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feat: add verbose output (#759)
--------- Co-authored-by: Abhishek Sharma <abhishek@zipteams.com> Co-authored-by: Barabazs <31799121+Barabazs@users.noreply.github.com>
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@ -171,7 +171,7 @@ class FasterWhisperPipeline(Pipeline):
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return final_iterator
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def transcribe(
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self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False
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self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False, verbose=False
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) -> TranscriptionResult:
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if isinstance(audio, str):
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audio = load_audio(audio)
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@ -223,6 +223,8 @@ class FasterWhisperPipeline(Pipeline):
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text = out['text']
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if batch_size in [0, 1, None]:
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text = text[0]
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if verbose:
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print(f"Transcript: [{round(vad_segments[idx]['start'], 3)} --> {round(vad_segments[idx]['end'], 3)}] {text}")
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segments.append(
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{
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"text": text,
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@ -87,6 +87,7 @@ def cli():
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device: str = args.pop("device")
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device_index: int = args.pop("device_index")
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compute_type: str = args.pop("compute_type")
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verbose: bool = args.pop("verbose")
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# model_flush: bool = args.pop("model_flush")
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os.makedirs(output_dir, exist_ok=True)
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@ -173,7 +174,7 @@ def cli():
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audio = load_audio(audio_path)
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# >> VAD & ASR
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print(">>Performing transcription...")
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result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size, print_progress=print_progress)
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result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size, print_progress=print_progress, verbose=verbose)
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results.append((result, audio_path))
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# Unload Whisper and VAD
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