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Introduction

SenseVoice is a speech foundation model with multiple speech understanding capabilities, including automatic speech recognition (ASR), spoken language identification (LID), speech emotion recognition (SER), and audio event detection (AED).

Requirements

pip install -r requirements.txt

Usage

Inference

Supports input of audio in any format and of any duration.

from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess

model_dir = "iic/SenseVoiceSmall"


model = AutoModel(
    model=model_dir,
    trust_remote_code=True,
    remote_code="./model.py",    
    vad_model="fsmn-vad",
    vad_kwargs={"max_single_segment_time": 30000},
    device="cuda:0",
)

# en
res = model.generate(
    input=f"{model.model_path}/example/en.mp3",
    cache={},
    language="auto",  # "zh", "en", "yue", "ja", "ko", "nospeech"
    use_itn=True,
    batch_size_s=60,
    merge_vad=True,  #
    merge_length_s=15,
)
text = rich_transcription_postprocess(res[0]["text"])
print(text)

Parameter Description (Click to Expand)

• model_dir: The name of the model, or the path to the model on the local disk.
• trust_remote_code:
  • When True, it means that the model's code implementation is loaded from remote_code, which specifies the exact location of the model code (for example, model.py in the current directory). It supports absolute paths, relative paths, and network URLs.
  • When False, it indicates that the model's code implementation is the integrated version within FunASR. At this time, modifications made to model.py in the current directory will not be effective, as the version loaded is the internal one from FunASR. For the model code, click here to view.
• vad_model: This indicates the activation of VAD (Voice Activity Detection). The purpose of VAD is to split long audio into shorter clips. In this case, the inference time includes both VAD and SenseVoice total consumption, and represents the end-to-end latency. If you wish to test the SenseVoice model's inference time separately, the VAD model can be disabled.
• vad_kwargs: Specifies the configurations for the VAD model. max_single_segment_time: denotes the maximum duration for audio segmentation by the vad_model, with the unit being milliseconds (ms).
• use_itn: Whether the output result includes punctuation and inverse text normalization.
• batch_size_s: Indicates the use of dynamic batching, where the total duration of audio in the batch is measured in seconds (s).
• merge_vad: Whether to merge short audio fragments segmented by the VAD model, with the merged length being merge_length_s, in seconds (s).
• ban_emo_unk: Whether to ban the output of the emo_unk token.

If all inputs are short audios (<30s), and batch inference is needed to speed up inference efficiency, the VAD model can be removed, and batch_size can be set accordingly.

model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")

res = model.generate(
    input=f"{model.model_path}/example/en.mp3",
    cache={},
    language="zh", # "zh", "en", "yue", "ja", "ko", "nospeech"
    use_itn=False,
    batch_size=64, 
)

Inference directly

Supports input of audio in any format, with an input duration limit of 30 seconds or less.

from model import SenseVoiceSmall
from funasr.utils.postprocess_utils import rich_transcription_postprocess

model_dir = "iic/SenseVoiceSmall"
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
m.eval()

res = m.inference(
    data_in=f"{kwargs['model_path']}/example/en.mp3",
    language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
    use_itn=False,
    ban_emo_unk=False,
    **kwargs,
)

text = rich_transcription_postprocess(res[0][0]["text"])
print(text)

Export and Test

ONNX and Libtorch Export

ONNX

# pip3 install -U funasr funasr-onnx
from pathlib import Path
from funasr_onnx import SenseVoiceSmall
from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess


model_dir = "iic/SenseVoiceSmall"

model = SenseVoiceSmall(model_dir, batch_size=10, quantize=True)

# inference
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]

res = model(wav_or_scp, language="auto", use_itn=True)
print([rich_transcription_postprocess(i) for i in res])

Note: ONNX model is exported to the original model directory.

Libtorch

from pathlib import Path
from funasr_torch import SenseVoiceSmall
from funasr_torch.utils.postprocess_utils import rich_transcription_postprocess


model_dir = "iic/SenseVoiceSmall"

model = SenseVoiceSmall(model_dir, batch_size=10, device="cuda:0")

wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]

res = model(wav_or_scp, language="auto", use_itn=True)
print([rich_transcription_postprocess(i) for i in res])

Note: Libtorch model is exported to the original model directory.

Finetune

Requirements

git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip3 install -e ./

Data prepare

Data examples

{"key": "YOU0000008470_S0000238_punc_itn", "text_language": "<|en|>", "emo_target": "<|NEUTRAL|>", "event_target": "<|Speech|>", "with_or_wo_itn": "<|withitn|>", "target": "Including legal due diligence, subscription agreement, negotiation.", "source": "/cpfs01/shared/Group-speech/beinian.lzr/data/industrial_data/english_all/audio/YOU0000008470_S0000238.wav", "target_len": 7, "source_len": 140}
{"key": "AUD0000001556_S0007580", "text_language": "<|en|>", "emo_target": "<|NEUTRAL|>", "event_target": "<|Speech|>", "with_or_wo_itn": "<|woitn|>", "target": "there is a tendency to identify the self or take interest in what one has got used to", "source": "/cpfs01/shared/Group-speech/beinian.lzr/data/industrial_data/english_all/audio/AUD0000001556_S0007580.wav", "target_len": 18, "source_len": 360}

Full ref to data/train_example.jsonl

Data Prepare Details

Description：

• key: audio file unique ID
• source：path to the audio file
• source_len：number of fbank frames of the audio file
• target：transcription
• target_len：length of target
• text_language：language id of the audio file
• emo_target：emotion label of the audio file
• event_target：event label of the audio file
• with_or_wo_itn：whether includes punctuation and inverse text normalization

train_text.txt

BAC009S0764W0121 甚至出现交易几乎停滞的情况
BAC009S0916W0489 湖北一公司以员工名义贷款数十员工负债千万
asr_example_cn_en 所有只要处理 data 不管你是做 machine learning 做 deep learning 做 data analytics 做 data science 也好 scientist 也好通通都要都做的基本功啊那 again 先先对有一些>也许对
ID0012W0014 he tried to think how it could be

train_wav.scp

BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
asr_example_cn_en https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wav
ID0012W0014 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav

train_text_language.txt

The language ids include <|zh|>、<|en|>、<|yue|>、<|ja|> and <|ko|>.

BAC009S0764W0121 <|zh|>
BAC009S0916W0489 <|zh|>
asr_example_cn_en <|zh|>
ID0012W0014 <|en|>

train_emo.txt

The emotion labels include<|HAPPY|>、<|SAD|>、<|ANGRY|>、<|NEUTRAL|>、<|FEARFUL|>、<|DISGUSTED|> and <|SURPRISED|>.

BAC009S0764W0121 <|NEUTRAL|>
BAC009S0916W0489 <|NEUTRAL|>
asr_example_cn_en <|NEUTRAL|>
ID0012W0014 <|NEUTRAL|>

train_event.txt

The event labels include<|BGM|>、<|Speech|>、<|Applause|>、<|Laughter|>、<|Cry|>、<|Sneeze|>、<|Breath|> and <|Cough|>.

BAC009S0764W0121 <|Speech|>
BAC009S0916W0489 <|Speech|>
asr_example_cn_en <|Speech|>
ID0012W0014 <|Speech|>

Command

# generate train.jsonl and val.jsonl from wav.scp, text.txt, text_language.txt, emo_target.txt, event_target.txt
sensevoice2jsonl \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt", "../../../data/list/train_text_language.txt", "../../../data/list/train_emo.txt", "../../../data/list/train_event.txt"]' \
++data_type_list='["source", "target", "text_language", "emo_target", "event_target"]' \
++jsonl_file_out="../../../data/list/train.jsonl"

If there is no train_text_language.txt, train_emo_target.txt and train_event_target.txt, the language, emotion and event label will be predicted automatically by using the SenseVoice model.

# generate train.jsonl and val.jsonl from wav.scp and text.txt
sensevoice2jsonl \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_out="../../../data/list/train.jsonl" \
++model_dir='iic/SenseVoiceSmall'

Finetune

Ensure to modify the train_tool in finetune.sh to the absolute path of funasr/bin/train_ds.py from the FunASR installation directory you have set up earlier.

bash finetune.sh