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Add mel_spectrogram_device parameter
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@ -51,6 +51,7 @@ def transcribe(
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append_punctuations: str = "\"'.。,,!!??::”)]}、",
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clip_timestamps: Union[str, List[float]] = "0",
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hallucination_silence_threshold: Optional[float] = None,
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mel_spectrogram_device: Optional[Union[str, torch.device]] = None,
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**decode_options,
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):
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"""
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@ -113,6 +114,9 @@ def transcribe(
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When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
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when a possible hallucination is detected
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mel_spectrogram_device: Optional[Union[str, torch.device]]
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If given, the audio tensor is moved to this device before STFT
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Returns
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-------
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A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
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@ -131,7 +135,7 @@ def transcribe(
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# Pad 30-seconds of silence to the input audio, for slicing
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mel = log_mel_spectrogram(
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audio, model.dims.n_mels, padding=N_SAMPLES, device=model.device
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audio, model.dims.n_mels, padding=N_SAMPLES, device=mel_spectrogram_device
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)
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content_frames = mel.shape[-1] - N_FRAMES
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content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE)
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