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https://github.com/m-bain/whisperX.git
synced 2025-07-01 18:17:27 -04:00
Update transcribe.py
added the ability to include HF access token in order to use PyAnnote models
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@ -385,7 +385,8 @@ def cli():
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parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
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parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
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parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
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parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
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args = parser.parse_args().__dict__
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model_name: str = args.pop("model")
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model_dir: str = args.pop("model_dir")
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@ -397,7 +398,8 @@ def cli():
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align_extend: float = args.pop("align_extend")
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align_from_prev: bool = args.pop("align_from_prev")
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interpolate_method: bool = args.pop("interpolate_method")
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hf_token: str = args.pop("hf_token")
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vad_filter: bool = args.pop("vad_filter")
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vad_input: bool = args.pop("vad_input")
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@ -410,12 +412,14 @@ def cli():
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vad_input = pd.read_csv(vad_input, header=None, sep= " ")
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elif vad_filter:
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from pyannote.audio import Pipeline
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vad_pipeline = Pipeline.from_pretrained("pyannote/voice-activity-detection")
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vad_pipeline = Pipeline.from_pretrained("pyannote/voice-activity-detection",
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use_auth_token=hf_token)
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diarize_pipeline = None
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if diarize:
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from pyannote.audio import Pipeline
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diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1")
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diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
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use_auth_token=hf_token)
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os.makedirs(output_dir, exist_ok=True)
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