35 lines
985 B
Python
35 lines
985 B
Python
import locale
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from pathlib import Path
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from whisper import Whisper, load_model
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from pyannote.audio import Pipeline
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import torch
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import static_ffmpeg
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import file_operations
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def prep() -> None:
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locale.getpreferredencoding = lambda: "UTF-8"
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# download and add ffmpeg to env
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static_ffmpeg.add_paths()
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def audiofile(url: str, input_path: Path) -> Path:
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file = file_operations.download_from_url(url, input_path)
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file_wav = file_operations.convert_to_wav(file, input_path)
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file.unlink()
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return file_wav
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def diarization(access_token: str | None) -> Pipeline:
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pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization", use_auth_token=access_token
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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return pipeline.to(device)
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def whispermodel() -> Whisper:
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# LOAD MODEL INTO VRAM
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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return load_model("large", device=device)
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