73 lines
2 KiB
Python
73 lines
2 KiB
Python
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# %% Utilisation de Whisper pour la transcription de podcasts en français
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import numpy as np
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import torch
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import torchaudio
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import tqdm
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from transformers import (
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AutoModelForCausalLM,
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AutoModelForSpeechSeq2Seq,
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AutoProcessor,
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pipeline,
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)
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# %% File paths
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audio_paths = ["METTRE LES LIENS DES FICHIERS MP3 OU WAV ICI"]
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# %% load PyTorch
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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# %% Load model
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model_name_or_path = "bofenghuang/whisper-large-v3-french"
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processor = AutoProcessor.from_pretrained(model_name_or_path)
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_name_or_path,
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torch_dtype=torch_dtype,
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low_cpu_mem_usage=True,
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)
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model.to(device)
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# %% Load draft model
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assistant_model_name_or_path = "bofenghuang/whisper-large-v3-french-distil-dec2"
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assistant_model = AutoModelForCausalLM.from_pretrained(
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assistant_model_name_or_path,
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torch_dtype=torch_dtype,
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low_cpu_mem_usage=True,
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)
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assistant_model.to(device)
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# %% Init pipeline
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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feature_extractor=processor.feature_extractor,
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tokenizer=processor.tokenizer,
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torch_dtype=torch_dtype,
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device=device,
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generate_kwargs={"assistant_model": assistant_model},
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max_new_tokens=128,
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)
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# %% Transcript function
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def transcript(audio_path):
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# Load audio
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model_sr = 16000
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speech, sr = torchaudio.load(audio_path)
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speech_16000 = torchaudio.functional.resample(speech, orig_freq=sr, new_freq=model_sr)
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speech_16000 = speech_16000.squeeze()
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# Run pipeline
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result = pipe(np.array(speech_16000))
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# Save text result to file
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transcript_path = f'whisper-large/{audio_path.replace(".mp3", "_transcript_whisper.txt")}'
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with open(transcript_path, "w") as f:
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f.write(result["text"])
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return None
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# %% Transcription loop
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for audio_path in tqdm.tqdm(audio_paths):
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transcript(audio_path)
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