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Improving timestamp accuracy in diarize.py by handling None #32

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Improving timestamp accuracy in diarize.py by handling None #32

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81 changes: 45 additions & 36 deletions src/speechbox/diarize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,9 @@

class ASRDiarizationPipeline:
def __init__(
self,
asr_pipeline,
diarization_pipeline,
self,
asr_pipeline,
diarization_pipeline,
):
self.asr_pipeline = asr_pipeline
self.sampling_rate = asr_pipeline.feature_extractor.sampling_rate
Expand All @@ -22,29 +22,29 @@ def __init__(

@classmethod
def from_pretrained(
cls,
asr_model: Optional[str] = "openai/whisper-medium",
*,
diarizer_model: Optional[str] = "pyannote/speaker-diarization",
chunk_length_s: Optional[int] = 30,
use_auth_token: Optional[Union[str, bool]] = True,
**kwargs,
cls,
asr_model: Optional[str] = "openai/whisper-medium",
*,
diarizer_model: Optional[str] = "pyannote/speaker-diarization",
chunk_length_s: Optional[int] = 30,
use_auth_token: Optional[Union[str, bool]] = True,
**kwargs,
):
asr_pipeline = pipeline(
"automatic-speech-recognition",
model=asr_model,
chunk_length_s=chunk_length_s,
token=use_auth_token, # 08/25/2023: Changed argument from use_auth_token to token
token=use_auth_token, # 08/25/2023: Changed argument from use_auth_token to token
**kwargs,
)
diarization_pipeline = Pipeline.from_pretrained(diarizer_model, use_auth_token=use_auth_token)
return cls(asr_pipeline, diarization_pipeline)

def __call__(
self,
inputs: Union[np.ndarray, List[np.ndarray]],
group_by_speaker: bool = True,
**kwargs,
self,
inputs: Union[np.ndarray, List[np.ndarray]],
group_by_speaker: bool = True,
**kwargs,
):
"""
Transcribe the audio sequence(s) given as inputs to text and label with speaker information. The input audio
Expand Down Expand Up @@ -77,7 +77,7 @@ def __call__(
- To update the asr configuration, use the prefix *asr_* for each configuration parameter.
- To update the diarization configuration, use the prefix *diarization_* for each configuration parameter.
- Added this support related to issue #25: 08/25/2023

Return:
A list of transcriptions. Each list item corresponds to one chunk / segment of transcription, and is a
dictionary with the following keys:
Expand All @@ -86,13 +86,14 @@ def __call__(
- **timestamps** (`tuple`) -- The start and end time for the chunk / segment.
"""
kwargs_asr = {
argument[len("asr_") :]: value for argument, value in kwargs.items() if argument.startswith("asr_")
argument[len("asr_"):]: value for argument, value in kwargs.items() if argument.startswith("asr_")
}

kwargs_diarization = {
argument[len("diarization_") :]: value for argument, value in kwargs.items() if argument.startswith("diarization_")
argument[len("diarization_"):]: value for argument, value in kwargs.items() if
argument.startswith("diarization_")
}

inputs, diarizer_inputs = self.preprocess(inputs)

diarization = self.diarization_pipeline(
Expand Down Expand Up @@ -141,31 +142,39 @@ def __call__(
transcript = asr_out["chunks"]

# get the end timestamps for each chunk from the ASR output
total_duration = inputs.shape[0] / self.sampling_rate
if transcript[-1]["timestamp"][-1] is None:
transcript[-1]["timestamp"] = (transcript[-1]["timestamp"][0], total_duration)

end_timestamps = np.array([chunk["timestamp"][-1] for chunk in transcript])

segmented_preds = []

# align the diarizer timestamps and the ASR timestamps
for segment in new_segments:
# get the diarizer end timestamp
end_time = segment["segment"]["end"]
# find the ASR end timestamp that is closest to the diarizer's end timestamp and cut the transcript to here
upto_idx = np.argmin(np.abs(end_timestamps - end_time))

if group_by_speaker:
segmented_preds.append(
{
"speaker": segment["speaker"],
"text": "".join([chunk["text"] for chunk in transcript[: upto_idx + 1]]),
"timestamp": (transcript[0]["timestamp"][0], transcript[upto_idx]["timestamp"][1]),
}
)
else:
for i in range(upto_idx + 1):
segmented_preds.append({"speaker": segment["speaker"], **transcript[i]})

# crop the transcripts and timestamp lists according to the latest timestamp (for faster argmin)
transcript = transcript[upto_idx + 1 :]
end_timestamps = end_timestamps[upto_idx + 1 :]
# find the ASR end timestamp that is closest to the diarizer's end timestamp
# only if the ending timestamp is not None
if transcript and transcript[0]["timestamp"][-1] is not None:
upto_idx = np.argmin(np.abs(end_timestamps - end_time))

if group_by_speaker:
segmented_preds.append(
{
"speaker": segment["speaker"],
"text": "".join([chunk["text"] for chunk in transcript[:upto_idx + 1]]),
"timestamp": (transcript[0]["timestamp"][0], transcript[upto_idx]["timestamp"][1]),
}
)
else:
for i in range(upto_idx + 1):
segmented_preds.append({"speaker": segment["speaker"], **transcript[i]})

# crop the transcripts and timestamp lists according to the latest timestamp (for faster argmin)
transcript = transcript[upto_idx + 1:]
end_timestamps = end_timestamps[upto_idx + 1:]

return segmented_preds

Expand Down