Jaikinator
163 lines
5.9 KiB
Python
163 lines
5.9 KiB
Python
from autotranscript.audio import AudioProcessor
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from autotranscript.diarisation import Diariser
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from autotranscript.transcriber import Transcriber, whisper
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from autotranscript.transcript_exporter import Transcript
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from typing import Union , TypeVar
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from tqdm import trange
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import torch
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import os
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from glob import iglob
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from subprocess import run
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from warnings import warn
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diarisation = TypeVar('diarisation')
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class AutoTranscribe:
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def __init__(self,
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whisper_model: Union[bool, str, whisper] = None,
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dia_model : Union[bool, str, diarisation] = None,
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dia_kwargs : dict = {},
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whisper_kwargs : dict = {}) -> None:
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"""
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AutoTranscribe class
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This class is the core Api Class of the autotranscript package.
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It allows to transcribe audio files with a whisper model and
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pyannote diarization model.
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Therefore it is do a fully automatic transcription of audio files.
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:param whisper_model: path to whisper model or whisper model
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:param dia_model: path to pyannote diarization model
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:param dia_kwargs: kwargs for pyannote diarization model
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:param whisper_kwargs: kwargs for whisper model
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"""
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if whisper_model is None:
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self.transcriber = Transcriber.load_model("medium", local=True)
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elif isinstance(whisper_model, str):
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self.transcriber = Transcriber.load_model(whisper_model, **whisper_kwargs)
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else:
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self.transcriber = whisper_model
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if dia_model is None:
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self.diariser = Diariser.load_model()
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elif isinstance(dia_model, str):
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self.diariser = Diariser.load_model(dia_model, **dia_kwargs)
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else:
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self.diariser = dia_model
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print("AutoTranscribe initialized all models successfully loaded.")
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def transcribe(self, audiofile : Union[str, torch.Tensor],
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remove_original : bool = False,
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*args, **kwargs) -> Transcript:
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"""
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Transcribe audiofile with whisper model and pyannote diarization model
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:param audiofile: path to audiofile or torch.Tensor
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:param remove_original: if True the original audiofile will be removed after
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transcription.
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:return: Transcript object which contains the transcript and can be used to
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export the transcript to differnt formats.
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"""
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audiofile = self.get_audiofile(audiofile)
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final_transcript = dict()
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dia_audio = {"waveform" :
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audiofile.waveform.reshape(1,len(audiofile.waveform)),
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"sample_rate": audiofile.sr}
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print("Starting diarisation.")
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diarisation = self.diariser.diarization(dia_audio,
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*args , **kwargs)
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print("Diarisation finished. Starting transcription.")
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audiofile.sr = torch.Tensor([audiofile.sr]).to(audiofile.waveform.device)
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for i in trange(len(diarisation["segments"]), desc= "Transcribing"):
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seg = diarisation["segments"][i]
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audio = audiofile.cut(seg[0], seg[1])
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transcript = self.transcriber.transcribe(audio, *args , **kwargs)
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final_transcript[i] = {"speaker" : diarisation["speakers"][i],
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"segment" : seg,
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"text" : transcript}
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if remove_original:
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if kwargs.get("shred") is True:
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self.remove_audio_file(audiofile, shred=True)
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else:
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self.remove_audio_file(audiofile, shred=False)
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return Transcript(final_transcript)
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@staticmethod
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def remove_audio_file(audiofile : str,
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shred : bool = False) -> None:
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"""
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removes orginal audiofile to avoid disk space problems
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or to enshure data privacy
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:param audiofile: path to audiofile
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:param shred: if True audiofile will be shredded and not only removed
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"""
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if not os.path.exists(audiofile):
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raise ValueError(f"Audiofile {audiofile} does not exist.")
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if shred:
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warn("Shredding audiofile can take a long time.", RuntimeWarning)
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gen = iglob(f'{audiofile}', recursive=True)
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cmd = ['shred', '-zvu', '-n', '10', f'{audiofile}']
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if os.path.isdir(audiofile):
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raise ValueError(f"Audiofile {audiofile} is a directory.")
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for file in gen:
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print(f'shredding {file} now\n')
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run(cmd , check=True)
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else:
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os.remove(audiofile)
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print(f"Audiofile {audiofile} removed.")
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@staticmethod
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def get_audiofile(audiofile : Union[str, torch.Tensor],
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*args, **kwargs) -> AudioProcessor:
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"""
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Get audiofile as TorchAudioProcessor
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:param audiofile: path to audiofile or torch.Tensor
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:type audiofile: Union[str, torch.Tensor]
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:return: object of audiofile containes
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waveform and sample_rate in torch.Tensor format.
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:rtype: TorchAudioProcessor
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"""
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if isinstance(audiofile, str):
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audiofile = AudioProcessor.from_file(audiofile)
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if isinstance(audiofile, torch.Tensor):
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audiofile = AudioProcessor(audiofile[0], audiofile[1])
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if not isinstance(audiofile, AudioProcessor):
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raise ValueError(f'Audiofile must be of type AudioProcessor,' \
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f'not {type(audiofile)}')
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return audiofile |