Jaikinator
360 lines
14 KiB
Python
360 lines
14 KiB
Python
"""
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AutoTranscribe Class
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--------------------
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This class serves as the core of the transcription system, responsible for handling
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transcription and diarization of audio files. It leverages pretrained models for
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speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio),
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providing an accessible interface for audio processing tasks such as transcription,
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speaker separation, and timestamping.
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By encapsulating the complexities of underlying models, it allows for straightforward
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integration into various applications, ranging from transcription services to voice assistants.
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Available Classes:
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- AutoTranscribe: Main class for performing transcription and diarization.
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Includes methods for loading models, processing audio files,
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and formatting the transcription output.
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Usage:
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from .autotranscribe import AutoTranscribe
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model = AutoTranscribe(whisper_model="path/to/whisper/model", dia_model="path/to/diarisation/model")
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transcript = model.transcribe("path/to/audiofile.wav")
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"""
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# Standard Library Imports
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import argparse
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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 typing import TypeVar, Union
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from warnings import warn
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# Third-Party Imports
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import torch
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from numpy import ndarray
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from tqdm import trange
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# Application-Specific Imports
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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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DiarisationType = TypeVar('DiarisationType')
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class AutoTranscribe:
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"""
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AutoTranscribe is a class responsible for managing the transcription and diarization of audio files.
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It serves as the core of the transcription system, incorporating pretrained models
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for speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio),
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allowing for comprehensive audio processing.
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Attributes:
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transcriber (Transcriber): The transcriber object to handle transcription.
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diariser (Diariser): The diariser object to handle diarization.
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Methods:
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__init__: Initializes the AutoTranscribe class with appropriate models.
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transcribe: Transcribes an audio file using the whisper model and pyannote diarization model.
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remove_audio_file: Removes the original audio file to avoid disk space issues or ensure data privacy.
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get_audio_file: Gets an audio file as an AudioProcessor object.
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"""
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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, DiarisationType] = None,
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**kwargs) -> None:
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"""Initializes the AutoTranscribe class.
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Args:
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whisper_model (Union[bool, str, whisper], optional):
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Path to whisper model or whisper model itself.
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diarisation_model (Union[bool, str, DiarisationType], optional):
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Path to pyannote diarization model or model itself.
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**kwargs: Additional keyword arguments for whisper
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and pyannote diarization models.
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"""
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if whisper_model is None:
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self.transcriber = Transcriber.load_model("medium")
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elif isinstance(whisper_model, str):
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self.transcriber = Transcriber.load_model(whisper_model, **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, **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, audio_file : Union[str, torch.Tensor, ndarray],
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remove_original : bool = False,
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**kwargs) -> Transcript:
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"""
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Transcribes an audio file using the whisper model and pyannote diarization model.
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Args:
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audio_file (Union[str, torch.Tensor, ndarray]):
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Path to audio file or a tensor representing the audio.
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remove_original (bool, optional): If True, the original audio file will
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be removed after transcription.
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*args: Additional positional arguments for diarization and transcription.
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**kwargs: Additional keyword arguments for diarization and transcription.
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Returns:
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Transcript: A Transcript object containing the transcription,
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which can be exported to different formats.
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"""
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# Get audio file as an AudioProcessor object
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audio_file = self.get_audio_file(audio_file)
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# Prepare waveform and sample rate for diarization
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dia_audio = {
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"waveform" : audio_file.waveform.reshape(1,len(audio_file.waveform)),
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"sample_rate": audio_file.sr
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}
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print("Starting diarisation.")
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diarisation = self.diariser.diarization(dia_audio, **kwargs)
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if not diarisation["segments"]:
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warn("No segments found. Try to run transcription without diarisation.")
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transcript = self.transcriber.transcribe(audio_file.waveform, **kwargs)
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final_transcript= {"speakers" : ["speaker01"],
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"segments" : [0, len(audio_file.waveform)],
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"text" : transcript}
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return Transcript(final_transcript)
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print("Diarisation finished. Starting transcription.")
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audio_file.sr = torch.Tensor([audio_file.sr]).to(audio_file.waveform.device)
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# Transcribe each segment and store the results
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final_transcript = dict()
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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 = audio_file.cut(seg[0], seg[1])
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transcript = self.transcriber.transcribe(audio, **kwargs)
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final_transcript[i] = {"speakers" : diarisation["speakers"][i],
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"segments" : seg,
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"text" : transcript}
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# Remove original file if needed
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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(audio_file, shred=True)
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else:
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self.remove_audio_file(audio_file, shred=False)
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return Transcript(final_transcript)
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@staticmethod
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def remove_audio_file(audio_file : str,
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shred : bool = False) -> None:
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"""
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Removes the original audio file to avoid disk space issues or ensure data privacy.
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Args:
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audio_file_path (str): Path to the audio file.
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shred (bool, optional): If True, the audio file will be shredded,
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not just removed.
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"""
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if not os.path.exists(audio_file):
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raise ValueError(f"Audiofile {audio_file} 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'{audio_file}', recursive=True)
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cmd = ['shred', '-zvu', '-n', '10', f'{audio_file}']
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if os.path.isdir(audio_file):
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raise ValueError(f"Audiofile {audio_file} 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(audio_file)
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print(f"Audiofile {audio_file} removed.")
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@staticmethod
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def get_audio_file(audio_file : Union[str, torch.Tensor, ndarray],
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*args, **kwargs) -> AudioProcessor:
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"""Gets an audio file as TorchAudioProcessor.
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Args:
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audio_file (Union[str, torch.Tensor, ndarray]): Path to the audio file or
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a tensor representing the audio.
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*args: Additional positional arguments.
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**kwargs: Additional keyword arguments.
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Returns:
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AudioProcessor: An object containing the waveform and sample rate in
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torch.Tensor format.
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"""
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if isinstance(audio_file, str):
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audio_file = AudioProcessor.from_file(audio_file)
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elif isinstance(audio_file, torch.Tensor):
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audio_file = AudioProcessor(audio_file[0], audio_file[1])
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elif isinstance(audio_file, ndarray):
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audio_file = AudioProcessor(torch.Tensor(audio_file[0]),
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audio_file[1])
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if not isinstance(audio_file, AudioProcessor):
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raise ValueError(f'Audiofile must be of type AudioProcessor,' \
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f'not {type(audio_file)}')
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return audio_file
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def cli():
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"""
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Command-Line Interface (CLI) for the AutoTranscribe class, allowing for user interaction to transcribe
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and diarize audio files. The function includes arguments for specifying the audio files, model paths,
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output formats, and other options necessary for transcription.
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This function can be executed from the command line to perform transcription tasks, providing a
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user-friendly way to access the AutoTranscribe class functionalities.
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"""
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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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from .diarisation import PYANNOTE_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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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("-f","--audio_files", nargs="+", type=str,
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help="List of audio files to transcribe.")
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parser.add_argument('--start_server', action='store_true',
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help='Start the Gradio app.')
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parser.add_argument("--whisper_model_name", default="medium",
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help="Name of the Whisper model to use.")
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parser.add_argument("--whisper_model_directory", type=str, default=WHISPER_DEFAULT_PATH,
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help="Path to save Whisper model files; defaults to ./models/whisper.")
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parser.add_argument("--diarization_directory", type=str, default=PYANNOTE_DEFAULT_PATH,
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help="Path to the diarization model directory.")
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parser.add_argument("--huggingface_token", default="", type=str,
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help="HuggingFace token for private model download.")
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parser.add_argument("--allow_download", type=str2bool, default=False,
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help="Allow model download if not found locally.")
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parser.add_argument("--inference_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("--num_threads", type=int, default=0,
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help="Number of threads used by torch for CPU inference; overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
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parser.add_argument("--output_directory", "-o", type=str, default=".",
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help="Directory to save the transcription outputs.")
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parser.add_argument("--output_format", "-of", type=str, default="txt",
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choices=["txt", "json", "md", "html"],
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help="Format of the output file; defaults to txt.")
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parser.add_argument("--verbose_output", type=str2bool, default=True,
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help="Enable or disable progress and debug messages.")
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parser.add_argument("--transcription_task", type=str, default="transcribe",
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choices=["transcribe", "diarize", "wtranscribe"],
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help="Choose to perform transcription, diarization, or Whisper transcription.")
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parser.add_argument("--spoken_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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args = parser.parse_args()
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output_directory = args.output_directory
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num_threads = args.num_threads
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whisper_model_directory = args.whisper_model_directory
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allow_download = args.allow_download
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inference_device = args.inference_device
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whisper_model_name = args.whisper_model_name
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diarization_directory = args.diarization_directory
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huggingface_token = args.huggingface_token
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transcription_task = args.transcription_task
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audio_files = args.audio_files
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spoken_language = args.spoken_language
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output_format = args.output_format
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start_server = args.start_server
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os.makedirs(output_directory, exist_ok=True)
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if num_threads > 0:
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torch.set_num_threads(num_threads)
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whisper_kwargs = {
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"download_root": whisper_model_directory,
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"local": allow_download,
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"device": inference_device
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}
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diarisation_kwargs = {
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"local": allow_download,
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"token": huggingface_token
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}
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model = AutoTranscribe(whisper_model=whisper_model_name,
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whisper_kwargs=whisper_kwargs,
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dia_model=diarization_directory,
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dia_kwargs=diarisation_kwargs)
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if transcription_task == "transcribe":
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for audio in audio_files:
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out = model.transcribe(audio, language=spoken_language)
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basename = audio.split("/")[-1].split(".")[0]
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spath = f"{output_directory}/{basename}.{output_format}"
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out.save(spath)
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# ... include other tasks here ...
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elif transcription_task == "diarize":
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# diarize code here
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pass
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elif transcription_task == "wtranscribe":
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# wtranscribe code here
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pass
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if start_server:
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from .gradio_app import gradio_app
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gradio_app(model)
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if __name__ == "__main__":
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cli() |