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Fix: Allow vad options to be configurable by correctly passing down to FasterWhisperPipeline.

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This commit is contained in:
Andrew Bettke
2023-10-05 10:06:34 -04:00
parent 07fafa37b3
commit 79801167ac
2 changed files with 16 additions and 3 deletions
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10
whisperx/asr.py
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@ -111,6 +111,7 @@ def load_model(whisper_arch,
tokenizer=tokenizer, tokenizer=tokenizer,
language=language, language=language,
suppress_numerals=suppress_numerals, suppress_numerals=suppress_numerals,
vad_params=default_vad_options,
) )
class WhisperModel(faster_whisper.WhisperModel): class WhisperModel(faster_whisper.WhisperModel):
@ -186,6 +187,7 @@ class FasterWhisperPipeline(Pipeline):
self, self,
model, model,
vad, vad,
vad_params: dict,
options : NamedTuple, options : NamedTuple,
tokenizer=None, tokenizer=None,
device: Union[int, str, "torch.device"] = -1, device: Union[int, str, "torch.device"] = -1,
@ -218,6 +220,7 @@ class FasterWhisperPipeline(Pipeline):
super(Pipeline, self).__init__() super(Pipeline, self).__init__()
self.vad_model = vad self.vad_model = vad
self._vad_params = vad_params
def _sanitize_parameters(self, **kwargs): def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {} preprocess_kwargs = {}
@ -266,7 +269,12 @@ class FasterWhisperPipeline(Pipeline):
yield {'inputs': audio[f1:f2]} yield {'inputs': audio[f1:f2]}
vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE}) vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
vad_segments = merge_chunks(vad_segments, chunk_size) vad_segments = merge_chunks(
vad_segments,
chunk_size,
onset=self._vad_params["vad_onset"],
offset=self._vad_params["vad_offset"],
)
if self.tokenizer is None: if self.tokenizer is None:
language = language or self.detect_language(audio) language = language or self.detect_language(audio)
task = task or "transcribe" task = task or "transcribe"

9
whisperx/vad.py
View File

@ -260,7 +260,12 @@ def merge_vad(vad_arr, pad_onset=0.0, pad_offset=0.0, min_duration_off=0.0, min_
active_segs = pd.DataFrame([x['segment'] for x in active['content']]) active_segs = pd.DataFrame([x['segment'] for x in active['content']])
return active_segs return active_segs
def merge_chunks(segments, chunk_size): def merge_chunks(
segments,
chunk_size,
onset: float = 0.5,
offset: Optional[float] = None,
):
""" """
Merge operation described in paper Merge operation described in paper
""" """
@ -270,7 +275,7 @@ def merge_chunks(segments, chunk_size):
speaker_idxs = [] speaker_idxs = []
assert chunk_size > 0 assert chunk_size > 0
binarize = Binarize(max_duration=chunk_size) binarize = Binarize(max_duration=chunk_size, onset=onset, offset=offset)
segments = binarize(segments) segments = binarize(segments)
segments_list = [] segments_list = []
for speech_turn in segments.get_timeline(): for speech_turn in segments.get_timeline():