qO- https://astral.sh/uv/install.sh | sh +``` -```` - ---- - -## Installation - -Clone the repository and set up the environment: +### Installation ```bash git clone https://github.com/mtuciru/balalaika cd balalaika -# Use this if you want to annotate/modify the dataset -bash create_dev_env.sh -# Use this if you only want to use the pre-annotated dataset -bash create_user_env.sh +bash create_dev_env.sh # For development/annotation +# or +bash create_user_env.sh # For using pre-annotated dataset only ``` ---- - -## Data Preparation - -### Quick Setup (Default Parameters) - -To download and prepare the dataset with default settings, choose one of the preconfigured dataset sizes: +### Basic Setup -* **100-hour dataset** - ```bash - bash use_meta_100h.sh - ``` - -* **500-hour dataset** - ```bash - bash use_meta_500h.sh - ``` - -* **1000-hour dataset** - ```bash - bash use_meta_1000h.sh - ``` - -* **2000-hour dataset** - ```bash - bash use_meta_2000h.sh - ``` - -All metadata can also be downloaded from [Hugging Face – MTUCI](https://huggingface.co/MTUCI). - -### Custom Metadata Download - -If you already have generated metadata files (`balalaika.parquet` and `balalaika.pkl`), place them in the project root and run: - -```bash -bash use_meta.sh +1. Create `.env` file: +```ini +HF_TOKEN= +YANDEX_KEY= ``` ---- - -## Running the Pipeline - - -### Basic Scenario (Local Processing) - - -This scenario will: - -1. Download datasets -2. Split audio into semantic chunks -3. Transcribe all segments -4. Perform speaker segmentation -5. Apply phonemization +2. Configure `configs/config.yaml`: +```yaml +podcasts_path: /absolute/path/to/your/data +``` -To execute locally, run: +3. Place required models in `models/` directory (see [Preparing Your Dataset](docs/preparing.md)) +4. Run the pipeline: ```bash bash base.sh configs/config.yaml ``` -All output metadata will be saved in `podcasts/result.csv`. - ---- - -## Configuration - -The main configuration file is located at `configs/config.yaml`. This file is organized into several sections, each corresponding to a specific stage of the podcast processing pipeline. Below is a detailed explanation of the key parameters within each section. - ---- - -### Global Parameters - -* `podcasts_path`: It specifies the **absolute path** to the directory where all downloaded podcast files will be stored and where subsequent processing (preprocessing, separation, transcription, etc.) will look for and save its output. ---- - -### `download` Section - -This section controls how podcast episodes are downloaded. - -* `podcasts_path`: (As explained above) The directory where downloaded podcasts will be saved. -* `episodes_limit`: This sets a **limit on the number of episodes** to download from a single podcast playlist. -* `num_workers`: Specifies the **number of parallel processes** to use for downloading. A higher number can speed up downloads but will consume more system resources. -* `podcasts_urls_file`: This parameter points to the **path of a `.pkl` file** that contains a list of podcast URLs to be downloaded. - ---- - -### `preprocess` Section - -This section handles the initial processing of downloaded audio files, such as chopping them into smaller segments. - -* `podcasts_path`: (As explained above) The directory containing the raw downloaded podcasts that need to be preprocessed. -* `duration`: Defines the **maximum length in seconds** for each audio sample (segment). -* `num_workers`: Specifies the **number of parallel processes** to use during preprocessing. -* `whisper_model`: Specifies the **name or path of the Faster-Whisper compatible model** to be used for initial audio processing. -* `compute_type`: Determines the **computation type** for the Whisper model, affecting performance and memory usage. -* `beam_size`: This parameter is related to the **beam search algorithm** used in the Whisper model's decoding process. - ---- - -### `separation` Section - -This section calculates metrics for each audio - -* `podcasts_path`: (As explained above) The directory where the chopped podcasts (from the `preprocess` stage) are located. -* `num_workers`: The **number of parallel processes** to use for audio separation. -* `nisqa_config`: Specifies the **path to the configuration file for NISQA** -* `one_speaker`: A **boolean flag** (`True`/`False`) that, when enabled (`True`), instructs the system to download and process only those audio recordings that should contain a single speaker. - --- -### `transcription` Section - -This section is responsible for converting audio into text. - -* `podcasts_path`: (As explained above) The directory containing the processed audio files ready for transcription. -* `model_name`: Specifies the **type of automatic speech recognition (ASR) model** to use. Options typically include `"ctc" or "rnnt"`. -* `num_workers`: The **number of parallel processes per GPU** to use for transcription. -* `with_timestamps`: A **boolean flag** (`True`/`False`) that, when enabled, allows the transcription process to generate timestamps for each word or segment. **it only works with ctc** -* `lm_path`: Specifies the **path to a language model file (`.bin`)**. A language model can improve transcription accuracy by providing contextual information. - ---- - -### `punctuation` Section - -This section focuses on adding proper punctuation to the transcribed text. - -* `podcasts_path`: (As explained above) The directory where the transcribed text files are located. -* `model_name`: Specifies the **name of the RUPunct model** to be used for punctuation restoration. -* `num_workers`: The **number of parallel processes per GPU** to use for punctuation. ---- - -### `accent` Section - -In the transcribed text this part is restored with accents. +## Documentation -* `podcasts_path`: (As explained above) The directory containing the relevant podcast files. -* `num_workers`: The **number of parallel processes per GPU** to use for accent processing. -* `model_name`: Specifies the **name of the ruAccent model** to be used. +- **[Usage Guide](docs/guide.md)**: Detailed guide on how to use the pipeline, what files are created, and how to run individual stages +- **[Preparing Your Dataset](docs/preparing.md)**: Setup instructions and how to prepare your own datasets for processing --- -### `phonemizer` Section +## Pipeline Overview -This section is responsible for converting text into phonetic representations (phonemes). +The pipeline consists of the following stages: -* `podcasts_path`: (As explained above) The directory where the text files (from transcription and punctuation stages) are located. -* `num_workers`: The **number of parallel processes per GPU** to use for phonemization. ---- - -### `classification` Section +1. **Download** - Downloads podcast episodes (optional, if using your own data) +2. **Preprocess** - Audio quality filtering and normalization: + - **Crest Factor Removal** - Removes files with excessive peak-to-RMS ratio + - **Loudness Normalization** - Normalizes audio loudness (ITU-R BS.1770-4) + - **Audio Segmentation** - Segments audio into chunks using VAD +3. **Separation** - Speaker diarization, quality assessment, music detection +4. **Transcription** - Multi-model ASR with ROVER consensus (optimized with early stopping when models agree) +5. **Punctuation** - Punctuation restoration +6. **Accents** - Accent restoration +7. **Phonemization** - Text-to-phoneme conversion +8. **Collate** - Aggregates metadata into Parquet -This section relates to global speaker clustering. +For detailed information about each stage, see [Usage Guide](docs/guide.md). -* `podcasts_path`: (As explained above) The directory containing the podcast files relevant for classification. -* `num_workers`: The **number of parallel processes per GPU** to use for classification. -* `threshold`: This is the **speaker classification confidence threshold**. Values typically range from `0.6` to `0.9`. A higher threshold means the model needs to be more confident in its classification to assign a label. -* `model_path`: Specifies the **path to the pretrained speaker classification model** in `.pt` format. --- -### Execution Scripts - -Each processing script (`*_yaml.sh` and `*_args.sh`) offers flexibility in how parameters are provided: - -* `*_yaml.sh`: These scripts read all necessary parameters directly from the main `config.yaml` file, ensuring consistency across different stages. -* `*_args.sh`: These scripts allow for hardcoded arguments directly within the shell script itself, which can be useful for quick tests or specific overrides without modifying the main configuration file. - -## Environment Variables +## Citation -Create a `.env` file in the project root with the following: +If you use this pipeline or the Balalaika dataset in your research, please cite: -```ini -HF_TOKEN= -YANDEX_KEY= +```bibtex +@article{borodin2025datacentric, + title={A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models}, + author={Borodin, Kirill and Vasiliev, Nikita and Kudryavtsev, Vasiliy and Maslov, Maxim and Gorodnichev, Mikhail and Rogov, Oleg and Mkrtchian, Grach}, + journal={arXiv preprint arXiv:2507.13563}, + year={2025} +} ``` -* `HF_TOKEN`: Required for speaker count estimation. -* `YANDEX_KEY`: Required for dataset downloads. +**Paper**: [arXiv:2507.13563](https://arxiv.org/abs/2507.13563) +**DOI**: [10.48550/arXiv.2507.13563](https://doi.org/10.48550/arXiv.2507.13563) --- -## Important Notes - -- All scripts must be executed from the **project root directory**. -- Paths in the config file must be **absolute**. -- The processing scripts (punctuation, accents, yofication) should be run **sequentially**. -- You’ll need: - - Yandex Music API key ([How to get one](https://yandex-music.readthedocs.io/en/main/token.html)) - - Hugging Face token - ## Models -Place all required models under the `models/` directory with the following structure: +The pipeline integrates the following models and tools: -``` -models/ -├── vosblink_resnet/ # Speaker classification model -│ └── ... -└── nisqa_s.tar # Audio quality assessment model -``` - -Supported models: - -- [NISQA](https://github.com/deepvk/NISQA-s) – Audio quality assessment. -- [GigaAM](https://github.com/salute-developers/GigaAM) – ASR. -- [ruAccent](https://github.com/Den4ikAI/ruaccent) – Accent restoration. -- [RUPunct](https://huggingface.co/RUPunct/RUPunct_big) – Punctuation restoration. -- [VoxBlink ResNet](https://github.com/wenet-e2e/wespeaker) – Speaker classification. -- [TryIPaG2P](https://github.com/NikiPshg/TryIPaG2P) – Phonemization. -- [Speaker Diarization](https://github.com/pyannote/pyannote-audio) – Speaker diarization. -- [Whisper](https://github.com/SYSTRAN/faster-whisper) – ASR + segmentation +- **[NISQA](https://github.com/deepvk/NISQA-s)**: Audio quality assessment +- **[GigaAM](https://github.com/salute-developers/GigaAM)**: ASR models (CTC, RNNT, CTC+LM) +- **[ruAccent](https://github.com/Den4ikAI/ruaccent)**: Accent restoration +- **[RUPunct](https://huggingface.co/RUPunct/RUPunct_big)**: Punctuation restoration +- **[Vosk](https://alphacephei.com/vosk/)**: ASR model +- **[T-one](https://github.com/mtuciru/T-one-cuda-onnx)**: ASR model +- **[TryIPaG2P](https://github.com/NikiPshg/TryIPaG2P)**: Phonemization +- **[Speaker Diarization](https://github.com/pyannote/pyannote-audio)**: Speaker diarization +- **[Smart Turn VAD](https://github.com/snakers4/silero-vad)**: Voice Activity Detection --- -## Citation +## License -If you use this pipeline in your research or production, please cite: -``` -``` +See [LICENSE](LICENSE) file for details. --- -## References and Acknowledgements - -Thanks to all the developers and contributors who made this project possible. - - - - - +## Acknowledgments +Thanks to all the developers and contributors who made this project possible, including the teams behind GigaAM, ruAccent, RUPunct, Vosk, and other integrated tools. diff --git a/docs/guide.md b/docs/guide.md index 1f96a7e..faee2f3 100644 --- a/docs/guide.md +++ b/docs/guide.md @@ -1,54 +1,251 @@ -# build vosk -```cmd -python -m pip install git+https://github.com/lhotse-speech/lhotse -``` -```cmd -python -m pip install https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.4.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl -```cmd -python -m pip install https://huggingface.co/csukuangfj/kaldifeat/resolve/main/cuda/1.25.5.dev20241029/linux/kaldifeat-1.25.5.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl -``` -``` -python3 -m pip install git+https://github.com/k2-fsa/icefall -``` +# Usage Guide -# balalaika on your data -if the raw data -``` -dataset/ -└── {album_id}/ - └── {episode_id}/ - ├── {start_time}_{end_time}_{album_id}_{episode_id}.mp3 - ├── {start_time}_{end_time}_{album_id}_{episode_id}_giga.txt - ├── {start_time}_{end_time}_{album_id}_{episode_id}_punct.txt - ├── {start_time}_{end_time}_{album_id}_{episode_id}_accent.txt - └── {start_time}_{end_time}_{album_id}_{episode_id}_giga_phonemes.txt -``` +This guide explains how to use the YapodDataset pipeline, what files are created at each stage, and how to run individual processing stages. + +--- + +## Table of Contents + +1. [Pipeline Stages](#pipeline-stages) +2. [Running the Pipeline](#running-the-pipeline) +3. [Output Files](#output-files) +4. [Configuration](#configuration) +5. [Running Individual Stages](#running-individual-stages) + +--- + +## Pipeline Stages + +### 1. Download (`src/download/`) +Downloads podcast episodes from Yandex Music based on provided URLs or playlists. + +**Input**: Podcast URLs or playlist IDs +**Output**: Raw audio files (`.mp3`) organized by `{album_id}/{episode_id}/` + +**Configuration**: `config.yaml` → `download` section + +--- + +### 2. Preprocess (`src/preprocess/`) +The preprocessing stage consists of three sequential steps: + +#### 2.1. Crest Factor Removal (`crest_factor_remover.py`) +Removes audio files that have excessive crest factor (peak/RMS ratio). Files with crest factor exceeding the threshold are deleted to filter out problematic audio with extreme dynamic range. + +**Input**: Raw audio files +**Output**: Filtered audio files (files with high crest factor are deleted) + +**Configuration**: `config.yaml` → `preprocess` section +- `crest_treshold`: Maximum allowed crest factor (peak/RMS). Files exceeding this value are deleted. Default: 10.0 + +#### 2.2. Loudness Normalization (`preprocess_audio.py`) +Normalizes audio loudness using ITU-R BS.1770-4 standard. All audio files are normalized to a consistent loudness level, overwriting the original files. + +**Input**: Filtered audio files +**Output**: Loudness-normalized audio files (original files overwritten) + +**Configuration**: `config.yaml` → `preprocess` section +- `peak`: Peak normalization level in dB. Default: -1.0 +- `loudness`: Target loudness level in LUFS. Default: -23.0 +- `block_size`: Block size for loudness measurement in seconds. Default: 0.400 + +#### 2.3. Audio Segmentation (`preprocess.py`) +Splits long audio files into shorter segments (default: 15 seconds) using Voice Activity Detection (VAD). Removes segments that are too short (< 1 second) or too long (> duration limit). + +**Input**: Normalized audio files +**Output**: Segmented audio files named `{start_time}_{end_time}_{album_id}_{episode_id}.mp3` + +**Configuration**: `config.yaml` → `preprocess` section +- `duration`: Maximum segment length in seconds +- `vad_args`: VAD thresholds and model path + - `smart_vad_model`: Path to Smart VAD model + - `silero_vad_threshold`: Threshold for Silero VAD (0.0-1.0) + - `smart_vad_threshold`: Threshold for Smart VAD (0.0-1.0) + +**Note**: The preprocessing stage runs all three steps sequentially. After preprocessing, the separation stage will create `balalaika.csv` with metadata including single speaker flags and audio quality metrics. Files with detected music will be automatically deleted during the separation stage. + +--- + +### 3. Separation (`src/separation/`) +Performs three types of analysis: +- **Diarization**: Identifies and separates different speakers, creates `.rttm` files +- **NISQA**: Assesses audio quality metrics +- **Music Detection**: Detects music segments in audio + +**Input**: Segmented audio files +**Output**: +- `.rttm` files (speaker diarization data) +- **`balalaika.csv`**: Metadata file containing: + - Single speaker flags (indicating whether each audio segment contains only one speaker) + - Audio quality metrics (from NISQA assessment) + - File paths and processing status +- Files with detected music are **automatically deleted** during music detection stage +- Can filter out multi-speaker files if `one_speaker: True` is set (files are deleted) + +**Configuration**: `config.yaml` → `separation` section + +**Note**: The `balalaika.csv` file is created/updated during the separation stage and contains important metadata about each audio segment, including speaker information and quality metrics. Files detected as containing music are removed from the dataset. + +--- + +### 4. Transcription (`src/transcription/`) +Transcribes audio using multiple ASR models in parallel. Each model creates its own transcription file, then all transcriptions are aggregated using ROVER (Recognizer Output Voting Error Reduction) consensus algorithm. + +**Optimization**: If `consensus_num` is set, the pipeline will automatically skip processing remaining models for files where the specified number of models have already produced identical transcriptions. This significantly speeds up processing when models agree. + +**Input**: Audio files (`.mp3`) +**Output**: +- Individual model transcriptions: `{filename}_{model_name}.txt` + - `{filename}_giga_ctc.txt` + - `{filename}_giga_rnnt.txt` + - `{filename}_giga_ctc_lm.txt` + - `{filename}_vosk.txt` + - `{filename}_tone.txt` +- Timestamp files (if enabled): `{filename}_{model_name}.tst` +- **Consensus transcription**: `{filename}_rover.txt` (aggregated from all models) + +**Configuration**: `config.yaml` → `transcription` section +- `model_names`: List of models to use +- `consensus_num`: Number of models that need to agree before skipping remaining models (e.g., `3` means if 3 models produce the same transcription, remaining models won't process that file). Set to `0` to process all models for all files. +- `with_timestamps`: Enable timestamp generation (works with `giga_ctc_lm` and `tone`) + +**Example**: With `consensus_num: 3` and 5 models, if the first 3 models produce identical transcriptions for a file, the remaining 2 models will skip that file, saving processing time. + +--- + +### 5. Punctuation (`src/punctuation/`) +Restores punctuation marks in transcribed text using RUPunct model. + +**Input**: `{filename}_rover.txt` +**Output**: `{filename}_punct.txt` + +**Configuration**: `config.yaml` → `punctuation` section +- `model_name`: RUPunct model name (e.g., `"RUPunct/RUPunct_big"`) + +--- + +### 6. Accents (`src/accents/`) +Restores stress marks (accents) in Russian text using ruAccent model. + +**Input**: `{filename}_punct.txt` +**Output**: `{filename}_accent.txt` + +**Configuration**: `config.yaml` → `accent` section +- `model_name`: ruAccent model name (e.g., `"turbo3.1"`) + +--- -if the raw data +### 7. Phonemizer (`src/phonemizer/`) +Converts text to phonetic representation (phonemes) using TryIPaG2P. + +**Input**: `{filename}_rover.txt` +**Output**: `{filename}_rover_phonemes.txt` + +**Configuration**: `config.yaml` → `phonemizer` section + +--- + +### 8. Collate (`src/collate.py`) +Collects all generated metadata files and aggregates them into a single Parquet file for easy access and analysis. + +**Input**: All generated text files (`_rover.txt`, `_punct.txt`, `_accent.txt`, `_rover_phonemes.txt`) +**Output**: `balalaika.parquet` (contains columns: filepath, rover, punct, accent, phonemes) + +**Usage**: +```bash +bash src/collate_yamls.sh configs/config.yaml ``` -dataset/ -└── {album_id}/ - └── {episode_id}/ - └── {start_time}_{end_time}_{album_id}_{episode_id}.mp3 - ... + +--- + +## Running the Pipeline + +### Complete Pipeline + +To run the complete annotation pipeline: + +```bash +bash base.sh configs/config.yaml ``` -if the audio is already cropped +This executes all enabled scripts in sequence: +1. Separation (diarization, quality assessment, music detection) +2. Transcription (multi-model ASR with ROVER consensus) +3. Punctuation restoration +4. Accent restoration +5. Phonemization + +### Collecting Metadata +After processing, collect all metadata: + +```bash +bash src/collate_yamls.sh configs/config.yaml ``` -dataset/ -└── {album_id}/ - └── {episode_id}/ - ├─ audio_1.mp3 - ├─ audio_2.wav - ├─ audio_3.opus - ... + +This creates `balalaika.parquet` in your `podcasts_path` directory. + +--- + +## Output Files + +For each audio segment, the pipeline generates: + ``` +{start_time}_{end_time}_{album_id}_{episode_id}.mp3 # Audio file +{start_time}_{end_time}_{album_id}_{episode_id}.rttm # Speaker diarization -# if only transcriptions are needed -`base.sh` +# Individual model transcriptions (if enabled) +{start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc.txt +{start_time}_{end_time}_{album_id}_{episode_id}_giga_rnnt.txt +{start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc_lm.txt +{start_time}_{end_time}_{album_id}_{episode_id}_vosk.txt +{start_time}_{end_time}_{album_id}_{episode_id}_tone.txt +{start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc_lm.tst # Timestamps (if enabled) +# Consensus and processed text files +{start_time}_{end_time}_{album_id}_{episode_id}_rover.txt # Consensus transcription +{start_time}_{end_time}_{album_id}_{episode_id}_punct.txt # With punctuation +{start_time}_{end_time}_{album_id}_{episode_id}_accent.txt # With accents +{start_time}_{end_time}_{album_id}_{episode_id}_rover_phonemes.txt # Phonetic representation ``` + +**Intermediate metadata:** +- `balalaika.csv`: Created during separation stage, contains: + - Single speaker flags (indicating one-speaker vs multi-speaker segments) + - Audio quality metrics (NISQA scores) + - File paths and processing status + - Files with music are automatically deleted and not included + +**Final aggregated metadata:** +- `balalaika.parquet`: All metadata in structured format (created by collate stage) +- Contains: filepath, rover, punct, accent, phonemes columns + +--- + +## Configuration + +The main configuration file is `configs/config.yaml`. Key sections: + +### Global Parameters +- `cache_path`: Path for caching temporary files +- `podcasts_path`: **Absolute path** to your data directory + +### Stage-Specific Configuration + +Each stage has its own configuration section. See `config.yaml` for all available parameters. + +**Important**: All paths must be **absolute paths**. + +--- + +## Running Individual Stages + +### Modify `base.sh` + +Edit the `SCRIPTS` array to run only specific stages: + +```bash SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" @@ -59,4 +256,54 @@ SCRIPTS=( # "./src/phonemizer/phonemizer_yaml.sh" # "./src/collate_yamls.sh" ) -``` \ No newline at end of file +``` + +### Run Scripts Directly + +```bash +# Activate virtual environment +source .dev_venv/bin/activate + +# Run specific stages +bash src/download/download_yaml.sh configs/config.yaml +bash src/preprocess/preprocess_yaml.sh configs/config.yaml +bash src/separation/separation_yaml.sh configs/config.yaml +bash src/transcription/transcription_yaml.sh configs/config.yaml +bash src/punctuation/punctuation_yaml.sh configs/config.yaml +bash src/accents/accents_yaml.sh configs/config.yaml +bash src/phonemizer/phonemizer_yaml.sh configs/config.yaml +bash src/collate_yamls.sh configs/config.yaml +``` + +### Processing Order + +The stages must be run in this order: +1. **Download** → Downloads raw audio files +2. **Preprocess** → Three sequential steps: + - **Crest Factor Removal** → Removes files with excessive peak/RMS ratio + - **Loudness Normalization** → Normalizes audio loudness (overwrites files) + - **Audio Segmentation** → Segments audio into chunks using VAD +3. **Separation** → Diarization, quality assessment, music detection +4. **Transcription** → Creates individual model transcriptions + `_rover.txt` (consensus) +5. **Punctuation** → Processes `_rover.txt` → `_punct.txt` +6. **Accents** → Processes `_punct.txt` → `_accent.txt` +7. **Phonemizer** → Processes `_rover.txt` → `_rover_phonemes.txt` +8. **Collate** → Aggregates all metadata into Parquet + +**Important Notes:** +- All scripts must be executed from the **project root directory** +- Processing scripts (punctuation, accents, phonemizer) should be run **sequentially** after transcription +- The pipeline processes files in place, so ensure you have backups if needed +- Transcription stage creates individual model files first, then aggregates them into `_rover.txt` + +--- + +## Troubleshooting + +### Common Issues + +1. **Path errors**: Ensure all paths in `config.yaml` are **absolute paths** +2. **Missing `_rover.txt` files**: Ensure transcription stage completed successfully. ROVER aggregation runs automatically after all model transcriptions finish +3. **File naming**: The pipeline expects specific file naming patterns. Ensure audio files follow the expected structure + +For more troubleshooting tips, see individual module READMEs in `src/*/README.md`. diff --git a/docs/preparing.md b/docs/preparing.md index e69de29..e25575d 100644 --- a/docs/preparing.md +++ b/docs/preparing.md @@ -0,0 +1,282 @@ +# Preparing Your Dataset + +This guide explains how to set up the pipeline and prepare your own audio datasets for processing. **The pipeline can process not only podcasts from Yandex Music, but also your own audio datasets** by organizing them in the expected format. + +--- + +## Table of Contents + +1. [Prerequisites](#prerequisites) +2. [Installation](#installation) +3. [Environment Setup](#environment-setup) +4. [Models Setup](#models-setup) +5. [Configuration](#configuration) +6. [Preparing Your Own Dataset](#preparing-your-own-dataset) +7. [Additional Requirements for Vosk](#additional-requirements-for-vosk) + +--- + +## Prerequisites + +Ensure you have the following tools installed: + +```bash +sudo apt update && sudo apt install -y ffmpeg +wget -qO- https://astral.sh/uv/install.sh | sh +``` + +--- + +## Installation + +Clone the repository and set up the environment: + +```bash +git clone https://github.com/mtuciru/balalaika +cd balalaika +``` + +Choose the appropriate environment: + +- **For development/annotation/modification**: + ```bash + bash create_dev_env.sh + ``` + +- **For using pre-annotated dataset only**: + ```bash + bash create_user_env.sh + ``` + +--- + +## Environment Setup + +### 1. Environment Variables + +Create a `.env` file in the project root: + +```ini +HF_TOKEN= +YANDEX_KEY= +``` + +- `HF_TOKEN`: Required for speaker diarization and accessing Hugging Face models +- `YANDEX_KEY`: Required only if downloading podcasts from Yandex Music (optional if using your own data) + +**How to get tokens:** +- **Hugging Face Token**: Create an account at [huggingface.co](https://huggingface.co) and generate a token in your account settings +- **Yandex Music Token**: Follow the guide at [yandex-music.readthedocs.io](https://yandex-music.readthedocs.io/en/main/token.html) + +--- + +## Models Setup + +Place all required models under the `models/` directory: + +- **Vosk Model**: `./models/vosk-model-ru` (directory with Vosk model files) +- **Smart VAD Model**: `./models/smart-turn-v3.0.onnx` +- **Music Detection Model**: `./models/music_detection.safetensors` +- **KenLM Language Model**: `./models/kenlm.bin` (for Giga CTC model) +- **NISQA Config**: `./configs/nisqa_b.yaml` + +**Note**: Some models are downloaded automatically from Hugging Face (RUPunct, ruAccent). Make sure your `HF_TOKEN` is set correctly. + +--- + +## Configuration + +Edit `configs/config.yaml` and set the `podcasts_path` to an **absolute path** where your data is located: + +```yaml +preprocess: + podcasts_path: /absolute/path/to/your/dataset + crest_treshold: 10 # Maximum allowed crest factor (peak/RMS). Files exceeding this are deleted + peak: -1.0 # Peak normalization level in dB (for loudness normalization) + loudness: -23.0 # Target loudness level in LUFS (for loudness normalization) + block_size: 0.400 # Block size for loudness measurement in seconds + duration: 15 # Maximum segment length in seconds + num_workers: 4 # Number of parallel workers +``` + +**Important**: +- All paths in the config file must be **absolute paths** +- The `podcasts_path` should point to the root directory containing your audio files +- Preprocessing runs three sequential steps: crest factor removal → loudness normalization → segmentation + +--- + +## Preparing Your Own Dataset + +**The pipeline is not limited to podcasts!** You can process any Russian audio dataset by organizing it in the expected format. + +### Supported Input Formats + +The pipeline accepts audio files in one of the following structures: + +#### Format 1: Pre-segmented Audio Files + +If your audio is already segmented into short clips: + +``` +your_dataset/ +└── {album_id}/ # Can be any identifier (e.g., speaker_id, session_id) + └── {episode_id}/ # Can be any identifier (e.g., recording_id, clip_id) + ├── audio_1.mp3 + ├── audio_2.wav + ├── audio_3.opus + ... +``` + +**Supported audio formats**: `.mp3`, `.wav`, `.opus`, `.flac`, `.m4a` + +#### Format 2: Long Audio Files + +If you have long audio files that need to be segmented: + +``` +your_dataset/ +└── {album_id}/ + └── {episode_id}/ + └── long_audio.mp3 # Will be automatically segmented + ... +``` + +The preprocessing stage will automatically perform three sequential steps: +1. **Crest Factor Removal**: Remove files with excessive peak/RMS ratio (crest factor > `crest_treshold`) +2. **Loudness Normalization**: Normalize all audio to consistent loudness (ITU-R BS.1770-4 standard) +3. **Audio Segmentation**: + - Segment files into chunks (default: 15 seconds) + - Remove segments that are too short (< 1 second) + - Use Voice Activity Detection to find speech segments + +#### Format 3: Already Processed Data + +If you already have processed data with the expected naming: + +``` +your_dataset/ +└── {album_id}/ + └── {episode_id}/ + ├── {start_time}_{end_time}_{album_id}_{episode_id}.mp3 + ... +``` + +### Dataset Organization Tips + +1. **Use meaningful identifiers**: Replace `{album_id}` and `{episode_id}` with identifiers that make sense for your dataset: + - Speaker IDs, session IDs, recording dates, etc. + - Example: `speaker_001/session_20240101/` + +2. **Keep directory structure shallow**: Avoid deeply nested directories for better performance + +3. **Audio quality**: The pipeline works best with: + - Clear speech audio + - Sample rate: 16kHz or higher (will be resampled automatically) + - Mono or stereo (will be converted to mono automatically) + - Reasonable dynamic range (excessive crest factor files will be filtered out) + +4. **Preprocessing stages**: The preprocessing stage includes: + - **Crest factor filtering**: Files with peak/RMS ratio > `crest_treshold` are removed + - **Loudness normalization**: All audio is normalized to consistent loudness (ITU-R BS.1770-4) + - **Segmentation**: Long files are split into shorter segments using VAD + +### Processing Your Dataset + +Once your dataset is organized, you can skip the download stage and start from preprocessing: + +1. **Set `podcasts_path`** in `config.yaml` to point to your dataset + +2. **Modify `base.sh`** to skip download: + ```bash + SCRIPTS=( + # "./src/download/download_yaml.sh" # Skip if using your own data + "./src/preprocess/preprocess_yaml.sh" + "./src/separation/separation_yaml.sh" + "./src/transcription/transcription_yaml.sh" + "./src/punctuation/punctuation_yaml.sh" + "./src/accents/accents_yaml.sh" + "./src/phonemizer/phonemizer_yaml.sh" + "./src/collate_yamls.sh" + ) + ``` + +3. **Run the pipeline**: + ```bash + bash base.sh configs/config.yaml + ``` + +**Important**: After the preprocessing stage (`./src/preprocess/preprocess_yaml.sh`), the separation stage creates a `balalaika.csv` file in your dataset directory. This file contains: +- **Single speaker flags**: Indicates whether each audio segment contains only one speaker +- **Audio quality metrics**: NISQA quality assessment scores for each segment +- **File paths**: References to all processed audio files + +Additionally, files detected as containing music are **automatically deleted** during the music detection step in the separation stage. + +### Example: Processing a Custom Dataset + +Let's say you have a dataset of Russian speech recordings organized like this: + +``` +/my_data/ +└── speaker_001/ + ├── recording_001.wav + ├── recording_002.wav + └── recording_003.wav +└── speaker_002/ + ├── recording_001.wav + └── recording_002.wav +``` + +1. Set in `config.yaml`: + ```yaml + podcasts_path: /my_data + ``` + +2. The pipeline will process all audio files and create: + - Transcriptions + - Punctuation + - Accents + - Phonemes + - Metadata in `balalaika.parquet` + +**Note**: The `{album_id}` and `{episode_id}` in file names are just organizational identifiers. You can use any naming scheme that fits your data structure. + +--- + +## Additional Requirements for Vosk + +If you plan to use Vosk models for transcription, install additional dependencies: + +```bash +python -m pip install git+https://github.com/lhotse-speech/lhotse +python -m pip install https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.4.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl +python -m pip install https://huggingface.co/csukuangfj/kaldifeat/resolve/main/cuda/1.25.5.dev20241029/linux/kaldifeat-1.25.5.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl +python3 -m pip install git+https://github.com/k2-fsa/icefall +``` + +**Note**: These dependencies are only needed if you enable Vosk in the transcription configuration. + +--- + +## Next Steps + +After setting up your environment and preparing your dataset: + +1. Review the [Usage Guide](guide.md) to understand how to run the pipeline +2. Check `configs/config.yaml` for all available configuration options +3. Start with a small subset of your data to test the pipeline +4. Monitor the output files to ensure everything is working correctly + +--- + +## Troubleshooting + +### Common Setup Issues + +1. **Model not found**: Verify all models are placed in the `models/` directory with correct paths in config +2. **Path errors**: Ensure all paths in `config.yaml` are **absolute paths** +3. **Permission errors**: Ensure the virtual environment is activated before running scripts +4. **CUDA errors**: Check GPU availability and CUDA installation if using GPU-accelerated models + +For more help, see individual module READMEs in `src/*/README.md`. From 5ce8462d23ad03aae62d10d584600d03fa9fda58 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Fri, 26 Dec 2025 23:47:26 +0000 Subject: [PATCH 22/43] docs: update readme --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 8c422b4..12b8508 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# YapodDataset Pipeline +# Balalaika Pipeline A complete production-ready pipeline for processing audio data, from download to feature extraction. This pipeline handles audio preprocessing, speaker diarization, transcription, punctuation restoration, accent restoration, and phonemization. @@ -96,15 +96,15 @@ If you use this pipeline or the Balalaika dataset in your research, please cite: The pipeline integrates the following models and tools: -- **[NISQA](https://github.com/deepvk/NISQA-s)**: Audio quality assessment +- **[NISQA](https://github.com/gabrielmittag/NISQA)**: Audio quality assessment - **[GigaAM](https://github.com/salute-developers/GigaAM)**: ASR models (CTC, RNNT, CTC+LM) - **[ruAccent](https://github.com/Den4ikAI/ruaccent)**: Accent restoration - **[RUPunct](https://huggingface.co/RUPunct/RUPunct_big)**: Punctuation restoration - **[Vosk](https://alphacephei.com/vosk/)**: ASR model -- **[T-one](https://github.com/mtuciru/T-one-cuda-onnx)**: ASR model -- **[TryIPaG2P](https://github.com/NikiPshg/TryIPaG2P)**: Phonemization +- **[T-one](https://github.com/voicekit-team/T-one)**: ASR model +- **[TryIPaG2P](https://github.com/mtuciru/IpaG2p)**: Phonemization - **[Speaker Diarization](https://github.com/pyannote/pyannote-audio)**: Speaker diarization -- **[Smart Turn VAD](https://github.com/snakers4/silero-vad)**: Voice Activity Detection +- **[Smart Turn VAD](https://github.com/pipecat-ai/smart-turn)**: Voice Activity Detection --- From 0e593c1269f4b890a2e1878260ed4d33321c32a6 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Sun, 18 Jan 2026 21:01:09 +0000 Subject: [PATCH 23/43] feat(silence):add silence detect, update read me --- README.md | 2 +- base.sh | 4 +- configs/config.yaml | 5 + docs/guide.md | 28 ++- docs/preparing.md | 3 + src/preprocess/preprocess.py | 2 - src/separation/separation_yaml.sh | 3 +- src/separation/silence_detect.py | 281 ++++++++++++++++++++++++++++++ 8 files changed, 318 insertions(+), 10 deletions(-) create mode 100644 src/separation/silence_detect.py diff --git a/README.md b/README.md index 12b8508..85e828a 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ The pipeline consists of the following stages: - **Crest Factor Removal** - Removes files with excessive peak-to-RMS ratio - **Loudness Normalization** - Normalizes audio loudness (ITU-R BS.1770-4) - **Audio Segmentation** - Segments audio into chunks using VAD -3. **Separation** - Speaker diarization, quality assessment, music detection +3. **Separation** - Speaker diarization, quality assessment, music detection, silence analysis 4. **Transcription** - Multi-model ASR with ROVER consensus (optimized with early stopping when models agree) 5. **Punctuation** - Punctuation restoration 6. **Accents** - Accent restoration diff --git a/base.sh b/base.sh index 027e892..1423f68 100644 --- a/base.sh +++ b/base.sh @@ -21,12 +21,12 @@ echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" - "./src/separation/separation_yaml.sh" + # "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" "./src/punctuation/punctuation_yaml.sh" "./src/accents/accents_yaml.sh" "./src/phonemizer/phonemizer_yaml.sh" - # "./src/collate_yamls.sh" + "./src/collate_yamls.sh" ) activate_venv ".dev_venv" diff --git a/configs/config.yaml b/configs/config.yaml index 40b7619..1907217 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -35,6 +35,11 @@ separation: music_detect_model: ./models/music_detection.safetensors threshold: 0.6 + silence_detect: + num_workers: 2 # per gpu + vad_threshold: 0.5 + max_silence_duration: 1.0 # maximum duration of silence to be marked + transcription: podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription diff --git a/docs/guide.md b/docs/guide.md index faee2f3..8238387 100644 --- a/docs/guide.md +++ b/docs/guide.md @@ -67,10 +67,11 @@ Splits long audio files into shorter segments (default: 15 seconds) using Voice --- ### 3. Separation (`src/separation/`) -Performs three types of analysis: +Performs four types of analysis: - **Diarization**: Identifies and separates different speakers, creates `.rttm` files - **NISQA**: Assesses audio quality metrics - **Music Detection**: Detects music segments in audio +- **Silence Detection**: Analyzes silence patterns in audio **Input**: Segmented audio files **Output**: @@ -78,13 +79,29 @@ Performs three types of analysis: - **`balalaika.csv`**: Metadata file containing: - Single speaker flags (indicating whether each audio segment contains only one speaker) - Audio quality metrics (from NISQA assessment) + - **Silence percent**: Percentage of silence in each audio segment + - **Max silence duration**: Maximum continuous silence duration in seconds - File paths and processing status - Files with detected music are **automatically deleted** during music detection stage - Can filter out multi-speaker files if `one_speaker: True` is set (files are deleted) **Configuration**: `config.yaml` → `separation` section - -**Note**: The `balalaika.csv` file is created/updated during the separation stage and contains important metadata about each audio segment, including speaker information and quality metrics. Files detected as containing music are removed from the dataset. +- `diarization`: Speaker diarization settings + - `num_workers`: Number of workers per GPU + - `one_speaker`: Filter for single-speaker audio only +- `nisqa`: Audio quality assessment settings + - `bs`: Batch size + - `num_workers`: Number of workers + - `nisqa_config_path`: Path to NISQA config +- `music_detect`: Music detection settings + - `bs`: Batch size + - `num_workers`: Number of workers per GPU + - `music_detect_model`: Path to model + - `threshold`: Detection threshold +- `silence_detect`: Silence detection settings + - `num_workers`: Number of workers per GPU + +**Note**: The `balalaika.csv` file is created/updated during the separation stage and contains important metadata about each audio segment, including speaker information, quality metrics, and silence analysis. Files detected as containing music are removed from the dataset. --- @@ -214,6 +231,9 @@ For each audio segment, the pipeline generates: - `balalaika.csv`: Created during separation stage, contains: - Single speaker flags (indicating one-speaker vs multi-speaker segments) - Audio quality metrics (NISQA scores) + - **Silence metrics**: + - `silence_percent`: Percentage of silence in each segment + - `max_silence_duration`: Maximum continuous silence duration (seconds) - File paths and processing status - Files with music are automatically deleted and not included @@ -283,7 +303,7 @@ The stages must be run in this order: - **Crest Factor Removal** → Removes files with excessive peak/RMS ratio - **Loudness Normalization** → Normalizes audio loudness (overwrites files) - **Audio Segmentation** → Segments audio into chunks using VAD -3. **Separation** → Diarization, quality assessment, music detection +3. **Separation** → Diarization, quality assessment, music detection, silence detection 4. **Transcription** → Creates individual model transcriptions + `_rover.txt` (consensus) 5. **Punctuation** → Processes `_rover.txt` → `_punct.txt` 6. **Accents** → Processes `_punct.txt` → `_accent.txt` diff --git a/docs/preparing.md b/docs/preparing.md index e25575d..b79887a 100644 --- a/docs/preparing.md +++ b/docs/preparing.md @@ -209,6 +209,9 @@ Once your dataset is organized, you can skip the download stage and start from p **Important**: After the preprocessing stage (`./src/preprocess/preprocess_yaml.sh`), the separation stage creates a `balalaika.csv` file in your dataset directory. This file contains: - **Single speaker flags**: Indicates whether each audio segment contains only one speaker - **Audio quality metrics**: NISQA quality assessment scores for each segment +- **Silence metrics**: + - `silence_percent`: Percentage of silence in each audio segment + - `max_silence_duration`: Maximum continuous silence duration in seconds - **File paths**: References to all processed audio files Additionally, files detected as containing music are **automatically deleted** during the music detection step in the separation stage. diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index cdba8ba..9b26648 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -15,8 +15,6 @@ from src.utils.utils import load_config, get_audio_paths from src.libs.smart_turn.offline_svad import OfflineVAD -from typing import List, Dict, Any, Tuple - torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index 906ffe5..de2e852 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -22,4 +22,5 @@ SCRIPT_DIR=$(dirname "$(realpath "$0")") # python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" # python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" -python3 -m src.separation.diarization --config_path "$CONFIG_PATH" +# python3 -m src.separation.diarization --config_path "$CONFIG_PATH" +python3 -m src.separation.silence_detect --config_path "$CONFIG_PATH" diff --git a/src/separation/silence_detect.py b/src/separation/silence_detect.py new file mode 100644 index 0000000..23259ee --- /dev/null +++ b/src/separation/silence_detect.py @@ -0,0 +1,281 @@ +import argparse +import multiprocessing +from concurrent.futures import ProcessPoolExecutor, as_completed +from functools import partial +from pathlib import Path +from typing import Dict, List + +import pandas as pd +import torch +import torchaudio +from loguru import logger +from silero_vad import load_silero_vad, read_audio, get_speech_timestamps +from tqdm import tqdm + +from src.utils.utils import get_audio_paths, load_config + +torch.set_num_threads(1) +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.enable_flash_sdp(True) +torch.backends.cuda.enable_mem_efficient_sdp(True) +torch.backends.cuda.enable_math_sdp(False) + + +silence_detect_model = None +vad_device = None + + +def init_worker(device_str: str): + """Initialize VAD model for the worker process.""" + global silence_detect_model, vad_device + logger.info(f"Initializing VAD model on {device_str}...") + + try: + if 'cuda' in device_str: + torch.cuda.set_device(device_str) + silence_detect_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad') + silence_detect_model.to(device_str) + vad_device = torch.device(device_str) + logger.info(f"Worker initialized successfully for silence detection on {device_str}.") + except Exception as e: + logger.error(f"Failed to initialize worker on {device_str}: {e}") + silence_detect_model = None + vad_device = None + + +def calculate_silence_metrics(path: Path, vad_threshold: float = 0.5) -> Dict: + """ + Calculate silence percentage and maximum silence duration using Silero VAD. + + Args: + path: Path to audio file + sample_rate: Target sample rate for VAD + + Returns: + Dict with silence_percent and max_silence_duration + """ + global silence_detect_model, vad_device + + if silence_detect_model is None: + logger.error(f"VAD model is not initialized. Skipping {path.name}.") + return None + + try: + # Load audio + audio = read_audio(str(path)) + if vad_device is not None: + audio = audio.to(vad_device) + + speech_timestamps = get_speech_timestamps( + audio, + silence_detect_model, + threshold=vad_threshold, + return_seconds=True + ) + # Calculate total audio duration in seconds + total_duration = audio.shape[-1] / 16_000 # 16_000 sample rate for silero vad + + # Calculate total speech duration + total_speech_duration = sum( + segment['end'] - segment['start'] + for segment in speech_timestamps + ) + + # Calculate silence duration + total_silence_duration = total_duration - total_speech_duration + + # Calculate silence percentage + silence_percent = (total_silence_duration / total_duration * 100) if total_duration > 0 else 0 + + # Calculate maximum silence duration + max_silence_duration = 0.0 + if len(speech_timestamps) > 1: + # Find gaps between speech segments + silence_gaps = [] + for i in range(len(speech_timestamps) - 1): + gap_start = speech_timestamps[i]['end'] + gap_end = speech_timestamps[i + 1]['start'] + gap_duration = gap_end - gap_start + silence_gaps.append(gap_duration) + + # Include silence at the beginning + if speech_timestamps[0]['start'] > 0: + silence_gaps.append(speech_timestamps[0]['start']) + + # Include silence at the end + if speech_timestamps[-1]['end'] < total_duration: + silence_gaps.append(total_duration - speech_timestamps[-1]['end']) + + max_silence_duration = max(silence_gaps) if silence_gaps else 0.0 + elif len(speech_timestamps) == 1: + # Only one speech segment, check before and after + max_silence_duration = max( + speech_timestamps[0]['start'], + total_duration - speech_timestamps[0]['end'] + ) + else: + # No speech detected, entire file is silence + max_silence_duration = total_duration + + return { + 'filepath': str(path), + 'silence_percent': round(silence_percent, 2), + 'max_silence_duration': round(max_silence_duration, 2) + } + + except Exception as e: + logger.error(f"Failed to process {path.name}: {e}") + return None + finally: + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def process_file(path: Path, vad_threshold: float = 0.5) -> Dict: + """Wrapper function for processing a single file.""" + return calculate_silence_metrics(path=path, vad_threshold=vad_threshold) + + +def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: Path) -> List[Path]: + """Get list of audio paths that haven't been processed yet.""" + all_audio_paths = set(get_audio_paths(podcasts_path)) + processed_audio_paths = [] + + if result_csv_path.exists(): + logger.info(f"Resuming from existing results file: {result_csv_path}") + df = pd.read_csv(result_csv_path) + if 'silence_percent' not in df.columns: + logger.warning("Column 'silence_percent' not found in results file. Treating all entries as unprocessed.") + processed_audio_paths = set() + else: + processed_mask = df['silence_percent'].notna() + processed_audio_paths = set(df.loc[processed_mask, 'filepath'].astype(str).tolist()) + else: + return list(all_audio_paths) + + unprocessed_paths = all_audio_paths - processed_audio_paths + return list(unprocessed_paths) + + +def main(args): + """Main function to detect silence metrics for all audio files.""" + config = load_config(args.config_path, 'separation') + podcasts_path = config.get('podcasts_path', './data') + + silence_config = config.get('silence_detect', {}) + num_workers_per_gpu = silence_config.get('num_workers', 1) + vad_threshold = silence_config.get('vad_threshold', 0.5) + + result_csv_path = Path(podcasts_path) / 'balalaika.csv' + available_gpu_ids = list(range(torch.cuda.device_count())) + + # Use CPU if no GPU available + if not available_gpu_ids: + logger.warning("No GPUs available. Using CPU for silence detection.") + available_gpu_ids = ['cpu'] + + logger.info(f""" + Podcasts path: {podcasts_path} + Workers per device: {num_workers_per_gpu} + VAD threshold: {vad_threshold} + Devices detected: {len(available_gpu_ids)} + """) + + all_audio_paths = get_unprocessed_audio_paths(podcasts_path, result_csv_path) + + if not all_audio_paths: + logger.success("All audio files have already been processed.") + return + + logger.info(f"Found {len(all_audio_paths)} new audio files to process.") + + num_devices = len(available_gpu_ids) + files_for_each_device = [[] for _ in range(num_devices)] + for i, path in enumerate(all_audio_paths): + device_assignment_index = i % num_devices + files_for_each_device[device_assignment_index].append(path) + + all_futures = [] + executors = [] + + for i, device_id in enumerate(available_gpu_ids): + device_str = f'cuda:{device_id}' if device_id != 'cpu' else 'cpu' + files_for_this_device = files_for_each_device[i] + + if not files_for_this_device: + continue + + logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") + + initializer_fn = partial(init_worker, device_str=device_str) + + executor = ProcessPoolExecutor( + max_workers=num_workers_per_gpu, + initializer=initializer_fn, + mp_context=multiprocessing.get_context('spawn') + ) + executors.append(executor) + + for path in files_for_this_device: + future = executor.submit(process_file, path, vad_threshold=vad_threshold) + all_futures.append(future) + + results = [] + for future in tqdm(as_completed(all_futures), total=len(all_futures), desc="Processing audio files"): + try: + result = future.result() + if result: + results.append(result) + except Exception as e: + logger.error(f"A task encountered an error: {e}") + + for executor in executors: + executor.shutdown(wait=True) + + if results: + try: + new_results_df = pd.DataFrame(results) + + if result_csv_path.exists(): + main_df = pd.read_csv(result_csv_path) + + main_df = main_df.set_index('filepath') + new_results_df = new_results_df.set_index('filepath') + + updated_df = new_results_df.combine_first(main_df) + + updated_df = updated_df.reset_index() + + original_cols = main_df.reset_index().columns.tolist() + new_cols = [col for col in updated_df.columns if col not in original_cols] + final_order = original_cols + new_cols + final_order_unique = list(dict.fromkeys(final_order)) + updated_df = updated_df[final_order_unique] + + if 'Unnamed: 0' in updated_df.columns: + updated_df = updated_df.drop('Unnamed: 0', axis=1) + + updated_df.to_csv(result_csv_path, index=False) + logger.success(f"Processing complete. Updated/added silence metrics for {len(results)} rows in {result_csv_path}") + else: + new_results_df.to_csv(result_csv_path, index=False) + logger.success(f"Created new results file with {len(results)} rows in {result_csv_path}") + + except Exception as e: + logger.error(f"An error occurred while updating the CSV file: {e}") + else: + logger.warning("Processing finished, but no new results were generated.") + + +if __name__ == "__main__": + multiprocessing.set_start_method('spawn', force=True) + + parser = argparse.ArgumentParser(description="Detect silence metrics (silence percentage and max silence duration) using Silero VAD.") + parser.add_argument( + "--config_path", + type=str, + help="Path to the main YAML configuration file." + ) + + args = parser.parse_args() + main(args) From f099c444f99168adfbf81f36df9430118c415828 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Sat, 31 Jan 2026 19:30:12 +0000 Subject: [PATCH 24/43] feat: update order separation_yaml --- base.sh | 14 +++++++------- configs/config.yaml | 16 ++++++++-------- src/preprocess/preprocess.py | 6 +++--- src/preprocess/preprocess_yaml.sh | 2 +- src/separation/music_detect.py | 9 ++++++--- src/separation/silence_detect.py | 7 ++++--- 6 files changed, 29 insertions(+), 25 deletions(-) diff --git a/base.sh b/base.sh index 1423f68..d32fd94 100644 --- a/base.sh +++ b/base.sh @@ -20,13 +20,13 @@ CONFIG_PATH=$(realpath "$1") echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" - # "./src/preprocess/preprocess_yaml.sh" - # "./src/separation/separation_yaml.sh" - "./src/transcription/transcription_yaml.sh" - "./src/punctuation/punctuation_yaml.sh" - "./src/accents/accents_yaml.sh" - "./src/phonemizer/phonemizer_yaml.sh" - "./src/collate_yamls.sh" + "./src/preprocess/preprocess_yaml.sh" + "./src/separation/separation_yaml.sh" + # "./src/transcription/transcription_yaml.sh" + # "./src/punctuation/punctuation_yaml.sh" + # "./src/accents/accents_yaml.sh" + # "./src/phonemizer/phonemizer_yaml.sh" + # "./src/collate_yamls.sh" ) activate_venv ".dev_venv" diff --git a/configs/config.yaml b/configs/config.yaml index 1907217..bd1745b 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,13 @@ cache_path: ./cache download: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the podcasts are located + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 1 crest_treshold: 10 # in dB @@ -18,7 +18,7 @@ preprocess: separation: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located diarization: num_workers: 1 # per gpu @@ -36,12 +36,12 @@ separation: threshold: 0.6 silence_detect: - num_workers: 2 # per gpu + num_workers: 1 # per gpu vad_threshold: 0.5 max_silence_duration: 1.0 # maximum duration of silence to be marked transcription: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True model_names: ['giga_ctc', 'giga_rnnt', 'giga_ctc_lm', 'vosk', 'tone',] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] @@ -64,15 +64,15 @@ transcription: batch_size: 16 # if supported punctuation: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /mnt/drive2_8tb/biggest-ru-book # full path to the folder where the text files are located + podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index 9b26648..5f77f83 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -196,9 +196,9 @@ def process_audio_file(path_audio: str, duration: float): # TODO: don't forget to remove the code audio, sr = torchaudio.load(path_audio) - if (audio.shape[-1] / sr) <= 1 or audio.shape[-1] / sr > 15: - logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") - os.remove(path_audio) + # if (audio.shape[-1] / sr) <= 1 or audio.shape[-1] / sr > 15: + # logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") + # os.remove(path_audio) # # TODO: don't forget to remove the code try: diff --git a/src/preprocess/preprocess_yaml.sh b/src/preprocess/preprocess_yaml.sh index 7e53ecb..88f86e6 100644 --- a/src/preprocess/preprocess_yaml.sh +++ b/src/preprocess/preprocess_yaml.sh @@ -21,6 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") +python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" python3 -m src.preprocess.crest_factor_remover --config_path "$CONFIG_PATH" python3 -m src.preprocess.preprocess_audio --config_path "$CONFIG_PATH" -python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/separation/music_detect.py b/src/separation/music_detect.py index 7c79a45..11beac5 100644 --- a/src/separation/music_detect.py +++ b/src/separation/music_detect.py @@ -105,16 +105,19 @@ def run_worker(rank: int, world_size: int, all_paths: List[str], config: dict): def main(args): mp.set_start_method('spawn', force=True) config = load_config(args.config_path, 'separation') + podcasts_path = config.get('podcasts_path') + all_paths = list(get_audio_paths(podcasts_path)) + n_gpus = torch.cuda.device_count() - if not (podcasts_path := config.get('podcasts_path')): + if not podcasts_path: logger.error("No podcasts_path in config") return - if not (all_paths := [str(p) for p in get_audio_paths(podcasts_path)]): + if not all_paths: logger.warning("No audio files found.") return - if (n_gpus := torch.cuda.device_count()) == 0: + if n_gpus == 0: logger.error("No GPU found.") return diff --git a/src/separation/silence_detect.py b/src/separation/silence_detect.py index 23259ee..0243f70 100644 --- a/src/separation/silence_detect.py +++ b/src/separation/silence_detect.py @@ -9,7 +9,7 @@ import torch import torchaudio from loguru import logger -from silero_vad import load_silero_vad, read_audio, get_speech_timestamps +from silero_vad import read_audio, get_speech_timestamps from tqdm import tqdm from src.utils.utils import get_audio_paths, load_config @@ -120,7 +120,8 @@ def calculate_silence_metrics(path: Path, vad_threshold: float = 0.5) -> Dict: return { 'filepath': str(path), 'silence_percent': round(silence_percent, 2), - 'max_silence_duration': round(max_silence_duration, 2) + 'max_silence_duration': round(max_silence_duration, 2), + 'total_duration': round(total_duration, 2), } except Exception as e: @@ -154,7 +155,7 @@ def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: Path) -> Li return list(all_audio_paths) unprocessed_paths = all_audio_paths - processed_audio_paths - return list(unprocessed_paths) + return list(all_audio_paths) def main(args): From c9e6e9b6e44dc52d0649f3c70dca0903da95167f Mon Sep 17 00:00:00 2001 From: root Date: Wed, 18 Feb 2026 19:15:58 +0300 Subject: [PATCH 25/43] feat: change transription backend --- configs/config.yaml | 25 +- docs/preparing.md | 116 +++++- requirements_dev.txt | Bin 1534 -> 1110 bytes src/transcription/README.md | 95 +++-- src/transcription/transcription.py | 454 +++++++++++----------- src/transcription/transcripton_base.py | 387 ------------------ src/transcription/transcripton_dataset.py | 169 -------- 7 files changed, 424 insertions(+), 822 deletions(-) delete mode 100644 src/transcription/transcripton_base.py delete mode 100644 src/transcription/transcripton_dataset.py diff --git a/configs/config.yaml b/configs/config.yaml index bd1745b..3df47e1 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -44,24 +44,23 @@ transcription: podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True - model_names: ['giga_ctc', 'giga_rnnt', 'giga_ctc_lm', 'vosk', 'tone',] # ['vosk', 'tone','giga_ctc', 'giga_rnnt', 'giga_ctc_lm'] + use_tensorrt: False # enable TensorRT execution provider (requires CUDA + TensorRT installed) + use_vad: False # enable Silero VAD for long-form audio (>30s) + use_rover: False # enable ROVER consensus aggregation (requires rover module) + # All models are loaded via onnx-asr from HuggingFace (or local path). + # Supported: giga_ctc, giga_rnnt, vosk, vosk_small, tone, parakeet_v2, parakeet_v3, canary, whisper_base, whisper_turbo + model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] - giga: - num_workers: 1 - batch_size: 16 # if supported - lm_path: './models/kenlm.bin' # path to lm.bin for giga ctc model + giga: + batch_size: 16 + # quantization: int8 # optional: int8 quantization for faster CPU/GPU inference vosk: - vosk_path: ./models/vosk-model-ru - num_workers: 1 - batch_size: 16 # if supported - beam_size: 4 - lm_scale: 0.2 - ngram_lm_scale: -0.1 + batch_size: 16 + # vosk_path: ./models/vosk-model-ru # optional: local model directory (otherwise downloaded from HF) tone: - num_workers: 1 - batch_size: 16 # if supported + batch_size: 16 punctuation: podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located diff --git a/docs/preparing.md b/docs/preparing.md index b79887a..c8b6471 100644 --- a/docs/preparing.md +++ b/docs/preparing.md @@ -12,7 +12,7 @@ This guide explains how to set up the pipeline and prepare your own audio datase 4. [Models Setup](#models-setup) 5. [Configuration](#configuration) 6. [Preparing Your Own Dataset](#preparing-your-own-dataset) -7. [Additional Requirements for Vosk](#additional-requirements-for-vosk) +7. [ASR Inference (onnx-asr)](#asr-inference-onnx-asr) --- @@ -72,15 +72,47 @@ YANDEX_KEY= ## Models Setup -Place all required models under the `models/` directory: +### Preprocessing & Separation Models + +Place the following models under the `models/` directory: -- **Vosk Model**: `./models/vosk-model-ru` (directory with Vosk model files) - **Smart VAD Model**: `./models/smart-turn-v3.0.onnx` - **Music Detection Model**: `./models/music_detection.safetensors` -- **KenLM Language Model**: `./models/kenlm.bin` (for Giga CTC model) - **NISQA Config**: `./configs/nisqa_b.yaml` -**Note**: Some models are downloaded automatically from Hugging Face (RUPunct, ruAccent). Make sure your `HF_TOKEN` is set correctly. +### ASR Models (onnx-asr) + +ASR-модели загружаются **автоматически** из Hugging Face при первом запуске через библиотеку [onnx-asr](https://github.com/istupakov/onnx-asr). Ручная установка не требуется. + +Поддерживаемые модели: + +| Имя в конфиге | onnx-asr модель | Язык | +|------------------|------------------------------------------|--------------| +| `giga_ctc` | `gigaam-v3-ctc` | Русский | +| `giga_rnnt` | `gigaam-v3-rnnt` | Русский | +| `vosk` | `alphacep/vosk-model-ru` | Русский | +| `tone` | `t-tech/t-one` | Русский | +| `parakeet_v3` | `nemo-parakeet-tdt-0.6b-v3` | Multilingual | +| `canary` | `nemo-canary-1b-v2` | Multilingual | +| `whisper_turbo` | `onnx-community/whisper-large-v3-turbo` | Multilingual | + +Инференс выполняется на GPU через ONNX Runtime с поддержкой CUDA и TensorRT execution providers. Батчевая обработка включена по умолчанию для максимальной утилизации GPU. + +**Конфигурация GPU-инференса** (`configs/config.yaml`): + +```yaml +transcription: + use_tensorrt: True # TensorRT EP — fp16, максимальная скорость на NVIDIA GPU + model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] + + giga: + batch_size: 16 # размер батча (подбирается под объем VRAM) + quantization: int8 # опционально: int8 квантизация +``` + +При включённом `use_tensorrt: True` используется TensorRT execution provider с fp16 для максимальной производительности. При нескольких GPU файлы автоматически распределяются между картами. + +**Note**: Some models (RUPunct, ruAccent) are downloaded automatically from Hugging Face. Make sure your `HF_TOKEN` is set correctly. --- @@ -247,18 +279,78 @@ Let's say you have a dataset of Russian speech recordings organized like this: --- -## Additional Requirements for Vosk +## ASR Inference (onnx-asr) + +Транскрипция реализована через [onnx-asr](https://github.com/istupakov/onnx-asr) — лёгкую Python-библиотеку для ASR на базе ONNX Runtime. Основные преимущества: + +- **Единый интерфейс** для всех моделей (GigaAM, Vosk, Parakeet, Whisper, T-one) +- **GPU-инференс** через CUDA / TensorRT execution providers +- **Батчевая обработка** для максимальной утилизации GPU +- **Мульти-GPU** — файлы автоматически распределяются между всеми доступными GPU +- **Автоматическая загрузка** моделей из Hugging Face +- **Минимум зависимостей** — не требуется PyTorch, Transformers или FFmpeg для ASR + +### Установка -If you plan to use Vosk models for transcription, install additional dependencies: +```bash +pip install onnx-asr[gpu,hub] +``` + +Для TensorRT (опционально, максимальная скорость): ```bash -python -m pip install git+https://github.com/lhotse-speech/lhotse -python -m pip install https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.4.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl -python -m pip install https://huggingface.co/csukuangfj/kaldifeat/resolve/main/cuda/1.25.5.dev20241029/linux/kaldifeat-1.25.5.dev20250807+cuda12.8.torch2.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl -python3 -m pip install git+https://github.com/k2-fsa/icefall +pip install onnxruntime-gpu[cuda,cudnn] tensorrt-cu12-libs +``` + +### Пример использования + +```python +import onnx_asr + +# Загрузка модели с TensorRT на конкретной GPU +providers = [ + ("TensorrtExecutionProvider", { + "device_id": 0, + "trt_fp16_enable": True, + "trt_max_workspace_size": 6 * 1024**3, + }), + ("CUDAExecutionProvider", {"device_id": 0}), +] + +model = onnx_asr.load_model("gigaam-v3-rnnt", providers=providers) + +# Батчевое распознавание — максимальная утилизация GPU +results = model.recognize(["audio1.wav", "audio2.wav", "audio3.wav"]) + +# С таймстемпами +model_ts = onnx_asr.load_model("gigaam-v3-ctc", providers=providers).with_timestamps() +result = model_ts.recognize("audio.wav") +``` + +### Конфигурация + +В `configs/config.yaml` секция `transcription`: + +```yaml +transcription: + podcasts_path: /path/to/dataset + consensus_num: 3 # пропуск файлов при совпадении N моделей + with_timestamps: True + use_tensorrt: True # TensorRT EP (fp16, максимальная скорость) + use_vad: False # Silero VAD для длинных аудио (>30с) + model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] + + giga: + batch_size: 16 # подбирается под VRAM (16 для 24GB, 8 для 12GB) + + vosk: + batch_size: 16 + + tone: + batch_size: 16 ``` -**Note**: These dependencies are only needed if you enable Vosk in the transcription configuration. +**Примечание**: дополнительные зависимости для Vosk (k2, kaldifeat, icefall) **больше не требуются** — onnx-asr включает всё необходимое. --- diff --git a/requirements_dev.txt b/requirements_dev.txt index d10a3f5afa0f638617f8d82287cbacc4eb2fa039..e6ce9c9b813bab2f1864ba862cd104aa44e57fc3 100644 GIT binary patch delta 229 zcmeyzeT`!xlZG@GLjgk~Lmop4kSu1f1wtbRJuo(&SSUMjg3QEqG850JO#GxYS&UI* zvIC>m1o?f>4g3K*Z=o>eva|1IXJ;n+wZ7QQ{48EdWsMEQcti^+R~sGT(C$8b)KEo(s|G!D zb9oU*7$dY-L{R79A&!Yhl-%Z&o;?e^76~7FgzvJ_CaVsJp2`!O$|WYMkQ2L%W1Hlas{x|n>0T*Pl`PbT(mpPqBI z&Fr}YmyGgWdjeP;HT)2Sp4=$_KCVqaszX+WRYW+d=%$>c6|=V-EZmw)NX zao5=@E*r@0mTmdB(|`3U)l8j|&A+jDVb0ce9QHKVj(gH)@|dZ8=0|K2at>B6*Mqe4 Q$OU{O={#biCpL=l3) int: + try: + import onnxruntime as ort + if 'CUDAExecutionProvider' not in ort.get_available_providers(): + return 0 + except ImportError: + return 0 + try: + import subprocess + result = subprocess.run( + ['nvidia-smi', '--query-gpu=index', '--format=csv,noheader'], + capture_output=True, text=True, timeout=5 + ) + if result.returncode == 0: + return len([line for line in result.stdout.strip().split('\n') if line.strip()]) + except Exception: + pass + return 1 + + +def get_providers(cuda_id: int, use_tensorrt: bool = False) -> list: + if use_tensorrt: + return [ + ("TensorrtExecutionProvider", { + "device_id": cuda_id, + "trt_max_workspace_size": 6 * 1024**3, + "trt_fp16_enable": True, + }), + ("CUDAExecutionProvider", {"device_id": cuda_id}), + ] + return [("CUDAExecutionProvider", {"device_id": cuda_id})] + + +def save_results(paths: List[str], texts: List[str], timestamps: Optional[List[str]], model_suffix: str): + for i, (path_str, text) in enumerate(zip(paths, texts)): path = Path(path_str) - + txt_path = path.with_name(f"{path.stem}_{model_suffix}.txt") try: with open(txt_path, "w", encoding="utf-8") as f: f.write(text) except Exception as e: - logger.error(f"Failed to write TXT for {path.name}: {e}") + logger.error(f"Write TXT failed {path.name}: {e}") - if ts_content: + ts = timestamps[i] if timestamps and i < len(timestamps) else '' + if ts: tst_path = path.with_name(f"{path.stem}_{model_suffix}.tst") try: with open(tst_path, "w", encoding="utf-8") as f: - f.write(ts_content) + f.write(ts) except Exception as e: - logger.error(f"Failed to write TST for {path.name}: {e}") + logger.error(f"Write TST failed {path.name}: {e}") -def run_inference_on_device(cuda_id: int, world_size: int, model_name: str, all_file_paths: List[Path], config: dict): - if not all_file_paths: - return +def load_batch(file_paths: List[str]): + """Read batch — pass WAV paths directly to onnx-asr; non-WAV read via soundfile.""" + all_wav = all(Path(f).suffix.lower() == '.wav' for f in file_paths) + if all_wav or not HAS_SOUNDFILE: + return file_paths, None + + waveforms, sr = [], None + for f in file_paths: + wf, file_sr = sf.read(f, dtype='float32') + if wf.ndim > 1: + wf = wf.mean(axis=1) + waveforms.append(wf) + sr = file_sr + return waveforms, sr + + +def format_timestamps(result) -> str: + if hasattr(result, 'tokens') and result.tokens: + return '\n'.join( + f"{getattr(t, 'start', 0):.3f}\t{getattr(t, 'end', 0):.3f}\t{getattr(t, 'text', '')}" + for t in result.tokens + ) + return '' + - my_files = all_file_paths[cuda_id::world_size] - +def run_worker(cuda_id: int, world_size: int, model_name: str, + all_files: List[str], config: dict): + """Inference worker: loads onnx-asr model on a single GPU and processes its shard.""" + my_files = all_files[cuda_id::world_size] if not my_files: return - device_str = f"cuda:{cuda_id}" - logger.info(f"Worker {cuda_id}/{world_size} started for '{model_name}' on {device_str}. Processing {len(my_files)} files.") + model_cfg = config.get('giga', {}) if 'giga' in model_name else config.get(model_name, {}) + if not isinstance(model_cfg, dict): + model_cfg = {} + batch_size = model_cfg.get('batch_size', 16) + use_trt = config.get('use_tensorrt', False) + quantization = model_cfg.get('quantization') - model_config = config.get('giga') if 'giga' in model_name else config.get(model_name, {}) - batch_size = model_config.get('batch_size', 8) - num_workers = model_config.get('num_workers', 4) + onnx_name = MODEL_MAP.get(model_name, model_name) + output_suffix = 'vosk' if 'vosk' in model_name else model_name + do_timestamps = config.get('with_timestamps', False) and model_name in SUPPORTED_TIMESTAMPS - lengths_cache_path = config.get('audio_lengths_cache', './cache/audio_lengths_cache.json') + local_path = model_cfg.get('vosk_path') if 'vosk' in model_name else model_cfg.get('model_path') - model_name_for_output = 'vosk' if 'vosk' in model_name else model_name - - with_timestamps = config.get('with_timestamps', False) - timestamps_supported = any(x in model_name for x in SUPPORTED_TIME_STAMPS) - process_timestamps = with_timestamps and timestamps_supported - - my_files_str = [str(p) for p in my_files] + logger.info(f"Worker {cuda_id}/{world_size}: {onnx_name} on cuda:{cuda_id}, {len(my_files)} files, batch={batch_size}") try: - if 'giga' in model_name: - model = GigaAMWrapper(model_id=model_name, device=device_str, **model_config) - - dataset = GigaAudioDataset(my_files_str, target_sr=16000) - sampler = LengthGroupedSampler(dataset, lengths_cache_path, batch_size) - - loader = torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - sampler=sampler, - num_workers=num_workers, - collate_fn=collate_giga, - pin_memory=True, - persistent_workers=True if num_workers > 0 else False - ) - - for batch_wavs, batch_lengths, batch_paths in tqdm(loader, desc=f"Giga-{cuda_id}", position=cuda_id): - if batch_wavs is None: continue - - if process_timestamps: - texts, tstamps = model.transcribe_tensors_with_timestamps(batch_wavs, batch_lengths) - else: - texts = model.transcribe_tensors(batch_wavs, batch_lengths) - tstamps = [''] * len(texts) - - save_results(batch_paths, texts, tstamps, model_name_for_output) - - elif 'tone' in model_name: - model = ToneWrapper(model_id=model_name, device=device_str, **model_config) - - dataset = ToneAudioDataset(my_files_str, target_sr=8000) - loader = torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - shuffle=False, - num_workers=num_workers, - collate_fn=collate_tone, - pin_memory=False - ) - - for batch_audios, batch_paths in tqdm(loader, desc=f"Tone-{cuda_id}", position=cuda_id): - if not batch_audios: continue - - if process_timestamps: - texts, tstamps = model.transcribe_audio_data_with_timestamps(batch_audios) - else: - texts = model.transcribe_audio_data(batch_audios) - tstamps = [''] * len(texts) - - save_results(batch_paths, texts, tstamps, model_name_for_output) - - elif 'vosk' in model_name: - model = VOSKCUDAWrapper(model_id=model_config.get('vosk_path'), device=device_str, **model_config) - - dataset = VoskAudioDataset(my_files_str, target_sr=16000) - sampler = LengthGroupedSampler(dataset, lengths_cache_path, batch_size) - - loader = torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - sampler=sampler, - num_workers=num_workers, - collate_fn=collate_vosk, - pin_memory=True - ) - - for batch_waveforms, batch_paths in tqdm(loader, desc=f"Vosk-{cuda_id}", position=cuda_id): - if not batch_waveforms: continue - - texts = model.transcribe_batch_data(batch_waveforms) - tstamps = [''] * len(texts) - - save_results(batch_paths, texts, tstamps, model_name_for_output) - - else: - logger.error(f"Unknown model type: {model_name}") + providers = get_providers(cuda_id, use_trt) + load_args = [onnx_name] + ([local_path] if local_path else []) + load_kwargs = {"providers": providers} + if quantization: + load_kwargs["quantization"] = quantization + + model = onnx_asr.load_model(*load_args, **load_kwargs) + + if do_timestamps: + model = model.with_timestamps() + + if config.get('use_vad', False): + vad_params = config.get('vad_params', {}) + vad = onnx_asr.load_vad("silero", **vad_params) + model = model.with_vad(vad) + + for i in tqdm(range(0, len(my_files), batch_size), desc=f"ASR-{cuda_id}", position=cuda_id): + batch = my_files[i:i + batch_size] + + try: + data, sr = load_batch(batch) + kw = {"sample_rate": sr} if sr else {} + results = model.recognize(data, **kw) + except Exception as e: + logger.error(f"Batch failed: {e}. Falling back to single-file mode.") + results = [] + for f in batch: + try: + results.append(model.recognize(f)) + except Exception as e2: + logger.error(f"File failed {f}: {e2}") + results.append("") + + if not isinstance(results, list): + results = [results] + + texts = [str(r) for r in results] + ts = [format_timestamps(r) for r in results] if do_timestamps else None + + save_results(batch, texts, ts, output_suffix) except Exception as e: - logger.exception(f"Critical error in worker {cuda_id} for model {model_name}: {e}") - - -def check_consensus_reached(audio_path: Path, model_names: List[str], consensus_num: int) -> bool: - """Check if consensus_num models have produced the same transcription for this audio file.""" - from collections import Counter - from src.utils.utils import read_file_content - - transcriptions = [] - for model_name in model_names: - model_name_for_file = 'vosk' if 'vosk' in model_name else model_name - transcript_path = audio_path.with_name(f"{audio_path.stem}_{model_name_for_file}.txt") - - if transcript_path.exists(): + logger.exception(f"Worker {cuda_id} fatal error ({model_name}): {e}") + + +def check_consensus(audio_path: Path, model_names: List[str], consensus_num: int) -> bool: + texts = [] + for mn in model_names: + suffix = 'vosk' if 'vosk' in mn else mn + tp = audio_path.with_name(f"{audio_path.stem}_{suffix}.txt") + if tp.exists(): try: - text = read_file_content(transcript_path) - if text: - # Normalize text for comparison (lowercase, strip) - normalized = text.lower().strip() - transcriptions.append(normalized) + t = read_file_content(tp) + if t: + texts.append(t.lower().strip()) except Exception: pass - - if len(transcriptions) < consensus_num: + if len(texts) < consensus_num: return False - - # Count occurrences of each transcription - counter = Counter(transcriptions) - # Check if any transcription appears at least consensus_num times - return max(counter.values()) >= consensus_num + return max(Counter(texts).values()) >= consensus_num -def get_valid_audio_paths(src_path: str, model_name_for_output: str, processed_models: List[str], consensus_num: int) -> List[Path]: - all_audio_paths = get_audio_paths(src_path) - if not all_audio_paths: +def get_valid_paths(src_path: str, output_suffix: str, + processed: List[str], consensus_num: int) -> List[str]: + all_paths = get_audio_paths(src_path) + if not all_paths: return [] - # Filter out files that already have transcription for this model - valid_paths = [ - p for p in all_audio_paths - if not p.with_name(f"{p.stem}_{model_name_for_output}.txt").exists() - ] - - # Filter out files that already reached consensus - if consensus_num > 0 and len(processed_models) >= consensus_num: - consensus_count = 0 - filtered_paths = [] - for p in valid_paths: - if check_consensus_reached(p, processed_models, consensus_num): - consensus_count += 1 + valid = [p for p in all_paths if not p.with_name(f"{p.stem}_{output_suffix}.txt").exists()] + + if consensus_num > 0 and len(processed) >= consensus_num: + skipped = 0 + filtered = [] + for p in valid: + if check_consensus(p, processed, consensus_num): + skipped += 1 else: - filtered_paths.append(p) - - if consensus_count > 0: - logger.info(f"Skipping {consensus_count} files that already reached consensus ({consensus_num} models agree).") - valid_paths = filtered_paths - - return valid_paths + filtered.append(p) + if skipped: + logger.info(f"Consensus reached for {skipped} files, skipping") + valid = filtered + + return [str(p) for p in valid] def main(args): config = load_config(args.config_path, 'transcription') model_names = config.get('model_names', ['giga_rnnt']) src_path = config.get('podcasts_path', '.') - consensus_num = config.get('consensus_num', 0) # 0 means process all models + consensus_num = config.get('consensus_num', 0) - available_gpu_ids = list(range(torch.cuda.device_count())) - if not available_gpu_ids: - logger.error("No CUDA GPUs detected. This optimized script requires GPU.") + num_gpus = get_gpu_count() + if num_gpus == 0: + logger.error("No CUDA GPUs detected. GPU required for transcription.") return - - num_gpus = len(available_gpu_ids) - logger.info(f"Detected {num_gpus} GPUs. Starting processing pipeline.") + + logger.info(f"{num_gpus} GPU(s) detected. Starting transcription pipeline.") if consensus_num > 0: - logger.info(f"Consensus mode enabled: will skip files where {consensus_num} models agree.") - - for model_idx, model_name in enumerate(model_names): - logger.info(f"=== Processing model {model_idx + 1}/{len(model_names)}: {model_name} ===") - - model_name_for_output = 'vosk' if 'vosk' in model_name else model_name - - # Get models processed so far (before current model) - processed_models = model_names[:model_idx] if consensus_num > 0 else [] - - all_paths = get_valid_audio_paths(src_path, model_name_for_output, processed_models, consensus_num) - - if not all_paths: - logger.info(f"No new files for {model_name}. Skipping.") + logger.info(f"Consensus mode: {consensus_num} models must agree") + + for idx, model_name in enumerate(model_names): + logger.info(f"=== [{idx + 1}/{len(model_names)}] {model_name} ===") + + output_suffix = 'vosk' if 'vosk' in model_name else model_name + processed = model_names[:idx] if consensus_num > 0 else [] + paths = get_valid_paths(src_path, output_suffix, processed, consensus_num) + + if not paths: + logger.info(f"No files to process for {model_name}") continue - - logger.info(f"Total files to process: {len(all_paths)}") - + + logger.info(f"{len(paths)} files to process") + + if num_gpus == 1: + run_worker(0, 1, model_name, paths, config) + else: + procs = [] + for gid in range(num_gpus): + p = mp.Process( + target=run_worker, + args=(gid, num_gpus, model_name, paths, config) + ) + p.start() + procs.append(p) + + for p in procs: + p.join() + + failed = [p.exitcode for p in procs if p.exitcode != 0] + if failed: + logger.error(f"Workers failed with exit codes: {failed}") + + if config.get('use_rover', False): + logger.info("ROVER aggregation...") try: - mp.spawn( - run_inference_on_device, - args=(num_gpus, model_name, all_paths, config), - nprocs=num_gpus, - join=True - ) + from src.transcription.rover import ROVERWrapper + ROVERWrapper(podcasts_path=src_path, model_names=model_names).aggregate_and_save() + logger.info("ROVER done.") + except ImportError: + logger.warning("ROVER module not available, skipping") except Exception as e: - logger.error(f"Multiprocessing error: {e}") - - logger.info("Starting ROVER processing...") - try: - rover_wrapper = ROVERWrapper(podcasts_path=src_path, model_names=model_names) - rover_wrapper.aggregate_and_save() - logger.info("ROVER processing finished.") - except Exception as e: - logger.error(f"ROVER failed: {e}") + logger.error(f"ROVER failed: {e}") + + logger.info("Transcription pipeline complete!") + if __name__ == "__main__": mp.set_start_method('spawn', force=True) - - parser = argparse.ArgumentParser(description="Optimized GPU Audio Transcription") - parser.add_argument("--config_path", type=str, required=True, help="Path to config.yaml") - args = parser.parse_args() - - main(args) \ No newline at end of file + + parser = argparse.ArgumentParser(description="ASR Transcription (onnx-asr)") + parser.add_argument("--config_path", type=str, required=True) + main(parser.parse_args()) diff --git a/src/transcription/transcripton_base.py b/src/transcription/transcripton_base.py deleted file mode 100644 index f4be06b..0000000 --- a/src/transcription/transcripton_base.py +++ /dev/null @@ -1,387 +0,0 @@ -import os -import math -from abc import ABC, abstractmethod -from collections import defaultdict -from pathlib import Path -from typing import List, Tuple - -import gigaam -import kaldifeat -import numpy as np -import pandas as pd -import pyctcdecode -import sentencepiece as spm -import torch -from crowdkit.aggregation import ROVER -from loguru import logger -from torch.nn.utils.rnn import pad_sequence -from tone import StreamingCTCPipeline, TextPhrase -from tqdm import tqdm -from huggingface_hub import snapshot_download, hf_hub_download - -from src.utils.utils import read_file_content, get_audio_paths -from src.utils.vosk_utils import ( - AttributeDict, LmScorer, NgramLm, - modified_beam_search_LODR - ) - - -class ASRWrapper(ABC): - """Abstract Base Class for ASR model wrappers.""" - - @abstractmethod - def __init__(self, model_id: str, device: str, **kwargs): - """ - Initializes the ASR model. - - Args: - model_id (str): The identifier for the model. - device (str): The device for computations (e.g., 'cuda:0'). - **kwargs: Additional arguments for model configuration. - """ - self.model_id = model_id - self.device = device - - -class GigaAMWrapper(ASRWrapper): - GIGA_AM_FRAME_SIZE_MS = 40 # Duration of a single frame in ms for CTC models - - def __init__(self, model_id: str, device: str, **kwargs): - super().__init__(model_id, device) - logger.info(f"Initializing GigaAM model '{self.model_id}' on device {self.device}") - - self.model_type = 'ctc' if 'ctc' in self.model_id else 'rnnt' - self.use_lm = 'lm' in self.model_id - - self.model = gigaam.load_model( - model_name=self.model_type, - device=self.device, - fp16_encoder=True, - use_flash=False - ) - self.model.eval() - - self.target_sr = 16_000 - self.decoder = None - self.sec_per_frame = self.GIGA_AM_FRAME_SIZE_MS / 1000.0 - - if self.use_lm: - if 'lm_path' in kwargs and not os.path.exists(kwargs['lm_path']): - try: - self._downlaod_lm() - except Exception as e: - logger.warning(f"Could not download LM: {e}") - - if self.model_type != 'ctc': - logger.warning(f"LM decoding is only supported for CTC models. LM ignored.") - self.use_lm = False - elif 'lm_path' not in kwargs: - logger.error("'lm_path' required for LM usage. LM ignored.") - self.use_lm = False - else: - self._init_lm(kwargs['lm_path']) - - def _init_lm(self, lm_path: str, alpha: float = 0.5, beta: float = 1.0): - logger.info(f"Building CTC decoder with LM: {lm_path}") - try: - vocab = self.model.decoding.tokenizer.vocab - self.decoder = pyctcdecode.build_ctcdecoder( - vocab, - kenlm_model_path=lm_path, - alpha=alpha, - beta=beta, - ) - except Exception as e: - logger.error(f"Failed to build CTC decoder: {e}") - self.decoder = None - self.use_lm = False - - @torch.inference_mode() - def transcribe_tensors(self, batch_wav: torch.Tensor, batch_lengths: torch.Tensor) -> List[str]: - if self.use_lm and self.decoder: - texts, _ = self.transcribe_tensors_with_timestamps(batch_wav, batch_lengths) - return texts - - batch_wav = batch_wav.to(self.device, non_blocking=True) - batch_lengths = batch_lengths.to(self.device, non_blocking=True) - - encoded, encoded_len = self.model.forward(batch_wav, batch_lengths) - transcriptions = self.model.decoding.decode(self.model.head, encoded, encoded_len) - - return [t if t else "" for t in transcriptions] - - @torch.inference_mode() - def transcribe_tensors_with_timestamps(self, batch_wav: torch.Tensor, batch_lengths: torch.Tensor) -> Tuple[List[str], List[str]]: - batch_wav = batch_wav.to(self.device, non_blocking=True) - batch_lengths = batch_lengths.to(self.device, non_blocking=True) - - result_texts = [] - result_timestamps = [] - - encoded, encoded_len = self.model.forward(batch_wav, batch_lengths) - - if self.model_type == 'ctc' and self.decoder: - logits_batch = self.model.head(encoded) - logits_batch = logits_batch.detach().cpu().numpy() - encoded_len_cpu = encoded_len.cpu().tolist() - - for i, valid_len in enumerate(encoded_len_cpu): - sample_logits = logits_batch[i, :valid_len, :] - try: - beams = self.decoder.decode_beams(sample_logits, beam_width=100) - if beams: - best_beam = beams[0] - text, _, word_timestamps_raw, _, _ = best_beam - formatted_timestamps = self._to_simple_timestamps(word_timestamps_raw) - result_texts.append(text) - result_timestamps.append(formatted_timestamps) - else: - result_texts.append("") - result_timestamps.append("") - except Exception as e: - logger.error(f"LM decoding error: {e}") - result_texts.append("") - result_timestamps.append("") - else: - transcriptions = self.model.decoding.decode(self.model.head, encoded, encoded_len) - result_texts = [t if t else "" for t in transcriptions] - result_timestamps = [""] * len(result_texts) - - return result_texts, result_timestamps - - def _to_simple_timestamps(self, word_timestamps: List[Tuple[str, Tuple[int, int]]]) -> str: - output_lines = [] - for word, (start_frame, end_frame) in word_timestamps: - start_time = start_frame * self.sec_per_frame - end_time = end_frame * self.sec_per_frame - output_lines.append(f"{word} {start_time:.3f} {end_time:.3f}") - return "\n".join(output_lines) - - def _downlaod_lm(self): - return hf_hub_download( - repo_id="MTUCI/lm", - filename="kenlm.bin", - local_dir="./models", - repo_type='dataset', - local_dir_use_symlinks=False - ) - - -class ToneWrapper(ASRWrapper): - def __init__(self, model_id: str, device: str, **kwargs): - super().__init__(model_id, device) - logger.info(f"Initializing Tone model '{self.model_id}' on device {self.device}") - - if 'cuda' in self.device: - try: - self.device_id = int(self.device.split(':')[-1]) - except (ValueError, IndexError): - logger.error(f"Invalid CUDA device format: {self.device}. Using ID 0.") - self.device_id = 0 - else: - self.device_id = -1 - - self.tone_pipeline = StreamingCTCPipeline.from_hugging_face(device_id=self.device_id) - self.target_sr = 8_000 - - def transcribe_audio_data(self, audios: List[np.ndarray]) -> List[str]: - result_texts = [] - - for audio_np in audios: - try: - phrases = self.tone_pipeline.forward_offline(audio_np) - result_text = ' '.join([phrase.text for phrase in phrases]).strip() - result_texts.append(result_text) - except Exception as e: - logger.error(f"Tone inference error: {e}") - result_texts.append("") - - return result_texts - - def transcribe_audio_data_with_timestamps(self, audios: List[np.ndarray]) -> Tuple[List[str], List[str]]: - result_texts = [] - result_timestamps = [] - - for audio_np in audios: - try: - phrases: List[TextPhrase] = self.tone_pipeline.forward_offline(audio_np) - - full_text_parts = [] - timestamp_lines = [] - - for phrase in phrases: - if phrase.text: - full_text_parts.append(phrase.text) - timestamp_lines.append(f"{phrase.text} {phrase.start_time:.3f} {phrase.end_time:.3f}") - - result_text = " ".join(full_text_parts).strip() - result_timestamps_str = "\n".join(timestamp_lines) - - result_texts.append(result_text) - result_timestamps.append(result_timestamps_str) - - except Exception as e: - logger.error(f"Tone inference error (timestamps): {e}") - result_texts.append("") - result_timestamps.append("") - - return result_texts, result_timestamps - - -class VOSKCUDAWrapper(ASRWrapper): - def __init__(self, model_id: str, device: str, **kwargs): - super().__init__(model_id, device) - logger.info(f"Initializing VOSK CUDA model '{self.model_id}' on device {self.device}") - vosk_path = Path(kwargs['vosk_path']) - - if not os.path.exists(str(vosk_path)): - self._load_from_hf() - - self.nn_model_filename = str(vosk_path / 'am' / 'jit_script.pt') - self.ngram_path = str(vosk_path / 'lm' / '2gram.fst.txt') - self.lm_exp_dir_vosk = str(vosk_path / 'lm') - self.bpe_model = str(vosk_path / 'lang' / 'bpe.model') - - self.target_sr = 16_000 - self.beam_size = kwargs.get('beam_size', 10) - self.lm_scale = kwargs.get('lm_scale', 0.5) - - self.model = torch.jit.load(self.nn_model_filename).to(self.device).eval() - - self.sp = spm.SentencePieceProcessor() - self.sp.load(self.bpe_model) - - opts = kaldifeat.FbankOptions() - opts.device = self.device - opts.frame_opts.dither = 3e-5 - opts.frame_opts.snip_edges = False - opts.frame_opts.samp_freq = self.target_sr - opts.mel_opts.num_bins = 80 - opts.mel_opts.high_freq = -400 - self.fbank = kaldifeat.Fbank(opts) - - params = AttributeDict({ - "lm_vocab_size": 500, - "rnn_lm_embedding_dim": 2048, - "rnn_lm_hidden_dim": 2048, - "rnn_lm_num_layers": 3, - "rnn_lm_tie_weights": True, - "lm_epoch": 99, - "lm_exp_dir": self.lm_exp_dir_vosk, - "lm_avg": 1, - }) - - self.LM = LmScorer( - lm_type="rnn", - params=params, - device=self.device, - lm_scale=self.lm_scale, - ).to(self.device).eval() - - self.ngram_lm = NgramLm( - self.ngram_path, - backoff_id=500, - is_binary=False, - ) - self.ngram_lm_scale = kwargs.get('ngram_lm_scale', 1.0) - - @torch.inference_mode() - def transcribe_batch_data(self, waveforms: List[torch.Tensor]) -> List[str]: - if not waveforms: - return [] - - waves_gpu = [w.to(self.device, non_blocking=True) for w in waveforms] - features = self.fbank(waves_gpu) - feature_lengths = torch.tensor([f.size(0) for f in features], device=self.device) - features_padded = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10)) - - encoder_out, encoder_out_lens = self.model.encoder( - features=features_padded, - feature_lengths=feature_lengths, - ) - - hyps = modified_beam_search_LODR( - model=self.model, - encoder_out=encoder_out, - encoder_out_lens=encoder_out_lens, - beam=self.beam_size, - LODR_lm=self.ngram_lm, - LODR_lm_scale=self.ngram_lm_scale, - LM=self.LM, - ) - - result_texts = [self.sp.decode(hyp) for hyp in hyps] - return result_texts - - - def _load_from_hf(self): - return snapshot_download( - repo_id="alphacep/vosk-model-ru", - local_dir="./models/vosk-model-ru", - local_dir_use_symlinks=False - ) - -class ROVERWrapper: - def __init__(self, podcasts_path: str, model_names: List[str]): - self.podcasts_path = Path(podcasts_path) - self.model_names = model_names - self.tokenizer = lambda s: s.lower().split() - self.detokenizer = lambda tokens: ' '.join(tokens) - self.rover_aggregator = ROVER(self.tokenizer, self.detokenizer) - - def aggregate_and_save(self): - logger.info("Starting transcription aggregation based on audio files.") - - all_audio_paths = get_audio_paths(str(self.podcasts_path)) - - if not all_audio_paths: - logger.warning("Audio files not found. Aggregation finished.") - return - - records = [] - excluded_patterns = ['_rover', '_phonemes', '_accent'] - - for audio_path in tqdm(all_audio_paths, desc="Aggregating transcriptions"): - if any(pattern in audio_path.stem for pattern in excluded_patterns): - continue - - for model_name in self.model_names: - transcript_path = audio_path.with_name(f"{audio_path.stem}_{model_name}.txt") - - if not transcript_path.exists(): - continue - - try: - text = read_file_content(transcript_path) - if not text: - continue - - records.append({ - 'task': str(audio_path), - 'worker': model_name, - 'text': text - }) - except Exception as e: - logger.error(f"Error reading file {transcript_path}: {e}") - - df = pd.DataFrame(records) - if df.empty: - logger.warning("No transcriptions found for aggregation. Check file paths and names.") - return - - df['text'] = df['text'].str.lower() - logger.info(f"Running ROVER on {len(df['task'].unique())} unique audio files...") - result = self.rover_aggregator.fit_predict(df) - - logger.info("Saving aggregated results...") - for task_path, agg_text in result.items(): - audio_path = Path(task_path) - output_path = audio_path.with_name(f"{audio_path.stem}_rover.txt") - - try: - with open(output_path, "w", encoding="utf-8") as f: - f.write(agg_text) - except IOError as e: - logger.error(f"Failed to write result to {output_path}: {e}") - - logger.info("Aggregation complete.") \ No newline at end of file diff --git a/src/transcription/transcripton_dataset.py b/src/transcription/transcripton_dataset.py deleted file mode 100644 index d9c221c..0000000 --- a/src/transcription/transcripton_dataset.py +++ /dev/null @@ -1,169 +0,0 @@ -import os -import json -import torch -import torchaudio -from typing import List, Tuple, Optional -import miniaudio -from torch.utils.data import Dataset, Sampler -from torch.nn.utils.rnn import pad_sequence -import numpy as np -from loguru import logger - -class GigaAudioDataset(Dataset): - def __init__(self, audio_paths: List[str], target_sr: int = 16000): - self.audio_paths = audio_paths - self.target_sr = target_sr - - def __len__(self): - return len(self.audio_paths) - - def __getitem__(self, idx): - path = self.audio_paths[idx] - try: - if not os.path.exists(path): - return None, path - - waveform, sr = torchaudio.load(path) - - if sr != self.target_sr: - waveform = torchaudio.functional.resample(waveform, sr, self.target_sr) - - if waveform.shape[0] > 1: - waveform = waveform.mean(dim=0, keepdim=True) - - return waveform.squeeze(0), path - - except Exception as e: - logger.warning(f"Error loading {path}: {e}") - return None, path - - -class LengthGroupedSampler(Sampler): - def __init__(self, data_source, json_cache_path: str, batch_size: int): - self.data_source = data_source - self.batch_size = batch_size - self.json_cache_path = json_cache_path - self.indices = self._build_indices() - - def _build_indices(self): - length_cache = {} - if os.path.exists(self.json_cache_path): - try: - with open(self.json_cache_path, 'r') as f: - length_cache = json.load(f) - except Exception as e: - logger.warning(f"Ошибка чтения кеша длин {self.json_cache_path}: {e}") - - pairs = [] - missing_count = 0 - - for idx, path in enumerate(self.data_source.audio_paths): - length = length_cache.get(path) - - if length is None: - length = 5.0 - missing_count += 1 - - pairs.append((idx, length)) - - if missing_count > 0: - logger.info(f"For {missing_count} files the length was not found in the cache (default used).") - - pairs.sort(key=lambda x: x[1], reverse=True) - - return [p[0] for p in pairs] - - def __iter__(self): - return iter(self.indices) - - def __len__(self): - return len(self.data_source) - - -def collate_giga(batch: List[Tuple[Optional[torch.Tensor], str]]): - batch = [item for item in batch if item[0] is not None] - - if not batch: - return None, None, [] - - waveforms = [item[0] for item in batch] - paths = [item[1] for item in batch] - - lengths = torch.tensor([w.size(0) for w in waveforms], dtype=torch.long) - padded_wavs = pad_sequence(waveforms, batch_first=True, padding_value=0.0) - - return padded_wavs, lengths, paths - - -class ToneAudioDataset(Dataset): - def __init__(self, audio_paths: List[str], target_sr: int = 8000): - self.audio_paths = audio_paths - self.target_sr = target_sr - - def __len__(self): - return len(self.audio_paths) - - def __getitem__(self, idx): - path = self.audio_paths[idx] - try: - if not os.path.exists(path): - return None, path - - audio = miniaudio.decode_file(str(path), nchannels=1, sample_rate=self.target_sr) - audio_np = np.asarray(audio.samples, dtype=np.int16).astype(np.int32) - - return audio_np, path - - except Exception as e: - logger.warning(f"Error processing {path} for Tone: {e}") - return None, path - -def collate_tone(batch: List[Tuple[Optional[np.ndarray], str]]): - valid_batch = [item for item in batch if item[0] is not None] - - if not valid_batch: - return [], [] - - audios = [item[0] for item in valid_batch] - paths = [item[1] for item in valid_batch] - - return audios, paths - - -class VoskAudioDataset(Dataset): - def __init__(self, audio_paths: List[str], target_sr: int = 16000): - self.audio_paths = audio_paths - self.target_sr = target_sr - - def __len__(self): - return len(self.audio_paths) - - def __getitem__(self, idx): - path = self.audio_paths[idx] - try: - if not os.path.exists(path): - return None, path - - waveform, sr = torchaudio.load(path) - - if sr != self.target_sr: - waveform = torchaudio.functional.resample(waveform, sr, self.target_sr) - - if waveform.shape[0] > 1: - waveform = waveform.mean(dim=0, keepdim=True) - - return waveform.squeeze(0), path - - except Exception as e: - logger.warning(f"Error loading {path}: {e}") - return None, path - -def collate_vosk(batch: List[Tuple[Optional[torch.Tensor], str]]): - batch = [item for item in batch if item[0] is not None] - if not batch: - return [], [] - - waveforms = [item[0] for item in batch] - paths = [item[1] for item in batch] - - return waveforms, paths \ No newline at end of file From 36e599cb910e264464816fa65c190f501be2ca0f Mon Sep 17 00:00:00 2001 From: root Date: Sat, 21 Feb 2026 20:18:57 +0300 Subject: [PATCH 26/43] feat: fix bugs transcription --- requirements_dev.txt | Bin 1110 -> 1132 bytes src/transcription/rover.py | 75 +++++++++++++++++++++++++++++ src/transcription/transcription.py | 47 +++++++++++++++--- 3 files changed, 115 insertions(+), 7 deletions(-) create mode 100644 src/transcription/rover.py diff --git a/requirements_dev.txt b/requirements_dev.txt index e6ce9c9b813bab2f1864ba862cd104aa44e57fc3..c435583d3c161b5497926aa001aafe2ac408477e 100644 GIT binary patch delta 39 tcmcb{@rHxx|Gy1pOu_{Wl?*uy`3$8DDGYf)x`-in@_c5)&1p;@7y%4Z4441_ delta 17 YcmaFEagAdmlNr str: + """Extract plain text from onnx-asr result (str or TimestampedResult).""" + if hasattr(result, 'text'): + return result.text + return str(result) + + def format_timestamps(result) -> str: - if hasattr(result, 'tokens') and result.tokens: - return '\n'.join( - f"{getattr(t, 'start', 0):.3f}\t{getattr(t, 'end', 0):.3f}\t{getattr(t, 'text', '')}" - for t in result.tokens - ) - return '' + """Format TimestampedResult as word-level TSV: start\\tend\\tword per line. + + onnx-asr TimestampedResult has parallel arrays: + .tokens = ['с', 'п', 'а', 'с', 'и', 'б', 'о', ' ', ...] + .timestamps = [0.39, 0.44, 0.51, 0.54, 0.57, 0.63, 0.66, 0.75, ...] + We group characters into words and produce word-level timestamps. + """ + tokens = getattr(result, 'tokens', None) + timestamps = getattr(result, 'timestamps', None) + + if not tokens or not timestamps or len(tokens) != len(timestamps): + return '' + + words = [] + current_word = '' + word_start = None + + for token, ts in zip(tokens, timestamps): + if token.strip() == '': + if current_word and word_start is not None: + words.append((word_start, ts, current_word)) + current_word = '' + word_start = None + else: + if word_start is None: + word_start = ts + current_word += token + + if current_word and word_start is not None: + words.append((word_start, timestamps[-1], current_word)) + + return '\n'.join(f"{start:.3f}\t{end:.3f}\t{word}" for start, end, word in words) def run_worker(cuda_id: int, world_size: int, model_name: str, @@ -171,7 +204,7 @@ def run_worker(cuda_id: int, world_size: int, model_name: str, if not isinstance(results, list): results = [results] - texts = [str(r) for r in results] + texts = [extract_text(r) for r in results] ts = [format_timestamps(r) for r in results] if do_timestamps else None save_results(batch, texts, ts, output_suffix) From 2cee2d6a0d99eb8dbca315c00401a8031e51a37d Mon Sep 17 00:00:00 2001 From: root Date: Mon, 23 Feb 2026 01:50:30 +0300 Subject: [PATCH 27/43] feat: update nisqa work --- base.sh | 12 +- configs/config.yaml | 16 +- configs/nisqa_b.yaml | 2 +- requirements_dev.txt | Bin 1132 -> 1072 bytes src/preprocess/preprocess_audio.py | 29 +- src/separation/music_detect.py | 36 +- src/separation/nisqa_process.py | 59 +- src/separation/separation_yaml.sh | 4 +- src/separation/silence_detect.py | 8 +- weights/nisqa.tar/nisqa.tar | 1483 ++++++++++++++++++++++++++++ weights/nisqa.tar/nisqa.tar.1 | 1483 ++++++++++++++++++++++++++++ 11 files changed, 3057 insertions(+), 75 deletions(-) create mode 100644 weights/nisqa.tar/nisqa.tar create mode 100644 weights/nisqa.tar/nisqa.tar.1 diff --git a/base.sh b/base.sh index d32fd94..d5bc6e6 100644 --- a/base.sh +++ b/base.sh @@ -20,13 +20,13 @@ CONFIG_PATH=$(realpath "$1") echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" - "./src/preprocess/preprocess_yaml.sh" + # "./src/preprocess/preprocess_yaml.sh" "./src/separation/separation_yaml.sh" - # "./src/transcription/transcription_yaml.sh" - # "./src/punctuation/punctuation_yaml.sh" - # "./src/accents/accents_yaml.sh" - # "./src/phonemizer/phonemizer_yaml.sh" - # "./src/collate_yamls.sh" + "./src/transcription/transcription_yaml.sh" + "./src/punctuation/punctuation_yaml.sh" + "./src/accents/accents_yaml.sh" + "./src/phonemizer/phonemizer_yaml.sh" + "./src/collate_yamls.sh" ) activate_venv ".dev_venv" diff --git a/configs/config.yaml b/configs/config.yaml index 3df47e1..00b1d8d 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,13 @@ cache_path: ./cache download: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr + podcasts_path: /root/datasets/50 episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the podcasts are located + podcasts_path: /root/datasets/50 # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 1 crest_treshold: 10 # in dB @@ -18,7 +18,7 @@ preprocess: separation: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located + podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located diarization: num_workers: 1 # per gpu @@ -41,12 +41,12 @@ separation: max_silence_duration: 1.0 # maximum duration of silence to be marked transcription: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located + podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True use_tensorrt: False # enable TensorRT execution provider (requires CUDA + TensorRT installed) use_vad: False # enable Silero VAD for long-form audio (>30s) - use_rover: False # enable ROVER consensus aggregation (requires rover module) + use_rover: True # enable ROVER consensus aggregation (requires rover module) # All models are loaded via onnx-asr from HuggingFace (or local path). # Supported: giga_ctc, giga_rnnt, vosk, vosk_small, tone, parakeet_v2, parakeet_v3, canary, whisper_base, whisper_turbo model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] @@ -63,15 +63,15 @@ transcription: batch_size: 16 punctuation: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located + podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the chopped podcasts are located + podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /mnt/drive2_8tb/shkolkovo-bobr # full path to the folder where the text files are located + podcasts_path: /root/datasets/50 # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/configs/nisqa_b.yaml b/configs/nisqa_b.yaml index 1e17354..e64d187 100644 --- a/configs/nisqa_b.yaml +++ b/configs/nisqa_b.yaml @@ -69,7 +69,7 @@ td_lstm_dropout: 0 td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 # Arguments for inference -ckp: /home/nikita/yapoddataset/models/nisqa.tar # checkpoint that will be used for inference +ckp: weights/nisqa.tar # checkpoint that will be used for inference sample: src/sample/gt.wav # path to file to run on inf_device: cuda # device for inference runs warmup: False # warmup run before inference; usually is not needed on CPU runs diff --git a/requirements_dev.txt b/requirements_dev.txt index c435583d3c161b5497926aa001aafe2ac408477e..6472a073cb686edcfadbf2861ec0b9a57776b6d3 100644 GIT binary patch delta 182 zcmaFEv4KPG|G#2}RE83UQicK`nGb|H48;sK3>FMV3|tHoncOC(yqjFXs1=w9RFcAw z2~=PUghmW{KqYz%20%q1ZAA>p3>kQ2l7VVcfEFc#)ffPEnonNOD9>vLd?pM4 delta 174 zcmdnM@rHxx|G$ZHZjyNnr3|?Y1q_u8wm@jepvPduUjCMJHAPi81#C}K!t$YUsG z$N|zN45>hAkU|rXlF8nThP)u25s+sJ#D#i%D*1T;1es6G>Dcq)S~Lpsok z(#f(+wv$zu^;C<2a)}JtK$#+-$tDc?Kr$0(1BjmwR9yy?O_^NCY`J+J( Tuple(path, prob)[[]] - predictions = zip(paths, probs.detach().flatten()) - - # Filter and Delete - for path, prob in predictions: - if prob > threshold: - try: - os.remove(path) - deleted_count += 1 - except OSError: - pass + for path, prob in zip(paths, probs.detach().flatten()): + # print(prob) + if prob > threshold: + try: + os.remove(path) + deleted_count += 1 + except OSError: + pass logger.success(f"[{device}] Done. Deleted {deleted_count} files.") diff --git a/src/separation/nisqa_process.py b/src/separation/nisqa_process.py index a384756..4b74308 100644 --- a/src/separation/nisqa_process.py +++ b/src/separation/nisqa_process.py @@ -122,7 +122,6 @@ def run_inference_worker(rank: int, world_size: int, file_paths: List[str], conf 'MOS': MOS, 'NOI': NOI, 'DISC': DISC, 'COL': COL, 'LOUD': LOUD }) - # Инкрементальное сохранение if len(results_buffer) >= save_every: save_chunk(results_buffer, worker_output_path) results_buffer = [] @@ -135,51 +134,59 @@ def run_inference_worker(rank: int, world_size: int, file_paths: List[str], conf def combine_results(final_output_path: Path, num_parts: int): - """Combines partial CSVs into one.""" - logger.info("Combining partial results...") - dfs = [] - - if final_output_path.exists(): - dfs.append(pd.read_csv(final_output_path)) - - found_parts = False + """Merges NISQA partial CSVs into the main balalaika.csv, filling MOS columns.""" + logger.info("Combining NISQA partial results...") + + nisqa_dfs = [] for i in range(num_parts): part_path = final_output_path.with_name(f"{final_output_path.stem}_part_{i}.csv") if part_path.exists(): try: - dfs.append(pd.read_csv(part_path)) - found_parts = True - os.remove(part_path) + nisqa_dfs.append(pd.read_csv(part_path)) + os.remove(part_path) except Exception as e: logger.error(f"Error reading {part_path}: {e}") - - if not found_parts and not dfs: - logger.warning("No results found to combine.") + + if not nisqa_dfs: + logger.warning("No NISQA results found to combine.") return - if dfs: - full_df = pd.concat(dfs, ignore_index=True) - full_df = full_df.drop_duplicates(subset=['filepath']) - full_df.to_csv(final_output_path, index=False) - logger.success(f"Combined results saved to {final_output_path}. Total rows: {len(full_df)}") + nisqa_df = pd.concat(nisqa_dfs, ignore_index=True).drop_duplicates(subset=['filepath']) + nisqa_df = nisqa_df.set_index('filepath') + + if final_output_path.exists(): + main_df = pd.read_csv(final_output_path).set_index('filepath') + main_df = main_df.combine_first(nisqa_df) + main_df = main_df.reset_index() + else: + main_df = nisqa_df.reset_index() + + main_df.to_csv(final_output_path, index=False) + mos_filled = main_df['MOS'].notna().sum() if 'MOS' in main_df.columns else 0 + logger.success(f"Combined results saved to {final_output_path}. Total: {len(main_df)}, with MOS: {mos_filled}") def get_unprocessed_audio_paths(podcasts_path: Path, result_csv_path: Path) -> List[str]: - """Get list of paths that are not in the result CSV yet.""" + """Get list of audio paths that don't have MOS scores in the result CSV yet.""" all_audio_paths = get_audio_paths(str(podcasts_path)) all_paths_str = [str(p.resolve()) for p in all_audio_paths] - + if not result_csv_path.exists(): return all_paths_str logger.info(f"Filtering existing results from: {result_csv_path}") try: df = pd.read_csv(result_csv_path) - if 'filepath' not in df.columns: + if 'filepath' not in df.columns or 'MOS' not in df.columns: return all_paths_str - processed = set(df['filepath'].astype(str).to_list()) - - return [p for p in all_paths_str if p not in processed] + + processed = set( + df.loc[df['MOS'].notna(), 'filepath'].astype(str).tolist() + ) + + unprocessed = [p for p in all_paths_str if p not in processed] + logger.info(f"Already processed (have MOS): {len(processed)}, remaining: {len(unprocessed)}") + return unprocessed except Exception as e: logger.warning(f"Could not read existing CSV ({e}), processing all files.") return all_paths_str diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index de2e852..c8b8466 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -21,6 +21,6 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") # python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" -# python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" -# python3 -m src.separation.diarization --config_path "$CONFIG_PATH" +python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" +python3 -m src.separation.diarization --config_path "$CONFIG_PATH" python3 -m src.separation.silence_detect --config_path "$CONFIG_PATH" diff --git a/src/separation/silence_detect.py b/src/separation/silence_detect.py index 0243f70..d4df5f8 100644 --- a/src/separation/silence_detect.py +++ b/src/separation/silence_detect.py @@ -1,4 +1,5 @@ import argparse +from importlib import reload import multiprocessing from concurrent.futures import ProcessPoolExecutor, as_completed from functools import partial @@ -33,7 +34,12 @@ def init_worker(device_str: str): try: if 'cuda' in device_str: torch.cuda.set_device(device_str) - silence_detect_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad') + silence_detect_model, utils = torch.hub.load( + repo_or_dir='snakers4/silero-vad', + model='silero_vad', + onnx=False, + force_reload=True + ) silence_detect_model.to(device_str) vad_device = torch.device(device_str) logger.info(f"Worker initialized successfully for silence detection on {device_str}.") diff --git a/weights/nisqa.tar/nisqa.tar b/weights/nisqa.tar/nisqa.tar new file mode 100644 index 0000000..1adce15 --- /dev/null +++ b/weights/nisqa.tar/nisqa.tar @@ -0,0 +1,1483 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + NISQA/weights/nisqa.tar at fe84f0f252abec382b24367d5b22498a7ce34dbb · gabrielmittag/NISQA · GitHub + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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+ + + From b7f406eba27a432878a802632c8c59e09c5b639c Mon Sep 17 00:00:00 2001 From: root Date: Mon, 23 Feb 2026 03:30:19 +0300 Subject: [PATCH 28/43] feat: update trt work and readme --- base.sh | 14 ++++- configs/config.yaml | 2 +- create_dev_env.sh | 7 +++ docs/guide.md | 28 +++++----- docs/preparing.md | 80 +++++++++++++-------------- requirements_dev.txt | Bin 1072 -> 750 bytes src/accents/README.md | 20 +++---- src/download/README.md | 12 ++-- src/phonemizer/README.md | 16 +++--- src/preprocess/README.md | 31 ++++------- src/punctuation/README.md | 20 +++---- src/separation/README.md | 27 +++------ src/separation/separation_yaml.sh | 2 +- src/transcription/README.md | 89 ++++++++++++------------------ 14 files changed, 159 insertions(+), 189 deletions(-) diff --git a/base.sh b/base.sh index d5bc6e6..a167c4f 100644 --- a/base.sh +++ b/base.sh @@ -9,6 +9,18 @@ activate_venv() { fi source "$venv_path/bin/activate" echo "Activated: $(which python)" + + local python_version=$(python3 -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')") + local nvidia_base="$venv_path/lib/python$python_version/site-packages/nvidia" + + if [ -d "$nvidia_base" ]; then + export LD_LIBRARY_PATH="${nvidia_base}/cublas/lib:${nvidia_base}/cudnn/lib:${nvidia_base}/cuda_runtime/lib:${nvidia_base}/cuda_nvrtc/lib:${nvidia_base}/cufft/lib:${nvidia_base}/nvjitlink/lib:${nvidia_base}/cusolver/lib:${nvidia_base}/cusparse/lib:${LD_LIBRARY_PATH:-}" + fi + + local trt_libs="$venv_path/lib/python$python_version/site-packages/tensorrt_libs" + if [ -d "$trt_libs" ]; then + export LD_LIBRARY_PATH="${trt_libs}:${LD_LIBRARY_PATH:-}" + fi } if [ -z "${1:-}" ]; then @@ -21,7 +33,7 @@ echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" - "./src/separation/separation_yaml.sh" + # "./src/separation/separation_yaml.sh" "./src/transcription/transcription_yaml.sh" "./src/punctuation/punctuation_yaml.sh" "./src/accents/accents_yaml.sh" diff --git a/configs/config.yaml b/configs/config.yaml index 00b1d8d..748af66 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -44,7 +44,7 @@ transcription: podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True - use_tensorrt: False # enable TensorRT execution provider (requires CUDA + TensorRT installed) + use_tensorrt: True # enable TensorRT execution provider (requires CUDA + TensorRT installed) use_vad: False # enable Silero VAD for long-form audio (>30s) use_rover: True # enable ROVER consensus aggregation (requires rover module) # All models are loaded via onnx-asr from HuggingFace (or local path). diff --git a/create_dev_env.sh b/create_dev_env.sh index e2dfab2..359142a 100644 --- a/create_dev_env.sh +++ b/create_dev_env.sh @@ -19,6 +19,13 @@ create_venv_env() { fi uv pip install -r "$requirements_file" + + echo "Installing ONNX Runtime GPU (CUDA 13 nightly)..." + uv pip install coloredlogs flatbuffers numpy packaging protobuf sympy + uv pip install --pre --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-13-nightly/pypi/simple/ onnxruntime-gpu + uv pip install tensorrt-cu13 + uv pip install onnx-asr[gpu,hub] + deactivate else echo "Environment $env_name already exists" diff --git a/docs/guide.md b/docs/guide.md index 8238387..08887e5 100644 --- a/docs/guide.md +++ b/docs/guide.md @@ -106,27 +106,26 @@ Performs four types of analysis: --- ### 4. Transcription (`src/transcription/`) -Transcribes audio using multiple ASR models in parallel. Each model creates its own transcription file, then all transcriptions are aggregated using ROVER (Recognizer Output Voting Error Reduction) consensus algorithm. +Transcribes audio using multiple ASR models in parallel. This stage is now powered by **onnx-asr**, which provides a unified, high-performance interface for all supported models without requiring manual dataloaders or complex PyTorch code. -**Optimization**: If `consensus_num` is set, the pipeline will automatically skip processing remaining models for files where the specified number of models have already produced identical transcriptions. This significantly speeds up processing when models agree. +**Key Features**: +- **Consensus Processing**: If `consensus_num` is set, the pipeline will automatically skip processing remaining models for files where the specified number of models have already produced identical transcriptions. +- **Direct GPU Inference**: Uses `onnxruntime-gpu` and `tensorrt-cu13` for native GPU acceleration. +- **Multiprocessing**: Automatically distributes workload across all available GPUs. +- **Word-Level Timestamps**: Supports extracting word-level timestamps for multiple models. **Input**: Audio files (`.mp3`) **Output**: - Individual model transcriptions: `{filename}_{model_name}.txt` - - `{filename}_giga_ctc.txt` - - `{filename}_giga_rnnt.txt` - - `{filename}_giga_ctc_lm.txt` - - `{filename}_vosk.txt` - - `{filename}_tone.txt` -- Timestamp files (if enabled): `{filename}_{model_name}.tst` -- **Consensus transcription**: `{filename}_rover.txt` (aggregated from all models) +- Timestamp files: `{filename}_{model_name}.tst` (available for supported models if enabled) +- **Consensus transcription**: `{filename}_rover.txt` (aggregated from all models using the ROVER consensus algorithm) **Configuration**: `config.yaml` → `transcription` section - `model_names`: List of models to use -- `consensus_num`: Number of models that need to agree before skipping remaining models (e.g., `3` means if 3 models produce the same transcription, remaining models won't process that file). Set to `0` to process all models for all files. -- `with_timestamps`: Enable timestamp generation (works with `giga_ctc_lm` and `tone`) - -**Example**: With `consensus_num: 3` and 5 models, if the first 3 models produce identical transcriptions for a file, the remaining 2 models will skip that file, saving processing time. +- `consensus_num`: Number of models that need to agree before skipping remaining models. +- `with_timestamps`: Enable word-level timestamp generation. +- `use_tensorrt`: Enable TensorRT 10 for maximum performance. +- `use_vad`: Use Silero VAD to process long audio files in chunks. --- @@ -215,10 +214,9 @@ For each audio segment, the pipeline generates: # Individual model transcriptions (if enabled) {start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc.txt {start_time}_{end_time}_{album_id}_{episode_id}_giga_rnnt.txt -{start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc_lm.txt {start_time}_{end_time}_{album_id}_{episode_id}_vosk.txt {start_time}_{end_time}_{album_id}_{episode_id}_tone.txt -{start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc_lm.tst # Timestamps (if enabled) +{start_time}_{end_time}_{album_id}_{episode_id}_giga_ctc.tst # Timestamps (if enabled) # Consensus and processed text files {start_time}_{end_time}_{album_id}_{episode_id}_rover.txt # Consensus transcription diff --git a/docs/preparing.md b/docs/preparing.md index c8b6471..33f932c 100644 --- a/docs/preparing.md +++ b/docs/preparing.md @@ -36,7 +36,7 @@ git clone https://github.com/mtuciru/balalaika cd balalaika ``` -Choose the appropriate environment: +Choose the appropriate environment. **Note**: For ASR with CUDA 13.1 support, the installation scripts now automatically set up the nightly ONNX Runtime and TensorRT 10. - **For development/annotation/modification**: ```bash @@ -61,12 +61,12 @@ HF_TOKEN= YANDEX_KEY= ``` -- `HF_TOKEN`: Required for speaker diarization and accessing Hugging Face models -- `YANDEX_KEY`: Required only if downloading podcasts from Yandex Music (optional if using your own data) +- `HF_TOKEN`: Required for speaker diarization and accessing Hugging Face models (like GigaAM, Canary). +- `YANDEX_KEY`: Required only if downloading podcasts from Yandex Music. **How to get tokens:** -- **Hugging Face Token**: Create an account at [huggingface.co](https://huggingface.co) and generate a token in your account settings -- **Yandex Music Token**: Follow the guide at [yandex-music.readthedocs.io](https://yandex-music.readthedocs.io/en/main/token.html) +- **Hugging Face Token**: Create an account at [huggingface.co](https://huggingface.co) and generate a token in your account settings. +- **Yandex Music Token**: Follow the guide at [yandex-music.readthedocs.io](https://yandex-music.readthedocs.io/en/main/token.html). --- @@ -82,35 +82,35 @@ Place the following models under the `models/` directory: ### ASR Models (onnx-asr) -ASR-модели загружаются **автоматически** из Hugging Face при первом запуске через библиотеку [onnx-asr](https://github.com/istupakov/onnx-asr). Ручная установка не требуется. +ASR models are downloaded **automatically** from Hugging Face upon first run using the [onnx-asr](https://github.com/istupakov/onnx-asr) library. Manual installation or complex wrappers are no longer required. -Поддерживаемые модели: +Supported models: -| Имя в конфиге | onnx-asr модель | Язык | +| Config Name | onnx-asr model | Language | |------------------|------------------------------------------|--------------| -| `giga_ctc` | `gigaam-v3-ctc` | Русский | -| `giga_rnnt` | `gigaam-v3-rnnt` | Русский | -| `vosk` | `alphacep/vosk-model-ru` | Русский | -| `tone` | `t-tech/t-one` | Русский | +| `giga_ctc` | `gigaam-v3-ctc` | Russian | +| `giga_rnnt` | `gigaam-v3-rnnt` | Russian | +| `vosk` | `alphacep/vosk-model-ru` | Russian | +| `tone` | `t-tech/t-one` | Russian | | `parakeet_v3` | `nemo-parakeet-tdt-0.6b-v3` | Multilingual | | `canary` | `nemo-canary-1b-v2` | Multilingual | | `whisper_turbo` | `onnx-community/whisper-large-v3-turbo` | Multilingual | -Инференс выполняется на GPU через ONNX Runtime с поддержкой CUDA и TensorRT execution providers. Батчевая обработка включена по умолчанию для максимальной утилизации GPU. +Inference is performed on GPU via **ONNX Runtime (GPU)** with CUDA and **TensorRT 10** support. You no longer need to write your own dataloaders — `onnx-asr` efficiently handles batching and distributes the workload across all available GPUs. -**Конфигурация GPU-инференса** (`configs/config.yaml`): +**GPU Inference Configuration** (`configs/config.yaml`): ```yaml transcription: - use_tensorrt: True # TensorRT EP — fp16, максимальная скорость на NVIDIA GPU + use_tensorrt: True # TensorRT EP (fp16) — up to 2x faster on NVIDIA GPUs model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] giga: - batch_size: 16 # размер батча (подбирается под объем VRAM) - quantization: int8 # опционально: int8 квантизация + batch_size: 16 # batch size (adjust based on VRAM) + quantization: int8 # optional: int8 quantization to save memory ``` -При включённом `use_tensorrt: True` используется TensorRT execution provider с fp16 для максимальной производительности. При нескольких GPU файлы автоматически распределяются между картами. +When `use_tensorrt: True` is enabled, the pipeline automatically configures TensorRT with fp16. If multiple GPUs are present, files are automatically distributed across them using `multiprocessing`. **Note**: Some models (RUPunct, ruAccent) are downloaded automatically from Hugging Face. Make sure your `HF_TOKEN` is set correctly. @@ -257,7 +257,7 @@ Let's say you have a dataset of Russian speech recordings organized like this: └── speaker_001/ ├── recording_001.wav ├── recording_002.wav - └── recording_003.wav + ├── recording_003.wav └── speaker_002/ ├── recording_001.wav └── recording_002.wav @@ -281,33 +281,33 @@ Let's say you have a dataset of Russian speech recordings organized like this: ## ASR Inference (onnx-asr) -Транскрипция реализована через [onnx-asr](https://github.com/istupakov/onnx-asr) — лёгкую Python-библиотеку для ASR на базе ONNX Runtime. Основные преимущества: +Transcription is implemented using [onnx-asr](https://github.com/istupakov/onnx-asr) — a lightweight Python library for ASR based on ONNX Runtime. Key benefits: -- **Единый интерфейс** для всех моделей (GigaAM, Vosk, Parakeet, Whisper, T-one) -- **GPU-инференс** через CUDA / TensorRT execution providers -- **Батчевая обработка** для максимальной утилизации GPU -- **Мульти-GPU** — файлы автоматически распределяются между всеми доступными GPU -- **Автоматическая загрузка** моделей из Hugging Face -- **Минимум зависимостей** — не требуется PyTorch, Transformers или FFmpeg для ASR +- **Unified Interface** for all models (GigaAM, Vosk, Parakeet, Canary, Whisper, T-one) +- **GPU Inference** via CUDA / TensorRT execution providers +- **Batch Processing** for maximum GPU utilization +- **Multi-GPU** — files are automatically distributed among all available GPUs +- **Automatic Download** of models from Hugging Face +- **Minimal Dependencies** — no PyTorch, Transformers, or FFmpeg required for ASR -### Установка +### Installation ```bash pip install onnx-asr[gpu,hub] ``` -Для TensorRT (опционально, максимальная скорость): +For TensorRT (optional, maximum speed): ```bash -pip install onnxruntime-gpu[cuda,cudnn] tensorrt-cu12-libs +pip install onnxruntime-gpu[cuda,cudnn] tensorrt-cu13-libs ``` -### Пример использования +### Usage Example ```python import onnx_asr -# Загрузка модели с TensorRT на конкретной GPU +# Loading a model with TensorRT on a specific GPU providers = [ ("TensorrtExecutionProvider", { "device_id": 0, @@ -319,29 +319,29 @@ providers = [ model = onnx_asr.load_model("gigaam-v3-rnnt", providers=providers) -# Батчевое распознавание — максимальная утилизация GPU +# Batch recognition — maximum GPU utilization results = model.recognize(["audio1.wav", "audio2.wav", "audio3.wav"]) -# С таймстемпами +# With timestamps model_ts = onnx_asr.load_model("gigaam-v3-ctc", providers=providers).with_timestamps() result = model_ts.recognize("audio.wav") ``` -### Конфигурация +### Configuration -В `configs/config.yaml` секция `transcription`: +In `configs/config.yaml`, the `transcription` section: ```yaml transcription: podcasts_path: /path/to/dataset - consensus_num: 3 # пропуск файлов при совпадении N моделей + consensus_num: 3 # skip files if N models agree with_timestamps: True - use_tensorrt: True # TensorRT EP (fp16, максимальная скорость) - use_vad: False # Silero VAD для длинных аудио (>30с) + use_tensorrt: True # TensorRT EP (fp16, maximum speed) + use_vad: False # Silero VAD for long audio (>30s) model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] giga: - batch_size: 16 # подбирается под VRAM (16 для 24GB, 8 для 12GB) + batch_size: 16 # adjust to VRAM (16 for 24GB, 8 for 12GB) vosk: batch_size: 16 @@ -350,7 +350,7 @@ transcription: batch_size: 16 ``` -**Примечание**: дополнительные зависимости для Vosk (k2, kaldifeat, icefall) **больше не требуются** — onnx-asr включает всё необходимое. +**Note**: Additional dependencies for Vosk (k2, kaldifeat, icefall) are **no longer required** — onnx-asr includes everything needed. --- diff --git a/requirements_dev.txt b/requirements_dev.txt index 6472a073cb686edcfadbf2861ec0b9a57776b6d3..6d44af982f898c18f3ea172987e5d233c8ec9e94 100644 GIT binary patch literal 750 zcmaJ8LDP}SrcI*G5eG)?-$8}VNgDvAPhE8o3(gd5^xMk zp@Uhv5(Q1BLV%xJS+19%5Q1)1GBctQ8$v8Ee_b>ftCvY=_5|pE4E0_hwA5+I0NE2RK{95qQ+WjW~y7BW~r*qH*k%?zXbJD4u%N+0Ayz zqhKT_rR4XA!&m+}d31y2cl9HG{{HcH$IBJ>u4%EKUU26ls=lW_&j0Q+Q<;Vyf;AL+ zWTcJ9bUIN=%%@?TXfwJ~jq?qq1qf7RaheMk8{yJ2BPahb(L*wxLT8Z{79cw-!y1P# zTf{NG&2u`FeW78?XuZnw`vQd(P&f{OnG516zIDSrslzE*cteGU9T{yLRwTCK@C gcUAb{I#k literal 1072 zcmb`GTT8=G5QO(Re?{AxdT9|A1VIEX75gIMLrl{)v`s@UYX7|Y%}LT0e6q+P3GB|! z%mW}xqpc8wyOO9`CJL2&jG9W&-fNJHs z#PuALTTnMeA>&a_HN84FMB&-4?ZNI{?u@wdQqQR?9qYtmSfWj}4{xQr1d)>s@mHWe z-C#~?g*8z*i|iB37QgHA2(Hv<{*uDTJ|sq0t$qz)NS5rY(uT&r90Xl4P3 zjNFG^k@ZdW&7?Ht48Jo_CB7OC)%R|koBmJ zncD%?1t$e{ZXHe)uQ=QEw1T5!H}sd_Ikq`> Date: Tue, 24 Feb 2026 15:53:25 +0000 Subject: [PATCH 29/43] feat: add distillMos and fix nisqa bug --- base.sh | 12 +- configs/config.yaml | 14 +-- create_dev_env.sh | 2 +- requirements_dev.txt | 6 +- src/separation/distillmos_process.py | 170 +++++++++++++++++++++++++++ src/separation/nisqa_process.py | 12 +- src/separation/separation_yaml.sh | 1 + 7 files changed, 195 insertions(+), 22 deletions(-) create mode 100644 src/separation/distillmos_process.py diff --git a/base.sh b/base.sh index a167c4f..7802e68 100644 --- a/base.sh +++ b/base.sh @@ -33,12 +33,12 @@ echo $CONFIG_PATH "--src" SCRIPTS=( # "./src/download/download_yaml.sh" # "./src/preprocess/preprocess_yaml.sh" - # "./src/separation/separation_yaml.sh" - "./src/transcription/transcription_yaml.sh" - "./src/punctuation/punctuation_yaml.sh" - "./src/accents/accents_yaml.sh" - "./src/phonemizer/phonemizer_yaml.sh" - "./src/collate_yamls.sh" + "./src/separation/separation_yaml.sh" + # "./src/transcription/transcription_yaml.sh" + # "./src/punctuation/punctuation_yaml.sh" + # "./src/accents/accents_yaml.sh" + # "./src/phonemizer/phonemizer_yaml.sh" + # "./src/collate_yamls.sh" ) activate_venv ".dev_venv" diff --git a/configs/config.yaml b/configs/config.yaml index 748af66..158c0b8 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,13 @@ cache_path: ./cache download: - podcasts_path: /root/datasets/50 + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /root/datasets/50 # full path to the folder where the podcasts are located + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 1 crest_treshold: 10 # in dB @@ -18,7 +18,7 @@ preprocess: separation: - podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located diarization: num_workers: 1 # per gpu @@ -41,7 +41,7 @@ separation: max_silence_duration: 1.0 # maximum duration of silence to be marked transcription: - podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True use_tensorrt: True # enable TensorRT execution provider (requires CUDA + TensorRT installed) @@ -63,15 +63,15 @@ transcription: batch_size: 16 punctuation: - podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /root/datasets/50 # full path to the folder where the chopped podcasts are located + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /root/datasets/50 # full path to the folder where the text files are located + podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/create_dev_env.sh b/create_dev_env.sh index 359142a..c2dc144 100644 --- a/create_dev_env.sh +++ b/create_dev_env.sh @@ -7,7 +7,7 @@ create_venv_env() { if [ ! -d "$env_name" ]; then echo "Creating $env_name environment..." - uv venv "$env_name" + uv venv "$env_name" --python 3.12 if [ -f "$env_name/Scripts/activate" ]; then source "$env_name/Scripts/activate" diff --git a/requirements_dev.txt b/requirements_dev.txt index 6d44af9..ddf4b21 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -14,20 +14,16 @@ torch==2.8.0 torchcodec==0.7 pyannote.audio>=4.0.0 librosa -miniaudio -# onnx-asr[gpu,hub] moved to script +distillmos pytest>=7.0.0 black>=24.0.0 flake8>=6.0.0 - -# === ONNX Runtime GPU (CUDA 13 nightly) installed in script === coloredlogs flatbuffers numpy packaging protobuf sympy -# onnxruntime-gpu moved to script pyloudnorm silero-vad git+https://github.com/Den4ikAI/ruaccent.git diff --git a/src/separation/distillmos_process.py b/src/separation/distillmos_process.py new file mode 100644 index 0000000..06cd018 --- /dev/null +++ b/src/separation/distillmos_process.py @@ -0,0 +1,170 @@ +import argparse +import os +import torch +import torch.multiprocessing as mp +import pandas as pd +import yaml +import torchaudio +from pathlib import Path +from typing import List, Dict +from loguru import logger +from tqdm import tqdm + +from src.utils.utils import get_audio_paths, load_config + +torch.backends.cuda.matmul.allow_tf32 = True + +def save_chunk(results: List[Dict], output_path: Path): + """Saves a chunk of results to CSV, appending if exists.""" + if not results: + return + df = pd.DataFrame(results) + output_path.parent.mkdir(parents=True, exist_ok=True) + header = not output_path.exists() + df.to_csv(output_path, mode='a', header=header, index=False) + +def run_inference_worker(rank: int, world_size: int, file_paths: List[str], config: dict, final_output_path: Path): + """ + Worker function running on a dedicated GPU for DistillMOS. + """ + my_files = file_paths[rank::world_size] + if not my_files: + logger.info(f"Worker {rank}: No files to process.") + return + + device = torch.device(f"cuda:{rank}") + worker_output_path = final_output_path.with_name(f"distillmos_part_{rank}.csv") + + logger.info(f"[cuda:{rank}] Loading DistillMOS model...") + try: + import distillmos + sqa_model = distillmos.ConvTransformerSQAModel() + sqa_model.to(device) + sqa_model.eval() + except Exception as e: + logger.error(f"Failed to load distillmos model on worker {rank}: {e}") + return + + results_buffer = [] + save_every = 500 + + logger.info(f"[cuda:{rank}] Starting inference for {len(my_files)} files.") + + for path_str in tqdm(my_files, desc=f"DistillMOS-{rank}", position=rank): + try: + x, sr = torchaudio.load(path_str) + + if x.shape[0] > 1: + x = x[0, None, :] + + if sr != 16000: + resampler = torchaudio.transforms.Resample(sr, 16000).to(device) + x = resampler(x.to(device)) + else: + x = x.to(device) + + with torch.no_grad(): + mos = sqa_model(x) + mos_val = mos[0].item() + + results_buffer.append({ + 'filepath': path_str, + 'DistillMOS': mos_val + }) + + except Exception as e: + logger.warning(f"Error processing {path_str}: {e}") + continue + + if len(results_buffer) >= save_every: + save_chunk(results_buffer, worker_output_path) + results_buffer = [] + + save_chunk(results_buffer, worker_output_path) + logger.success(f"[cuda:{rank}] Finished.") + +def combine_results(final_output_path: Path, num_parts: int): + """Merges partial CSVs into the main balalaika.csv.""" + logger.info("Combining DistillMOS results...") + dfs = [] + for i in range(num_parts): + part_path = final_output_path.with_name(f"distillmos_part_{i}.csv") + if part_path.exists(): + try: + dfs.append(pd.read_csv(part_path)) + os.remove(part_path) + except Exception as e: + logger.error(f"Error reading {part_path}: {e}") + + if not dfs: + logger.warning("No DistillMOS results found.") + return + + new_df = pd.concat(dfs, ignore_index=True).drop_duplicates(subset=['filepath'], keep='last') + + if final_output_path.exists(): + logger.info(f"Merging with existing CSV: {final_output_path}") + main_df = pd.read_csv(final_output_path) + + # Обновляем существующий DF новыми результатами + main_df = pd.concat([main_df, new_df], ignore_index=True) + main_df = main_df.drop_duplicates(subset=['filepath'], keep='last') + else: + main_df = new_df + + main_df.to_csv(final_output_path, index=False) + logger.success(f"Combined. Total rows: {len(main_df)}") + +def get_unprocessed_paths(podcasts_path: Path, result_csv_path: Path) -> List[str]: + all_audio_paths = get_audio_paths(str(podcasts_path)) + all_paths_str = [str(p.resolve()) for p in all_audio_paths] + + if not result_csv_path.exists(): + return all_paths_str + + try: + df = pd.read_csv(result_csv_path) + if 'DistillMOS' not in df.columns: + return all_paths_str + + processed = set(df.loc[df['DistillMOS'].notna(), 'filepath'].astype(str).tolist()) + return [p for p in all_paths_str if p not in processed] + except: + return all_paths_str + +def main(): + mp.set_start_method('spawn', force=True) + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, required=True) + args = parser.parse_args() + + config = load_config(args.config_path, 'separation') + podcasts_path = Path(config.get('podcasts_path', '.')) + final_output_path = podcasts_path / 'balalaika.csv' + + available_gpus = torch.cuda.device_count() + if available_gpus == 0: + logger.error("No GPU detected.") + return + + unprocessed = get_unprocessed_paths(podcasts_path, final_output_path) + if not unprocessed: + logger.success("All files already have DistillMOS. Exiting.") + return + + logger.info(f"Processing {len(unprocessed)} files on {available_gpus} GPUs.") + + try: + mp.spawn( + run_inference_worker, + args=(available_gpus, unprocessed, config, final_output_path), + nprocs=available_gpus, + join=True + ) + except Exception as e: + logger.critical(f"Multiprocessing failed: {e}") + + combine_results(final_output_path, available_gpus) + +if __name__ == "__main__": + main() diff --git a/src/separation/nisqa_process.py b/src/separation/nisqa_process.py index 4b74308..2db2ecd 100644 --- a/src/separation/nisqa_process.py +++ b/src/separation/nisqa_process.py @@ -109,9 +109,15 @@ def run_inference_worker(rank: int, world_size: int, file_paths: List[str], conf for batch in tqdm(dataloader, desc=f"NISQA-{rank}", position=rank): x_batch = batch['x_spec_seg'].to(device, dtype=dtype) n_wins_batch = batch['n_wins'].to(device) - - with torch.amp.autocast(device_type="cuda", dtype=dtype): - outputs = model(x_batch, n_wins_batch) + + try: + with torch.amp.autocast(device_type="cuda", dtype=dtype): + outputs = model(x_batch, n_wins_batch) + except Exception as e: + logger.ERROR(f"Error in NISQA inference: {e}") + outputs = torch.zerros([len(batch['file_paths']), 5]) + + outputs_cpu = outputs.float().cpu() diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index 43eb3db..3864f14 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -22,5 +22,6 @@ SCRIPT_DIR=$(dirname "$(realpath "$0")") python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" +python3 -m src.separation.distillmos_procrss --config_path "$CONFIG_PATH" python3 -m src.separation.diarization --config_path "$CONFIG_PATH" python3 -m src.separation.silence_detect --config_path "$CONFIG_PATH" From 9762a3e6dca3cb38040774b69e5a39fabe34013f Mon Sep 17 00:00:00 2001 From: Vasiliy Date: Mon, 9 Mar 2026 20:12:44 +0300 Subject: [PATCH 30/43] add benchmarking --- benchmarking/.gitignore | 2 + benchmarking/README.md | 12 ++ benchmarking/__init__.py | 1 + benchmarking/bench.py | 15 ++ benchmarking/cli.py | 203 +++++++++++++++++++ benchmarking/common.py | 156 +++++++++++++++ benchmarking/models.py | 35 ++++ benchmarking/resources.py | 366 ++++++++++++++++++++++++++++++++++ benchmarking/run_benchmark.sh | 89 +++++++++ benchmarking/runner.py | 235 ++++++++++++++++++++++ benchmarking/sampling.py | 97 +++++++++ benchmarking/targets.py | 366 ++++++++++++++++++++++++++++++++++ 12 files changed, 1577 insertions(+) create mode 100644 benchmarking/.gitignore create mode 100644 benchmarking/README.md create mode 100644 benchmarking/__init__.py create mode 100755 benchmarking/bench.py create mode 100644 benchmarking/cli.py create mode 100644 benchmarking/common.py create mode 100644 benchmarking/models.py create mode 100644 benchmarking/resources.py create mode 100755 benchmarking/run_benchmark.sh create mode 100644 benchmarking/runner.py create mode 100644 benchmarking/sampling.py create mode 100644 benchmarking/targets.py diff --git a/benchmarking/.gitignore b/benchmarking/.gitignore new file mode 100644 index 0000000..21c1a3a --- /dev/null +++ b/benchmarking/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +reports/ diff --git a/benchmarking/README.md b/benchmarking/README.md new file mode 100644 index 0000000..790aea3 --- /dev/null +++ b/benchmarking/README.md @@ -0,0 +1,12 @@ +# Benchmarking + +```bash +DISABLE_DIARIZATION=1 \ +TARGET=pipeline.base \ +DATASET=/home/basil/bal/0 \ +NUM_SAMPLES=512 \ +REPEATS=1 \ +GPU_IDS=0 \ +benchmarking/run_benchmark.sh +``` + diff --git a/benchmarking/__init__.py b/benchmarking/__init__.py new file mode 100644 index 0000000..68b72ff --- /dev/null +++ b/benchmarking/__init__.py @@ -0,0 +1 @@ +"""Benchmarking harness package for Balalaika.""" diff --git a/benchmarking/bench.py b/benchmarking/bench.py new file mode 100755 index 0000000..18a6c10 --- /dev/null +++ b/benchmarking/bench.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from benchmarking.cli import main + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarking/cli.py b/benchmarking/cli.py new file mode 100644 index 0000000..dc6f6a4 --- /dev/null +++ b/benchmarking/cli.py @@ -0,0 +1,203 @@ +from __future__ import annotations + +import argparse +import copy +import json +from dataclasses import asdict +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + +from .common import REPO_ROOT, load_full_config, parse_gpu_ids, run_git, utc_now +from .resources import filter_gpu_inventory, query_gpu_inventory +from .runner import ( + aggregate_repeats, + host_metadata, + make_env, + repeat_label, + resolve_source_dataset, + run_single_repeat, + selected_gpu_ids, +) +from .sampling import collect_source_samples +from .targets import TARGETS, list_targets + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Benchmark harness for Balalaika stages and models.") + parser.add_argument("--target", type=str, help="Benchmark target name") + parser.add_argument("--config-path", type=Path, default=REPO_ROOT / "configs" / "config.yaml") + parser.add_argument("--dataset", type=Path, help="Source dataset root. Defaults to the dataset from config.") + parser.add_argument( + "--num-examples", + type=int, + default=None, + help="How many examples to benchmark. Omit or set <= 0 to use all eligible files.", + ) + parser.add_argument("--sample-mode", choices=("first", "random"), default="first") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--repeats", type=int, default=3) + parser.add_argument("--warmup-repeats", type=int, default=0) + parser.add_argument("--gpu-ids", type=parse_gpu_ids) + parser.add_argument("--num-gpus", type=int) + parser.add_argument("--cpu-workers-per-gpu", type=int) + parser.add_argument("--cpu-workers-total", type=int) + parser.add_argument("--batch-size-override", type=int) + parser.add_argument("--model-name-override", type=str) + parser.add_argument("--disable-diarization", action="store_true") + parser.add_argument("--sample-interval-sec", type=float, default=0.5) + parser.add_argument("--output-root", type=Path, default=REPO_ROOT / "benchmarking" / "reports") + parser.add_argument("--keep-workdirs", action="store_true") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--list-targets", action="store_true") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + + if args.list_targets: + list_targets() + return 0 + + if not args.target: + raise SystemExit("--target is required unless --list-targets is used") + + if args.target not in TARGETS: + raise SystemExit(f"Unknown target: {args.target}. Use --list-targets.") + + target = TARGETS[args.target] + base_config = load_full_config(args.config_path.resolve()) + args.dataset = resolve_source_dataset(args, copy.deepcopy(base_config), target) + + if not args.dataset.exists(): + raise SystemExit(f"Dataset does not exist: {args.dataset}") + + gpu_inventory = query_gpu_inventory() + gpu_ids = selected_gpu_ids(args, gpu_inventory) + scoped_gpu_inventory = filter_gpu_inventory(gpu_inventory, gpu_ids) + env = make_env(args, gpu_ids if target.uses_gpu else []) + + selected_samples = collect_source_samples( + source_dataset=args.dataset, + target=target, + config=base_config, + sample_mode=args.sample_mode, + num_examples=args.num_examples, + seed=args.seed, + ) + + if not selected_samples: + raise SystemExit( + "No eligible samples were found. Check dataset contents and target prerequisites." + ) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + run_root = (args.output_root / f"{timestamp}__{target.name.replace('.', '_')}").resolve() + run_root.mkdir(parents=True, exist_ok=True) + + selected_samples_path = run_root / "selected_samples.json" + with selected_samples_path.open("w", encoding="utf-8") as handle: + json.dump([asdict(sample) for sample in selected_samples], handle, indent=2, ensure_ascii=False) + + summary_stub = { + "report_version": 1, + "created_at_utc": utc_now(), + "repo_root": str(REPO_ROOT), + "git": { + "branch": run_git(("git", "branch", "--show-current")), + "commit": run_git(("git", "rev-parse", "HEAD")), + "status_short": run_git(("git", "status", "--short")), + }, + "host": host_metadata(), + "gpu_inventory": scoped_gpu_inventory, + "target": { + "name": target.name, + "description": target.description, + "modules": list(target.modules), + "required_sidecars": list(target.required_sidecars), + "copied_sidecars": list(target.copied_sidecars), + "uses_gpu": target.uses_gpu, + }, + "params": { + "config_path": str(args.config_path.resolve()), + "source_dataset": str(args.dataset), + "num_examples_requested": args.num_examples, + "num_examples_selected": len(selected_samples), + "sample_mode": args.sample_mode, + "seed": args.seed, + "repeats": args.repeats, + "warmup_repeats": args.warmup_repeats, + "gpu_ids": gpu_ids, + "cpu_workers_per_gpu": args.cpu_workers_per_gpu, + "cpu_workers_total": args.cpu_workers_total, + "batch_size_override": args.batch_size_override, + "model_name_override": args.model_name_override, + "disable_diarization": args.disable_diarization, + "sample_interval_sec": args.sample_interval_sec, + "keep_workdirs": args.keep_workdirs, + "cuda_visible_devices": env.get("CUDA_VISIBLE_DEVICES"), + }, + "selected_samples_path": str(selected_samples_path), + } + + if args.dry_run: + print(json.dumps(summary_stub, indent=2, ensure_ascii=False)) + return 0 + + warmups: List[Dict[str, Any]] = [] + for warmup_index in range(1, args.warmup_repeats + 1): + label = repeat_label("warmup", warmup_index) + warmups.append( + run_single_repeat( + label=label, + target=target, + sample_records=selected_samples, + base_config=base_config, + run_root=run_root, + args=args, + gpu_ids=gpu_ids, + env=env, + ) + ) + + repeats: List[Dict[str, Any]] = [] + for repeat_index in range(1, args.repeats + 1): + label = repeat_label("repeat", repeat_index) + repeats.append( + run_single_repeat( + label=label, + target=target, + sample_records=selected_samples, + base_config=base_config, + run_root=run_root, + args=args, + gpu_ids=gpu_ids, + env=env, + ) + ) + + report = dict(summary_stub) + report["warmups"] = warmups + report["repeats"] = repeats + report["aggregate"] = aggregate_repeats(repeats) + report["successful"] = report["aggregate"]["failed_repeats"] == 0 + + report_path = run_root / "report.json" + with report_path.open("w", encoding="utf-8") as handle: + json.dump(report, handle, indent=2, ensure_ascii=False) + handle.write("\n") + + final_summary = { + "report_path": str(report_path), + "target": target.name, + "successful": report["successful"], + "successful_repeats": report["aggregate"]["successful_repeats"], + "avg_rtf": report["aggregate"]["rtf"]["avg"], + "avg_gpu_util_percent": report["aggregate"]["gpu_util_percent"]["avg"], + "avg_gpu_vram_mb": report["aggregate"]["gpu_vram_mb"]["avg"], + "avg_rss_gb": report["aggregate"]["rss_gb"]["avg"], + "avg_cpu_util_percent": report["aggregate"]["cpu_util_percent"]["avg"], + } + print(json.dumps(final_summary, ensure_ascii=False)) + return 0 if report["successful"] else 1 diff --git a/benchmarking/common.py b/benchmarking/common.py new file mode 100644 index 0000000..c940b72 --- /dev/null +++ b/benchmarking/common.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +import os +import shutil +import subprocess +import sys +import warnings +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional, Sequence + +import yaml + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +TRANSCRIPTION_MODELS: tuple[str, ...] = ( + "giga_ctc", + "giga_rnnt", + "giga_ctc_lm", + "tone", + "vosk", + "vosk_small", + "parakeet_v2", + "parakeet_v3", + "canary", + "whisper_base", + "whisper_turbo", +) + +COLLATE_SIDECARS: tuple[str, ...] = ( + "_rover.txt", + "_punct.txt", + "_accent.txt", + "_rover_phonemes.txt", +) + +CLK_TCK = os.sysconf(os.sysconf_names["SC_CLK_TCK"]) +PAGE_SIZE = os.sysconf("SC_PAGE_SIZE") + + +def eprint(message: str) -> None: + print(message, file=sys.stderr, flush=True) + + +def utc_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def preserve_repeat_artifacts(dataset_root: Path, repeat_root: Path) -> List[str]: + artifacts_dir = repeat_root / "artifacts" + preserved_paths: List[str] = [] + + for filename in ("balalaika.csv", "balalaika.parquet"): + source_path = dataset_root / filename + if not source_path.exists(): + continue + artifacts_dir.mkdir(parents=True, exist_ok=True) + destination_path = artifacts_dir / filename + shutil.copy2(source_path, destination_path) + preserved_paths.append(str(destination_path)) + + return preserved_paths + + +def ensure_dict(mapping: Dict[str, Any], key: str) -> Dict[str, Any]: + value = mapping.get(key) + if not isinstance(value, dict): + value = {} + mapping[key] = value + return value + + +def load_full_config(config_path: Path) -> Dict[str, Any]: + with config_path.open("r", encoding="utf-8") as handle: + data = yaml.safe_load(handle) or {} + if not isinstance(data, dict): + raise ValueError(f"Config {config_path} must be a YAML mapping") + return data + + +def write_yaml(path: Path, data: Dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as handle: + yaml.safe_dump(data, handle, sort_keys=False, allow_unicode=False) + + +def run_git(command: Sequence[str]) -> str: + try: + result = subprocess.run( + list(command), + cwd=REPO_ROOT, + capture_output=True, + text=True, + check=True, + ) + except Exception: + return "" + return result.stdout.strip() + + +def get_audio_duration(path: Path) -> float: + try: + import torchaudio + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info = torchaudio.info(str(path)) + if info.sample_rate > 0 and info.num_frames > 0: + return float(info.num_frames) / float(info.sample_rate) + except Exception: + pass + + try: + import soundfile as sf + + info = sf.info(str(path)) + if info.samplerate > 0 and info.frames > 0: + return float(info.frames) / float(info.samplerate) + except Exception: + pass + + raise RuntimeError(f"Unable to determine duration for {path}") + + +def sidecar_path(audio_path: Path, suffix: str) -> Path: + return audio_path.with_name(f"{audio_path.stem}{suffix}") + + +def copy_file(src: Path, dst: Path) -> None: + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + + +def summarize_numeric(values: Iterable[Optional[float]]) -> Dict[str, Optional[float]]: + filtered = [float(value) for value in values if value is not None] + if not filtered: + return {"avg": None, "max": None, "min": None} + return { + "avg": sum(filtered) / len(filtered), + "max": max(filtered), + "min": min(filtered), + } + + +def parse_gpu_ids(raw: Optional[str]) -> Optional[List[int]]: + if not raw: + return None + values: List[int] = [] + for part in raw.split(","): + part = part.strip() + if not part: + continue + values.append(int(part)) + return values or None diff --git a/benchmarking/models.py b/benchmarking/models.py new file mode 100644 index 0000000..d62059c --- /dev/null +++ b/benchmarking/models.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable, Dict, Optional, TypeAlias + + +@dataclass(frozen=True) +class SampleRecord: + audio_path: str + relative_path: str + duration_sec: float + copied_sidecars: tuple[str, ...] + + +@dataclass(frozen=True) +class CommandSpec: + name: str + argv: tuple[str, ...] + + +ConfigMutator: TypeAlias = Callable[[Dict[str, Any], argparse.Namespace, Path], None] + + +@dataclass(frozen=True) +class TargetSpec: + name: str + description: str + modules: tuple[str, ...] + required_sidecars: tuple[str, ...] = () + copied_sidecars: tuple[str, ...] = () + mutator: Optional[ConfigMutator] = None + min_input_duration_from_config: Optional[tuple[str, ...]] = None + uses_gpu: bool = True diff --git a/benchmarking/resources.py b/benchmarking/resources.py new file mode 100644 index 0000000..3bc28f1 --- /dev/null +++ b/benchmarking/resources.py @@ -0,0 +1,366 @@ +from __future__ import annotations + +import os +import subprocess +import sys +import threading +import time +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence + +from .common import CLK_TCK, PAGE_SIZE, REPO_ROOT, summarize_numeric, utc_now + + +def build_runtime_library_paths() -> List[str]: + python_version = f"{sys.version_info.major}.{sys.version_info.minor}" + site_packages = Path(sys.prefix) / "lib" / f"python{python_version}" / "site-packages" + nvidia_root = site_packages / "nvidia" + + library_dirs: List[Path] = [] + for candidate in [ + nvidia_root / "cu13" / "lib", + nvidia_root / "cublas" / "lib", + nvidia_root / "cudnn" / "lib", + nvidia_root / "cuda_runtime" / "lib", + nvidia_root / "cuda_nvrtc" / "lib", + nvidia_root / "cufft" / "lib", + nvidia_root / "nvjitlink" / "lib", + nvidia_root / "cusolver" / "lib", + nvidia_root / "cusparse" / "lib", + nvidia_root / "cusparselt" / "lib", + nvidia_root / "nccl" / "lib", + site_packages / "tensorrt_libs", + ]: + if candidate.exists(): + library_dirs.append(candidate) + + seen: set[str] = set() + resolved: List[str] = [] + for path in library_dirs: + path_str = str(path) + if path_str in seen: + continue + seen.add(path_str) + resolved.append(path_str) + return resolved + + +def nvidia_smi_env() -> Dict[str, str]: + env = os.environ.copy() + env.pop("LD_LIBRARY_PATH", None) + return env + + +def proc_root() -> Path: + return Path("/proc") + + +def system_cpu_snapshot() -> Optional[tuple[int, int]]: + try: + first_line = (proc_root() / "stat").read_text(encoding="utf-8").splitlines()[0] + except Exception: + return None + + parts = first_line.split() + if not parts or parts[0] != "cpu": + return None + + values = [int(value) for value in parts[1:]] + total = sum(values) + idle = values[3] + values[4] if len(values) > 4 else values[3] + return total, idle + + +def system_memory_snapshot() -> Dict[str, Optional[float]]: + meminfo_path = proc_root() / "meminfo" + values: Dict[str, int] = {} + try: + for line in meminfo_path.read_text(encoding="utf-8").splitlines(): + key, _, raw_value = line.partition(":") + parts = raw_value.strip().split() + if not parts: + continue + values[key] = int(parts[0]) * 1024 + except Exception: + return {"used_gb": None, "percent": None} + + total = values.get("MemTotal") + available = values.get("MemAvailable") + if not total or available is None: + return {"used_gb": None, "percent": None} + + used = total - available + return { + "used_gb": used / (1024 ** 3), + "percent": (used / total) * 100.0, + } + + +def read_process_stat(pid: int) -> Optional[Dict[str, int]]: + stat_path = proc_root() / str(pid) / "stat" + statm_path = proc_root() / str(pid) / "statm" + + try: + stat_text = stat_path.read_text(encoding="utf-8") + statm_text = statm_path.read_text(encoding="utf-8") + except Exception: + return None + + right_paren = stat_text.rfind(")") + if right_paren == -1: + return None + fields = stat_text[right_paren + 2 :].split() + if len(fields) < 13: + return None + + try: + ppid = int(fields[1]) + utime = int(fields[11]) + stime = int(fields[12]) + resident_pages = int(statm_text.split()[1]) + except (IndexError, ValueError): + return None + + return { + "pid": pid, + "ppid": ppid, + "cpu_ticks": utime + stime, + "rss_bytes": resident_pages * PAGE_SIZE, + } + + +def process_tree_snapshot(root_pid: int) -> Dict[int, Dict[str, int]]: + stats: Dict[int, Dict[str, int]] = {} + children: Dict[int, List[int]] = {} + + for entry in proc_root().iterdir(): + if not entry.name.isdigit(): + continue + pid = int(entry.name) + stat = read_process_stat(pid) + if not stat: + continue + stats[pid] = stat + children.setdefault(stat["ppid"], []).append(pid) + + if root_pid not in stats: + return {} + + result: Dict[int, Dict[str, int]] = {} + pending = [root_pid] + while pending: + pid = pending.pop() + if pid in result: + continue + stat = stats.get(pid) + if not stat: + continue + result[pid] = stat + pending.extend(children.get(pid, [])) + + return result + + +def query_gpu_inventory() -> Dict[str, Any]: + command = [ + "nvidia-smi", + "--query-gpu=index,name,memory.total", + "--format=csv,noheader,nounits", + ] + try: + result = subprocess.run( + command, + cwd=REPO_ROOT, + capture_output=True, + text=True, + timeout=5, + env=nvidia_smi_env(), + check=True, + ) + except Exception as exc: + return {"gpus": [], "error": str(exc)} + + gpus: List[Dict[str, Any]] = [] + for line in result.stdout.splitlines(): + parts = [item.strip() for item in line.split(",")] + if len(parts) != 3: + continue + try: + gpus.append( + { + "index": int(parts[0]), + "name": parts[1], + "memory_total_mb": float(parts[2]), + } + ) + except ValueError: + continue + return {"gpus": gpus, "error": None} + + +def filter_gpu_inventory(inventory: Dict[str, Any], gpu_ids: Optional[List[int]]) -> Dict[str, Any]: + if gpu_ids is None: + return inventory + if gpu_ids == []: + return {"gpus": [], "error": inventory.get("error")} + gpus = [gpu for gpu in inventory.get("gpus", []) if gpu.get("index") in gpu_ids] + return {"gpus": gpus, "error": inventory.get("error")} + + +class ResourceSampler: + def __init__(self, pid: int, gpu_ids: Optional[List[int]], interval_sec: float) -> None: + self.pid = pid + self.gpu_ids = gpu_ids + self.interval_sec = interval_sec + self.samples: List[Dict[str, Any]] = [] + self.gpu_query_error: Optional[str] = None + self._stop_event = threading.Event() + self._thread: Optional[threading.Thread] = None + self._cpu_count = max(os.cpu_count() or 1, 1) + self._prev_process_cpu_ticks: Dict[int, int] = {} + self._prev_process_sample_time: Optional[float] = None + self._prev_system_cpu_snapshot: Optional[tuple[int, int]] = None + + def start(self) -> None: + self._thread = threading.Thread(target=self._run, name=f"resource-sampler-{self.pid}", daemon=True) + self._thread.start() + + def stop(self) -> None: + self._stop_event.set() + if self._thread: + self._thread.join(timeout=max(self.interval_sec * 2, 1.0)) + + def _query_gpu(self) -> List[Dict[str, Any]]: + if self.gpu_ids == []: + return [] + command = [ + "nvidia-smi", + "--query-gpu=index,utilization.gpu,memory.used,memory.total", + "--format=csv,noheader,nounits", + ] + try: + result = subprocess.run( + command, + cwd=REPO_ROOT, + capture_output=True, + text=True, + timeout=5, + env=nvidia_smi_env(), + check=True, + ) + except Exception as exc: + if self.gpu_query_error is None: + self.gpu_query_error = str(exc) + return [] + + metrics: List[Dict[str, Any]] = [] + for line in result.stdout.splitlines(): + parts = [item.strip() for item in line.split(",")] + if len(parts) != 4: + continue + try: + index = int(parts[0]) + if self.gpu_ids is not None and index not in self.gpu_ids: + continue + metrics.append( + { + "index": index, + "gpu_util_percent": float(parts[1]), + "memory_used_mb": float(parts[2]), + "memory_total_mb": float(parts[3]), + } + ) + except ValueError: + continue + return metrics + + def _run(self) -> None: + while not self._stop_event.is_set(): + now = time.monotonic() + process_stats = process_tree_snapshot(self.pid) + cpu_ticks_total = sum(stat["cpu_ticks"] for stat in process_stats.values()) + rss_bytes = sum(stat["rss_bytes"] for stat in process_stats.values()) + + cpu_percent_raw = 0.0 + if self._prev_process_sample_time is not None: + elapsed = now - self._prev_process_sample_time + if elapsed > 0: + previous_total = sum( + self._prev_process_cpu_ticks.get(pid, stat["cpu_ticks"]) + for pid, stat in process_stats.items() + ) + delta_ticks = max(cpu_ticks_total - previous_total, 0) + cpu_percent_raw = (delta_ticks / (elapsed * CLK_TCK)) * 100.0 + + self._prev_process_cpu_ticks = {pid: stat["cpu_ticks"] for pid, stat in process_stats.items()} + self._prev_process_sample_time = now + + system_cpu_percent = 0.0 + current_system_cpu_snapshot = system_cpu_snapshot() + if self._prev_system_cpu_snapshot and current_system_cpu_snapshot: + prev_total, prev_idle = self._prev_system_cpu_snapshot + total, idle = current_system_cpu_snapshot + total_delta = total - prev_total + idle_delta = idle - prev_idle + if total_delta > 0: + system_cpu_percent = (1.0 - (idle_delta / total_delta)) * 100.0 + self._prev_system_cpu_snapshot = current_system_cpu_snapshot + + memory_snapshot = system_memory_snapshot() + + sample = { + "timestamp_utc": utc_now(), + "process_count": len(process_stats), + "cpu_percent_raw": cpu_percent_raw, + "cpu_util_percent": cpu_percent_raw / self._cpu_count, + "cpu_cores_used": cpu_percent_raw / 100.0, + "rss_bytes": rss_bytes, + "rss_gb": rss_bytes / (1024 ** 3), + "system_cpu_percent": system_cpu_percent, + "system_ram_used_gb": memory_snapshot["used_gb"], + "system_ram_percent": memory_snapshot["percent"], + "gpus": self._query_gpu(), + } + self.samples.append(sample) + self._stop_event.wait(self.interval_sec) + + +def summarize_gpu_samples(samples: Sequence[Dict[str, Any]]) -> Dict[str, Any]: + per_gpu: Dict[str, Dict[str, List[float]]] = {} + all_utils: List[float] = [] + all_vram_mb: List[float] = [] + + for sample in samples: + for gpu in sample.get("gpus", []): + gpu_key = str(gpu["index"]) + bucket = per_gpu.setdefault(gpu_key, {"gpu_util_percent": [], "memory_used_mb": []}) + bucket["gpu_util_percent"].append(gpu["gpu_util_percent"]) + bucket["memory_used_mb"].append(gpu["memory_used_mb"]) + all_utils.append(gpu["gpu_util_percent"]) + all_vram_mb.append(gpu["memory_used_mb"]) + + return { + "gpu_util_percent": summarize_numeric(all_utils), + "gpu_vram_mb": summarize_numeric(all_vram_mb), + "per_gpu": { + gpu_key: { + "gpu_util_percent": summarize_numeric(values["gpu_util_percent"]), + "memory_used_mb": summarize_numeric(values["memory_used_mb"]), + } + for gpu_key, values in per_gpu.items() + }, + } + + +def summarize_resource_samples(samples: Sequence[Dict[str, Any]]) -> Dict[str, Any]: + summary = { + "sample_count": len(samples), + "cpu_util_percent": summarize_numeric(sample["cpu_util_percent"] for sample in samples), + "cpu_cores_used": summarize_numeric(sample["cpu_cores_used"] for sample in samples), + "rss_gb": summarize_numeric(sample["rss_gb"] for sample in samples), + "system_cpu_percent": summarize_numeric(sample["system_cpu_percent"] for sample in samples), + "system_ram_used_gb": summarize_numeric(sample["system_ram_used_gb"] for sample in samples), + "system_ram_percent": summarize_numeric(sample["system_ram_percent"] for sample in samples), + } + summary.update(summarize_gpu_samples(samples)) + return summary diff --git a/benchmarking/run_benchmark.sh b/benchmarking/run_benchmark.sh new file mode 100755 index 0000000..c2a1a64 --- /dev/null +++ b/benchmarking/run_benchmark.sh @@ -0,0 +1,89 @@ +#!/usr/bin/env bash +set -euo pipefail + +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +REPO_ROOT=$(cd "${SCRIPT_DIR}/.." && pwd) +cd "${REPO_ROOT}" + +if [[ ! -f ".dev_venv/bin/activate" ]]; then + echo "Missing virtual environment: ${REPO_ROOT}/.dev_venv" >&2 + exit 1 +fi + +source ".dev_venv/bin/activate" + +TARGET="${TARGET:-${1:-}}" +if [[ -z "${TARGET}" ]]; then + echo "Usage: TARGET= DATASET=/path/to/data ${SCRIPT_DIR}/run_benchmark.sh" >&2 + echo "Or: ${SCRIPT_DIR}/run_benchmark.sh " >&2 + echo "Use TARGET=list to print available benchmark targets." >&2 + exit 1 +fi + +CONFIG_PATH="${CONFIG_PATH:-${REPO_ROOT}/configs/config.yaml}" +SAMPLE_MODE="${SAMPLE_MODE:-first}" +SEED="${SEED:-42}" +REPEATS="${REPEATS:-3}" +WARMUP_REPEATS="${WARMUP_REPEATS:-0}" +SAMPLE_INTERVAL_SEC="${SAMPLE_INTERVAL_SEC:-0.5}" +OUTPUT_ROOT="${OUTPUT_ROOT:-${REPO_ROOT}/benchmarking/reports}" + +ARGS=( + "--config-path" "${CONFIG_PATH}" + "--sample-mode" "${SAMPLE_MODE}" + "--seed" "${SEED}" + "--repeats" "${REPEATS}" + "--warmup-repeats" "${WARMUP_REPEATS}" + "--sample-interval-sec" "${SAMPLE_INTERVAL_SEC}" + "--output-root" "${OUTPUT_ROOT}" +) + +if [[ "${TARGET}" == "list" ]]; then + exec python3 "${SCRIPT_DIR}/bench.py" --list-targets +fi + +ARGS+=("--target" "${TARGET}") + +if [[ -n "${DATASET:-}" ]]; then + ARGS+=("--dataset" "${DATASET}") +fi + +if [[ -n "${NUM_SAMPLES:-}" ]]; then + ARGS+=("--num-examples" "${NUM_SAMPLES}") +fi + +if [[ -n "${GPU_IDS:-}" ]]; then + ARGS+=("--gpu-ids" "${GPU_IDS}") +elif [[ -n "${NUM_GPUS:-}" ]]; then + ARGS+=("--num-gpus" "${NUM_GPUS}") +fi + +if [[ -n "${CPU_WORKERS_PER_GPU:-}" ]]; then + ARGS+=("--cpu-workers-per-gpu" "${CPU_WORKERS_PER_GPU}") +fi + +if [[ -n "${CPU_WORKERS_TOTAL:-}" ]]; then + ARGS+=("--cpu-workers-total" "${CPU_WORKERS_TOTAL}") +fi + +if [[ -n "${BATCH_SIZE_OVERRIDE:-}" ]]; then + ARGS+=("--batch-size-override" "${BATCH_SIZE_OVERRIDE}") +fi + +if [[ -n "${MODEL_NAME_OVERRIDE:-}" ]]; then + ARGS+=("--model-name-override" "${MODEL_NAME_OVERRIDE}") +fi + +if [[ "${DISABLE_DIARIZATION:-0}" == "1" ]]; then + ARGS+=("--disable-diarization") +fi + +if [[ "${KEEP_WORKDIRS:-0}" == "1" ]]; then + ARGS+=("--keep-workdirs") +fi + +if [[ "${DRY_RUN:-0}" == "1" ]]; then + ARGS+=("--dry-run") +fi + +exec python3 "${SCRIPT_DIR}/bench.py" "${ARGS[@]}" diff --git a/benchmarking/runner.py b/benchmarking/runner.py new file mode 100644 index 0000000..b891b4f --- /dev/null +++ b/benchmarking/runner.py @@ -0,0 +1,235 @@ +from __future__ import annotations + +import argparse +import copy +import json +import os +import platform +import shutil +import socket +import subprocess +import sys +import time +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence + +from .common import REPO_ROOT, eprint, ensure_dict, preserve_repeat_artifacts, summarize_numeric, utc_now, write_yaml +from .models import CommandSpec, SampleRecord, TargetSpec +from .resources import ResourceSampler, build_runtime_library_paths, summarize_resource_samples +from .sampling import copy_benchmark_dataset +from .targets import build_commands + + +def run_command( + command: CommandSpec, + config_path: Path, + log_path: Path, + gpu_ids: Optional[List[int]], + sample_interval_sec: float, + env: Dict[str, str], +) -> Dict[str, Any]: + argv = list(command.argv) + [str(config_path)] + log_path.parent.mkdir(parents=True, exist_ok=True) + started_at = utc_now() + started = time.monotonic() + + with log_path.open("w", encoding="utf-8") as handle: + process = subprocess.Popen( + argv, + cwd=REPO_ROOT, + stdout=handle, + stderr=subprocess.STDOUT, + env=env, + ) + sampler = ResourceSampler(pid=process.pid, gpu_ids=gpu_ids, interval_sec=sample_interval_sec) + sampler.start() + return_code = process.wait() + sampler.stop() + + wall_time_sec = time.monotonic() - started + finished_at = utc_now() + summary = summarize_resource_samples(sampler.samples) + return { + "name": command.name, + "argv": argv, + "log_path": str(log_path), + "started_at_utc": started_at, + "finished_at_utc": finished_at, + "wall_time_sec": wall_time_sec, + "return_code": return_code, + "resource_summary": summary, + "resource_samples": sampler.samples, + "gpu_query_error": sampler.gpu_query_error, + } + + +def aggregate_repeats(repeats: Sequence[Dict[str, Any]]) -> Dict[str, Any]: + successful = [repeat for repeat in repeats if repeat.get("success")] + return { + "total_repeats": len(repeats), + "successful_repeats": len(successful), + "failed_repeats": len(repeats) - len(successful), + "wall_time_sec": summarize_numeric(repeat.get("wall_time_sec") for repeat in successful), + "rtf": summarize_numeric(repeat.get("rtf") for repeat in successful), + "x_realtime": summarize_numeric(repeat.get("x_realtime") for repeat in successful), + "cpu_util_percent": summarize_numeric( + repeat.get("resource_summary", {}).get("cpu_util_percent", {}).get("avg") + for repeat in successful + ), + "rss_gb": summarize_numeric( + repeat.get("resource_summary", {}).get("rss_gb", {}).get("avg") for repeat in successful + ), + "gpu_util_percent": summarize_numeric( + repeat.get("resource_summary", {}).get("gpu_util_percent", {}).get("avg") + for repeat in successful + ), + "gpu_vram_mb": summarize_numeric( + repeat.get("resource_summary", {}).get("gpu_vram_mb", {}).get("avg") + for repeat in successful + ), + } + + +def selected_gpu_ids(args: argparse.Namespace, inventory: Dict[str, Any]) -> Optional[List[int]]: + if args.gpu_ids is not None: + return args.gpu_ids + if args.num_gpus is not None: + return list(range(args.num_gpus)) + gpu_inventory = inventory.get("gpus", []) + if not gpu_inventory: + return [] + return [int(gpu["index"]) for gpu in gpu_inventory] + + +def make_env(args: argparse.Namespace, gpu_ids: Optional[List[int]]) -> Dict[str, str]: + env = os.environ.copy() + pythonpath_parts = [str(REPO_ROOT)] + if env.get("PYTHONPATH"): + pythonpath_parts.append(env["PYTHONPATH"]) + env["PYTHONPATH"] = os.pathsep.join(pythonpath_parts) + + ld_library_parts = build_runtime_library_paths() + if env.get("LD_LIBRARY_PATH"): + ld_library_parts.append(env["LD_LIBRARY_PATH"]) + if ld_library_parts: + env["LD_LIBRARY_PATH"] = os.pathsep.join(ld_library_parts) + + if gpu_ids is not None and gpu_ids != []: + env["CUDA_VISIBLE_DEVICES"] = ",".join(str(gpu_id) for gpu_id in gpu_ids) + return env + + +def repeat_label(prefix: str, index: int) -> str: + return f"{prefix}_{index:02d}" + + +def run_single_repeat( + label: str, + target: TargetSpec, + sample_records: Sequence[SampleRecord], + base_config: Dict[str, Any], + run_root: Path, + args: argparse.Namespace, + gpu_ids: Optional[List[int]], + env: Dict[str, str], +) -> Dict[str, Any]: + repeat_root = run_root / label + repeat_root.mkdir(parents=True, exist_ok=True) + + dataset_root = repeat_root / "dataset" + copy_benchmark_dataset(destination_dataset=dataset_root, sample_records=sample_records) + + effective_config = copy.deepcopy(base_config) + if target.mutator is not None: + target.mutator(effective_config, args, dataset_root) + + config_path = repeat_root / "config.yaml" + write_yaml(config_path, effective_config) + + commands = build_commands(target.modules) + command_results: List[Dict[str, Any]] = [] + all_samples: List[Dict[str, Any]] = [] + success = True + error_message = None + + for index, command in enumerate(commands, start=1): + log_path = repeat_root / f"command_{index:02d}_{command.name.replace('.', '_')}.log" + eprint(f"[{label}] running {command.name}") + result = run_command( + command=command, + config_path=config_path, + log_path=log_path, + gpu_ids=gpu_ids if target.uses_gpu else [], + sample_interval_sec=args.sample_interval_sec, + env=env, + ) + all_samples.extend(result.pop("resource_samples")) + command_results.append(result) + if result["return_code"] != 0: + success = False + error_message = f"{command.name} failed with exit code {result['return_code']}" + break + + samples_path = repeat_root / "resource_samples.jsonl" + with samples_path.open("w", encoding="utf-8") as handle: + for sample in all_samples: + handle.write(json.dumps(sample, ensure_ascii=False) + "\n") + + total_audio_sec = sum(record.duration_sec for record in sample_records) + wall_time_sec = sum(command_result["wall_time_sec"] for command_result in command_results) + rtf = (wall_time_sec / total_audio_sec) if total_audio_sec > 0 else None + x_realtime = (total_audio_sec / wall_time_sec) if wall_time_sec > 0 else None + + repeat_result = { + "label": label, + "repeat_root": str(repeat_root), + "dataset_root": str(dataset_root), + "config_path": str(config_path), + "samples_path": str(samples_path), + "success": success, + "error": error_message, + "command_results": command_results, + "wall_time_sec": wall_time_sec, + "total_audio_sec": total_audio_sec, + "rtf": rtf, + "x_realtime": x_realtime, + "resource_summary": summarize_resource_samples(all_samples), + "preserved_artifacts": preserve_repeat_artifacts(dataset_root, repeat_root), + } + + if not args.keep_workdirs: + shutil.rmtree(dataset_root, ignore_errors=True) + + return repeat_result + + +def host_metadata() -> Dict[str, Any]: + return { + "hostname": socket.gethostname(), + "platform": platform.platform(), + "python": sys.version, + "logical_cpu_count": os.cpu_count(), + "physical_cpu_count": None, + } + + +def resolve_source_dataset(args: argparse.Namespace, config: Dict[str, Any], target: TargetSpec) -> Path: + if args.dataset: + return args.dataset.resolve() + + fallback_sections = { + "collate.stage": "download", + "pipeline.base": "preprocess", + } + preferred_section = fallback_sections.get(target.name) + if preferred_section: + preferred = ensure_dict(config, preferred_section).get("podcasts_path") + if preferred: + return Path(preferred).resolve() + + for key in ("preprocess", "separation", "transcription", "punctuation", "accent", "phonemizer", "download"): + value = ensure_dict(config, key).get("podcasts_path") + if value: + return Path(value).resolve() + + raise ValueError("Dataset path was not provided and could not be inferred from config") diff --git a/benchmarking/sampling.py b/benchmarking/sampling.py new file mode 100644 index 0000000..305786c --- /dev/null +++ b/benchmarking/sampling.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +import random +import shutil +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence + +from src.utils.utils import get_audio_paths + +from .common import copy_file, eprint, get_audio_duration, sidecar_path +from .models import SampleRecord, TargetSpec +from .targets import min_duration_for_target + + +def collect_source_samples( + source_dataset: Path, + target: TargetSpec, + config: Dict[str, Any], + sample_mode: str, + num_examples: Optional[int], + seed: int, +) -> List[SampleRecord]: + all_audio_paths = sorted(Path(path) for path in get_audio_paths(str(source_dataset))) + minimum_duration = min_duration_for_target(target, config) + invalid_audio_count = 0 + invalid_audio_examples: List[str] = [] + + def build_record(audio_path: Path) -> Optional[SampleRecord]: + nonlocal invalid_audio_count + for suffix in target.required_sidecars: + if not sidecar_path(audio_path, suffix).exists(): + return None + try: + if audio_path.stat().st_size == 0: + raise RuntimeError("empty file") + duration_sec = get_audio_duration(audio_path) + except Exception as exc: + invalid_audio_count += 1 + if len(invalid_audio_examples) < 5: + invalid_audio_examples.append(f"{audio_path}: {exc}") + return None + if minimum_duration is not None and duration_sec <= minimum_duration: + return None + try: + relative_path = str(audio_path.relative_to(source_dataset)) + except ValueError: + return None + copied_sidecars = tuple( + suffix for suffix in target.copied_sidecars if sidecar_path(audio_path, suffix).exists() + ) + return SampleRecord( + audio_path=str(audio_path), + relative_path=relative_path, + duration_sec=duration_sec, + copied_sidecars=copied_sidecars, + ) + + if sample_mode == "first": + records: List[SampleRecord] = [] + for audio_path in all_audio_paths: + record = build_record(audio_path) + if not record: + continue + records.append(record) + if num_examples is not None and num_examples > 0 and len(records) >= num_examples: + break + if invalid_audio_count: + eprint(f"Skipped {invalid_audio_count} unreadable audio files during sample selection.") + for example in invalid_audio_examples: + eprint(f" {example}") + return records + + candidates = [record for record in (build_record(path) for path in all_audio_paths) if record] + if invalid_audio_count: + eprint(f"Skipped {invalid_audio_count} unreadable audio files during sample selection.") + for example in invalid_audio_examples: + eprint(f" {example}") + random.Random(seed).shuffle(candidates) + if num_examples is not None and num_examples > 0: + return candidates[:num_examples] + return candidates + + +def copy_benchmark_dataset(destination_dataset: Path, sample_records: Sequence[SampleRecord]) -> None: + if destination_dataset.exists(): + shutil.rmtree(destination_dataset) + destination_dataset.mkdir(parents=True, exist_ok=True) + + for record in sample_records: + src_audio = Path(record.audio_path) + dst_audio = destination_dataset / record.relative_path + copy_file(src_audio, dst_audio) + for suffix in record.copied_sidecars: + src_sidecar = sidecar_path(src_audio, suffix) + dst_sidecar = sidecar_path(dst_audio, suffix) + if src_sidecar.exists(): + copy_file(src_sidecar, dst_sidecar) diff --git a/benchmarking/targets.py b/benchmarking/targets.py new file mode 100644 index 0000000..52713ce --- /dev/null +++ b/benchmarking/targets.py @@ -0,0 +1,366 @@ +from __future__ import annotations + +import argparse +import sys +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence + +from .common import COLLATE_SIDECARS, TRANSCRIPTION_MODELS, ensure_dict +from .models import CommandSpec, ConfigMutator, TargetSpec + + +def build_commands(modules: Sequence[str]) -> List[CommandSpec]: + return [ + CommandSpec( + name=module, + argv=(sys.executable, "-m", module, "--config_path"), + ) + for module in modules + ] + + +def base_mutator(config: Dict[str, Any], _: argparse.Namespace, work_dataset_path: Path) -> None: + config["cache_path"] = str(work_dataset_path.parent / "cache") + + +def set_dataset_everywhere(config: Dict[str, Any], work_dataset_path: Path) -> None: + dataset_path = str(work_dataset_path) + for key in ("download", "preprocess", "separation", "transcription", "punctuation", "accent", "phonemizer"): + section = ensure_dict(config, key) + section["podcasts_path"] = dataset_path + + +def patch_preprocess(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "preprocess") + section["podcasts_path"] = str(work_dataset_path) + if args.cpu_workers_total is not None: + section["num_workers"] = args.cpu_workers_total + elif args.cpu_workers_per_gpu is not None: + section["num_workers"] = args.cpu_workers_per_gpu + + +def patch_separation_common(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> Dict[str, Any]: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "separation") + section["podcasts_path"] = str(work_dataset_path) + section["cache_path"] = str(work_dataset_path.parent / "cache") + return section + + +def patch_separation_music_detect(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + section = patch_separation_common(config, args, work_dataset_path) + music_detect = ensure_dict(section, "music_detect") + music_detect["cache_path"] = str(work_dataset_path.parent / "cache") + if args.cpu_workers_per_gpu is not None: + music_detect["num_workers"] = args.cpu_workers_per_gpu + if args.batch_size_override is not None: + music_detect["bs"] = args.batch_size_override + + +def patch_separation_nisqa(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + section = patch_separation_common(config, args, work_dataset_path) + nisqa = ensure_dict(section, "nisqa") + if args.batch_size_override is not None: + nisqa["bs"] = args.batch_size_override + if args.cpu_workers_per_gpu is not None: + nisqa["num_workers"] = args.cpu_workers_per_gpu + section["bs"] = nisqa.get("bs", 32) + section["num_workers_nisqa"] = nisqa.get("num_workers", 4) + section["nisqa_config_path"] = nisqa.get("nisqa_config_path", "./configs/nisqa_b.yaml") + + +def patch_separation_distillmos(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + patch_separation_common(config, args, work_dataset_path) + + +def patch_separation_diarization(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + section = patch_separation_common(config, args, work_dataset_path) + diarization = ensure_dict(section, "diarization") + if args.disable_diarization: + diarization["enabled"] = False + if args.cpu_workers_per_gpu is not None: + diarization["num_workers"] = args.cpu_workers_per_gpu + + +def patch_separation_silence(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + section = patch_separation_common(config, args, work_dataset_path) + silence_detect = ensure_dict(section, "silence_detect") + if args.cpu_workers_per_gpu is not None: + silence_detect["num_workers"] = args.cpu_workers_per_gpu + + +def patch_separation_stage(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + patch_separation_common(config, args, work_dataset_path) + patch_separation_music_detect(config, args, work_dataset_path) + patch_separation_nisqa(config, args, work_dataset_path) + patch_separation_diarization(config, args, work_dataset_path) + patch_separation_silence(config, args, work_dataset_path) + + +def transcription_batch_section(model_name: str) -> str: + if "giga" in model_name: + return "giga" + if "vosk" in model_name: + return "vosk" + return model_name + + +def patch_transcription_common(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> Dict[str, Any]: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "transcription") + section["podcasts_path"] = str(work_dataset_path) + return section + + +def patch_transcription_stage(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + section = patch_transcription_common(config, args, work_dataset_path) + if args.batch_size_override is not None: + for model_name in section.get("model_names", []): + model_section = ensure_dict(section, transcription_batch_section(str(model_name))) + model_section["batch_size"] = args.batch_size_override + + +def patch_transcription_model(model_name: str) -> ConfigMutator: + def mutator(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + section = patch_transcription_common(config, args, work_dataset_path) + section["model_names"] = [model_name] + section["consensus_num"] = 0 + section["use_rover"] = False + if args.batch_size_override is not None: + model_section = ensure_dict(section, transcription_batch_section(model_name)) + model_section["batch_size"] = args.batch_size_override + + return mutator + + +def patch_punctuation(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "punctuation") + section["podcasts_path"] = str(work_dataset_path) + if args.cpu_workers_per_gpu is not None: + section["num_workers"] = args.cpu_workers_per_gpu + if args.model_name_override: + section["model_name"] = args.model_name_override + + +def patch_accent(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "accent") + section["podcasts_path"] = str(work_dataset_path) + if args.cpu_workers_per_gpu is not None: + section["num_workers"] = args.cpu_workers_per_gpu + if args.model_name_override: + section["model_name"] = args.model_name_override + + +def patch_phonemizer(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "phonemizer") + section["podcasts_path"] = str(work_dataset_path) + if args.cpu_workers_per_gpu is not None: + section["num_workers"] = args.cpu_workers_per_gpu + + +def patch_collate(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + base_mutator(config, args, work_dataset_path) + section = ensure_dict(config, "download") + section["podcasts_path"] = str(work_dataset_path) + if args.cpu_workers_total is not None: + section["num_workers"] = args.cpu_workers_total + elif args.cpu_workers_per_gpu is not None: + section["num_workers"] = args.cpu_workers_per_gpu + + +def patch_pipeline(config: Dict[str, Any], args: argparse.Namespace, work_dataset_path: Path) -> None: + set_dataset_everywhere(config, work_dataset_path) + patch_preprocess(config, args, work_dataset_path) + patch_separation_stage(config, args, work_dataset_path) + patch_transcription_stage(config, args, work_dataset_path) + patch_punctuation(config, args, work_dataset_path) + patch_accent(config, args, work_dataset_path) + patch_phonemizer(config, args, work_dataset_path) + patch_collate(config, args, work_dataset_path) + + +TARGETS: Dict[str, TargetSpec] = { + "preprocess.stage": TargetSpec( + name="preprocess.stage", + description="Full preprocess sequence: VAD chunking, crest-factor filter, loudness normalization.", + modules=( + "src.preprocess.preprocess", + "src.preprocess.crest_factor_remover", + "src.preprocess.preprocess_audio", + ), + mutator=patch_preprocess, + uses_gpu=True, + ), + "preprocess.vad": TargetSpec( + name="preprocess.vad", + description="Smart-turn VAD chunking only.", + modules=("src.preprocess.preprocess",), + mutator=patch_preprocess, + uses_gpu=True, + ), + "preprocess.crest_factor": TargetSpec( + name="preprocess.crest_factor", + description="Crest-factor filtering only.", + modules=("src.preprocess.crest_factor_remover",), + mutator=patch_preprocess, + uses_gpu=False, + ), + "preprocess.audio_normalize": TargetSpec( + name="preprocess.audio_normalize", + description="Loudness normalization only.", + modules=("src.preprocess.preprocess_audio",), + mutator=patch_preprocess, + uses_gpu=False, + ), + "separation.stage": TargetSpec( + name="separation.stage", + description="Full separation sequence: music_detect, NISQA, DistillMOS, diarization, silence_detect.", + modules=( + "src.separation.music_detect", + "src.separation.nisqa_process", + "src.separation.distillmos_process", + "src.separation.diarization", + "src.separation.silence_detect", + ), + mutator=patch_separation_stage, + uses_gpu=True, + ), + "separation.music_detect": TargetSpec( + name="separation.music_detect", + description="Music detection model only.", + modules=("src.separation.music_detect",), + mutator=patch_separation_music_detect, + uses_gpu=True, + ), + "separation.nisqa": TargetSpec( + name="separation.nisqa", + description="NISQA MOS model only.", + modules=("src.separation.nisqa_process",), + mutator=patch_separation_nisqa, + uses_gpu=True, + ), + "separation.distillmos": TargetSpec( + name="separation.distillmos", + description="DistillMOS model only.", + modules=("src.separation.distillmos_process",), + mutator=patch_separation_distillmos, + uses_gpu=True, + ), + "separation.diarization": TargetSpec( + name="separation.diarization", + description="Pyannote diarization only.", + modules=("src.separation.diarization",), + mutator=patch_separation_diarization, + uses_gpu=True, + ), + "separation.silence_detect": TargetSpec( + name="separation.silence_detect", + description="Silero silence metrics only.", + modules=("src.separation.silence_detect",), + mutator=patch_separation_silence, + uses_gpu=True, + ), + "transcription.stage": TargetSpec( + name="transcription.stage", + description="Full transcription stage using model_names from config.", + modules=("src.transcription.transcription",), + mutator=patch_transcription_stage, + uses_gpu=True, + ), + "punctuation.stage": TargetSpec( + name="punctuation.stage", + description="Punctuation restoration stage.", + modules=("src.punctuation.punctuation",), + required_sidecars=("_rover.txt",), + copied_sidecars=("_rover.txt",), + mutator=patch_punctuation, + uses_gpu=True, + ), + "accents.stage": TargetSpec( + name="accents.stage", + description="Accent restoration stage.", + modules=("src.accents.accents",), + required_sidecars=("_punct.txt",), + copied_sidecars=("_punct.txt",), + mutator=patch_accent, + uses_gpu=True, + ), + "phonemizer.stage": TargetSpec( + name="phonemizer.stage", + description="Phonemizer stage.", + modules=("src.phonemizer.phonemizer",), + required_sidecars=("_rover.txt",), + copied_sidecars=("_rover.txt",), + mutator=patch_phonemizer, + uses_gpu=True, + ), + "collate.stage": TargetSpec( + name="collate.stage", + description="Final collate into parquet.", + modules=("src.collate",), + copied_sidecars=COLLATE_SIDECARS, + mutator=patch_collate, + uses_gpu=False, + ), + "pipeline.base": TargetSpec( + name="pipeline.base", + description="Full base pipeline from preprocess to collate.", + modules=( + "src.preprocess.preprocess", + "src.preprocess.crest_factor_remover", + "src.preprocess.preprocess_audio", + "src.separation.music_detect", + "src.separation.nisqa_process", + "src.separation.distillmos_process", + "src.separation.diarization", + "src.separation.silence_detect", + "src.transcription.transcription", + "src.punctuation.punctuation", + "src.accents.accents", + "src.phonemizer.phonemizer", + "src.collate", + ), + mutator=patch_pipeline, + uses_gpu=True, + ), +} + +for transcription_model in TRANSCRIPTION_MODELS: + TARGETS[f"transcription.{transcription_model}"] = TargetSpec( + name=f"transcription.{transcription_model}", + description=f"Transcription benchmark for {transcription_model}.", + modules=("src.transcription.transcription",), + mutator=patch_transcription_model(transcription_model), + uses_gpu=True, + ) + + +def target_names() -> List[str]: + return sorted(TARGETS.keys()) + + +def min_duration_for_target(target: TargetSpec, config: Dict[str, Any]) -> Optional[float]: + if not target.min_input_duration_from_config: + return None + current: Any = config + for key in target.min_input_duration_from_config: + if not isinstance(current, dict): + return None + current = current.get(key) + if current is None: + return None + try: + return float(current) + except (TypeError, ValueError): + return None + + +def list_targets() -> None: + for name in target_names(): + target = TARGETS[name] + sidecars = ",".join(target.required_sidecars) if target.required_sidecars else "-" + print(f"{name}\tGPU={int(target.uses_gpu)}\tinputs={sidecars}\t{target.description}") From ad3288407f208c6306f71835445009fb870d3d5e Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Mon, 9 Mar 2026 23:42:16 +0000 Subject: [PATCH 31/43] feat: upload sortformer and remove pyannote,nisqa,silero --- configs/config.yaml | 34 +- create_dev_env.sh | 1 + requirements_dev.txt | 3 - src/libs/smart_turn/inference.py | 2 - src/libs/smart_turn/model.py | 98 ----- src/libs/smart_turn/offline_svad.py | 146 ++----- src/preprocess/preprocess.py | 618 +++++++++++++++++++--------- src/preprocess/sortformer_onnx.py | 391 ++++++++++++++++++ src/separation/diarization.py | 247 ----------- src/separation/nisqa_process.py | 241 ----------- src/separation/separation_yaml.sh | 5 +- src/separation/silence_detect.py | 288 ------------- 12 files changed, 844 insertions(+), 1230 deletions(-) delete mode 100644 src/libs/smart_turn/model.py create mode 100644 src/preprocess/sortformer_onnx.py delete mode 100644 src/separation/diarization.py delete mode 100644 src/separation/nisqa_process.py delete mode 100644 src/separation/silence_detect.py diff --git a/configs/config.yaml b/configs/config.yaml index 158c0b8..48c5f39 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,28 +1,27 @@ cache_path: ./cache download: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks + podcasts_path: /home/nikita/balalaika/datkamatka episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the podcasts are located + podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) - num_workers: 1 + num_workers: 1 # per GPU; keep at 1 — Sortformer is a large model crest_treshold: 10 # in dB + peak: -1.0 # peak normalization level in dB + loudness: -23.0 # target loudness level in LUFS + block_size: 0.400 # block size for loudness measurement in seconds + chunk_duration: 900 # max chunk duration for Sortformer (seconds). Audio >15 min is split. + sortformer_model: nvidia/diar_streaming_sortformer_4spk-v2 # HF model ID or path to .nemo file vad_args: smart_vad_model: ./models/smart-turn-v3.0.onnx - silero_vad_threshold: 0.4 - smart_vad_threshold: 0.4 + smart_vad_threshold: 0.4 - separation: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located - - diarization: - num_workers: 1 # per gpu - one_speaker: False # When this option is enabled, only single-speaker audio recordings are downloaded. + podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located nisqa: bs: 32 @@ -35,13 +34,8 @@ separation: music_detect_model: ./models/music_detection.safetensors threshold: 0.6 - silence_detect: - num_workers: 1 # per gpu - vad_threshold: 0.5 - max_silence_duration: 1.0 # maximum duration of silence to be marked - transcription: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True use_tensorrt: True # enable TensorRT execution provider (requires CUDA + TensorRT installed) @@ -63,15 +57,15 @@ transcription: batch_size: 16 punctuation: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /mnt/drive2_8tb/RuDevicesAudiobooks # full path to the folder where the text files are located + podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/create_dev_env.sh b/create_dev_env.sh index c2dc144..7dde07f 100644 --- a/create_dev_env.sh +++ b/create_dev_env.sh @@ -22,6 +22,7 @@ create_venv_env() { echo "Installing ONNX Runtime GPU (CUDA 13 nightly)..." uv pip install coloredlogs flatbuffers numpy packaging protobuf sympy + uv pip install nemo_toolkit[asr] uv pip install --pre --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-13-nightly/pypi/simple/ onnxruntime-gpu uv pip install tensorrt-cu13 uv pip install onnx-asr[gpu,hub] diff --git a/requirements_dev.txt b/requirements_dev.txt index ddf4b21..62353f4 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -12,7 +12,6 @@ pyarrow>=14.0.1 torchaudio==2.8.0 torch==2.8.0 torchcodec==0.7 -pyannote.audio>=4.0.0 librosa distillmos pytest>=7.0.0 @@ -25,9 +24,7 @@ packaging protobuf sympy pyloudnorm -silero-vad git+https://github.com/Den4ikAI/ruaccent.git git+https://github.com/mtuciru/TryIPaG2P.git git+https://github.com/Toloka/crowd-kit.git -git+https://github.com/NikiPshg/NISQA-b.git git+https://github.com/NikiPshg/musicdetection \ No newline at end of file diff --git a/src/libs/smart_turn/inference.py b/src/libs/smart_turn/inference.py index 7349a84..034f029 100644 --- a/src/libs/smart_turn/inference.py +++ b/src/libs/smart_turn/inference.py @@ -2,8 +2,6 @@ from transformers import WhisperFeatureExtractor import onnxruntime as ort -# MODEL_PATH = "path/to/your/trained/model" -# MODEL_PATH = "pipecat-ai/smart-turn-v2" ONNX_MODEL_PATH = "smart-turn-v3.0.onnx" # Load model and processor diff --git a/src/libs/smart_turn/model.py b/src/libs/smart_turn/model.py deleted file mode 100644 index 7e2d416..0000000 --- a/src/libs/smart_turn/model.py +++ /dev/null @@ -1,98 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F -from transformers import Wav2Vec2PreTrainedModel, Wav2Vec2Model - - -class Wav2Vec2ForEndpointing(Wav2Vec2PreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.wav2vec2 = Wav2Vec2Model(config) - - self.pool_attention = nn.Sequential( - nn.Linear(config.hidden_size, 256), - nn.Tanh(), - nn.Linear(256, 1) - ) - - self.classifier = nn.Sequential( - nn.Linear(config.hidden_size, 256), - nn.LayerNorm(256), - nn.GELU(), - nn.Dropout(0.1), - nn.Linear(256, 64), - nn.GELU(), - nn.Linear(64, 1) - ) - - for module in self.classifier: - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=0.1) - if module.bias is not None: - module.bias.data.zero_() - - for module in self.pool_attention: - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=0.1) - if module.bias is not None: - module.bias.data.zero_() - - def attention_pool(self, hidden_states, attention_mask): - # Calculate attention weights - attention_weights = self.pool_attention(hidden_states) - - if attention_mask is None: - raise ValueError("attention_mask must be provided for attention pooling") - - attention_weights = attention_weights + ( - (1.0 - attention_mask.unsqueeze(-1).to(attention_weights.dtype)) * -1e9 - ) - - attention_weights = F.softmax(attention_weights, dim=1) - - # Apply attention to hidden states - weighted_sum = torch.sum(hidden_states * attention_weights, dim=1) - - return weighted_sum - - def forward(self, input_values, attention_mask=None, labels=None): - outputs = self.wav2vec2(input_values, attention_mask=attention_mask) - hidden_states = outputs[0] - - # Create transformer padding mask - if attention_mask is not None: - input_length = attention_mask.size(1) - hidden_length = hidden_states.size(1) - ratio = input_length / hidden_length - indices = (torch.arange(hidden_length, device=attention_mask.device) * ratio).long() - attention_mask = attention_mask[:, indices] - attention_mask = attention_mask.bool() - else: - attention_mask = None - - pooled = self.attention_pool(hidden_states, attention_mask) - - logits = self.classifier(pooled) - - if torch.isnan(logits).any(): - raise ValueError("NaN values detected in logits") - - if labels is not None: - # Calculate positive sample weight based on batch statistics - pos_weight = ((labels == 0).sum() / (labels == 1).sum()).clamp(min=0.1, max=10.0) - loss_fct = nn.BCEWithLogitsLoss(pos_weight=pos_weight) - labels = labels.float() - loss = loss_fct(logits.view(-1), labels.view(-1)) - - # Add L2 regularization for classifier layers - l2_lambda = 0.01 - l2_reg = torch.tensor(0., device=logits.device) - for param in self.classifier.parameters(): - l2_reg += torch.norm(param) - loss += l2_lambda * l2_reg - - probs = torch.sigmoid(logits.detach()) - return {"loss": loss, "logits": probs} - - probs = torch.sigmoid(logits) - return {"logits": probs} \ No newline at end of file diff --git a/src/libs/smart_turn/offline_svad.py b/src/libs/smart_turn/offline_svad.py index 4ddef40..86e79e1 100644 --- a/src/libs/smart_turn/offline_svad.py +++ b/src/libs/smart_turn/offline_svad.py @@ -1,60 +1,36 @@ -import torch import os from loguru import logger import numpy as np import onnxruntime as ort -import time from transformers import WhisperFeatureExtractor from huggingface_hub import hf_hub_download -class OfflineVAD: + +class SmartVAD: + """EOS (End of Speech) classifier using Smart Turn model. + Classifies whether a speech segment represents a complete utterance. + """ + def __init__( self, - silero_vad_threshold: float = 0.4, smart_vad_threshold: float = 0.4, - device: str = 'cuda:1', + device: str = 'cuda:0', resample_rate: int = 16_000, - max_speech_duration_s: float = 8, smart_vad_model: str = "pipecat-ai/smart-turn-v3" ): - - self.silero_vad_threshold = silero_vad_threshold self.smart_vad_threshold = smart_vad_threshold - self.max_speech_duration_s= max_speech_duration_s self.sample_rate = resample_rate self.smart_vad_model = smart_vad_model self.device = device - self.device_id = device.split(':')[1] if ':' in self.device else 0 + self.device_id = int(device.split(':')[1]) if ':' in device else 0 - # download smart vad if not os.path.exists(self.smart_vad_model): self._load_from_hf() - # Initialize VAD models - self._init_silero_vad() self._init_smart_vad() - def _init_silero_vad(self): - logger.info('Initializing Silero VAD model...') - torch.hub.set_dir("./.torch_hub") - silero_vad_model, utils = torch.hub.load( - repo_or_dir="snakers4/silero-vad", - model="silero_vad", - force_reload=False, - onnx=True, - trust_repo=True - ) - logger.info('Silero VAD model successfully initialized.') - self.silero_vad_model = silero_vad_model - - (self.get_speech_timestamps, - _, - self.read_audio, - _, _) = utils - - def _init_smart_vad(self): - logger.info('Initializing Smart VAD model...') + logger.info('Initializing Smart VAD (EOS classifier)...') self.feature_extractor = WhisperFeatureExtractor(chunk_length=8) so = ort.SessionOptions() @@ -62,34 +38,26 @@ def _init_smart_vad(self): so.inter_op_num_threads = 1 so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL self.session = ort.InferenceSession( - self.smart_vad_model, sess_options=so, + self.smart_vad_model, sess_options=so, providers=[ ( "CUDAExecutionProvider", {"device_id": self.device_id} ), - "CPUExecutionProvider", + "CPUExecutionProvider", ] ) - logger.info('Smart VAD model successfully initialized.') - - def _load_and_validate_audio(self, audio_path: str) -> torch.Tensor: - if not os.path.exists(audio_path): - raise FileNotFoundError(f"File not found: {audio_path}") + logger.info('Smart VAD (EOS classifier) initialized.') - wav_tensor = self.read_audio(audio_path, sampling_rate=self.sample_rate) - return wav_tensor - def _load_from_hf(self): return hf_hub_download( repo_id="pipecat-ai/smart-turn-v3", filename="smart-turn-v3.0.onnx", - local_dir="./models", - local_dir_use_symlinks=False + local_dir="./models", + local_dir_use_symlinks=False ) - - def predict_endpoint(self, audio_array): + def predict_endpoint(self, audio_array: np.ndarray) -> dict: """ Predict whether an audio segment is complete (turn ended) or incomplete. @@ -97,16 +65,11 @@ def predict_endpoint(self, audio_array): audio_array: Numpy array containing audio samples at 16kHz Returns: - Dictionary containing prediction results: - - prediction: 1 for complete, 0 for incomplete - - probability: Probability of completion (sigmoid output) + Dictionary with 'prediction' (1=complete, 0=incomplete) and 'probability' """ + audio_array = self._truncate_audio(audio_array, n_seconds=8) - # Truncate to 8 seconds (keeping the end) or pad to 8 seconds - audio_array = self.truncate_audio_to_last_n_seconds(audio_array, n_seconds=8) - - # Process audio using Whisper's feature extractor - inputs = self.feature_extractor( + inputs = self.feature_extractor( audio_array, sampling_rate=16000, return_tensors="pt", @@ -116,89 +79,28 @@ def predict_endpoint(self, audio_array): do_normalize=True ) - # Convert to numpy and ensure correct shape for ONNX input_features = inputs.input_features.squeeze(0).numpy().astype(np.float32) - input_features = np.expand_dims(input_features, axis=0) # Add batch dimension + input_features = np.expand_dims(input_features, axis=0) - # Run ONNX inference outputs = self.session.run(None, {"input_features": input_features}) - - # Extract probability (ONNX model returns sigmoid probabilities) probability = outputs[0][0].item() - - # Make prediction (1 for Complete, 0 for Incomplete) prediction = 1 if probability > self.smart_vad_threshold else 0 return { "prediction": prediction, - "probability": probability, + "probability": round(probability, 4), } - - def truncate_audio_to_last_n_seconds(self, audio_array, n_seconds=8, sample_rate=16000): + + @staticmethod + def _truncate_audio(audio_array: np.ndarray, n_seconds: int = 8, sample_rate: int = 16000) -> np.ndarray: """Truncate audio to last n seconds or pad with zeros to meet n seconds.""" max_samples = n_seconds * sample_rate if len(audio_array) > max_samples: return audio_array[-max_samples:] elif len(audio_array) < max_samples: - # Pad with zeros at the beginning padding = max_samples - len(audio_array) return np.pad(audio_array, (padding, 0), mode='constant', constant_values=0) return audio_array - def process_file(self, audio_path: str) -> list: - audio_tensor = self._load_and_validate_audio(audio_path) - speech_timestamps = self.get_speech_timestamps( - audio_tensor, - self.silero_vad_model, - sampling_rate = self.sample_rate, - threshold = self.silero_vad_threshold, - max_speech_duration_s = self.max_speech_duration_s, - min_silence_duration_ms = 200 - ) - - if not speech_timestamps: - logger.debug("No speech found in audio.") - os.remove(audio_path) - return [] - - results = [] - for segment in speech_timestamps: - start_sample = segment['start'] - end_sample = segment['end'] - - start_time = round(start_sample / self.sample_rate, 2) - end_time = round(end_sample / self.sample_rate, 2) - - segment_audio_tensor = audio_tensor[start_sample:end_sample] - - # Ensure the segment_audio_tensor is on CPU for .numpy() if device is 'cuda' - segment_audio_np = segment_audio_tensor.cpu().numpy() - - prediction_result = self.predict_endpoint(segment_audio_np) # Call instance method - - final_result = { - "start_time": start_time, - "end_time": end_time, - "prediction": prediction_result['prediction'], - "probability": round(prediction_result['probability'], 4) - } - results.append(final_result) - - return results - -if __name__ == "__main__": - from pathlib import Path - TEST_WAV_FILES = list(Path("/home/nikita/balalaika/6271311").glob('*.mp3')) - print(len(TEST_WAV_FILES)) - - vad_processor = OfflineVAD( - smart_vad_model='/home/nikita/yapoddataset/src/libs/smart_turn/smart-turn-v3.0.onnx', - device='cuda:2' - ) - - start = time.time() - for path in TEST_WAV_FILES: - result = vad_processor.process_file(path) - print(result) - logger.info(time.time() - start) \ No newline at end of file +OfflineVAD = SmartVAD diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index 5f77f83..03f69b3 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -1,237 +1,416 @@ import argparse import os import multiprocessing +import tempfile from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path from typing import Dict, List, Tuple, Any import torch import torchaudio +import numpy as np +import pandas as pd from loguru import logger from tqdm import tqdm from dotenv import load_dotenv - from huggingface_hub import login from src.utils.utils import load_config, get_audio_paths -from src.libs.smart_turn.offline_svad import OfflineVAD +from src.libs.smart_turn.offline_svad import SmartVAD + +from dotenv import load_dotenv -torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) torch.backends.cuda.enable_math_sdp(False) +load_dotenv() + +CHUNK_DURATION_S = 15 * 60 # 15 minutes — Sortformer OOM limit on 48GB +sortformer_model = None smart_vad = None -def initializer_wrapper(args_dict: Dict[str, Any], gpus_count: int): - """ - Initializer for each worker process. Assigns a GPU and creates a VAD instance. - This function runs once when each worker process is created. - """ - # Get a unique ID for each worker process (note: _identity is an internal API) +def initializer_wrapper(config: Dict[str, Any], gpus_count: int): worker_id = multiprocessing.current_process()._identity[0] - 1 - # Assign a GPU to the worker in a round-robin fashion gpu_id = worker_id % gpus_count if gpus_count > 0 else 0 - - # Call the original VAD initialization function for this specific worker - init_vad_process(gpu_id=gpu_id, vad_args=args_dict) + init_models(gpu_id, config) + + +def init_models(gpu_id: int, config: Dict[str, Any]): + global sortformer_model, smart_vad + + device = f"cuda:{gpu_id}" + providers = [ + ("TensorrtExecutionProvider", { + "trt_max_workspace_size": 6 * 1024**3, + "trt_fp16_enable": True, + "trt_engine_cache_enable": True, + "trt_engine_cache_path": "./trt_cache", + }), + ("CUDAExecutionProvider", + { + "device_id": gpu_id + } + ), + "CPUExecutionProvider" + ] + if torch.cuda.is_available(): + torch.cuda.set_device(device) + + try: + from src.preprocess.sortformer_onnx import Sortformer + from src.preprocess.sortformer_onnx import DiarizationConfig + except ImportError: + logger.error("Sortformer model not found") + raise -def postprocess_vad_result( - vad_result: List[Dict[str, Any]], - duration: float = 15.0, + config = DiarizationConfig() + sortformer_model = Sortformer(model_path=config.get('sortformer_model'), config=config, providers=providers) + + sortformer_model = sortformer_model.to(device) + sortformer_model.eval() + + vad_args = config.get('vad_args', {}) + smart_vad = SmartVAD( + smart_vad_model=vad_args.get('smart_vad_model', './models/smart-turn-v3.0.onnx'), + smart_vad_threshold=vad_args.get('smart_vad_threshold', 0.4), + device=device + ) + + logger.info(f"Sortformer + SmartVAD initialized on {device}") + + +# --------------------------------------------------------------------------- +# Sortformer diarization +# --------------------------------------------------------------------------- + +def parse_diarization_output(raw_results) -> List[Tuple[float, float, int]]: + """Parse Sortformer diarize() output into (start, end, speaker_id) tuples.""" + segments = [] + if not raw_results or len(raw_results) == 0: + return segments + + file_results = raw_results[0] if raw_results else [] + for seg in file_results: + try: + if isinstance(seg, str): + cleaned = seg.strip('[] \n') + parts = [p.strip() for p in cleaned.split(',')] + if len(parts) >= 3: + segments.append((float(parts[0]), float(parts[1]), int(float(parts[2])))) + elif isinstance(seg, (list, tuple)) and len(seg) >= 3: + segments.append((float(seg[0]), float(seg[1]), int(seg[2]))) + except (ValueError, IndexError) as e: + logger.warning(f"Failed to parse segment {seg}: {e}") + return sorted(segments, key=lambda x: x[0]) + + +def _diarize_chunk( + chunk_audio: torch.Tensor, + sr: int, + offset: float +) -> List[Tuple[float, float, int]]: + """Diarize a single audio chunk via Sortformer temp-file interface.""" + global sortformer_model + + fd, temp_path = tempfile.mkstemp(suffix='.wav') + os.close(fd) + + try: + torchaudio.save(temp_path, chunk_audio, sr) + raw = sortformer_model.diarize(audio=temp_path, batch_size=1, verbose=False) + logger.debug(f"Raw diarization output: {raw}") + segments = parse_diarization_output(raw) + return [(s + offset, e + offset, spk) for s, e, spk in segments] + except Exception as e: + logger.error(f"Diarization chunk failed: {e}") + return [] + finally: + if os.path.exists(temp_path): + os.unlink(temp_path) + + +def diarize_audio( + audio: torch.Tensor, + sr: int, + chunk_duration: float = CHUNK_DURATION_S +) -> List[Tuple[float, float, int]]: + """Run Sortformer diarization; split into <=chunk_duration pieces if needed. + For each chunk the first and last segments are discarded (edge artefacts). + """ + total_samples = audio.shape[-1] + total_duration = total_samples / sr + + if total_duration <= chunk_duration: + return _diarize_chunk(audio, sr, offset=0.0) + + logger.info(f"Audio {total_duration:.0f}s > {chunk_duration:.0f}s, splitting into chunks") + chunk_samples = int(chunk_duration * sr) + all_segments: List[Tuple[float, float, int]] = [] + offset = 0 + + while offset < total_samples: + end = min(offset + chunk_samples, total_samples) + chunk = audio[:, offset:end] + offset_sec = offset / sr + + segments = _diarize_chunk(chunk, sr, offset_sec) + + if len(segments) > 2: + segments = segments[1:-1] + else: + offset = end + continue + + all_segments.extend(segments) + offset = end + + return sorted(all_segments, key=lambda x: x[0]) + + +# --------------------------------------------------------------------------- +# Filtering & EOS classification +# --------------------------------------------------------------------------- + +def filter_single_speaker_segments( + segments: List[Tuple[float, float, int]], min_duration: float = 1.0, - gap_threshold: float = 30.0 -) -> Tuple[List[float], List[float]]: - - if not vad_result: - return [], [] - - primary_segments = [] - - current_segment_start = None - - for i, item in enumerate(vad_result): - if item['prediction'] == 1: - if current_segment_start is None: - current_segment_start = item['start_time'] - segment_end = item['end_time'] - segment_duration = segment_end - current_segment_start - - if segment_duration > duration: - if i > 0 and vad_result[i-1]['prediction'] == 1: - primary_segments.append((current_segment_start, vad_result[i-1]['end_time'])) - current_segment_start = item['start_time'] - - if current_segment_start is not None: - last_item = vad_result[-1] - primary_segments.append((current_segment_start, last_item['end_time'])) - - primary_segments = [(s, e) for s, e in primary_segments if min_duration <= e - s <= duration] - - primary_segments = sorted(list(set(primary_segments)), key=lambda x: x[0]) - - if not primary_segments: - return [], [] - - # stage 2 - filled_segments = [] - last_end_time = 0.0 - - for start, end in primary_segments: - gap_duration = start - last_end_time - if gap_duration > gap_threshold: - filled_segments.extend(_fill_gap(vad_result, last_end_time, start, duration, min_duration)) - last_end_time = end - - audio_end = vad_result[-1]['end_time'] - if audio_end - last_end_time > gap_threshold: - filled_segments.extend(_fill_gap(vad_result, last_end_time, audio_end, duration, min_duration)) - - all_segments = primary_segments + filled_segments - all_segments = sorted(list(set(all_segments)), key=lambda x: x[0]) - - all_starts = [s for s, e in all_segments] - all_ends = [e for s, e in all_segments] - - return all_starts, all_ends - - -def _fill_gap( - vad_result: List[Dict[str, Any]], - gap_start: float, - gap_end: float, - duration: float, - min_duration: float -) -> List[Tuple[float, float]]: - - filled_segments = [] - - current_time = gap_start - while current_time < gap_end: - next_interval_index = -1 - for i, item in enumerate(vad_result): - if item['start_time'] >= current_time: - next_interval_index = i + max_duration: float = 15.0 +) -> List[Tuple[float, float, int]]: + """Keep only non-overlapping segments within duration limits.""" + if not segments: + return [] + + segments = sorted(segments, key=lambda x: x[0]) + filtered = [] + + for i, (start, end, spk) in enumerate(segments): + dur = end - start + if dur < min_duration or dur > max_duration: + continue + + has_overlap = any( + j != i and start < e2 and end > s2 + for j, (s2, e2, _) in enumerate(segments) + ) + if not has_overlap: + filtered.append((start, end, spk)) + + return filtered + + +def apply_eos_classification( + audio: torch.Tensor, + sr: int, + segments: List[Tuple[float, float, int]], + max_duration: float = 15.0 +) -> List[Tuple[float, float, int]]: + """Run EOS classification; merge incomplete segments with the next + same-speaker segment when possible (respecting *max_duration*).""" + global smart_vad + if not smart_vad or not segments: + return segments + + audio_np = audio.squeeze(0).numpy() if audio.dim() > 1 else audio.numpy() + + classified = [] + for start, end, spk in segments: + s_sample = int(start * sr) + e_sample = min(int(end * sr), len(audio_np)) + seg_audio = audio_np[s_sample:e_sample] + result = smart_vad.predict_endpoint(seg_audio) + classified.append((start, end, spk, result['prediction'])) + + merged: List[Tuple[float, float, int]] = [] + i = 0 + while i < len(classified): + start, end, spk, pred = classified[i] + + if pred == 1: + merged.append((start, end, spk)) + i += 1 + continue + + j = i + 1 + while j < len(classified): + ns, ne, nspk, npred = classified[j] + if nspk != spk or ne - start > max_duration: break - - if next_interval_index == -1: - break - - seg_start = vad_result[next_interval_index]['start_time'] - seg_end = vad_result[next_interval_index]['end_time'] - - for j in range(next_interval_index + 1, len(vad_result)): - if vad_result[j]['end_time'] - seg_start > duration or vad_result[j]['end_time'] > gap_end: + end = ne + j += 1 + if npred == 1: break - seg_end = vad_result[j]['end_time'] - - seg_duration = seg_end - seg_start - if min_duration <= seg_duration <= duration: - filled_segments.append((seg_start, seg_end)) - - current_time = seg_end - - return filled_segments + + if end - start >= 1.0: + merged.append((start, end, spk)) + i = max(j, i + 1) + + return merged + + +# --------------------------------------------------------------------------- +# Silence metrics +# --------------------------------------------------------------------------- + +def calculate_silence_metrics( + segments: List[Tuple[float, float, int]], + total_duration: float +) -> Tuple[float, float]: + """Derive silence percentage and max silence duration from diarization segments.""" + if not segments: + return 100.0, total_duration + + sorted_segs = sorted(segments, key=lambda x: x[0]) + total_speech = sum(e - s for s, e, _ in sorted_segs) + silence_pct = ((total_duration - total_speech) / total_duration * 100) if total_duration > 0 else 0 + + gaps = [sorted_segs[0][0]] + for i in range(len(sorted_segs) - 1): + gaps.append(sorted_segs[i + 1][0] - sorted_segs[i][1]) + gaps.append(total_duration - sorted_segs[-1][1]) + max_silence = max(gaps) + + return round(silence_pct, 2), round(max_silence, 2) + + +# --------------------------------------------------------------------------- +# RTTM & audio cutting +# --------------------------------------------------------------------------- + +def save_rttm(segments: List[Tuple[float, float, int]], audio_path: str): + rttm_path = str(Path(audio_path).with_suffix('.rttm')) + file_id = Path(audio_path).stem + with open(rttm_path, 'w') as f: + for start, end, spk in segments: + f.write( + f"SPEAKER {file_id} 1 {start:.3f} {end - start:.3f} " + f" speaker_{spk} \n" + ) + def cut_audio( audio: torch.Tensor, sr: int, - start_timestamps: List[float], - end_timestamps: List[float], + segments: List[Tuple[float, float, int]], output_folder: str, album_id: str, episode_id: str, - format: str = 'opus', - duration: float = 15.0 -): - try: - os.makedirs(output_folder, exist_ok=True) - segments_created = 0 - for start_time, end_time in zip(start_timestamps, end_timestamps): - if end_time - start_time <= duration / 2: - continue - - start_sample = int(start_time * sr) - end_sample = int(end_time * sr) - end_sample = min(audio.shape[-1], end_sample) - if end_sample <= start_sample: - continue - - segment = audio[:, start_sample:end_sample] - output_audio_filename = f"{start_time:.2f}_{end_time:.2f}_{album_id}_{episode_id}.{format}" - output_audio_path = os.path.join(output_folder, output_audio_filename) - - torchaudio.save(output_audio_path, segment, sr) - segments_created += 1 - - logger.success(f"Processed {segments_created} segments: {output_folder}") + fmt: str = 'mp3', + max_duration: float = 15.0 +) -> List[Dict]: + os.makedirs(output_folder, exist_ok=True) + results = [] + + for start, end, spk in segments: + if end - start <= max_duration / 5: + continue + + s_sample = int(start * sr) + e_sample = min(int(end * sr), audio.shape[-1]) + if e_sample <= s_sample: + continue + + segment = audio[:, s_sample:e_sample] + fname = f"{start:.2f}_{end:.2f}_{album_id}_{episode_id}.{fmt}" + out_path = os.path.join(output_folder, fname) + torchaudio.save(out_path, segment, sr) + + results.append({ + 'filepath': os.path.abspath(out_path), + 'is_single_speaker': True, + 'speaker_id': spk, + 'start': f"{start:.2f}", + 'end': f"{end:.2f}", + 'total_duration': round(end - start, 2), + 'playlist_id': album_id, + 'podcast_id': episode_id, + }) + + return results + + +# --------------------------------------------------------------------------- +# Per-file processing +# --------------------------------------------------------------------------- + +def process_audio_file(path_audio: str, config: Dict[str, Any]) -> List[Dict]: + """Process a single audio file: Sortformer diarization -> EOS check -> cut.""" + global sortformer_model, smart_vad - except Exception as e: - logger.error(f"Error in cut_audio: {e}") - raise - -def init_vad_process(gpu_id: int, vad_args: Dict[str, Any]): - global smart_vad - - if torch.cuda.is_available(): - device = f"cuda:{gpu_id}" - torch.cuda.set_device(device) - else: - device = "cpu" - logger.warning(f"No GPU available, using CPU for process") - - smart_vad = OfflineVAD( - **vad_args, - device=device - ) - logger.info(f"VAD initialized on {device}") + duration = config.get('duration', 15) + chunk_duration = config.get('chunk_duration', CHUNK_DURATION_S) -def process_audio_file(path_audio: str, duration: float): - global smart_vad - album_id = os.path.basename(os.path.dirname(path_audio)) episode_id = os.path.splitext(os.path.basename(path_audio))[0] episode_folder = os.path.join(os.path.dirname(path_audio), episode_id) - # TODO: don't forget to remove the code - audio, sr = torchaudio.load(path_audio) - # if (audio.shape[-1] / sr) <= 1 or audio.shape[-1] / sr > 15: - # logger.info(f"{path_audio} -- removed {audio.shape[-1] / sr} duration") - # os.remove(path_audio) - # # TODO: don't forget to remove the code - try: - if audio.shape[-1] / sr <= duration: - return + audio, sr = torchaudio.load(path_audio) except Exception as e: logger.error(f"Broken file {path_audio}: {e}") if os.path.exists(path_audio): os.remove(path_audio) - return + return [] + + total_duration = audio.shape[-1] / sr + if total_duration <= duration: + return [] try: - vad_result = smart_vad.process_file(path_audio) - timesteps_starts, timesteps_ends = postprocess_vad_result(vad_result, duration=duration) - if not timesteps_starts: - logger.warning(f"No speech segments found in {path_audio}, removed") + if audio.shape[0] > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + + # 1. Sortformer diarization + all_segments = diarize_audio(audio, sr, chunk_duration) + + if not all_segments: + logger.warning(f"No speech segments in {path_audio}, removing") os.remove(path_audio) - return - - cut_audio( - audio=audio, - sr=sr, - start_timestamps=timesteps_starts, - end_timestamps=timesteps_ends, - output_folder=episode_folder, - album_id=album_id, - episode_id=episode_id, - format='mp3', - duration=duration + return [] + + save_rttm(all_segments, path_audio) + + # 2. Filter: single-speaker, no overlap, duration bounds + filtered = filter_single_speaker_segments( + all_segments, min_duration=1.0, max_duration=duration ) + if not filtered: + logger.warning(f"No clean single-speaker segments in {path_audio}, removing") + os.remove(path_audio) + return [] + + logger.debug(f"Filtered segments: {filtered}") + # 3. EOS semantic classification + merging incomplete segments + filtered = apply_eos_classification(audio, sr, filtered, max_duration=duration) + + if not filtered: + logger.warning(f"No segments after EOS filtering in {path_audio}, removing") + # os.remove(path_audio) + return [] + + # 4. Silence metrics (derived from full diarization output) + silence_pct, max_sil = calculate_silence_metrics(all_segments, total_duration) + + # 5. Cut audio + seg_results = cut_audio( + audio=audio, sr=sr, segments=filtered, + output_folder=episode_folder, album_id=album_id, + episode_id=episode_id, fmt='mp3', max_duration=duration + ) + + for r in seg_results: + r['silence_percent'] = silence_pct + r['max_silence_duration'] = max_sil + + logger.success(f"Processed {len(seg_results)} segments: {path_audio}") + except Exception as e: logger.error(f"Processing error {path_audio}: {e}") + return [] finally: if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -240,6 +419,13 @@ def process_audio_file(path_audio: str, duration: float): os.remove(path_audio) logger.info(f"Original file deleted: {path_audio}") + return seg_results + + +# --------------------------------------------------------------------------- +# Main entry +# --------------------------------------------------------------------------- + def main(args): load_dotenv() hf_key = os.environ.get('HF_TOKEN') @@ -252,53 +438,75 @@ def main(args): podcasts_path = config.get('podcasts_path', '../../../podcasts') duration = config.get('duration', 15) - vad_args = config.get('vad_args') - num_gpus = torch.cuda.device_count() - - workers_per_gpu = config.get('num_workers', 4) + workers_per_gpu = config.get('num_workers', 1) if num_gpus > 0: total_workers = num_gpus * workers_per_gpu - logger.info (f"Found {num_gpus} GPU. We run {workers_per_gpu} processes for each one.") - logger.info (f"Total number of work processes: {total_workers}.") + logger.info( + f"Found {num_gpus} GPU(s), {workers_per_gpu} worker(s)/GPU, " + f"total: {total_workers}" + ) + else: + total_workers = workers_per_gpu + logger.warning("No GPUs available, using CPU") audio_paths = get_audio_paths(podcasts_path) if not audio_paths: - logger.info ("No audio files found for processing.") + logger.info("No audio files found for processing.") return - - logger.info (f""" - Running parallel processing: - The path to Podcasts: {podcasts_path} - Segment duration: {duration} seconds - Total number of workers: {total_workers} + logger.info(f""" + Running Sortformer segmentation pipeline: + Podcasts path: {podcasts_path} + Max segment duration: {duration}s + Total workers: {total_workers} Files to process: {len(audio_paths)} - """) + """) + all_results: List[Dict] = [] with ProcessPoolExecutor( max_workers=total_workers, initializer=initializer_wrapper, - initargs=(vad_args, num_gpus) + initargs=(config, num_gpus) ) as executor: - - futures = [executor.submit(process_audio_file, path, duration) for path in audio_paths] + futures = [ + executor.submit(process_audio_file, str(p), config) + for p in audio_paths + ] - for future in tqdm(as_completed(futures), total=len(audio_paths), desc="Podcast Processing"): + for future in tqdm(as_completed(futures), total=len(audio_paths), + desc="Sortformer Processing"): try: - future.result() + results = future.result() + if results: + all_results.extend(results) except Exception as e: - logger.error(f"The task ended with an error:{e}") + logger.error(f"Task error: {e}") + + if all_results: + csv_path = Path(podcasts_path) / 'balalaika.csv' + df = pd.DataFrame(all_results) + + if csv_path.exists(): + existing = pd.read_csv(csv_path) + df = pd.concat([existing, df], ignore_index=True) + df = df.drop_duplicates(subset=['filepath'], keep='last') + + df.to_csv(csv_path, index=False) + logger.success(f"Saved metadata for {len(all_results)} segments to {csv_path}") logger.info("All files have been processed.") -if __name__ == "__main__": + +if __name__ == "__main__": torchaudio.set_audio_backend('soundfile') multiprocessing.set_start_method('spawn', force=True) - - parser = argparse.ArgumentParser(description="Process audio files using smart-turn VAD model.") - parser.add_argument("--config_path", type=str, help="Path to YAML configuration file") + + parser = argparse.ArgumentParser( + description="Audio segmentation using Sortformer diarization + Smart VAD EOS" + ) + parser.add_argument("--config_path", type=str, help="Path to YAML config file") args = parser.parse_args() - main(args) \ No newline at end of file + main(args) diff --git a/src/preprocess/sortformer_onnx.py b/src/preprocess/sortformer_onnx.py new file mode 100644 index 0000000..e9a9159 --- /dev/null +++ b/src/preprocess/sortformer_onnx.py @@ -0,0 +1,391 @@ +import numpy as np +import onnxruntime as ort +import librosa +from typing import List, Tuple, Union +import time + +# Model constants +N_FFT = 512 +WIN_LENGTH = 400 +HOP_LENGTH = 160 +N_MELS = 128 +PREEMPH = 0.97 +LOG_ZERO_GUARD = 5.9604645e-8 +SAMPLE_RATE = 16000 + +# Streaming constants defaults +CHUNK_LEN = 124 +FIFO_LEN = 124 +SPKCACHE_LEN = 188 +RIGHT_CONTEXT = 1 +SUBSAMPLING = 8 +EMB_DIM = 512 +NUM_SPEAKERS = 4 +FRAME_DURATION = 0.08 + +# Cache compression params +SPKCACHE_SIL_FRAMES_PER_SPK = 3 +PRED_SCORE_THRESHOLD = 0.25 +STRONG_BOOST_RATE = 0.75 +WEAK_BOOST_RATE = 1.5 +MIN_POS_SCORES_RATE = 0.5 +SIL_THRESHOLD = 0.2 +MAX_INDEX = 999999 + + +class DiarizationConfig: + def __init__(self, onset=0.5, offset=0.5, pad_onset=0.0, pad_offset=0.0, + min_duration_on=0.0, min_duration_off=0.0, median_window=1): + self.onset = onset + self.offset = offset + self.pad_onset = pad_onset + self.pad_offset = pad_offset + self.min_duration_on = min_duration_on + self.min_duration_off = min_duration_off + self.median_window = median_window + + +class Sortformer: + def __init__(self, model_path: str, config: DiarizationConfig = None, providers: List[str] = None): + if config is None: + self.config = DiarizationConfig() + else: + self.config = config + + if providers is None: + providers = [ + ( + "TensorrtExecutionProvider", + { + "trt_max_workspace_size": 6 * 1024**3, + "trt_fp16_enable": True, + "trt_engine_cache_enable": True, + "trt_engine_cache_path": "./trt_cache", + } + ), + "CUDAExecutionProvider", + "CPUExecutionProvider" + ] + + self.session = ort.InferenceSession(model_path, providers=providers) + + meta = self.session.get_modelmeta().custom_metadata_map + self.chunk_len = int(meta.get("chunk_len", CHUNK_LEN)) + self.fifo_len = int(meta.get("fifo_len", FIFO_LEN)) + self.spkcache_len = int(meta.get("spkcache_len", SPKCACHE_LEN)) + self.right_context = int(meta.get("right_context", RIGHT_CONTEXT)) + + self.mel_basis = librosa.filters.mel(sr=SAMPLE_RATE, n_fft=N_FFT, n_mels=N_MELS, norm='slaney') + + self.reset_state() + + def reset_state(self): + self.spkcache = np.zeros((1, 0, EMB_DIM), dtype=np.float32) + self.spkcache_preds = None + self.fifo = np.zeros((1, 0, EMB_DIM), dtype=np.float32) + self.fifo_preds = np.zeros((1, 0, NUM_SPEAKERS), dtype=np.float32) + self.mean_sil_emb = np.zeros((1, EMB_DIM), dtype=np.float32) + self.n_sil_frames = 0 + + def extract_mel_features(self, audio: np.ndarray) -> np.ndarray: + preemphasized = np.append(audio[0], audio[1:] - PREEMPH * audio[:-1]) + S = librosa.stft(preemphasized, n_fft=N_FFT, hop_length=HOP_LENGTH, + win_length=WIN_LENGTH, window='hann', center=True) + power_spec = np.abs(S) ** 2 + mel_spec = np.dot(self.mel_basis, power_spec) + log_mel_spec = np.log(mel_spec + LOG_ZERO_GUARD) + return log_mel_spec.T[np.newaxis, :, :].astype(np.float32) + + def diarize(self, audio: np.ndarray, sample_rate: int = 16000, include_tensor_outputs: bool = False) -> Union[List[List[str]], Tuple[List[List[str]], np.ndarray]]: + if sample_rate != SAMPLE_RATE: + audio = librosa.resample(audio, orig_sr=sample_rate, target_sr=SAMPLE_RATE) + + if len(audio.shape) > 1: + audio = np.mean(audio, axis=0) + + self.reset_state() + + features = self.extract_mel_features(audio) + full_preds = self._process_features(features) + + if self.config.median_window > 1: + from scipy.ndimage import median_filter + filtered_preds = median_filter(full_preds, size=(self.config.median_window, 1)) + else: + filtered_preds = full_preds + + audio_duration_sec = len(audio) / SAMPLE_RATE + segments = self._binarize(filtered_preds, audio_duration_sec) + + formatted_result = [segments] + + if include_tensor_outputs: + return formatted_result, full_preds + return formatted_result + + def _process_features(self, features: np.ndarray) -> np.ndarray: + total_frames = features.shape[1] + chunk_stride = self.chunk_len * SUBSAMPLING + feed_size = (self.chunk_len + self.right_context) * SUBSAMPLING + num_chunks = int(np.ceil(total_frames / chunk_stride)) + + all_chunk_preds = [] + + for chunk_idx in range(num_chunks): + start = chunk_idx * chunk_stride + end = min(start + feed_size, total_frames) + current_len = end - start + + chunk_feat = features[:, start:end, :] + + if current_len < feed_size: + padded = np.zeros((1, feed_size, N_MELS), dtype=np.float32) + padded[:, :current_len, :] = chunk_feat + chunk_feat = padded + + chunk_preds = self._streaming_update(chunk_feat, current_len) + all_chunk_preds.append(chunk_preds) + + if len(all_chunk_preds) == 0: + return np.zeros((0, NUM_SPEAKERS), dtype=np.float32) + + return np.concatenate(all_chunk_preds, axis=0) + + def _streaming_update(self, chunk_feat: np.ndarray, current_len: int) -> np.ndarray: + spkcache_len = self.spkcache.shape[1] + fifo_len = self.fifo.shape[1] + + inputs = { + "chunk": chunk_feat, + "chunk_lengths": np.array([current_len], dtype=np.int64), + "spkcache": self.spkcache, + "spkcache_lengths": np.array([spkcache_len], dtype=np.int64), + "fifo": self.fifo, + "fifo_lengths": np.array([fifo_len], dtype=np.int64) + } + + outputs = self.session.run(["spkcache_fifo_chunk_preds", "chunk_pre_encode_embs"], inputs) + + preds = outputs[0] + new_embs = outputs[1] + + valid_frames = int(np.ceil(current_len / SUBSAMPLING)) + fifo_preds = preds[:, spkcache_len:spkcache_len+fifo_len, :] if fifo_len > 0 else np.zeros((1, 0, NUM_SPEAKERS)) + + keep = min(self.chunk_len, valid_frames) + chunk_preds_idx_start = spkcache_len + fifo_len + chunk_preds = preds[:, chunk_preds_idx_start:chunk_preds_idx_start+keep, :] + chunk_embs = new_embs[:, :keep, :] + + self.fifo = np.concatenate([self.fifo, chunk_embs], axis=1) + self.fifo_preds = np.concatenate([fifo_preds, chunk_preds], axis=1) + + fifo_len_after = self.fifo.shape[1] + + if fifo_len_after > self.fifo_len: + pop_out_len = max(self.chunk_len, valid_frames - self.fifo_len + fifo_len) + pop_out_len = min(pop_out_len, fifo_len_after) + + pop_out_embs = self.fifo[:, :pop_out_len, :] + pop_out_preds = self.fifo_preds[:, :pop_out_len, :] + + self._update_silence_profile(pop_out_embs[0], pop_out_preds[0]) + + self.fifo = self.fifo[:, pop_out_len:, :] + self.fifo_preds = self.fifo_preds[:, pop_out_len:, :] + + self.spkcache = np.concatenate([self.spkcache, pop_out_embs], axis=1) + + if self.spkcache_preds is not None: + self.spkcache_preds = np.concatenate([self.spkcache_preds, pop_out_preds], axis=1) + + if self.spkcache.shape[1] > self.spkcache_len: + if self.spkcache_preds is None: + initial_cache_preds = preds[:, :spkcache_len, :] + self.spkcache_preds = np.concatenate([initial_cache_preds, pop_out_preds], axis=1) + self._compress_spkcache() + + return chunk_preds[0] + + def _update_silence_profile(self, embs: np.ndarray, preds: np.ndarray): + sums = np.sum(preds, axis=1) + sil_mask = sums < SIL_THRESHOLD + if np.any(sil_mask): + sil_embs = embs[sil_mask] + for emb in sil_embs: + self.mean_sil_emb[0] = (self.mean_sil_emb[0] * self.n_sil_frames + emb) / (self.n_sil_frames + 1) + self.n_sil_frames += 1 + + def _compress_spkcache(self): + if self.spkcache_preds is None: return + + n_frames = self.spkcache.shape[1] + per_spk = self.spkcache_len // NUM_SPEAKERS + if per_spk <= SPKCACHE_SIL_FRAMES_PER_SPK: + self.spkcache = self.spkcache[:, :self.spkcache_len, :] + self.spkcache_preds = self.spkcache_preds[:, :self.spkcache_len, :] + return + + spkcache_len_per_spk = per_spk - SPKCACHE_SIL_FRAMES_PER_SPK + strong_boost = int(spkcache_len_per_spk * STRONG_BOOST_RATE) + weak_boost = int(spkcache_len_per_spk * WEAK_BOOST_RATE) + min_pos = int(spkcache_len_per_spk * MIN_POS_SCORES_RATE) + + preds_2d = self.spkcache_preds[0] + scores = self._get_log_pred_scores(preds_2d) + scores = self._disable_low_scores(preds_2d, scores, min_pos) + scores = self._boost_topk_scores(scores, strong_boost, 2.0) + scores = self._boost_topk_scores(scores, weak_boost, 1.0) + + if SPKCACHE_SIL_FRAMES_PER_SPK > 0: + padded = np.full((n_frames + SPKCACHE_SIL_FRAMES_PER_SPK, NUM_SPEAKERS), -np.inf, dtype=np.float32) + padded[:n_frames, :] = scores + padded[n_frames:, :] = np.inf + scores = padded + + topk_indices, is_disabled = self._get_topk_indices(scores, n_frames) + new_embs, new_preds = self._gather_spkcache(topk_indices, is_disabled) + + self.spkcache = new_embs + self.spkcache_preds = new_preds + + def _get_log_pred_scores(self, preds: np.ndarray) -> np.ndarray: + p = np.maximum(preds, PRED_SCORE_THRESHOLD) + log_p = np.log(p) + log_1_p = np.log(np.maximum(1.0 - preds, PRED_SCORE_THRESHOLD)) + log_1_probs_sum = np.sum(log_1_p, axis=1, keepdims=True) + return log_p - log_1_p + log_1_probs_sum - np.log(0.5) + + def _disable_low_scores(self, preds: np.ndarray, scores: np.ndarray, min_pos: int) -> np.ndarray: + pos_count = np.sum(scores > 0.0, axis=0) + is_speech = preds > 0.5 + is_pos = scores > 0.0 + mask = (~is_speech) | ((~is_pos) & (pos_count >= min_pos)) + scores[mask] = -np.inf + return scores + + def _boost_topk_scores(self, scores: np.ndarray, n_boost: int, scale_factor: float) -> np.ndarray: + for s in range(NUM_SPEAKERS): + col = scores[:, s].copy() + top_idx = np.argsort(col)[::-1][:n_boost] + valid_mask = scores[top_idx, s] != -np.inf + scores[top_idx[valid_mask], s] -= scale_factor * np.log(0.5) + return scores + + def _get_topk_indices(self, scores: np.ndarray, n_frames_no_sil: int) -> Tuple[List[int], List[bool]]: + n_frames = scores.shape[0] + flat_scores = scores.flatten('F') + sorted_flat_idx = np.argsort(flat_scores)[::-1] + + topk_flat = [] + for idx in sorted_flat_idx[:self.spkcache_len]: + if flat_scores[idx] == -np.inf: + topk_flat.append(MAX_INDEX) + else: + topk_flat.append(idx) + topk_flat.sort() + + is_disabled = [False] * self.spkcache_len + frame_indices = [0] * self.spkcache_len + + for i, flat_idx in enumerate(topk_flat): + if flat_idx == MAX_INDEX: + is_disabled[i] = True + else: + frame_idx = flat_idx % n_frames + if frame_idx >= n_frames_no_sil: + is_disabled[i] = True + else: + frame_indices[i] = frame_idx + return frame_indices, is_disabled + + def _gather_spkcache(self, indices: List[int], is_disabled: List[bool]) -> Tuple[np.ndarray, np.ndarray]: + new_embs = np.zeros((1, self.spkcache_len, EMB_DIM), dtype=np.float32) + new_preds = np.zeros((1, self.spkcache_len, NUM_SPEAKERS), dtype=np.float32) + cache_preds = self.spkcache_preds[0] + cache_embs = self.spkcache[0] + + for i, (idx, disabled) in enumerate(zip(indices, is_disabled)): + if disabled: + new_embs[0, i, :] = self.mean_sil_emb[0] + elif idx < cache_embs.shape[0]: + new_embs[0, i, :] = cache_embs[idx] + new_preds[0, i, :] = cache_preds[idx] + return new_embs, np.expand_dims(new_preds[0], axis=0) + + def _binarize(self, preds: np.ndarray, audio_duration_sec: float) -> List[str]: + raw_segments = [] + num_frames = preds.shape[0] + + for spk in range(NUM_SPEAKERS): + raw_intervals = [] + in_seg = False + start_t = 0.0 + + for t in range(num_frames): + p = preds[t, spk] + if p >= self.config.onset and not in_seg: + in_seg = True + start_t = t * FRAME_DURATION + elif p < self.config.offset and in_seg: + in_seg = False + raw_intervals.append([start_t, t * FRAME_DURATION, spk]) + + if in_seg: + raw_intervals.append([start_t, num_frames * FRAME_DURATION, spk]) + + if not raw_intervals: + continue + + merged_intervals = [raw_intervals[0]] + for i in range(1, len(raw_intervals)): + gap = raw_intervals[i][0] - merged_intervals[-1][1] + if gap <= self.config.min_duration_off: + merged_intervals[-1][1] = raw_intervals[i][1] + else: + merged_intervals.append(raw_intervals[i]) + + filtered_intervals = [] + for seg in merged_intervals: + if (seg[1] - seg[0]) >= self.config.min_duration_on: + filtered_intervals.append(seg) + + padded_intervals = [] + for seg in filtered_intervals: + start_s = max(0.0, seg[0] - self.config.pad_onset) + end_s = min(audio_duration_sec, seg[1] + self.config.pad_offset) + + if not padded_intervals: + padded_intervals.append([start_s, end_s, spk]) + else: + if start_s <= padded_intervals[-1][1]: + padded_intervals[-1][1] = max(padded_intervals[-1][1], end_s) + else: + padded_intervals.append([start_s, end_s, spk]) + + raw_segments.extend(padded_intervals) + + raw_segments.sort(key=lambda x: (x[0], x[2])) + + str_segments = [] + for seg in raw_segments: + str_segments.append(f"{seg[0]} {seg[1]} speaker_{seg[2]}") + + return str_segments + + + +if __name__ == "__main__": + model_path = "model.onnx" + audio_path = "audio.wav" + + audio, sr = librosa.load(audio_path, sr=16000, mono=True) + + config = DiarizationConfig() + diarizer = Sortformer(model_path, config=config) + + start_time = time.time() + results = diarizer.diarize(audio, sample_rate=16000, include_tensor_outputs=False) + end_time = time.time() + + print(f"RTF: {(end_time - start_time) / (audio.shape[-1] / 16_000):.3f}") diff --git a/src/separation/diarization.py b/src/separation/diarization.py deleted file mode 100644 index cf20cfa..0000000 --- a/src/separation/diarization.py +++ /dev/null @@ -1,247 +0,0 @@ -import argparse -import multiprocessing -import os -from concurrent.futures import ProcessPoolExecutor, as_completed -from functools import partial -from pathlib import Path -from typing import Dict, List - -import pandas as pd -import torch -import torchaudio -from dotenv import load_dotenv -from loguru import logger -from pyannote.audio import Pipeline -from tqdm import tqdm - -from src.utils.utils import get_audio_paths, load_config - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cuda.enable_flash_sdp(True) -torch.backends.cuda.enable_mem_efficient_sdp(True) -torch.backends.cuda.enable_math_sdp(False) - - -diarization_pipeline = None - -def init_worker(hf_token: str, device_str: str): - global diarization_pipeline - logger.info(f"Initializing worker for diarization on {device_str}...") - - try: - torch.cuda.set_device(device_str) - diarization_pipeline = Pipeline.from_pretrained( - "pyannote/speaker-diarization-community-1", - token=hf_token - ).to(torch.device(device_str)) - logger.info(f"Worker initialized successfully for diarization on {device_str}.") - except Exception as e: - logger.error(f"Failed to initialize worker on {device_str}: {e}") - diarization_pipeline = None - -def _save_rttm_file(diarization, rttm_path: str): - with open(rttm_path, "w") as rttm_file: - diarization.write_rttm(rttm_file) - -def process_file(path: Path, one_speaker: bool) -> Dict: - - global diarization_pipeline - if diarization_pipeline is None: - logger.error(f"Diarization pipeline is not initialized. Skipping {path.name}.") - return None - - try: - # 1. Preprocess audio: Load, convert to mono - audio, sr = torchaudio.load(path) - if audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True) - - # 2. Perform diarization - diarization = diarization_pipeline({ - "waveform": audio, - "sample_rate": sr - }) - speaker_diarization = diarization.speaker_diarization - # 3. Save RTTM and check speaker count - base_path_str = str(path.with_suffix('')) - rttm_path = base_path_str + '.rttm' - _save_rttm_file(diarization=speaker_diarization, rttm_path=rttm_path) - - num_speakers = len(set([speaker for _, speaker in speaker_diarization])) - is_single_speaker = num_speakers == 1 - - # 4. Handle multi-speaker files if one_speaker mode is enabled - if not is_single_speaker and one_speaker: - logger.info(f"Multiple speakers ({num_speakers}) detected in {path.name}. Deleting associated files.") - for ext in ['.mp3', '_giga.txt', '_punct.txt', '_accent.txt', '_e.txt', '_e_phonemes.txt', '.rttm']: - file_to_delete = Path(base_path_str + ext) - if file_to_delete.exists(): - file_to_delete.unlink() - logger.info(f"Deleted {file_to_delete}") - - # 5. Extract metadata from the filename - file_parts = path.name.split('_') - playlist_id = file_parts[-2] if len(file_parts) > 1 else 'N/A' - podcast_id = file_parts[-1].split('.')[0] if len(file_parts) > 0 else 'N/A' - - return { - 'filepath': str(path), - 'is_single_speaker': is_single_speaker, - 'playlist_id': playlist_id, - 'podcast_id': podcast_id, - 'start': file_parts[0] if len(file_parts) > 0 else 'N/A', - 'end': file_parts[1] if len(file_parts) > 1 else 'N/A' - } - - except Exception as e: - logger.error(f"Failed to process {path.name}: {e}") - return None - finally: - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - -def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: str) -> List[Path]: - all_audio_paths = set(get_audio_paths(podcasts_path)) - processed_audio_paths = [] - - if result_csv_path.exists(): - logger.info(f"Resuming from existing results file: {result_csv_path}") - df = pd.read_csv(result_csv_path) - if 'is_single_speaker' not in df.columns: - logger.warning("Column 'is_single_speaker' not found in results file. Treating all entries as unprocessed.") - processed_audio_paths = set() - else: - processed_mask = df['is_single_speaker'].notna() - processed_audio_paths = set(df.loc[processed_mask, 'filepath'].astype(str).tolist()) - else: - return all_audio_paths - - unprocessed_paths = all_audio_paths - processed_audio_paths - return list(unprocessed_paths) - - -def main(args): - load_dotenv() - hf_token = os.getenv("HF_TOKEN") - print(args.config_path) - config = load_config(args.config_path, 'separation') - podcasts_path = config.get('podcasts_path', './data') - - diarization_config = config.get('diarization') - one_speaker = diarization_config.get('one_speaker', False) - num_workers_per_gpu = diarization_config.get('num_workers', 1) - - result_csv_path = Path(podcasts_path) / 'balalaika.csv' - available_gpu_ids = list(range(torch.cuda.device_count())) - if not available_gpu_ids: - logger.error("No GPUs available. This script requires at least one GPU. Exiting.") - return - - logger.info(f""" - Podcasts path: {podcasts_path} - One speaker mode: {one_speaker} - Workers per GPU: {num_workers_per_gpu} - GPUs detected: {len(available_gpu_ids)} - """) - - all_audio_paths = get_unprocessed_audio_paths( - podcasts_path, - result_csv_path - ) - - if not all_audio_paths: - logger.success("All audio files have already been processed.") - return - - logger.info(f"Found {len(all_audio_paths)} new audio files to process.") - - num_devices = len(available_gpu_ids) - files_for_each_device = [[] for _ in range(num_devices)] - for i, path in enumerate(all_audio_paths): - device_assignment_index = i % num_devices - files_for_each_device[device_assignment_index].append(path) - - all_futures = [] - executors = [] - task_fn = partial(process_file, one_speaker=one_speaker) - - for i, device_id in enumerate(available_gpu_ids): - device_str = f'cuda:{device_id}' - files_for_this_device = files_for_each_device[i] - - if not files_for_this_device: - continue - - logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") - - initializer_fn = partial(init_worker, hf_token=hf_token, device_str=device_str) - - executor = ProcessPoolExecutor( - max_workers=num_workers_per_gpu, - initializer=initializer_fn, - mp_context=multiprocessing.get_context('spawn') - ) - executors.append(executor) - - for path in files_for_this_device: - future = executor.submit(task_fn, path) - all_futures.append(future) - - results = [] - for future in tqdm(as_completed(all_futures), total=len(all_futures), desc="Processing audio files"): - try: - result = future.result() - if result: - results.append(result) - except Exception as e: - logger.error(f"A task encountered an error: {e}") - - for executor in executors: - executor.shutdown(wait=True) - - if results: - try: - new_results_df = pd.DataFrame(results) - main_df = pd.read_csv(result_csv_path) - - main_df = main_df.set_index('filepath') - new_results_df = new_results_df.set_index('filepath') - - updated_df = new_results_df.combine_first(main_df) - - updated_df = updated_df.reset_index() - - original_cols = main_df.reset_index().columns.tolist() - new_cols = [col for col in updated_df.columns if col not in original_cols] - final_order = original_cols + new_cols - final_order_unique = list(dict.fromkeys(final_order)) - updated_df = updated_df[final_order_unique] - - if 'Unnamed: 0' in updated_df.columns: - updated_df = updated_df.drop('Unnamed: 0', axis=1) - - updated_df.to_csv(result_csv_path, index=False) - logger.success(f"Processing complete. Updated/added diarization for {len(results)} rows in {result_csv_path}") - - except FileNotFoundError: - logger.error(f"Error: The result file {result_csv_path} was not found. Please run the NISQA script first.") - except Exception as e: - logger.error(f"An error occurred while updating the CSV file: {e}") - else: - logger.warning("Processing finished, but no new results were generated.") - - -if __name__ == "__main__": - multiprocessing.set_start_method('spawn', force=True) - torchaudio.set_audio_backend('soundfile') - - parser = argparse.ArgumentParser(description="Perform speaker diarization on audio files using multiple GPUs.") - parser.add_argument( - "--config_path", - type=str, - help="Path to the main YAML configuration file." - ) - - args = parser.parse_args() - main(args) \ No newline at end of file diff --git a/src/separation/nisqa_process.py b/src/separation/nisqa_process.py deleted file mode 100644 index 2db2ecd..0000000 --- a/src/separation/nisqa_process.py +++ /dev/null @@ -1,241 +0,0 @@ -import argparse -import os -import torch -import torch.multiprocessing as mp -import pandas as pd -import yaml -from pathlib import Path -from typing import List, Dict -from loguru import logger -from multiprocessing import cpu_count -from torch.utils.data import DataLoader -from tqdm import tqdm - -from src.utils.utils import get_audio_paths, load_config - -from nisqab.core.model_torch import model_init -from nisqab.utils.dataset import NISQADataset, collate_fn -from nisqab.utils.audio_cache import create_audio_length_cache -from nisqab.utils.audio_sampler import LengthBasedBatchSampler - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cuda.enable_flash_sdp(True) - -def save_chunk(results: List[Dict], output_path: Path): - """Saves a chunk of results to CSV, appending if exists.""" - if not results: - return - - df = pd.DataFrame(results) - output_path.parent.mkdir(parents=True, exist_ok=True) - - header = not output_path.exists() - df.to_csv(output_path, mode='a', header=header, index=False) - - -def run_inference_worker(rank: int, world_size: int, file_paths: List[str], config: dict, final_output_path: Path): - """ - Worker function running on a dedicated GPU. - """ - my_files = file_paths[rank::world_size] - if not my_files: - logger.info(f"Worker {rank}: No files to process.") - return - - device_str = f"cuda:{rank}" - device = torch.device(device_str) - - # Читаем конфиг - bs = config.get('bs', 32) - num_workers = config.get('num_workers_nisqa', 4) - - cache_dir = Path(config.get('cache_path', './cache')) / f'nisqa_temp_worker_{rank}' - cache_dir.mkdir(parents=True, exist_ok=True) - cache_file = cache_dir / 'audio_lengths.json' - - worker_output_path = final_output_path.with_name(f"{final_output_path.stem}_part_{rank}.csv") - - logger.info(f"[{device_str}] Starting worker. Processing {len(my_files)} files.") - - try: - nisqa_config_path = Path(config.get('nisqa_config_path', 'configs/nisqa_b.yaml')) - with open(nisqa_config_path, "r") as ymlfile: - args_yaml = yaml.load(ymlfile, Loader=yaml.FullLoader) - - model = model_init(args_yaml) - model = model.to(device) - model.eval() - - - audio_lengths = create_audio_length_cache( - file_paths=my_files, - cache_file=str(cache_file), - num_workers=min(num_workers, cpu_count() // world_size), - force_rebuild=False - ) - - dataset = NISQADataset( - file_paths=my_files, - audio_lengths=audio_lengths, - seg_length=15, seg_hop=1, max_length=2000, - n_fft=4096, hop_length=0.01, win_length=0.02, n_mels=48, fmax=20000 - ) - - sampler = LengthBasedBatchSampler( - file_paths=my_files, - audio_lengths=audio_lengths, - batch_size=bs, - drop_last=False, - shuffle=True - ) - - dataloader = DataLoader( - dataset, - batch_sampler=sampler, - num_workers=num_workers, - collate_fn=collate_fn, - pin_memory=True, - persistent_workers=True if num_workers > 0 else False, - prefetch_factor=2 if num_workers > 0 else None - ) - - dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 - model = model.to(dtype) - - results_buffer = [] - save_every = 1000 - - with torch.inference_mode(): - for batch in tqdm(dataloader, desc=f"NISQA-{rank}", position=rank): - x_batch = batch['x_spec_seg'].to(device, dtype=dtype) - n_wins_batch = batch['n_wins'].to(device) - - try: - with torch.amp.autocast(device_type="cuda", dtype=dtype): - outputs = model(x_batch, n_wins_batch) - except Exception as e: - logger.ERROR(f"Error in NISQA inference: {e}") - outputs = torch.zerros([len(batch['file_paths']), 5]) - - - - outputs_cpu = outputs.float().cpu() - - for i in range(len(batch['file_paths'])): - MOS, NOI, DISC, COL, LOUD = outputs_cpu[i].tolist() - results_buffer.append({ - 'filepath': batch['file_paths'][i], - 'MOS': MOS, 'NOI': NOI, 'DISC': DISC, 'COL': COL, 'LOUD': LOUD - }) - - if len(results_buffer) >= save_every: - save_chunk(results_buffer, worker_output_path) - results_buffer = [] - - save_chunk(results_buffer, worker_output_path) - logger.success(f"[{device_str}] Finished. Saved to {worker_output_path}") - - except Exception as e: - logger.exception(f"Worker {rank} failed: {e}") - - -def combine_results(final_output_path: Path, num_parts: int): - """Merges NISQA partial CSVs into the main balalaika.csv, filling MOS columns.""" - logger.info("Combining NISQA partial results...") - - nisqa_dfs = [] - for i in range(num_parts): - part_path = final_output_path.with_name(f"{final_output_path.stem}_part_{i}.csv") - if part_path.exists(): - try: - nisqa_dfs.append(pd.read_csv(part_path)) - os.remove(part_path) - except Exception as e: - logger.error(f"Error reading {part_path}: {e}") - - if not nisqa_dfs: - logger.warning("No NISQA results found to combine.") - return - - nisqa_df = pd.concat(nisqa_dfs, ignore_index=True).drop_duplicates(subset=['filepath']) - nisqa_df = nisqa_df.set_index('filepath') - - if final_output_path.exists(): - main_df = pd.read_csv(final_output_path).set_index('filepath') - main_df = main_df.combine_first(nisqa_df) - main_df = main_df.reset_index() - else: - main_df = nisqa_df.reset_index() - - main_df.to_csv(final_output_path, index=False) - mos_filled = main_df['MOS'].notna().sum() if 'MOS' in main_df.columns else 0 - logger.success(f"Combined results saved to {final_output_path}. Total: {len(main_df)}, with MOS: {mos_filled}") - - -def get_unprocessed_audio_paths(podcasts_path: Path, result_csv_path: Path) -> List[str]: - """Get list of audio paths that don't have MOS scores in the result CSV yet.""" - all_audio_paths = get_audio_paths(str(podcasts_path)) - all_paths_str = [str(p.resolve()) for p in all_audio_paths] - - if not result_csv_path.exists(): - return all_paths_str - - logger.info(f"Filtering existing results from: {result_csv_path}") - try: - df = pd.read_csv(result_csv_path) - if 'filepath' not in df.columns or 'MOS' not in df.columns: - return all_paths_str - - processed = set( - df.loc[df['MOS'].notna(), 'filepath'].astype(str).tolist() - ) - - unprocessed = [p for p in all_paths_str if p not in processed] - logger.info(f"Already processed (have MOS): {len(processed)}, remaining: {len(unprocessed)}") - return unprocessed - except Exception as e: - logger.warning(f"Could not read existing CSV ({e}), processing all files.") - return all_paths_str - - -def main(): - mp.set_start_method('spawn', force=True) - - parser = argparse.ArgumentParser() - parser.add_argument("--config_path", type=str, required=True) - args = parser.parse_args() - - config = load_config(args.config_path, 'separation') - podcasts_path = Path(config.get('podcasts_path', '.')) - final_output_path = podcasts_path / 'balalaika.csv' - - available_gpus = torch.cuda.device_count() - if available_gpus == 0: - logger.error("No GPU detected. NISQA is too slow on CPU for large datasets.") - return - - logger.info(f"Detected {available_gpus} GPUs. Preparing pipeline...") - - unprocessed_files = get_unprocessed_audio_paths(podcasts_path, final_output_path) - - if not unprocessed_files: - logger.success("All files processed. Exiting.") - return - - logger.info(f"Total files to process: {len(unprocessed_files)}") - - try: - mp.spawn( - run_inference_worker, - args=(available_gpus, unprocessed_files, config, final_output_path), - nprocs=available_gpus, - join=True - ) - except Exception as e: - logger.critical(f"Multiprocessing failed: {e}") - - combine_results(final_output_path, available_gpus) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/separation/separation_yaml.sh b/src/separation/separation_yaml.sh index 3864f14..0241487 100644 --- a/src/separation/separation_yaml.sh +++ b/src/separation/separation_yaml.sh @@ -21,7 +21,4 @@ activate_venv ".dev_venv" SCRIPT_DIR=$(dirname "$(realpath "$0")") python3 -m src.separation.music_detect --config_path "$CONFIG_PATH" -python3 -m src.separation.nisqa_process --config_path "$CONFIG_PATH" -python3 -m src.separation.distillmos_procrss --config_path "$CONFIG_PATH" -python3 -m src.separation.diarization --config_path "$CONFIG_PATH" -python3 -m src.separation.silence_detect --config_path "$CONFIG_PATH" +python3 -m src.separation.distillmos_process --config_path "$CONFIG_PATH" diff --git a/src/separation/silence_detect.py b/src/separation/silence_detect.py deleted file mode 100644 index d4df5f8..0000000 --- a/src/separation/silence_detect.py +++ /dev/null @@ -1,288 +0,0 @@ -import argparse -from importlib import reload -import multiprocessing -from concurrent.futures import ProcessPoolExecutor, as_completed -from functools import partial -from pathlib import Path -from typing import Dict, List - -import pandas as pd -import torch -import torchaudio -from loguru import logger -from silero_vad import read_audio, get_speech_timestamps -from tqdm import tqdm - -from src.utils.utils import get_audio_paths, load_config - -torch.set_num_threads(1) -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cuda.enable_flash_sdp(True) -torch.backends.cuda.enable_mem_efficient_sdp(True) -torch.backends.cuda.enable_math_sdp(False) - - -silence_detect_model = None -vad_device = None - - -def init_worker(device_str: str): - """Initialize VAD model for the worker process.""" - global silence_detect_model, vad_device - logger.info(f"Initializing VAD model on {device_str}...") - - try: - if 'cuda' in device_str: - torch.cuda.set_device(device_str) - silence_detect_model, utils = torch.hub.load( - repo_or_dir='snakers4/silero-vad', - model='silero_vad', - onnx=False, - force_reload=True - ) - silence_detect_model.to(device_str) - vad_device = torch.device(device_str) - logger.info(f"Worker initialized successfully for silence detection on {device_str}.") - except Exception as e: - logger.error(f"Failed to initialize worker on {device_str}: {e}") - silence_detect_model = None - vad_device = None - - -def calculate_silence_metrics(path: Path, vad_threshold: float = 0.5) -> Dict: - """ - Calculate silence percentage and maximum silence duration using Silero VAD. - - Args: - path: Path to audio file - sample_rate: Target sample rate for VAD - - Returns: - Dict with silence_percent and max_silence_duration - """ - global silence_detect_model, vad_device - - if silence_detect_model is None: - logger.error(f"VAD model is not initialized. Skipping {path.name}.") - return None - - try: - # Load audio - audio = read_audio(str(path)) - if vad_device is not None: - audio = audio.to(vad_device) - - speech_timestamps = get_speech_timestamps( - audio, - silence_detect_model, - threshold=vad_threshold, - return_seconds=True - ) - # Calculate total audio duration in seconds - total_duration = audio.shape[-1] / 16_000 # 16_000 sample rate for silero vad - - # Calculate total speech duration - total_speech_duration = sum( - segment['end'] - segment['start'] - for segment in speech_timestamps - ) - - # Calculate silence duration - total_silence_duration = total_duration - total_speech_duration - - # Calculate silence percentage - silence_percent = (total_silence_duration / total_duration * 100) if total_duration > 0 else 0 - - # Calculate maximum silence duration - max_silence_duration = 0.0 - if len(speech_timestamps) > 1: - # Find gaps between speech segments - silence_gaps = [] - for i in range(len(speech_timestamps) - 1): - gap_start = speech_timestamps[i]['end'] - gap_end = speech_timestamps[i + 1]['start'] - gap_duration = gap_end - gap_start - silence_gaps.append(gap_duration) - - # Include silence at the beginning - if speech_timestamps[0]['start'] > 0: - silence_gaps.append(speech_timestamps[0]['start']) - - # Include silence at the end - if speech_timestamps[-1]['end'] < total_duration: - silence_gaps.append(total_duration - speech_timestamps[-1]['end']) - - max_silence_duration = max(silence_gaps) if silence_gaps else 0.0 - elif len(speech_timestamps) == 1: - # Only one speech segment, check before and after - max_silence_duration = max( - speech_timestamps[0]['start'], - total_duration - speech_timestamps[0]['end'] - ) - else: - # No speech detected, entire file is silence - max_silence_duration = total_duration - - return { - 'filepath': str(path), - 'silence_percent': round(silence_percent, 2), - 'max_silence_duration': round(max_silence_duration, 2), - 'total_duration': round(total_duration, 2), - } - - except Exception as e: - logger.error(f"Failed to process {path.name}: {e}") - return None - finally: - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - -def process_file(path: Path, vad_threshold: float = 0.5) -> Dict: - """Wrapper function for processing a single file.""" - return calculate_silence_metrics(path=path, vad_threshold=vad_threshold) - - -def get_unprocessed_audio_paths(podcasts_path: str, result_csv_path: Path) -> List[Path]: - """Get list of audio paths that haven't been processed yet.""" - all_audio_paths = set(get_audio_paths(podcasts_path)) - processed_audio_paths = [] - - if result_csv_path.exists(): - logger.info(f"Resuming from existing results file: {result_csv_path}") - df = pd.read_csv(result_csv_path) - if 'silence_percent' not in df.columns: - logger.warning("Column 'silence_percent' not found in results file. Treating all entries as unprocessed.") - processed_audio_paths = set() - else: - processed_mask = df['silence_percent'].notna() - processed_audio_paths = set(df.loc[processed_mask, 'filepath'].astype(str).tolist()) - else: - return list(all_audio_paths) - - unprocessed_paths = all_audio_paths - processed_audio_paths - return list(all_audio_paths) - - -def main(args): - """Main function to detect silence metrics for all audio files.""" - config = load_config(args.config_path, 'separation') - podcasts_path = config.get('podcasts_path', './data') - - silence_config = config.get('silence_detect', {}) - num_workers_per_gpu = silence_config.get('num_workers', 1) - vad_threshold = silence_config.get('vad_threshold', 0.5) - - result_csv_path = Path(podcasts_path) / 'balalaika.csv' - available_gpu_ids = list(range(torch.cuda.device_count())) - - # Use CPU if no GPU available - if not available_gpu_ids: - logger.warning("No GPUs available. Using CPU for silence detection.") - available_gpu_ids = ['cpu'] - - logger.info(f""" - Podcasts path: {podcasts_path} - Workers per device: {num_workers_per_gpu} - VAD threshold: {vad_threshold} - Devices detected: {len(available_gpu_ids)} - """) - - all_audio_paths = get_unprocessed_audio_paths(podcasts_path, result_csv_path) - - if not all_audio_paths: - logger.success("All audio files have already been processed.") - return - - logger.info(f"Found {len(all_audio_paths)} new audio files to process.") - - num_devices = len(available_gpu_ids) - files_for_each_device = [[] for _ in range(num_devices)] - for i, path in enumerate(all_audio_paths): - device_assignment_index = i % num_devices - files_for_each_device[device_assignment_index].append(path) - - all_futures = [] - executors = [] - - for i, device_id in enumerate(available_gpu_ids): - device_str = f'cuda:{device_id}' if device_id != 'cpu' else 'cpu' - files_for_this_device = files_for_each_device[i] - - if not files_for_this_device: - continue - - logger.info(f"Creating ProcessPoolExecutor for {device_str} with {num_workers_per_gpu} workers for {len(files_for_this_device)} files.") - - initializer_fn = partial(init_worker, device_str=device_str) - - executor = ProcessPoolExecutor( - max_workers=num_workers_per_gpu, - initializer=initializer_fn, - mp_context=multiprocessing.get_context('spawn') - ) - executors.append(executor) - - for path in files_for_this_device: - future = executor.submit(process_file, path, vad_threshold=vad_threshold) - all_futures.append(future) - - results = [] - for future in tqdm(as_completed(all_futures), total=len(all_futures), desc="Processing audio files"): - try: - result = future.result() - if result: - results.append(result) - except Exception as e: - logger.error(f"A task encountered an error: {e}") - - for executor in executors: - executor.shutdown(wait=True) - - if results: - try: - new_results_df = pd.DataFrame(results) - - if result_csv_path.exists(): - main_df = pd.read_csv(result_csv_path) - - main_df = main_df.set_index('filepath') - new_results_df = new_results_df.set_index('filepath') - - updated_df = new_results_df.combine_first(main_df) - - updated_df = updated_df.reset_index() - - original_cols = main_df.reset_index().columns.tolist() - new_cols = [col for col in updated_df.columns if col not in original_cols] - final_order = original_cols + new_cols - final_order_unique = list(dict.fromkeys(final_order)) - updated_df = updated_df[final_order_unique] - - if 'Unnamed: 0' in updated_df.columns: - updated_df = updated_df.drop('Unnamed: 0', axis=1) - - updated_df.to_csv(result_csv_path, index=False) - logger.success(f"Processing complete. Updated/added silence metrics for {len(results)} rows in {result_csv_path}") - else: - new_results_df.to_csv(result_csv_path, index=False) - logger.success(f"Created new results file with {len(results)} rows in {result_csv_path}") - - except Exception as e: - logger.error(f"An error occurred while updating the CSV file: {e}") - else: - logger.warning("Processing finished, but no new results were generated.") - - -if __name__ == "__main__": - multiprocessing.set_start_method('spawn', force=True) - - parser = argparse.ArgumentParser(description="Detect silence metrics (silence percentage and max silence duration) using Silero VAD.") - parser.add_argument( - "--config_path", - type=str, - help="Path to the main YAML configuration file." - ) - - args = parser.parse_args() - main(args) From db49b7626161b7961670a3e62de8a0fbc3346d1b Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Tue, 10 Mar 2026 02:25:34 +0000 Subject: [PATCH 32/43] feat: update csv update in distillmos --- configs/nisqa_b.yaml | 85 ----- create_dev_env.sh | 1 - src/libs/smart_turn/offline_svad.py | 2 - src/preprocess/crest_factor_remover.py | 1 - src/preprocess/preprocess.py | 492 +++++++++---------------- src/preprocess/preprocess_audio.py | 4 +- src/preprocess/preprocess_yaml.sh | 2 +- src/preprocess/sortformer_onnx.py | 24 +- src/separation/distillmos_process.py | 60 ++- src/transcription/transcription.py | 2 + 10 files changed, 231 insertions(+), 442 deletions(-) delete mode 100644 configs/nisqa_b.yaml diff --git a/configs/nisqa_b.yaml b/configs/nisqa_b.yaml deleted file mode 100644 index e64d187..0000000 --- a/configs/nisqa_b.yaml +++ /dev/null @@ -1,85 +0,0 @@ -# Runname and paths -name: nisqa_s_np # name of current training run -data_dir: /data/Data/NISQA_Corpus # main input dir with dataset samples and csv files -output_dir: ./weights # output dir, a new subfolder for current run will be created with yaml, results csv, and stored model weights -pretrained_model: false - -# Dataset options -csv_file: NISQA_corpus_file.csv # csv-file with MOS labels and filepaths of all datasets, must be placed in 'data_dir', must contain columns 'mos', 'noi', 'dis', 'col', 'loud' with overall and dimension quality ratings -csv_con: null # csv-file with per-condition MOS used for evaluation (optional) -csv_deg: filepath_deg # csv column name of filepath to degraded speech sample, path must be relative to 'data_dir' -csv_ref: filepath_ref # csv column name of filepath to reference speech sample, path must be relative to 'data_dir' -csv_mos_train: mos # csv column name of target training value (usually MOS) -csv_mos_val: mos # csv column name of target validation value (usually MOS) -csv_db_train: # dataset names of training sets, the dataset names must be in 'db' column of csv file - - NISQA_TRAIN_SIM - - NISQA_TRAIN_LIVE -csv_db_val: # dataset names of validation sets, the dataset names must be in 'db' column of csv file - - NISQA_VAL_SIM - - NISQA_VAL_LIVE - -# Training options -tr_epochs: 500 # number of max training epochs -tr_early_stop: 20 # stop training if neither validation RMSE nor correlation 'r_p' does improve for 'tr_early_stop' epochs -tr_bs: 120 # training dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_bs_val: 120 # validation dataset mini-batch size (should be increased to 100-200 if enough GPU memory available) -tr_lr: 0.001 # learning rate of ADAM optimiser -tr_lr_patience: 15 # learning rate patience, decrease learning rate if loss does not improve for 'tr_lr_patience' epochs -tr_num_workers: 4 # number of workers to be used by PyTorch Dataloader (may cause problems on Windows machines -> set to 0) -tr_parallel: True # use PyTorch DataParallel for training on multiple GPUs -tr_ds_to_memory: False # load dataset in CPU RAM before starting training (increases speed on some systems, 'tr_num_workers' should be set to 0 or 1) -tr_ds_to_memory_workers: 0 # number of workers used for loading data into CPU RAM (experimental) -tr_device: null # train on 'cpu' or 'cuda', if null 'cuda' is used if available. -tr_checkpoint: best_only # 'every_epoch' stores model weights at each training epoch | 'best_only' stores only the weights with best validation correlation | 'null' only stores results but no model weights -tr_verbose: 2 # '0' only basic results after each epoch | '1' more detailed results and bias loss information | '2' adds progression bar - -# Bias loss options (optional) -tr_bias_mapping: null # set to 'first_order' if bias loss should be applied, otherwise 'null' -tr_bias_min_r: null # minimum correlation threshold to be reached before estimating bias (e.g. 0.7), set to 'null' if no bias loss should be applied -tr_bias_anchor_db: null # name of anchor dataset (optional) - -# Mel-Specs options -ms_sr: null # resample speech signal to 'ms_sr' -ms_fmax: 20000 # maximum considered Mel-band frequency (in Hz), set to 20k for fullband speech samples -ms_n_fft: 960 # fft size -ms_hop_length: 480 # hop length of fft windowing -ms_win_length: 960 # fft window length, will be padded with zeros to match 'ms_n_fft' -ms_n_mels: 48 # number of Mel bands -ms_seg_length: 15 # width of extracted Mel-spec segments (in bins) -ms_seg_hop_length: 3 # hop length of segments (in bins), decreasing this may improve performance but increases memory usage and runtime. -ms_channel: null # audio channel in case of stereo file (0->left, 1->right). if null, mono mix is used -ms_max_length: 1300 # spec length for training only (in bins). if samples of different duration are used in dataloader they will be padded. one segment corresponds to 40ms -> 0.04*1300=52sec max sample duration. change if you want to train on different samples - - -# CNN parameters -cnn_c_out_1: 16 # number of output channels of first convolutional layer -cnn_c_out_2: 32 # number of output channels of the second convolutional layer -cnn_c_out_3: 64 # number of output channels of the last four convolutional layer -cnn_kernel_size: !!python/tuple [3,3] -cnn_dropout: 0.2 -cnn_fc_out_h: null # length of the CNN output feature vector, if 'null' the last fully connected layer is omitted -cnn_pool_1: [24,7] # output dimensions of first adaptive pooling ('adaptive' CNN only) -cnn_pool_2: [12,5] # output dimensions of second adaptive pooling ('adaptive' CNN only) -cnn_pool_3: [6,3] # output dimensions of third adaptive pooling ('adaptive' CNN only) - -# LSTM parameters -td_lstm_h: 128 # number of LSTM hidden units -td_lstm_num_layers: 1 # LSTM depth -td_lstm_dropout: 0 -td_lstm_bidirectional: true # use bidirectional LSTM -> hidden units x 2 - -# Arguments for inference -ckp: weights/nisqa.tar # checkpoint that will be used for inference -sample: src/sample/gt.wav # path to file to run on -inf_device: cuda # device for inference runs -warmup: False # warmup run before inference; usually is not needed on CPU runs - -#Microphone inference specifics -frame: 2 # framesize for file/mic capture in seconds -updates: null # if null, metrics will be calculated over whole available frame (every [frame] seconds); if int - metrics will be calculated every n bins (which equivalent to ms_n_fft / sr * n seconds, for updates=1, n_fft=960 and sr=48000 it will be 960/48000*1 = 20ms) -sd_device: null # check mic device ID in sounddevice or leave null to use default input device (most probably your system default mic) -sd_dump: null # set this to filename if you want to dump mic signal into file - - - - diff --git a/create_dev_env.sh b/create_dev_env.sh index 7dde07f..c2dc144 100644 --- a/create_dev_env.sh +++ b/create_dev_env.sh @@ -22,7 +22,6 @@ create_venv_env() { echo "Installing ONNX Runtime GPU (CUDA 13 nightly)..." uv pip install coloredlogs flatbuffers numpy packaging protobuf sympy - uv pip install nemo_toolkit[asr] uv pip install --pre --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-13-nightly/pypi/simple/ onnxruntime-gpu uv pip install tensorrt-cu13 uv pip install onnx-asr[gpu,hub] diff --git a/src/libs/smart_turn/offline_svad.py b/src/libs/smart_turn/offline_svad.py index 86e79e1..fffd575 100644 --- a/src/libs/smart_turn/offline_svad.py +++ b/src/libs/smart_turn/offline_svad.py @@ -102,5 +102,3 @@ def _truncate_audio(audio_array: np.ndarray, n_seconds: int = 8, sample_rate: in return np.pad(audio_array, (padding, 0), mode='constant', constant_values=0) return audio_array - -OfflineVAD = SmartVAD diff --git a/src/preprocess/crest_factor_remover.py b/src/preprocess/crest_factor_remover.py index ec5043e..2a54096 100644 --- a/src/preprocess/crest_factor_remover.py +++ b/src/preprocess/crest_factor_remover.py @@ -174,7 +174,6 @@ def main(args): if __name__ == "__main__": - torchaudio.set_audio_backend('soundfile') mp.set_start_method('spawn', force=True) parser = argparse.ArgumentParser( diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index 03f69b3..b767e40 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -1,14 +1,13 @@ import argparse import os import multiprocessing -import tempfile +import re from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path from typing import Dict, List, Tuple, Any import torch import torchaudio -import numpy as np import pandas as pd from loguru import logger from tqdm import tqdm @@ -18,60 +17,34 @@ from src.utils.utils import load_config, get_audio_paths from src.libs.smart_turn.offline_svad import SmartVAD -from dotenv import load_dotenv - torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) torch.backends.cuda.enable_math_sdp(False) -load_dotenv() - -CHUNK_DURATION_S = 15 * 60 # 15 minutes — Sortformer OOM limit on 48GB +CHUNK_DURATION_S = 15 * 60 # 15 минут — лимит Sortformer для избежания OOM sortformer_model = None smart_vad = None - def initializer_wrapper(config: Dict[str, Any], gpus_count: int): worker_id = multiprocessing.current_process()._identity[0] - 1 gpu_id = worker_id % gpus_count if gpus_count > 0 else 0 init_models(gpu_id, config) - def init_models(gpu_id: int, config: Dict[str, Any]): global sortformer_model, smart_vad - device = f"cuda:{gpu_id}" - providers = [ - ("TensorrtExecutionProvider", { - "trt_max_workspace_size": 6 * 1024**3, - "trt_fp16_enable": True, - "trt_engine_cache_enable": True, - "trt_engine_cache_path": "./trt_cache", - }), - ("CUDAExecutionProvider", - { - "device_id": gpu_id - } - ), - "CPUExecutionProvider" - ] - if torch.cuda.is_available(): - torch.cuda.set_device(device) + providers = [("CUDAExecutionProvider", {"device_id": gpu_id}), "CPUExecutionProvider"] try: - from src.preprocess.sortformer_onnx import Sortformer - from src.preprocess.sortformer_onnx import DiarizationConfig + from src.preprocess.sortformer_onnx import Sortformer, DiarizationConfig except ImportError: logger.error("Sortformer model not found") raise - config = DiarizationConfig() - sortformer_model = Sortformer(model_path=config.get('sortformer_model'), config=config, providers=providers) - - sortformer_model = sortformer_model.to(device) - sortformer_model.eval() + model_config = DiarizationConfig() + sortformer_model = Sortformer(model_path=config.get('sortformer_model'), config=model_config, providers=providers) vad_args = config.get('vad_args', {}) smart_vad = SmartVAD( @@ -79,239 +52,168 @@ def init_models(gpu_id: int, config: Dict[str, Any]): smart_vad_threshold=vad_args.get('smart_vad_threshold', 0.4), device=device ) - - logger.info(f"Sortformer + SmartVAD initialized on {device}") + logger.info(f"Models initialized on {device}") # --------------------------------------------------------------------------- -# Sortformer diarization +# 1. Диаризация большого файла (1 раз) # --------------------------------------------------------------------------- def parse_diarization_output(raw_results) -> List[Tuple[float, float, int]]: - """Parse Sortformer diarize() output into (start, end, speaker_id) tuples.""" segments = [] - if not raw_results or len(raw_results) == 0: - return segments - - file_results = raw_results[0] if raw_results else [] - for seg in file_results: + if not raw_results or len(raw_results) == 0: return segments + for seg in (raw_results[0] if raw_results else []): try: if isinstance(seg, str): - cleaned = seg.strip('[] \n') - parts = [p.strip() for p in cleaned.split(',')] + parts = seg.strip().split() if len(parts) >= 3: - segments.append((float(parts[0]), float(parts[1]), int(float(parts[2])))) + segments.append((float(parts[0]), float(parts[1]), int(parts[2].replace('speaker_', '')))) elif isinstance(seg, (list, tuple)) and len(seg) >= 3: segments.append((float(seg[0]), float(seg[1]), int(seg[2]))) - except (ValueError, IndexError) as e: - logger.warning(f"Failed to parse segment {seg}: {e}") + except (ValueError, IndexError): + pass return sorted(segments, key=lambda x: x[0]) - -def _diarize_chunk( - chunk_audio: torch.Tensor, - sr: int, - offset: float -) -> List[Tuple[float, float, int]]: - """Diarize a single audio chunk via Sortformer temp-file interface.""" +def diarize_audio(audio: torch.Tensor, sr: int, chunk_duration: float = CHUNK_DURATION_S) -> List[Tuple[float, float, int]]: global sortformer_model - - fd, temp_path = tempfile.mkstemp(suffix='.wav') - os.close(fd) - - try: - torchaudio.save(temp_path, chunk_audio, sr) - raw = sortformer_model.diarize(audio=temp_path, batch_size=1, verbose=False) - logger.debug(f"Raw diarization output: {raw}") - segments = parse_diarization_output(raw) - return [(s + offset, e + offset, spk) for s, e, spk in segments] - except Exception as e: - logger.error(f"Diarization chunk failed: {e}") - return [] - finally: - if os.path.exists(temp_path): - os.unlink(temp_path) - - -def diarize_audio( - audio: torch.Tensor, - sr: int, - chunk_duration: float = CHUNK_DURATION_S -) -> List[Tuple[float, float, int]]: - """Run Sortformer diarization; split into <=chunk_duration pieces if needed. - For each chunk the first and last segments are discarded (edge artefacts). - """ total_samples = audio.shape[-1] - total_duration = total_samples / sr - - if total_duration <= chunk_duration: - return _diarize_chunk(audio, sr, offset=0.0) - - logger.info(f"Audio {total_duration:.0f}s > {chunk_duration:.0f}s, splitting into chunks") chunk_samples = int(chunk_duration * sr) - all_segments: List[Tuple[float, float, int]] = [] - offset = 0 + all_segments, offset = [], 0 + # Обрабатываем большой файл чанками чисто для избежания OOM, + # логически это 1 прогон на весь файл. while offset < total_samples: end = min(offset + chunk_samples, total_samples) chunk = audio[:, offset:end] + + audio_np = chunk.squeeze(0).numpy() if chunk.dim() > 1 else chunk.numpy() + raw = sortformer_model.diarize(audio=audio_np, sample_rate=sr, include_tensor_outputs=False) + segs = parse_diarization_output(raw) + offset_sec = offset / sr - - segments = _diarize_chunk(chunk, sr, offset_sec) - - if len(segments) > 2: - segments = segments[1:-1] - else: - offset = end - continue - - all_segments.extend(segments) + segs = [(s + offset_sec, e + offset_sec, spk) for s, e, spk in segs] + + if len(segs) > 2 and total_samples > chunk_samples: + segs = segs[1:-1] # Убираем артефакты склейки + + all_segments.extend(segs) offset = end return sorted(all_segments, key=lambda x: x[0]) # --------------------------------------------------------------------------- -# Filtering & EOS classification +# 2. Фильтрация и EOS (получаем границы мелких файлов) # --------------------------------------------------------------------------- -def filter_single_speaker_segments( - segments: List[Tuple[float, float, int]], - min_duration: float = 1.0, - max_duration: float = 15.0 -) -> List[Tuple[float, float, int]]: - """Keep only non-overlapping segments within duration limits.""" - if not segments: - return [] - - segments = sorted(segments, key=lambda x: x[0]) +def filter_single_speaker_segments(segments: List[Tuple[float, float, int]], min_duration: float = 1.0, max_duration: float = 15.0) -> List[Tuple[float, float, int]]: filtered = [] - + segments = sorted(segments, key=lambda x: x[0]) for i, (start, end, spk) in enumerate(segments): - dur = end - start - if dur < min_duration or dur > max_duration: - continue - - has_overlap = any( - j != i and start < e2 and end > s2 - for j, (s2, e2, _) in enumerate(segments) - ) - if not has_overlap: + if not (min_duration <= end - start <= max_duration): continue + # Проверка на пересечение спикеров + if not any(j != i and start < e2 and end > s2 for j, (s2, e2, _) in enumerate(segments)): filtered.append((start, end, spk)) - return filtered - -def apply_eos_classification( - audio: torch.Tensor, - sr: int, - segments: List[Tuple[float, float, int]], - max_duration: float = 15.0 -) -> List[Tuple[float, float, int]]: - """Run EOS classification; merge incomplete segments with the next - same-speaker segment when possible (respecting *max_duration*).""" +def apply_eos_classification(audio: torch.Tensor, sr: int, segments: List[Tuple[float, float, int]], max_duration: float = 15.0) -> List[Tuple[float, float, int]]: global smart_vad - if not smart_vad or not segments: - return segments + if not smart_vad or not segments: return segments audio_np = audio.squeeze(0).numpy() if audio.dim() > 1 else audio.numpy() - classified = [] - for start, end, spk in segments: - s_sample = int(start * sr) - e_sample = min(int(end * sr), len(audio_np)) - seg_audio = audio_np[s_sample:e_sample] - result = smart_vad.predict_endpoint(seg_audio) - classified.append((start, end, spk, result['prediction'])) - - merged: List[Tuple[float, float, int]] = [] + for s, e, spk in segments: + pred = smart_vad.predict_endpoint(audio_np[int(s * sr):min(int(e * sr), len(audio_np))])['prediction'] + classified.append((s, e, spk, pred)) + + merged = [] i = 0 while i < len(classified): start, end, spk, pred = classified[i] - if pred == 1: merged.append((start, end, spk)) i += 1 continue - + j = i + 1 while j < len(classified): ns, ne, nspk, npred = classified[j] - if nspk != spk or ne - start > max_duration: - break + if nspk != spk or ne - start > max_duration: break end = ne j += 1 - if npred == 1: - break + if npred == 1: break if end - start >= 1.0: merged.append((start, end, spk)) i = max(j, i + 1) - return merged # --------------------------------------------------------------------------- -# Silence metrics +# 3. Вычисление тишины КОНКРЕТНО для мелкого куска (математически) # --------------------------------------------------------------------------- -def calculate_silence_metrics( - segments: List[Tuple[float, float, int]], - total_duration: float -) -> Tuple[float, float]: - """Derive silence percentage and max silence duration from diarization segments.""" - if not segments: - return 100.0, total_duration - - sorted_segs = sorted(segments, key=lambda x: x[0]) - total_speech = sum(e - s for s, e, _ in sorted_segs) - silence_pct = ((total_duration - total_speech) / total_duration * 100) if total_duration > 0 else 0 - - gaps = [sorted_segs[0][0]] - for i in range(len(sorted_segs) - 1): - gaps.append(sorted_segs[i + 1][0] - sorted_segs[i][1]) - gaps.append(total_duration - sorted_segs[-1][1]) - max_silence = max(gaps) +def get_chunk_silence_metrics(c_start: float, c_end: float, raw_segments: List[Tuple[float, float, int]]) -> Tuple[float, float]: + """Считает тишину внутри конкретного куска, используя сырые метки 1 прогона диаризатора""" + chunk_dur = c_end - c_start + if chunk_dur <= 0: return 0.0, 0.0 + + # 1. Находим все сырые куски речи, которые попали в наш финальный чанк + intervals = [] + for rs, re, _ in raw_segments: + overlap_s = max(c_start, rs) + overlap_e = min(c_end, re) + if overlap_s < overlap_e: + intervals.append([overlap_s, overlap_e]) + + # 2. Склеиваем пересекающиеся интервалы речи (на всякий случай) + intervals.sort(key=lambda x: x[0]) + merged_speech = [] + for interval in intervals: + if not merged_speech: + merged_speech.append(interval) + else: + prev = merged_speech[-1] + if interval[0] <= prev[1]: + prev[1] = max(prev[1], interval[1]) + else: + merged_speech.append(interval) + + # 3. Считаем длину речи и тишины в чанке + speech_dur = sum(e - s for s, e in merged_speech) + silence_dur = max(0.0, chunk_dur - speech_dur) + silence_pct = (silence_dur / chunk_dur) * 100 + + # 4. Считаем максимальную непрерывную паузу внутри чанка + if not merged_speech: + max_gap = chunk_dur + else: + gaps = [merged_speech[0][0] - c_start] # от начала до первой речи + for i in range(len(merged_speech) - 1): + gaps.append(merged_speech[i+1][0] - merged_speech[i][1]) # между репликами + gaps.append(c_end - merged_speech[-1][1]) # от последней речи до конца + max_gap = max(gaps) - return round(silence_pct, 2), round(max_silence, 2) + return round(silence_pct, 2), round(max_gap, 2) # --------------------------------------------------------------------------- -# RTTM & audio cutting +# 4. Нарезка файлов # --------------------------------------------------------------------------- -def save_rttm(segments: List[Tuple[float, float, int]], audio_path: str): - rttm_path = str(Path(audio_path).with_suffix('.rttm')) - file_id = Path(audio_path).stem - with open(rttm_path, 'w') as f: - for start, end, spk in segments: - f.write( - f"SPEAKER {file_id} 1 {start:.3f} {end - start:.3f} " - f" speaker_{spk} \n" - ) - - -def cut_audio( - audio: torch.Tensor, - sr: int, - segments: List[Tuple[float, float, int]], - output_folder: str, - album_id: str, - episode_id: str, - fmt: str = 'mp3', - max_duration: float = 15.0 -) -> List[Dict]: +def cut_audio(audio: torch.Tensor, sr: int, final_segments: List[Tuple[float, float, int]], raw_segments: List[Tuple[float, float, int]], output_folder: str, album_id: str, episode_id: str, fmt: str = 'mp3', max_duration: float = 15.0) -> List[Dict]: os.makedirs(output_folder, exist_ok=True) results = [] - for start, end, spk in segments: - if end - start <= max_duration / 5: - continue + for start, end, spk in final_segments: + dur = end - start + if dur <= max_duration / 5: continue + + s_sample, e_sample = int(start * sr), min(int(end * sr), audio.shape[-1]) + if e_sample <= s_sample: continue - s_sample = int(start * sr) - e_sample = min(int(end * sr), audio.shape[-1]) - if e_sample <= s_sample: - continue + # Вытаскиваем тишину ИМЕННО для этого куска из сырых меток: + sil_pct, max_sil = get_chunk_silence_metrics(start, end, raw_segments) segment = audio[:, s_sample:e_sample] fname = f"{start:.2f}_{end:.2f}_{album_id}_{episode_id}.{fmt}" @@ -320,15 +222,15 @@ def cut_audio( results.append({ 'filepath': os.path.abspath(out_path), - 'is_single_speaker': True, 'speaker_id': spk, - 'start': f"{start:.2f}", - 'end': f"{end:.2f}", - 'total_duration': round(end - start, 2), + 'start': round(start, 2), + 'end': round(end, 2), + 'total_duration': round(dur, 2), 'playlist_id': album_id, 'podcast_id': episode_id, + 'silence_percent': sil_pct, + 'max_silence_duration': max_sil }) - return results @@ -337,176 +239,126 @@ def cut_audio( # --------------------------------------------------------------------------- def process_audio_file(path_audio: str, config: Dict[str, Any]) -> List[Dict]: - """Process a single audio file: Sortformer diarization -> EOS check -> cut.""" - global sortformer_model, smart_vad - duration = config.get('duration', 15) chunk_duration = config.get('chunk_duration', CHUNK_DURATION_S) - album_id = os.path.basename(os.path.dirname(path_audio)) - episode_id = os.path.splitext(os.path.basename(path_audio))[0] - episode_folder = os.path.join(os.path.dirname(path_audio), episode_id) + p_audio = Path(path_audio) + album_id, episode_id = p_audio.parent.name, p_audio.stem + episode_folder = p_audio.parent / episode_id try: audio, sr = torchaudio.load(path_audio) except Exception as e: logger.error(f"Broken file {path_audio}: {e}") - if os.path.exists(path_audio): - os.remove(path_audio) return [] - total_duration = audio.shape[-1] / sr - if total_duration <= duration: + if (audio.shape[-1] / sr) <= duration: return [] try: - if audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True) + if audio.shape[0] > 1: audio = torch.mean(audio, dim=0, keepdim=True) - # 1. Sortformer diarization - all_segments = diarize_audio(audio, sr, chunk_duration) + # 1. Один прогон диаризатора на всё большое аудио + raw_segments = diarize_audio(audio, sr, chunk_duration) + if not raw_segments: return [] - if not all_segments: - logger.warning(f"No speech segments in {path_audio}, removing") - os.remove(path_audio) - return [] - - save_rttm(all_segments, path_audio) - - # 2. Filter: single-speaker, no overlap, duration bounds - filtered = filter_single_speaker_segments( - all_segments, min_duration=1.0, max_duration=duration - ) - - if not filtered: - logger.warning(f"No clean single-speaker segments in {path_audio}, removing") - os.remove(path_audio) - return [] - - logger.debug(f"Filtered segments: {filtered}") - # 3. EOS semantic classification + merging incomplete segments - filtered = apply_eos_classification(audio, sr, filtered, max_duration=duration) + # 2. Фильтрация и склейка коротких пауз (определяем границы нарезки) + clean_segments = filter_single_speaker_segments(raw_segments, min_duration=1.0, max_duration=duration) + final_segments = apply_eos_classification(audio, sr, clean_segments, max_duration=duration) + if not final_segments: return [] - if not filtered: - logger.warning(f"No segments after EOS filtering in {path_audio}, removing") - # os.remove(path_audio) - return [] + # 3. Нарезка и формирование словарей (с вычислением тишины по каждому куску) + seg_results = cut_audio(audio, sr, final_segments, raw_segments, str(episode_folder), album_id, episode_id, max_duration=duration) - # 4. Silence metrics (derived from full diarization output) - silence_pct, max_sil = calculate_silence_metrics(all_segments, total_duration) + logger.success(f"Processed {len(seg_results)} chunks from: {path_audio}") - # 5. Cut audio - seg_results = cut_audio( - audio=audio, sr=sr, segments=filtered, - output_folder=episode_folder, album_id=album_id, - episode_id=episode_id, fmt='mp3', max_duration=duration - ) - - for r in seg_results: - r['silence_percent'] = silence_pct - r['max_silence_duration'] = max_sil - - logger.success(f"Processed {len(seg_results)} segments: {path_audio}") + # 4. Удаляем большой файл в конце + if seg_results and p_audio.exists(): + os.remove(p_audio) + logger.info(f"Original large file deleted: {path_audio}") except Exception as e: logger.error(f"Processing error {path_audio}: {e}") return [] finally: - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - if os.path.exists(episode_folder) and os.listdir(episode_folder): - os.remove(path_audio) - logger.info(f"Original file deleted: {path_audio}") + if torch.cuda.is_available(): torch.cuda.empty_cache() return seg_results - # --------------------------------------------------------------------------- # Main entry # --------------------------------------------------------------------------- def main(args): load_dotenv() - hf_key = os.environ.get('HF_TOKEN') - if hf_key: - login(token=hf_key) - else: - logger.warning("HF_TOKEN not found in environment.") + if hf_key := os.environ.get('HF_TOKEN'): login(token=hf_key) config = load_config(args.config_path, 'preprocess') - - podcasts_path = config.get('podcasts_path', '../../../podcasts') - duration = config.get('duration', 15) + podcasts_path = Path(config.get('podcasts_path', '../../../podcasts')) + num_gpus = torch.cuda.device_count() workers_per_gpu = config.get('num_workers', 1) + total_workers = max(1, num_gpus * workers_per_gpu) + + # Загружаем существующий CSV (в нем могут быть DistillMOS для УЖЕ нарезанных файлов) + csv_path = podcasts_path / 'balalaika.csv' + existing_df = pd.DataFrame() + if csv_path.exists(): + existing_df = pd.read_csv(csv_path) + + # Собираем файлы и ОТСЕИВАЕМ мелкие куски, если вдруг они попали в поиск + raw_audio_paths = get_audio_paths(str(podcasts_path)) + paths_to_process = [] + chunk_pattern = re.compile(r'^\d+\.\d+_\d+\.\d+_') # Паттерн: 29.04_37.92_... + + for p_str in raw_audio_paths: + p = Path(p_str) + # Если это УЖЕ нарезанный мелкий файл — пропускаем его + if chunk_pattern.match(p.name): + continue + paths_to_process.append(p) - if num_gpus > 0: - total_workers = num_gpus * workers_per_gpu - logger.info( - f"Found {num_gpus} GPU(s), {workers_per_gpu} worker(s)/GPU, " - f"total: {total_workers}" - ) - else: - total_workers = workers_per_gpu - logger.warning("No GPUs available, using CPU") - - audio_paths = get_audio_paths(podcasts_path) - if not audio_paths: - logger.info("No audio files found for processing.") + if not paths_to_process: + logger.info("No new large files to process.") return - logger.info(f""" - Running Sortformer segmentation pipeline: - Podcasts path: {podcasts_path} - Max segment duration: {duration}s - Total workers: {total_workers} - Files to process: {len(audio_paths)} - """) + logger.info(f"Large files to process: {len(paths_to_process)} / Total workers: {total_workers}") all_results: List[Dict] = [] - - with ProcessPoolExecutor( - max_workers=total_workers, - initializer=initializer_wrapper, - initargs=(config, num_gpus) - ) as executor: - futures = [ - executor.submit(process_audio_file, str(p), config) - for p in audio_paths - ] - - for future in tqdm(as_completed(futures), total=len(audio_paths), - desc="Sortformer Processing"): + with ProcessPoolExecutor(max_workers=total_workers, initializer=initializer_wrapper, initargs=(config, num_gpus)) as executor: + futures = [executor.submit(process_audio_file, str(p), config) for p in paths_to_process] + for future in tqdm(as_completed(futures), total=len(paths_to_process), desc="Processing"): try: - results = future.result() - if results: - all_results.extend(results) + if results := future.result(): all_results.extend(results) except Exception as e: logger.error(f"Task error: {e}") + # БЕЗОПАСНОЕ слияние Датафреймов (не затираем DistillMOS) if all_results: - csv_path = Path(podcasts_path) / 'balalaika.csv' - df = pd.DataFrame(all_results) + new_df = pd.DataFrame(all_results) + + if not existing_df.empty: + # Устанавливаем filepath как индекс, чтобы merge прошел четко по файлу + existing_df.set_index('filepath', inplace=True) + new_df.set_index('filepath', inplace=True) + + # combine_first заполнит пустые метрики (start, end, тишина) у строк, + # где был только DistillMOS, и добавит новые строки! + df = existing_df.combine_first(new_df).reset_index() + else: + df = new_df - if csv_path.exists(): - existing = pd.read_csv(csv_path) - df = pd.concat([existing, df], ignore_index=True) - df = df.drop_duplicates(subset=['filepath'], keep='last') + base_cols = ['filepath', 'speaker_id', 'start', 'end', 'total_duration', + 'playlist_id', 'podcast_id', 'silence_percent', 'max_silence_duration', 'DistillMOS'] + final_cols = [c for c in base_cols if c in df.columns] + [c for c in df.columns if c not in base_cols] + + df = df[final_cols] df.to_csv(csv_path, index=False) - logger.success(f"Saved metadata for {len(all_results)} segments to {csv_path}") - - logger.info("All files have been processed.") - + logger.success(f"Saved metadata for {len(all_results)} new chunks to {csv_path}") if __name__ == "__main__": - torchaudio.set_audio_backend('soundfile') multiprocessing.set_start_method('spawn', force=True) - - parser = argparse.ArgumentParser( - description="Audio segmentation using Sortformer diarization + Smart VAD EOS" - ) + parser = argparse.ArgumentParser() parser.add_argument("--config_path", type=str, help="Path to YAML config file") - args = parser.parse_args() - main(args) + main(parser.parse_args()) \ No newline at end of file diff --git a/src/preprocess/preprocess_audio.py b/src/preprocess/preprocess_audio.py index 733f11f..e363f52 100644 --- a/src/preprocess/preprocess_audio.py +++ b/src/preprocess/preprocess_audio.py @@ -1,5 +1,4 @@ import argparse -import os import torch import torch.multiprocessing as mp import torchaudio @@ -11,6 +10,8 @@ from loguru import logger from tqdm import tqdm +import soundfile as sf + from src.utils.utils import load_config, get_audio_paths torch.backends.cuda.matmul.allow_tf32 = True @@ -198,7 +199,6 @@ def main(args): if __name__ == "__main__": - torchaudio.set_audio_backend('soundfile') mp.set_start_method('spawn', force=True) parser = argparse.ArgumentParser( diff --git a/src/preprocess/preprocess_yaml.sh b/src/preprocess/preprocess_yaml.sh index 88f86e6..adb2f1e 100644 --- a/src/preprocess/preprocess_yaml.sh +++ b/src/preprocess/preprocess_yaml.sh @@ -23,4 +23,4 @@ SCRIPT_DIR=$(dirname "$(realpath "$0")") python3 -m src.preprocess.preprocess --config_path "$CONFIG_PATH" python3 -m src.preprocess.crest_factor_remover --config_path "$CONFIG_PATH" -python3 -m src.preprocess.preprocess_audio --config_path "$CONFIG_PATH" +python3 -m src.preprocess.preprocess_audio --config_path "$CONFIG_PATH" \ No newline at end of file diff --git a/src/preprocess/sortformer_onnx.py b/src/preprocess/sortformer_onnx.py index e9a9159..ff0a505 100644 --- a/src/preprocess/sortformer_onnx.py +++ b/src/preprocess/sortformer_onnx.py @@ -54,15 +54,15 @@ def __init__(self, model_path: str, config: DiarizationConfig = None, providers: if providers is None: providers = [ - ( - "TensorrtExecutionProvider", - { - "trt_max_workspace_size": 6 * 1024**3, - "trt_fp16_enable": True, - "trt_engine_cache_enable": True, - "trt_engine_cache_path": "./trt_cache", - } - ), + # ( + # "TensorrtExecutionProvider", + # { + # "trt_max_workspace_size": 6 * 1024**3, + # "trt_fp16_enable": True, + # "trt_engine_cache_enable": True, + # "trt_engine_cache_path": "./trt_cache", + # } + # ), "CUDAExecutionProvider", "CPUExecutionProvider" ] @@ -376,8 +376,8 @@ def _binarize(self, preds: np.ndarray, audio_duration_sec: float) -> List[str]: if __name__ == "__main__": - model_path = "model.onnx" - audio_path = "audio.wav" + model_path = "/home/nikita/balalaika/models/diar_streaming_sortformer_4spk-v2.1.onnx" + audio_path = "/home/nikita/balalaika/datkamatka/12.mp3" audio, sr = librosa.load(audio_path, sr=16000, mono=True) @@ -385,7 +385,9 @@ def _binarize(self, preds: np.ndarray, audio_duration_sec: float) -> List[str]: diarizer = Sortformer(model_path, config=config) start_time = time.time() + # print(audio.shape) results = diarizer.diarize(audio, sample_rate=16000, include_tensor_outputs=False) end_time = time.time() print(f"RTF: {(end_time - start_time) / (audio.shape[-1] / 16_000):.3f}") + print(results) diff --git a/src/separation/distillmos_process.py b/src/separation/distillmos_process.py index 06cd018..48c1567 100644 --- a/src/separation/distillmos_process.py +++ b/src/separation/distillmos_process.py @@ -1,9 +1,9 @@ import argparse import os +import re import torch import torch.multiprocessing as mp import pandas as pd -import yaml import torchaudio from pathlib import Path from typing import List, Dict @@ -84,7 +84,7 @@ def run_inference_worker(rank: int, world_size: int, file_paths: List[str], conf logger.success(f"[cuda:{rank}] Finished.") def combine_results(final_output_path: Path, num_parts: int): - """Merges partial CSVs into the main balalaika.csv.""" + """Merges partial CSVs into the main balalaika.csv securely.""" logger.info("Combining DistillMOS results...") dfs = [] for i in range(num_parts): @@ -97,40 +97,62 @@ def combine_results(final_output_path: Path, num_parts: int): logger.error(f"Error reading {part_path}: {e}") if not dfs: - logger.warning("No DistillMOS results found.") + logger.warning("No DistillMOS results found to merge.") return - new_df = pd.concat(dfs, ignore_index=True).drop_duplicates(subset=['filepath'], keep='last') + # Это DataFrame только с результатами воркеров (filepath и DistillMOS) + new_df = pd.concat(dfs, ignore_index=True) if final_output_path.exists(): - logger.info(f"Merging with existing CSV: {final_output_path}") + logger.info(f"Safely merging with existing CSV: {final_output_path}") main_df = pd.read_csv(final_output_path) - # Обновляем существующий DF новыми результатами - main_df = pd.concat([main_df, new_df], ignore_index=True) - main_df = main_df.drop_duplicates(subset=['filepath'], keep='last') + # Устанавливаем filepath как индекс для точного слияния + main_df.set_index('filepath', inplace=True) + new_df.set_index('filepath', inplace=True) + + # combine_first заполняет пустые ячейки (NaN) в main_df данными из new_df + # При этом он НЕ удаляет другие колонки (start, silence_percent и т.д.) + main_df = main_df.combine_first(new_df).reset_index() else: main_df = new_df - main_df.to_csv(final_output_path, index=False) - logger.success(f"Combined. Total rows: {len(main_df)}") + # Чистим старый флаг is_single_speaker, если он остался в старых файлах + if 'is_single_speaker' in main_df.columns: + main_df.drop(columns=['is_single_speaker'], inplace=True) + + # Красиво сортируем колонки + base_cols = ['filepath', 'speaker_id', 'start', 'end', 'total_duration', + 'playlist_id', 'podcast_id', 'silence_percent', 'max_silence_duration', 'DistillMOS'] + final_cols = [c for c in base_cols if c in main_df.columns] + [c for c in main_df.columns if c not in base_cols] + + main_df[final_cols].to_csv(final_output_path, index=False) + logger.success(f"Combined successfully. Total rows: {len(main_df)}") def get_unprocessed_paths(podcasts_path: Path, result_csv_path: Path) -> List[str]: + """Finds all chunks that haven't been processed by DistillMOS yet.""" all_audio_paths = get_audio_paths(str(podcasts_path)) - all_paths_str = [str(p.resolve()) for p in all_audio_paths] + + # Паттерн для поиска только нарезанных мелких чанков + chunk_pattern = re.compile(r'^\d+\.\d+_\d+\.\d+_') + all_chunks = [str(Path(p).resolve()) for p in all_audio_paths if chunk_pattern.match(Path(p).name)] if not result_csv_path.exists(): - return all_paths_str + return all_chunks try: df = pd.read_csv(result_csv_path) if 'DistillMOS' not in df.columns: - return all_paths_str + return all_chunks + + # Ищем файлы, у которых УЖЕ ЕСТЬ не пустая оценка DistillMOS + processed = set(df.dropna(subset=['DistillMOS'])['filepath'].astype(str).tolist()) - processed = set(df.loc[df['DistillMOS'].notna(), 'filepath'].astype(str).tolist()) - return [p for p in all_paths_str if p not in processed] - except: - return all_paths_str + # Возвращаем только те чанки, которых еще нет в списке обработанных + return [p for p in all_chunks if p not in processed] + except Exception as e: + logger.warning(f"Could not read CSV to filter paths: {e}. Processing all chunks.") + return all_chunks def main(): mp.set_start_method('spawn', force=True) @@ -149,7 +171,7 @@ def main(): unprocessed = get_unprocessed_paths(podcasts_path, final_output_path) if not unprocessed: - logger.success("All files already have DistillMOS. Exiting.") + logger.success("All small audio files already have a DistillMOS score. Exiting.") return logger.info(f"Processing {len(unprocessed)} files on {available_gpus} GPUs.") @@ -167,4 +189,4 @@ def main(): combine_results(final_output_path, available_gpus) if __name__ == "__main__": - main() + main() \ No newline at end of file diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 42b00cf..9ab9e93 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -60,6 +60,8 @@ def get_providers(cuda_id: int, use_tensorrt: bool = False) -> list: "device_id": cuda_id, "trt_max_workspace_size": 6 * 1024**3, "trt_fp16_enable": True, + "trt_engine_cache_enable": True, + "trt_engine_cache_path": f"./trt_cache_{cuda_id}", }), ("CUDAExecutionProvider", {"device_id": cuda_id}), ] From 03aaeae7c7ec1a7e0bd4b4108237b6ca0f1f58e4 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Tue, 10 Mar 2026 02:34:43 +0000 Subject: [PATCH 33/43] feat: auto-load sortformer --- configs/config.yaml | 2 +- src/preprocess/sortformer_onnx.py | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/configs/config.yaml b/configs/config.yaml index 48c5f39..46089d2 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -15,7 +15,7 @@ preprocess: loudness: -23.0 # target loudness level in LUFS block_size: 0.400 # block size for loudness measurement in seconds chunk_duration: 900 # max chunk duration for Sortformer (seconds). Audio >15 min is split. - sortformer_model: nvidia/diar_streaming_sortformer_4spk-v2 # HF model ID or path to .nemo file + sortformer_model: ./models/diar_streaming_sortformer_4spk-v2.1.onnx vad_args: smart_vad_model: ./models/smart-turn-v3.0.onnx smart_vad_threshold: 0.4 diff --git a/src/preprocess/sortformer_onnx.py b/src/preprocess/sortformer_onnx.py index ff0a505..c5c78f3 100644 --- a/src/preprocess/sortformer_onnx.py +++ b/src/preprocess/sortformer_onnx.py @@ -3,6 +3,8 @@ import librosa from typing import List, Tuple, Union import time +import os +import huggingface_hub # Model constants N_FFT = 512 @@ -52,6 +54,9 @@ def __init__(self, model_path: str, config: DiarizationConfig = None, providers: else: self.config = config + if not os.path.exists(model_path): + model_path = huggingface_hub.hf_hub_download(repo_id="altunenes/parakeet-rs", filename="diar_streaming_sortformer_4spk-v2.1.onnx", local_dir="./models") + if providers is None: providers = [ # ( From 46669a59ae5691826c9c4184856cbb4e7c9e55d5 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Tue, 10 Mar 2026 02:41:29 +0000 Subject: [PATCH 34/43] feat: update config --- configs/config.yaml | 17 +---------------- src/transcription/transcription.py | 3 +-- 2 files changed, 2 insertions(+), 18 deletions(-) diff --git a/configs/config.yaml b/configs/config.yaml index 46089d2..391bbb0 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -23,11 +23,6 @@ preprocess: separation: podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located - nisqa: - bs: 32 - num_workers: 12 - nisqa_config_path: ./configs/nisqa_b.yaml - music_detect: bs: 32 num_workers: 1 # per gpu @@ -44,17 +39,7 @@ transcription: # All models are loaded via onnx-asr from HuggingFace (or local path). # Supported: giga_ctc, giga_rnnt, vosk, vosk_small, tone, parakeet_v2, parakeet_v3, canary, whisper_base, whisper_turbo model_names: ['giga_ctc', 'giga_rnnt', 'vosk', 'tone'] - - giga: - batch_size: 16 - # quantization: int8 # optional: int8 quantization for faster CPU/GPU inference - - vosk: - batch_size: 16 - # vosk_path: ./models/vosk-model-ru # optional: local model directory (otherwise downloaded from HF) - - tone: - batch_size: 16 + batch_size: 16 # batch size for the model punctuation: podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 9ab9e93..13ad4ec 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -153,8 +153,7 @@ def run_worker(cuda_id: int, world_size: int, model_name: str, my_files = all_files[cuda_id::world_size] if not my_files: return - - model_cfg = config.get('giga', {}) if 'giga' in model_name else config.get(model_name, {}) + if not isinstance(model_cfg, dict): model_cfg = {} batch_size = model_cfg.get('batch_size', 16) From d32995a2545a55343fe750f8ea5fd3cdfe9dd42b Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Tue, 10 Mar 2026 03:52:52 +0000 Subject: [PATCH 35/43] fear: update transctipton trt cache path, clear repo --- configs/config.yaml | 23 +- src/to_webdataset.py | 15 + src/transcription/transcription.py | 12 +- weights/nisqa.tar/nisqa.tar | 1483 ---------------------------- weights/nisqa.tar/nisqa.tar.1 | 1483 ---------------------------- 5 files changed, 32 insertions(+), 2984 deletions(-) create mode 100644 src/to_webdataset.py delete mode 100644 weights/nisqa.tar/nisqa.tar delete mode 100644 weights/nisqa.tar/nisqa.tar.1 diff --git a/configs/config.yaml b/configs/config.yaml index 391bbb0..85ca3c1 100644 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -1,13 +1,13 @@ cache_path: ./cache download: - podcasts_path: /home/nikita/balalaika/datkamatka + podcasts_path: /home/nikita/balalaika/balalaika_data episodes_limit: 100 # limit on downloading episodes from one playlist num_workers: 4 podcasts_urls_file: '' # pkl with urls preprocess: - podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the podcasts are located + podcasts_path: /home/nikita/balalaika/balalaika_data # full path to the folder where the podcasts are located duration: 15 # in seconds (each sample will be no longer than this time.) num_workers: 1 # per GPU; keep at 1 — Sortformer is a large model crest_treshold: 10 # in dB @@ -16,21 +16,22 @@ preprocess: block_size: 0.400 # block size for loudness measurement in seconds chunk_duration: 900 # max chunk duration for Sortformer (seconds). Audio >15 min is split. sortformer_model: ./models/diar_streaming_sortformer_4spk-v2.1.onnx + use_tensorrt: True # enable TensorRT execution provider (requires CUDA + TensorRT installed) vad_args: - smart_vad_model: ./models/smart-turn-v3.0.onnx - smart_vad_threshold: 0.4 + smart_vad_model: ./models/smart-turn-v3.0.onnx # path to the smart-turn-v3.0.onnx model + smart_vad_threshold: 0.4 # threshold for the smart-turn-v3.0 model separation: - podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/balalaika_data # full path to the folder where the chopped podcasts are located music_detect: bs: 32 num_workers: 1 # per gpu - music_detect_model: ./models/music_detection.safetensors - threshold: 0.6 + music_detect_model: ./models/music_detection.safetensors # path to the music_detection.safetensors model + threshold: 0.6 # threshold for the music_detection.safetensors model transcription: - podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/balalaika_data # full path to the folder where the chopped podcasts are located consensus_num: 3 # number of models to use for consensus transcription with_timestamps: True use_tensorrt: True # enable TensorRT execution provider (requires CUDA + TensorRT installed) @@ -42,15 +43,15 @@ transcription: batch_size: 16 # batch size for the model punctuation: - podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/balalaika_data # full path to the folder where the chopped podcasts are located model_name: "RUPunct/RUPunct_big" # RUPunct model name num_workers: 1 # per one gpu accent: - podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the chopped podcasts are located + podcasts_path: /home/nikita/balalaika/balalaika_data # full path to the folder where the chopped podcasts are located num_workers: 1 # per one gpu model_name: turbo3.1 # ruAccent model name phonemizer: - podcasts_path: /home/nikita/balalaika/datkamatka # full path to the folder where the text files are located + podcasts_path: /home/nikita/balalaika/balalaika_data # full path to the folder where the text files are located num_workers: 1 # per one gpu diff --git a/src/to_webdataset.py b/src/to_webdataset.py new file mode 100644 index 0000000..d0f54b6 --- /dev/null +++ b/src/to_webdataset.py @@ -0,0 +1,15 @@ +import argparse +from src.utils.utils import load_config + +def main(config): + # TODO: Implement the function + print(config) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, required=True) + args = parser.parse_args() + + config = load_config(args.config_path) + + main(config) \ No newline at end of file diff --git a/src/transcription/transcription.py b/src/transcription/transcription.py index 13ad4ec..777054d 100644 --- a/src/transcription/transcription.py +++ b/src/transcription/transcription.py @@ -61,7 +61,7 @@ def get_providers(cuda_id: int, use_tensorrt: bool = False) -> list: "trt_max_workspace_size": 6 * 1024**3, "trt_fp16_enable": True, "trt_engine_cache_enable": True, - "trt_engine_cache_path": f"./trt_cache_{cuda_id}", + "trt_engine_cache_path": f".cache/trt_cache_{cuda_id}", }), ("CUDAExecutionProvider", {"device_id": cuda_id}), ] @@ -153,18 +153,16 @@ def run_worker(cuda_id: int, world_size: int, model_name: str, my_files = all_files[cuda_id::world_size] if not my_files: return - - if not isinstance(model_cfg, dict): - model_cfg = {} - batch_size = model_cfg.get('batch_size', 16) + + batch_size = config.get('batch_size', 16) use_trt = config.get('use_tensorrt', False) - quantization = model_cfg.get('quantization') + quantization = config.get('quantization') onnx_name = MODEL_MAP.get(model_name, model_name) output_suffix = 'vosk' if 'vosk' in model_name else model_name do_timestamps = config.get('with_timestamps', False) and model_name in SUPPORTED_TIMESTAMPS - local_path = model_cfg.get('vosk_path') if 'vosk' in model_name else model_cfg.get('model_path') + local_path = config.get('vosk_path') if 'vosk' in model_name else config.get('model_path') logger.info(f"Worker {cuda_id}/{world_size}: {onnx_name} on cuda:{cuda_id}, {len(my_files)} files, batch={batch_size}") diff --git a/weights/nisqa.tar/nisqa.tar b/weights/nisqa.tar/nisqa.tar deleted file mode 100644 index 1adce15..0000000 --- a/weights/nisqa.tar/nisqa.tar +++ /dev/null @@ -1,1483 +0,0 @@ - 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- - - From 22ae752ab2f635c679635ec45f0fa081b335eca7 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Tue, 10 Mar 2026 04:02:33 +0000 Subject: [PATCH 36/43] feat: update SmartVAD 3.0 --> 3.2 --- src/libs/smart_turn/offline_svad.py | 2 +- src/separation/distillmos_process.py | 9 --------- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/src/libs/smart_turn/offline_svad.py b/src/libs/smart_turn/offline_svad.py index fffd575..236e0b4 100644 --- a/src/libs/smart_turn/offline_svad.py +++ b/src/libs/smart_turn/offline_svad.py @@ -52,7 +52,7 @@ def _init_smart_vad(self): def _load_from_hf(self): return hf_hub_download( repo_id="pipecat-ai/smart-turn-v3", - filename="smart-turn-v3.0.onnx", + filename="smart-turn-v3.2-gpu.onnx", local_dir="./models", local_dir_use_symlinks=False ) diff --git a/src/separation/distillmos_process.py b/src/separation/distillmos_process.py index 48c1567..26f18e1 100644 --- a/src/separation/distillmos_process.py +++ b/src/separation/distillmos_process.py @@ -100,28 +100,22 @@ def combine_results(final_output_path: Path, num_parts: int): logger.warning("No DistillMOS results found to merge.") return - # Это DataFrame только с результатами воркеров (filepath и DistillMOS) new_df = pd.concat(dfs, ignore_index=True) if final_output_path.exists(): logger.info(f"Safely merging with existing CSV: {final_output_path}") main_df = pd.read_csv(final_output_path) - # Устанавливаем filepath как индекс для точного слияния main_df.set_index('filepath', inplace=True) new_df.set_index('filepath', inplace=True) - # combine_first заполняет пустые ячейки (NaN) в main_df данными из new_df - # При этом он НЕ удаляет другие колонки (start, silence_percent и т.д.) main_df = main_df.combine_first(new_df).reset_index() else: main_df = new_df - # Чистим старый флаг is_single_speaker, если он остался в старых файлах if 'is_single_speaker' in main_df.columns: main_df.drop(columns=['is_single_speaker'], inplace=True) - # Красиво сортируем колонки base_cols = ['filepath', 'speaker_id', 'start', 'end', 'total_duration', 'playlist_id', 'podcast_id', 'silence_percent', 'max_silence_duration', 'DistillMOS'] final_cols = [c for c in base_cols if c in main_df.columns] + [c for c in main_df.columns if c not in base_cols] @@ -133,7 +127,6 @@ def get_unprocessed_paths(podcasts_path: Path, result_csv_path: Path) -> List[st """Finds all chunks that haven't been processed by DistillMOS yet.""" all_audio_paths = get_audio_paths(str(podcasts_path)) - # Паттерн для поиска только нарезанных мелких чанков chunk_pattern = re.compile(r'^\d+\.\d+_\d+\.\d+_') all_chunks = [str(Path(p).resolve()) for p in all_audio_paths if chunk_pattern.match(Path(p).name)] @@ -145,10 +138,8 @@ def get_unprocessed_paths(podcasts_path: Path, result_csv_path: Path) -> List[st if 'DistillMOS' not in df.columns: return all_chunks - # Ищем файлы, у которых УЖЕ ЕСТЬ не пустая оценка DistillMOS processed = set(df.dropna(subset=['DistillMOS'])['filepath'].astype(str).tolist()) - # Возвращаем только те чанки, которых еще нет в списке обработанных return [p for p in all_chunks if p not in processed] except Exception as e: logger.warning(f"Could not read CSV to filter paths: {e}. Processing all chunks.") From 678dd27c962a5c28adc91c635130752cadd26192 Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Thu, 19 Mar 2026 15:28:35 +0000 Subject: [PATCH 37/43] docs: update sortformer readme --- src/preprocess/README.md | 54 +++++++++++++----- src/preprocess/preprocess.py | 106 ++++++++++++++--------------------- 2 files changed, 82 insertions(+), 78 deletions(-) diff --git a/src/preprocess/README.md b/src/preprocess/README.md index 3b1443a..3aa23f3 100644 --- a/src/preprocess/README.md +++ b/src/preprocess/README.md @@ -1,36 +1,62 @@ +## Overview +This script is an advanced audio preprocessing pipeline designed to prepare datasets for TTS (Text-to-Speech) or ASR (Automatic Speech Recognition). It processes long podcast audio files by isolating clean, **single-speaker segments** and removing silences or overlapping speech. + +### What it does: +1. **Speaker Diarization (`Sortformer`)**: Analyzes the audio in 15-minute chunks to identify who is speaking and when. +2. **Filtering**: Isolates segments where only a **single speaker** is talking (ignoring overlapping voices). +3. **End-of-Speech Detection (`SmartVAD`)**: Refines segment boundaries by cutting the audio at natural pauses. +4. **Metrics Calculation**: Computes the percentage of silence, maximum silence gap, and sets a boolean flag if the segment contains exactly one speaker. +5. **Segmentation**: Slices the original audio into short chunks (e.g., up to 15 seconds) and saves them as `.mp3`. +6. **Metadata Generation**: Saves all chunk metadata (paths, speaker IDs, durations, silence metrics, and single-speaker flags) into a centralized `balalaika.csv` file. +7. **Cleanup**: Automatically **deletes the original large audio file** after successful processing to save disk space. + ## Usage/Examples ### Running the Code via Command-Line Arguments You can modify the parameters directly in the shell script (`preprocess_args.sh`) and then run it: -~~~ +~~~bash sh preprocess/preprocess_args.sh ~~~ ### Running the Code via Config File -Example: -~~~ +The python script is executed by passing a YAML configuration file. Example: +~~~bash sh preprocess/preprocess_yaml.sh config_path ~~~ -## Explanation of Parameters +## Explanation of Parameters (in YAML config) -- `--config_path`: Path to the YAML configuration file. -- `--podcasts_path`: Root directory containing audio files. -- `--duration`: Maximum duration in seconds for each audio segment (default: 15). -- `--num_workers`: Number of parallel processes for audio processing. +- `podcasts_path`: Root directory containing the raw audio files. +- `duration`: Maximum duration in seconds for each final audio segment (default: `15.0`). +- `chunk_duration`: Duration in seconds for processing large files in RAM (default: `900` / 15 minutes). +- `num_workers`: Number of parallel processes **per GPU**. The total number of workers is `num_gpus * num_workers`. +- `sortformer_model`: Path to the ONNX Sortformer model used for diarization. +- `vad_args`: Dictionary containing VAD settings (e.g., `smart_vad_model` path and `smart_vad_threshold`). ## Output Structure -After processing, the original audio files are normalized and segmented into shorter clips. The resulting structure will be: +After processing, the original large audio files are deleted and replaced with short, normalized, single-speaker clips. The resulting directory structure will look like this: -~~~ +~~~text podcasts/ +├── balalaika.csv <-- Generated metadata file └── {album_id}/ ├── {episode_id}/ - │ ├── {start_time}_{end_time}_{album_id}_{episode_id}.mp3 + │ ├── 12.50_26.30_{album_id}_{episode_id}.mp3 + │ ├── 27.15_39.80_{album_id}_{episode_id}.mp3 │ └── ... (other segments) ~~~ -Each audio file is moved to its own folder named after the episode ID (within its album folder) and then segmented. - -The `{start_time}` and `{end_time}` in the filenames represent the timestamp positions (in seconds) of the segment in the original audio file. +### Filename Convention +The `{start_time}` and `{end_time}` in the filenames (e.g., `12.50_26.30`) represent the timestamp positions (in seconds) of the segment from the original audio file. + +### CSV Metadata (`balalaika.csv`) +The script automatically updates a CSV file with the following columns for each generated audio chunk: +- `filepath`: Absolute path to the generated chunk. +- `speaker_id`: ID of the speaker identified by the diarization model. +- `is_single_speaker`: Boolean flag (`True` or `False`) indicating if only one person speaks in the chunk. +- `start` / `end`: Original timestamps. +- `total_duration`: Length of the chunk in seconds. +- `playlist_id` / `podcast_id`: Source album and episode names. +- `silence_percent`: Percentage of silence within the chunk. +- `max_silence_duration`: Longest continuous silence gap in the chunk. \ No newline at end of file diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py index b767e40..0491348 100644 --- a/src/preprocess/preprocess.py +++ b/src/preprocess/preprocess.py @@ -22,7 +22,7 @@ torch.backends.cuda.enable_mem_efficient_sdp(True) torch.backends.cuda.enable_math_sdp(False) -CHUNK_DURATION_S = 15 * 60 # 15 минут — лимит Sortformer для избежания OOM +CHUNK_DURATION_S = 15 * 60 sortformer_model = None smart_vad = None @@ -55,10 +55,6 @@ def init_models(gpu_id: int, config: Dict[str, Any]): logger.info(f"Models initialized on {device}") -# --------------------------------------------------------------------------- -# 1. Диаризация большого файла (1 раз) -# --------------------------------------------------------------------------- - def parse_diarization_output(raw_results) -> List[Tuple[float, float, int]]: segments = [] if not raw_results or len(raw_results) == 0: return segments @@ -80,8 +76,6 @@ def diarize_audio(audio: torch.Tensor, sr: int, chunk_duration: float = CHUNK_DU chunk_samples = int(chunk_duration * sr) all_segments, offset = [], 0 - # Обрабатываем большой файл чанками чисто для избежания OOM, - # логически это 1 прогон на весь файл. while offset < total_samples: end = min(offset + chunk_samples, total_samples) chunk = audio[:, offset:end] @@ -94,7 +88,7 @@ def diarize_audio(audio: torch.Tensor, sr: int, chunk_duration: float = CHUNK_DU segs = [(s + offset_sec, e + offset_sec, spk) for s, e, spk in segs] if len(segs) > 2 and total_samples > chunk_samples: - segs = segs[1:-1] # Убираем артефакты склейки + segs = segs[1:-1] all_segments.extend(segs) offset = end @@ -102,16 +96,11 @@ def diarize_audio(audio: torch.Tensor, sr: int, chunk_duration: float = CHUNK_DU return sorted(all_segments, key=lambda x: x[0]) -# --------------------------------------------------------------------------- -# 2. Фильтрация и EOS (получаем границы мелких файлов) -# --------------------------------------------------------------------------- - def filter_single_speaker_segments(segments: List[Tuple[float, float, int]], min_duration: float = 1.0, max_duration: float = 15.0) -> List[Tuple[float, float, int]]: filtered = [] segments = sorted(segments, key=lambda x: x[0]) for i, (start, end, spk) in enumerate(segments): if not (min_duration <= end - start <= max_duration): continue - # Проверка на пересечение спикеров if not any(j != i and start < e2 and end > s2 for j, (s2, e2, _) in enumerate(segments)): filtered.append((start, end, spk)) return filtered @@ -149,24 +138,20 @@ def apply_eos_classification(audio: torch.Tensor, sr: int, segments: List[Tuple[ return merged -# --------------------------------------------------------------------------- -# 3. Вычисление тишины КОНКРЕТНО для мелкого куска (математически) -# --------------------------------------------------------------------------- - -def get_chunk_silence_metrics(c_start: float, c_end: float, raw_segments: List[Tuple[float, float, int]]) -> Tuple[float, float]: - """Считает тишину внутри конкретного куска, используя сырые метки 1 прогона диаризатора""" +def get_chunk_metrics(c_start: float, c_end: float, raw_segments: List[Tuple[float, float, int]]) -> Tuple[float, float, int]: chunk_dur = c_end - c_start - if chunk_dur <= 0: return 0.0, 0.0 + if chunk_dur <= 0: return 0.0, 0.0, 0 - # 1. Находим все сырые куски речи, которые попали в наш финальный чанк intervals = [] - for rs, re, _ in raw_segments: + speakers_in_chunk = set() + + for rs, re, spk in raw_segments: overlap_s = max(c_start, rs) overlap_e = min(c_end, re) if overlap_s < overlap_e: intervals.append([overlap_s, overlap_e]) + speakers_in_chunk.add(spk) - # 2. Склеиваем пересекающиеся интервалы речи (на всякий случай) intervals.sort(key=lambda x: x[0]) merged_speech = [] for interval in intervals: @@ -179,28 +164,22 @@ def get_chunk_silence_metrics(c_start: float, c_end: float, raw_segments: List[T else: merged_speech.append(interval) - # 3. Считаем длину речи и тишины в чанке speech_dur = sum(e - s for s, e in merged_speech) silence_dur = max(0.0, chunk_dur - speech_dur) silence_pct = (silence_dur / chunk_dur) * 100 - # 4. Считаем максимальную непрерывную паузу внутри чанка if not merged_speech: max_gap = chunk_dur else: - gaps = [merged_speech[0][0] - c_start] # от начала до первой речи + gaps = [merged_speech[0][0] - c_start] for i in range(len(merged_speech) - 1): - gaps.append(merged_speech[i+1][0] - merged_speech[i][1]) # между репликами - gaps.append(c_end - merged_speech[-1][1]) # от последней речи до конца + gaps.append(merged_speech[i+1][0] - merged_speech[i][1]) + gaps.append(c_end - merged_speech[-1][1]) max_gap = max(gaps) - return round(silence_pct, 2), round(max_gap, 2) + return round(silence_pct, 2), round(max_gap, 2), len(speakers_in_chunk) -# --------------------------------------------------------------------------- -# 4. Нарезка файлов -# --------------------------------------------------------------------------- - def cut_audio(audio: torch.Tensor, sr: int, final_segments: List[Tuple[float, float, int]], raw_segments: List[Tuple[float, float, int]], output_folder: str, album_id: str, episode_id: str, fmt: str = 'mp3', max_duration: float = 15.0) -> List[Dict]: os.makedirs(output_folder, exist_ok=True) results = [] @@ -212,8 +191,7 @@ def cut_audio(audio: torch.Tensor, sr: int, final_segments: List[Tuple[float, fl s_sample, e_sample = int(start * sr), min(int(end * sr), audio.shape[-1]) if e_sample <= s_sample: continue - # Вытаскиваем тишину ИМЕННО для этого куска из сырых меток: - sil_pct, max_sil = get_chunk_silence_metrics(start, end, raw_segments) + sil_pct, max_sil, unique_spk = get_chunk_metrics(start, end, raw_segments) segment = audio[:, s_sample:e_sample] fname = f"{start:.2f}_{end:.2f}_{album_id}_{episode_id}.{fmt}" @@ -229,15 +207,12 @@ def cut_audio(audio: torch.Tensor, sr: int, final_segments: List[Tuple[float, fl 'playlist_id': album_id, 'podcast_id': episode_id, 'silence_percent': sil_pct, - 'max_silence_duration': max_sil + 'max_silence_duration': max_sil, + 'is_single_speaker': unique_spk == 1 }) return results -# --------------------------------------------------------------------------- -# Per-file processing -# --------------------------------------------------------------------------- - def process_audio_file(path_audio: str, config: Dict[str, Any]) -> List[Dict]: duration = config.get('duration', 15) chunk_duration = config.get('chunk_duration', CHUNK_DURATION_S) @@ -252,27 +227,41 @@ def process_audio_file(path_audio: str, config: Dict[str, Any]) -> List[Dict]: logger.error(f"Broken file {path_audio}: {e}") return [] - if (audio.shape[-1] / sr) <= duration: - return [] + total_audio_duration = audio.shape[-1] / sr try: if audio.shape[0] > 1: audio = torch.mean(audio, dim=0, keepdim=True) - # 1. Один прогон диаризатора на всё большое аудио raw_segments = diarize_audio(audio, sr, chunk_duration) if not raw_segments: return [] - # 2. Фильтрация и склейка коротких пауз (определяем границы нарезки) + if total_audio_duration <= duration: + sil_pct, max_sil, unique_spk = get_chunk_metrics(0.0, total_audio_duration, raw_segments) + main_spk = raw_segments[0][2] if raw_segments else -1 + + logger.success(f"Processed short file | Spk: {unique_spk} | Sil: {sil_pct}% -> {path_audio}") + + return [{ + 'filepath': os.path.abspath(path_audio), + 'speaker_id': main_spk, + 'start': 0.0, + 'end': round(total_audio_duration, 2), + 'total_duration': round(total_audio_duration, 2), + 'playlist_id': album_id, + 'podcast_id': episode_id, + 'silence_percent': sil_pct, + 'max_silence_duration': max_sil, + 'is_single_speaker': unique_spk == 1 + }] + clean_segments = filter_single_speaker_segments(raw_segments, min_duration=1.0, max_duration=duration) final_segments = apply_eos_classification(audio, sr, clean_segments, max_duration=duration) if not final_segments: return [] - # 3. Нарезка и формирование словарей (с вычислением тишины по каждому куску) seg_results = cut_audio(audio, sr, final_segments, raw_segments, str(episode_folder), album_id, episode_id, max_duration=duration) logger.success(f"Processed {len(seg_results)} chunks from: {path_audio}") - # 4. Удаляем большой файл в конце if seg_results and p_audio.exists(): os.remove(p_audio) logger.info(f"Original large file deleted: {path_audio}") @@ -285,10 +274,6 @@ def process_audio_file(path_audio: str, config: Dict[str, Any]) -> List[Dict]: return seg_results -# --------------------------------------------------------------------------- -# Main entry -# --------------------------------------------------------------------------- - def main(args): load_dotenv() if hf_key := os.environ.get('HF_TOKEN'): login(token=hf_key) @@ -300,29 +285,27 @@ def main(args): workers_per_gpu = config.get('num_workers', 1) total_workers = max(1, num_gpus * workers_per_gpu) - # Загружаем существующий CSV (в нем могут быть DistillMOS для УЖЕ нарезанных файлов) csv_path = podcasts_path / 'balalaika.csv' existing_df = pd.DataFrame() if csv_path.exists(): existing_df = pd.read_csv(csv_path) - # Собираем файлы и ОТСЕИВАЕМ мелкие куски, если вдруг они попали в поиск raw_audio_paths = get_audio_paths(str(podcasts_path)) paths_to_process = [] - chunk_pattern = re.compile(r'^\d+\.\d+_\d+\.\d+_') # Паттерн: 29.04_37.92_... + + chunk_pattern = re.compile(r'^\d+\.\d+_\d+\.\d+_') for p_str in raw_audio_paths: p = Path(p_str) - # Если это УЖЕ нарезанный мелкий файл — пропускаем его if chunk_pattern.match(p.name): continue paths_to_process.append(p) if not paths_to_process: - logger.info("No new large files to process.") + logger.info("No new files to process.") return - logger.info(f"Large files to process: {len(paths_to_process)} / Total workers: {total_workers}") + logger.info(f"Files to process: {len(paths_to_process)} / Total workers: {total_workers}") all_results: List[Dict] = [] with ProcessPoolExecutor(max_workers=total_workers, initializer=initializer_wrapper, initargs=(config, num_gpus)) as executor: @@ -333,29 +316,24 @@ def main(args): except Exception as e: logger.error(f"Task error: {e}") - # БЕЗОПАСНОЕ слияние Датафреймов (не затираем DistillMOS) if all_results: new_df = pd.DataFrame(all_results) if not existing_df.empty: - # Устанавливаем filepath как индекс, чтобы merge прошел четко по файлу existing_df.set_index('filepath', inplace=True) new_df.set_index('filepath', inplace=True) - - # combine_first заполнит пустые метрики (start, end, тишина) у строк, - # где был только DistillMOS, и добавит новые строки! df = existing_df.combine_first(new_df).reset_index() else: df = new_df - base_cols = ['filepath', 'speaker_id', 'start', 'end', 'total_duration', - 'playlist_id', 'podcast_id', 'silence_percent', 'max_silence_duration', 'DistillMOS'] + 'playlist_id', 'podcast_id', 'silence_percent', 'max_silence_duration', + 'is_single_speaker', 'DistillMOS'] final_cols = [c for c in base_cols if c in df.columns] + [c for c in df.columns if c not in base_cols] df = df[final_cols] df.to_csv(csv_path, index=False) - logger.success(f"Saved metadata for {len(all_results)} new chunks to {csv_path}") + logger.success(f"Saved metadata for {len(all_results)} files to {csv_path}") if __name__ == "__main__": multiprocessing.set_start_method('spawn', force=True) From c71b74edee9595df4b91377d806485453f15255b Mon Sep 17 00:00:00 2001 From: NikiPshg Date: Thu, 19 Mar 2026 16:50:57 +0000 Subject: [PATCH 38/43] feat: add webdataset support --- .../train/shard_000_0000.tar | Bin 0 -> 3614720 bytes .../train/shard_001_0000.tar | Bin 0 -> 10240 bytes .../train/shard_002_0000.tar | Bin 0 -> 10240 bytes .../train/shard_003_0000.tar | Bin 0 -> 10240 bytes .../train/shard_004_0000.tar | Bin 0 -> 10240 bytes .../train/shard_005_0000.tar | Bin 0 -> 10240 bytes .../train/shard_006_0000.tar | Bin 0 -> 10240 bytes .../train/shard_007_0000.tar | Bin 0 -> 10240 bytes .../train/shard_008_0000.tar | Bin 0 -> 10240 bytes .../train/shard_009_0000.tar | Bin 0 -> 10240 bytes .../train/shard_010_0000.tar | Bin 0 -> 10240 bytes .../train/shard_011_0000.tar | Bin 0 -> 10240 bytes .../train/shard_012_0000.tar | Bin 0 -> 10240 bytes .../train/shard_013_0000.tar | Bin 0 -> 10240 bytes base.sh | 4 +- configs/config.yaml | 6 + docs/preparing.md | 377 -------------- example/README.md | 137 +++-- example/dataset.py | 44 -- example/example.py | 28 +- requirements_dev.txt | 2 + src/collate_yamls.sh | 3 +- src/separation/README.md | 59 ++- src/to_webdataset.py | 170 +++++- src/utils/vosk_utils.py | 490 ------------------ 25 files changed, 323 insertions(+), 997 deletions(-) create mode 100644 balalaika_data_webdataset/train/shard_000_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_001_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_002_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_003_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_004_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_005_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_006_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_007_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_008_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_009_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_010_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_011_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_012_0000.tar create mode 100644 balalaika_data_webdataset/train/shard_013_0000.tar delete mode 100644 docs/preparing.md delete mode 100644 example/dataset.py delete mode 100644 src/utils/vosk_utils.py diff --git a/balalaika_data_webdataset/train/shard_000_0000.tar b/balalaika_data_webdataset/train/shard_000_0000.tar new file mode 100644 index 0000000000000000000000000000000000000000..b0a2caa1092b3559b46834afee8a1aaecf2050f4 GIT binary patch literal 3614720 zcmeFY2UJu~m*{(%+{7klY;wjX3JB6oZi3_tl5@_8fo^inSwV6}0RaI)aug+qih_~^ zNlHdQpdao3eRJ=8GxO%n%$mFAu9>seNxODc{c3mBKKoRi7P&5R9j1dDr6sH#Yy;eFJ+TLCpTGcD 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