ABANDA-OMGBA-Ulrich-Michel
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Parent(s):
00b651f
Create app.py
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app.py
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import torch
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from transformers import VitsModel, AutoTokenizer
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import numpy as np
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import scipy.io.wavfile as wavfile
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def yes(texte):
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model = VitsModel.from_pretrained("facebook/mms-tts-eng")
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tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
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text = texte
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inputs = tokenizer(text, return_tensors="pt")
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with torch.no_grad():
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output = model(**inputs).waveform
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# Normaliser les données audio dans la plage [-1, 1]
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output_normalized = output / torch.max(torch.abs(output))
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# Convertir en tableau numpy avec le bon type
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audio_data = output_normalized.squeeze().cpu().numpy()
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# Mettre à l'échelle dans la plage de valeurs acceptées par WAV
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audio_data_scaled = np.int16(audio_data * 32767)
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# Enregistrer les données audio dans un fichier WAV
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wavfile.write("techno.wav", rate=model.config.sampling_rate, data=audio_data_scaled)
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with open("techno.wav",'rb') as audio:
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audio_data = audio.read()
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return audio_data
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text = gr.Interface(fn=yes, inputs='text', outputs='audio')
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text.launch(debug=True)
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