app.py

import datasets 
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import soundfile as sf
import numpy as np
import gradio as gr
import io
import sentencepiece

# Charger les modèles et les embeddings du locuteur une seule fois pour éviter de les recharger à chaque appel
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)


def text_to_speech(text):
    # Prétraiter le texte
    inputs = processor(text=text, return_tensors="pt")

    # Générer la parole
    speech = model.generate_speech(
        inputs["input_ids"], speaker_embeddings, vocoder=vocoder
    )

    # Enregistrer l'audio dans un buffer
    buffer = io.BytesIO()
    sf.write(buffer, speech.numpy(), samplerate=16000, format="WAV")

    return buffer.getvalue()


# Créer l'interface Gradio
interface = gr.Interface(
    fn=text_to_speech,
    inputs="text",
    outputs=gr.Audio(label="Processed Audio"),
    title="Application du type Text to speech",
    description="Entrez un texte en anglais et l'application va la traduire en audio"
).launch()