Jaikinator
113 lines
3.0 KiB
Python
113 lines
3.0 KiB
Python
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import os
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from typing import TypeVar
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from whisper import load_model
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from glob import glob
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whisper = TypeVar('whisper')
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Transcriber = TypeVar('Transcriber')
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def get_whisper_default_path() -> str:
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"""
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Get default path for whisper models
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Returns
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-------
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str
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path
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"""
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_path = os.path.dirname(os.path.dirname(__file__))
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return os.path.join(_path, "models", "whisper")
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WHISPER_DEFAULT_PATH = get_whisper_default_path()
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class Transcriber:
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def __init__(self, model: whisper ) -> None:
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"""
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Initialize Transcriber class with a whisper model
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:param model: whisper model
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"""
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self.model = model
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def transcribe(self, file : str, language:str = "German"):
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"""
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transcribe audio file
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:param file: audio file to transcribe
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:param language: language of the audio file
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:return: transcript as string
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"""
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result = self.model.transcribe(file, language = language)
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return result["text"]
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@staticmethod
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def save_transcript(transcript:str , save_path : str) -> None:
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"""
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Save transcript to file
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:param transcript: transcript as string
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:param savepath: path to save the transcript
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:return: None
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"""
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with open(save_path, 'w') as f:
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f.write(transcript)
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f.close()
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print(f'Transcript saved to {save_path}')
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@classmethod
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def load_whisper_model(cls,
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model: str = "medium",
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local : bool = True,
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download_root: str = WHISPER_DEFAULT_PATH) -> Transcriber:
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"""
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Load whisper module
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Parameters
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----------
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whisper : str
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whisper model
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available models:
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- 'tiny.en'
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- 'tiny'
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- 'base.en'
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- 'base'
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- 'small.en'
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- 'small'
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- 'medium.en'
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- 'medium'
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- 'large-v1'
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- 'large-v2'
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- 'large'
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local : bool
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If true, load from local cache
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download_root : str
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Path to download the model
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default: /models/whisper
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Returns
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-------
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Whisper Object
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"""
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if local:
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available_models = [os.path.basename(x) for x in glob(os.path.join(download_root, "*"))]
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for i, module in enumerate(available_models):
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available_models[i] = module.split(".")[0]
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if model not in available_models:
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raise RuntimeError("Model not found. Consider downloading the "/
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"model first. By deactivating the local flag, " /
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"the model will be downloaded automatically.")
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_model = load_model(model, download_root=download_root)
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return cls(_model)
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