Jaikinator
265 lines
10 KiB
Python
265 lines
10 KiB
Python
from .audio import AudioProcessor
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from .diarisation import Diariser
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from .transcriber import Transcriber, whisper
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from .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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import argparse
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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
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def cli():
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from whisper import available_models
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from whisper.utils import get_writer
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from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
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from .transcriber import WHISPER_DEFAULT_PATH
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def str2bool(string):
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str2val = {"True": True, "False": False}
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if string in str2val:
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return str2val[string]
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else:
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raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
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# fmt: off
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parser = argparse.ArgumentParser(formatter_class=
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argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("audio", nargs="+", type=str,
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help="audio file(s) to transcribe")
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parser.add_argument("--wmodel", default="medium",
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help="name of the Whisper model to use")
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parser.add_argument("--wmodel_dir", type=str, default= WHISPER_DEFAULT_PATH,
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help="the path to save model files; uses ./models/whisper by default")
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parser.add_argument("--device",
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default="cuda" if torch.cuda.is_available() else "cpu",
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help="device to use for PyTorch inference")
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parser.add_argument("--threads", type=int, default=0,
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help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
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parser.add_argument("--output_dir", "-o", type=str, default=".",
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help="directory to save the outputs")
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parser.add_argument("--output_format", "-f", type=str, default="txt",
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choices=["txt", "json", "md", "html"],
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help="format of the output file; if not specified, all available formats will be produced")
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parser.add_argument("--verbose", type=str2bool, default=True,
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help="whether to print out the progress and debug messages")
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parser.add_argument("--task", type=str, default="transcribe",
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choices=["transcribe", "diarize","wtranscribe"],
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help="whether to perfrom transcription and diazation or only one of them")
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parser.add_argument("--language", type=str, default=None,
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choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]),
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help="language spoken in the audio, specify None to perform language detection")
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# fmt: on
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args = parser.parse_args().__dict__
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model_name: str = args.pop("wmodel")
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model_dir: str = args.pop("wmodel_dir")
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output_dir: str = args.pop("output_dir")
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output_format: str = args.pop("output_format")
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task = args.pop("task")
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device: str = args.pop("device")
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os.makedirs(output_dir, exist_ok=True)
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if (threads := args.pop("threads")) > 0:
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torch.set_num_threads(threads)
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wkwargs = {"download_root": model_dir,
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"device": device,
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"language" : args.pop("language")}
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model = AutoTranscribe(whisper_model= model_name, whisper_kwargs= wkwargs)
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if task == "transcribe":
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for audio in args.pop("audio"):
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out = model.transcribe(audio)
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basename = audio.split("/")[-1].split(".")[0]
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spath = f"{output_dir}/{basename}.{output_format}"
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out.save(spath)
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elif task == "diarize":
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warn("Diarization is still in beta and may not work as expected.",
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RuntimeWarning)
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for audio in args.pop("audio"):
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out = model.diariser.diarization(audio)
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basename = audio.split("/")[-1].split(".")[0]
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spath = f"{output_dir}/{basename}.json"
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print(f"diairization results saved to {spath}")
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out.save(spath)
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elif task == "wtranscribe":
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writer = get_writer(output_format, output_dir)
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warn("whisper transcription is poorly supported and may not work as expected." \
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"It is recommendet to use the whisper cli directly",
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RuntimeWarning)
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for audio in args.pop("audio"):
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out = model.transcriber.transcribe(audio, diarisation=True)
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basename = audio.split("/")[-1].split(".")[0]
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writer(out, audio)
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if __name__ == "__main__":
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cli() |