Update app.py

app.py

@@ -2,6 +2,7 @@ import torch
 import gradio as gr
 import soundfile as sf
 import tempfile
+import librosa
 
 from transformers import (
     SpeechT5Processor,
@@ -9,35 +10,30 @@ from transformers import (
     SpeechT5HifiGan
 )
 from datasets import load_dataset
-import librosa
 
-# Carga de modelos y recursos
+# Modelos
 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 de voz
 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
+    audio, sr = librosa.load(audio_file, sr=16000)
     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
+# Interfaz
 interface = gr.Interface(
     fn=voice_conversion,
-    inputs=gr.Audio(source="upload", type="filepath", label="Sube un audio (voz hablada)"),
+    inputs=gr.Audio(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"