renamed module
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Jaikinator
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"""
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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 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", **kwargs)
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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(**kwargs)
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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 autotranscribe(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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print("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= {0 : {"speakers" : 'SPEAKER_01',
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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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def diarization(self, audio_file : Union[str, torch.Tensor, ndarray],
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**kwargs) -> dict:
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"""
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Perform diarization on an audio file using the pyannote diarization model.
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Args:
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audio_file (Union[str, torch.Tensor, ndarray]):
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The audio source which can either be a path to the audio file or a tensor representation.
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**kwargs:
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Additional keyword arguments for diarization.
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Returns:
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dict:
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A dictionary containing the results of the diarization process.
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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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return diarisation
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def transcribe(self, audio_file : Union[str, torch.Tensor, ndarray],
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**kwargs):
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"""
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Transcribe the provided audio file.
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Args:
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audio_file (Union[str, torch.Tensor, ndarray]):
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The audio source, which can either be a path or a tensor representation.
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**kwargs:
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Additional keyword arguments for transcription.
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Returns:
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str:
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The transcribed text from the audio source.
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"""
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audio_file = self.get_audio_file(audio_file)
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return self.transcriber.transcribe(audio_file.waveform, **kwargs)
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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 __repr__(self):
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return f"AutoTranscribe(transcriber={self.transcriber}, diariser={self.diariser})"
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