""" AutoTranscribe Class -------------------- This class serves as the core of the transcription system, responsible for handling transcription and diarization of audio files. It leverages pretrained models for speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio), providing an accessible interface for audio processing tasks such as transcription, speaker separation, and timestamping. By encapsulating the complexities of underlying models, it allows for straightforward integration into various applications, ranging from transcription services to voice assistants. Available Classes: - AutoTranscribe: Main class for performing transcription and diarization. Includes methods for loading models, processing audio files, and formatting the transcription output. Usage: from .autotranscribe import AutoTranscribe model = AutoTranscribe(whisper_model="path/to/whisper/model", dia_model="path/to/diarisation/model") transcript = model.transcribe("path/to/audiofile.wav") """ # Standard Library Imports import os from glob import iglob import re from subprocess import run from typing import TypeVar, Union from warnings import warn # Third-Party Imports import torch from numpy import ndarray from tqdm import trange # Application-Specific Imports from .audio import AudioProcessor from .diarisation import Diariser from .transcriber import Transcriber, whisper from .transcript_exporter import Transcript DiarisationType = TypeVar('DiarisationType') class AutoTranscribe: """ AutoTranscribe is a class responsible for managing the transcription and diarization of audio files. It serves as the core of the transcription system, incorporating pretrained models for speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio), allowing for comprehensive audio processing. Attributes: transcriber (Transcriber): The transcriber object to handle transcription. diariser (Diariser): The diariser object to handle diarization. Methods: __init__: Initializes the AutoTranscribe class with appropriate models. transcribe: Transcribes an audio file using the whisper model and pyannote diarization model. remove_audio_file: Removes the original audio file to avoid disk space issues or ensure data privacy. get_audio_file: Gets an audio file as an AudioProcessor object. """ def __init__(self, whisper_model: Union[bool, str, whisper] = None, dia_model : Union[bool, str, DiarisationType] = None, **kwargs) -> None: """Initializes the AutoTranscribe class. Args: whisper_model (Union[bool, str, whisper], optional): Path to whisper model or whisper model itself. diarisation_model (Union[bool, str, DiarisationType], optional): Path to pyannote diarization model or model itself. **kwargs: Additional keyword arguments for whisper and pyannote diarization models. """ if whisper_model is None: self.transcriber = Transcriber.load_model("medium") elif isinstance(whisper_model, str): self.transcriber = Transcriber.load_model(whisper_model, **kwargs) else: self.transcriber = whisper_model if dia_model is None: self.diariser = Diariser.load_model() elif isinstance(dia_model, str): self.diariser = Diariser.load_model(dia_model, **kwargs) else: self.diariser = dia_model print("AutoTranscribe initialized all models successfully loaded.") def autotranscribe(self, audio_file : Union[str, torch.Tensor, ndarray], remove_original : bool = False, **kwargs) -> Transcript: """ Transcribes an audio file using the whisper model and pyannote diarization model. Args: audio_file (Union[str, torch.Tensor, ndarray]): Path to audio file or a tensor representing the audio. remove_original (bool, optional): If True, the original audio file will be removed after transcription. *args: Additional positional arguments for diarization and transcription. **kwargs: Additional keyword arguments for diarization and transcription. Returns: Transcript: A Transcript object containing the transcription, which can be exported to different formats. """ # Get audio file as an AudioProcessor object audio_file = self.get_audio_file(audio_file) # Prepare waveform and sample rate for diarization dia_audio = { "waveform" : audio_file.waveform.reshape(1,len(audio_file.waveform)), "sample_rate": audio_file.sr } print("Starting diarisation.") diarisation = self.diariser.diarization(dia_audio, **kwargs) if not diarisation["segments"]: warn("No segments found. Try to run transcription without diarisation.") transcript = self.transcriber.transcribe(audio_file.waveform, **kwargs) final_transcript= {"speakers" : ["speaker01"], "segments" : [0, len(audio_file.waveform)], "text" : transcript} return Transcript(final_transcript) print("Diarisation finished. Starting transcription.") audio_file.sr = torch.Tensor([audio_file.sr]).to(audio_file.waveform.device) # Transcribe each segment and store the results final_transcript = dict() for i in trange(len(diarisation["segments"]), desc= "Transcribing"): seg = diarisation["segments"][i] audio = audio_file.cut(seg[0], seg[1]) transcript = self.transcriber.transcribe(audio, **kwargs) final_transcript[i] = {"speakers" : diarisation["speakers"][i], "segments" : seg, "text" : transcript} # Remove original file if needed if remove_original: if kwargs.get("shred") is True: self.remove_audio_file(audio_file, shred=True) else: self.remove_audio_file(audio_file, shred=False) return Transcript(final_transcript) def diarization(self, audio_file : Union[str, torch.Tensor, ndarray], **kwargs) -> dict: """ Perform diarization on an audio file using the pyannote diarization model. Args: audio_file (Union[str, torch.Tensor, ndarray]): The audio source which can either be a path to the audio file or a tensor representation. **kwargs: Additional keyword arguments for diarization. Returns: dict: A dictionary containing the results of the diarization process. """ # Get audio file as an AudioProcessor object audio_file = self.get_audio_file(audio_file) # Prepare waveform and sample rate for diarization dia_audio = { "waveform" : audio_file.waveform.reshape(1,len(audio_file.waveform)), "sample_rate": audio_file.sr } print("Starting diarisation.") diarisation = self.diariser.diarization(dia_audio, **kwargs) return diarisation def transcribe(self, audio_file : Union[str, torch.Tensor, ndarray], **kwargs): """ Transcribe the provided audio file. Args: audio_file (Union[str, torch.Tensor, ndarray]): The audio source, which can either be a path or a tensor representation. **kwargs: Additional keyword arguments for transcription. Returns: str: The transcribed text from the audio source. """ audio_file = self.get_audio_file(audio_file) return self.transcriber.transcribe(audio_file.waveform, **kwargs) @staticmethod def remove_audio_file(audio_file : str, shred : bool = False) -> None: """ Removes the original audio file to avoid disk space issues or ensure data privacy. Args: audio_file_path (str): Path to the audio file. shred (bool, optional): If True, the audio file will be shredded, not just removed. """ if not os.path.exists(audio_file): raise ValueError(f"Audiofile {audio_file} does not exist.") if shred: warn("Shredding audiofile can take a long time.", RuntimeWarning) gen = iglob(f'{audio_file}', recursive=True) cmd = ['shred', '-zvu', '-n', '10', f'{audio_file}'] if os.path.isdir(audio_file): raise ValueError(f"Audiofile {audio_file} is a directory.") for file in gen: print(f'shredding {file} now\n') run(cmd , check=True) else: os.remove(audio_file) print(f"Audiofile {audio_file} removed.") @staticmethod def get_audio_file(audio_file : Union[str, torch.Tensor, ndarray], *args, **kwargs) -> AudioProcessor: """Gets an audio file as TorchAudioProcessor. Args: audio_file (Union[str, torch.Tensor, ndarray]): Path to the audio file or a tensor representing the audio. *args: Additional positional arguments. **kwargs: Additional keyword arguments. Returns: AudioProcessor: An object containing the waveform and sample rate in torch.Tensor format. """ if isinstance(audio_file, str): audio_file = AudioProcessor.from_file(audio_file) elif isinstance(audio_file, torch.Tensor): audio_file = AudioProcessor(audio_file[0], audio_file[1]) elif isinstance(audio_file, ndarray): audio_file = AudioProcessor(torch.Tensor(audio_file[0]), audio_file[1]) if not isinstance(audio_file, AudioProcessor): raise ValueError(f'Audiofile must be of type AudioProcessor,' \ f'not {type(audio_file)}') return audio_file