Update app.py

app.py

@@ -1,17 +1,42 @@
-import transformers
-from transformers import pipeline
-text_speech=pipeline(model="suno/bark")
+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
-def text_to_speech(message):
-    texte = text_speech(message)
+import io
+import sentencepiece
 
-    return texte
+# 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)
 
-iface = gr.Interface(text_to_speech, inputs = 'text',
-                     outputs = 'audio',
-                     title = 'text to Audio Application',
-                     description = 'A simple application to convert PDF files in audio speech. Upload your own file, or click one of the examples to load them.',
-                      )
 
-iface.launch()
+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()