Create app.py

app.py

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+import torch
+import gradio as gr
+import soundfile as sf
+import tempfile
+
+from transformers import (
+    SpeechT5Processor,
+    SpeechT5ForSpeechToSpeech,
+    SpeechT5HifiGan
+)
+from datasets import load_dataset
+import librosa
+
+# Carga de modelos y recursos
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_vc")
+model = SpeechT5ForSpeechToSpeech.from_pretrained("microsoft/speecht5_vc")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+# Speaker embeddings fijos (puedes extender a selección en el UI)
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+# Función principal
+def voice_conversion(audio_file):
+    # Cargar audio con librosa
+    audio, sr = librosa.load(audio_file, sr=16000)  # asegúrate de que esté en 16kHz
+    inputs = processor(audio=audio, sampling_rate=16000, return_tensors="pt")
+
+    with torch.no_grad():
+        speech = model.generate_speech(inputs["input_values"], speaker_embeddings, vocoder=vocoder)
+
+    # Guardar en archivo temporal
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        sf.write(f.name, speech.numpy(), samplerate=16000)
+        return f.name
+
+# Interfaz Gradio
+interface = gr.Interface(
+    fn=voice_conversion,
+    inputs=gr.Audio(source="upload", type="filepath", label="Sube un audio (voz hablada)"),
+    outputs=gr.Audio(type="filepath", label="Voz convertida"),
+    title="SpeechT5 Voice Conversion",
+    description="Convierte una voz hablada en otra con SpeechT5 de Microsoft"
+)
+
+interface.launch()