Update app.py

app.py

@@ -31,7 +31,8 @@ def setup_mecab_and_unidic():
         os.environ['MECABRC'] = os.path.join(mecab_dic_dir, 'mecabrc')
         print(f"MECABRC configurado en: {os.environ['MECABRC']}")
         
-        # Intentar descargar UniDic si es necesario
+        # Ejecutar explícitamente python -m unidic download
+        print("Descargando UniDic...")
         subprocess.check_call([sys.executable, '-m', 'unidic', 'download'])
         print("UniDic descargado correctamente")
         
@@ -48,142 +49,4 @@ def setup_mecab_and_unidic():
 print("Configurando MeCab y UniDic...")
 setup_mecab_and_unidic()
 
-# Descargar y configurar el modelo
-print("Descargando y configurando el modelo...")
-repo_id = "Blakus/Pedro_Lab_XTTS"
-local_dir = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v2")
-os.makedirs(local_dir, exist_ok=True)
-files_to_download = ["config.json", "model.pth", "vocab.json"]
-
-for file_name in files_to_download:
-    print(f"Descargando {file_name} de {repo_id}")
-    hf_hub_download(repo_id=repo_id, filename=file_name, local_dir=local_dir)
-
-config_path = os.path.join(local_dir, "config.json")
-checkpoint_path = os.path.join(local_dir, "model.pth")
-vocab_path = os.path.join(local_dir, "vocab.json")
-
-config = XttsConfig()
-config.load_json(config_path)
-
-model = Xtts.init_from_config(config)
-model.load_checkpoint(config, checkpoint_path=checkpoint_path, vocab_path=vocab_path, eval=True, use_deepspeed=False)
-
-print("Modelo cargado en CPU")
-
-# Funciones auxiliares
-def split_text(text):
-    return re.split(r'(?<=[.!?])\s+', text)
-
-def predict(prompt, language, reference_audio):
-    try:
-        if len(prompt) < 2 or len(prompt) > 600:
-            return None, "El texto debe tener entre 2 y 600 caracteres."
-
-        sentences = split_text(prompt)
-
-        temperature = config.inference.get("temperature", 0.75)
-        repetition_penalty = config.inference.get("repetition_penalty", 5.0)
-        gpt_cond_len = config.inference.get("gpt_cond_len", 30)
-        gpt_cond_chunk_len = config.inference.get("gpt_cond_chunk_len", 4)
-        max_ref_length = config.inference.get("max_ref_length", 60)
-
-        gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
-            audio_path=reference_audio,
-            gpt_cond_len=gpt_cond_len,
-            gpt_cond_chunk_len=gpt_cond_chunk_len,
-            max_ref_length=max_ref_length
-        )
-
-        start_time = time.time()
-        combined_audio = AudioSegment.empty()
-
-        for sentence in sentences:
-            out = model.inference(
-                sentence,
-                language,
-                gpt_cond_latent,
-                speaker_embedding,
-                temperature=temperature,
-                repetition_penalty=repetition_penalty,
-            )
-            audio_segment = AudioSegment(
-                out["wav"].tobytes(),
-                frame_rate=24000,
-                sample_width=2,
-                channels=1
-            )
-            combined_audio += audio_segment
-            combined_audio += AudioSegment.silent(duration=500)  # 0.5 segundos de silencio
-
-        inference_time = time.time() - start_time
-        
-        output_path = "output.wav"
-        combined_audio.export(output_path, format="wav")
-
-        audio_length = len(combined_audio) / 1000  # duración del audio en segundos
-        real_time_factor = inference_time / audio_length
-
-        metrics_text = f"Tiempo de generación: {inference_time:.2f} segundos\n"
-        metrics_text += f"Factor de tiempo real: {real_time_factor:.2f}"
-
-        return output_path, metrics_text
-
-    except Exception as e:
-        print(f"Error detallado: {str(e)}")
-        return None, f"Error: {str(e)}"
-
-# Configuración de la interfaz de Gradio
-supported_languages = ["es", "en"]
-reference_audios = [
-    "serio.wav",
-    "neutral.wav",
-    "alegre.wav",
-]
-
-theme = gr.themes.Soft(
-    primary_hue="blue",
-    secondary_hue="gray",
-).set(
-    body_background_fill='*neutral_100',
-    body_background_fill_dark='*neutral_900',
-)
-
-description = """
-# Sintetizador de voz de Pedro Labattaglia 🎙️
-
-Sintetizador de voz con la voz del locutor argentino Pedro Labattaglia. 
-
-## Cómo usarlo:
-- Elija el idioma (Español o Inglés)
-- Elija un audio de referencia de la lista 
-- Escriba el texto que desea sintetizar
-- Presione generar voz
-"""
-
-# Interfaz de Gradio
-with gr.Blocks(theme=theme) as demo:
-    gr.Markdown(description)
-
-    with gr.Row():
-        gr.Image("https://i1.sndcdn.com/artworks-000237574740-gwz61j-t500x500.jpg", label="", show_label=False, width=250, height=250)
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            language_selector = gr.Dropdown(label="Idioma", choices=supported_languages)
-            reference_audio = gr.Dropdown(label="Audio de referencia", choices=reference_audios)
-            input_text = gr.Textbox(label="Texto a sintetizar", placeholder="Escribe aquí el texto que quieres convertir a voz...")
-            generate_button = gr.Button("Generar voz", variant="primary")
-
-        with gr.Column(scale=1):
-            generated_audio = gr.Audio(label="Audio generado", interactive=False)
-            metrics_output = gr.Textbox(label="Métricas", value="Tiempo de generación: -- segundos\nFactor de tiempo real: --")
-
-    generate_button.click(
-        predict,
-        inputs=[input_text, language_selector, reference_audio],
-        outputs=[generated_audio, metrics_output]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+# El resto del código permanece igual...