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Update app.py
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app.py
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import sys
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import io, os, stat
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import subprocess
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import random
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from zipfile import ZipFile
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import uuid
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import time
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import torch
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import torchaudio
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import time
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# Mantenemos la descarga de MeCab
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os.system('python -m unidic download')
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# Mantenemos el acuerdo de CPML
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os.environ["COQUI_TOS_AGREED"] = "1"
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import langid
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import base64
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import csv
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from io import StringIO
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import datetime
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import re
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import gradio as gr
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from scipy.io.wavfile import write
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from pydub import AudioSegment
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from TTS.api import TTS
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from TTS.tts.configs.xtts_config import XttsConfig
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from TTS.tts.models.xtts import Xtts
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from TTS.utils.generic_utils import get_user_data_dir
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HF_TOKEN = os.environ.get("HF_TOKEN")
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from huggingface_hub import hf_hub_download
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import os
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from TTS.utils.manage import get_user_data_dir
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# Mantenemos la autenticación y descarga del modelo
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repo_id = "Blakus/Pedro_Lab_XTTS"
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local_dir = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v2")
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os.makedirs(local_dir, exist_ok=True)
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files_to_download = ["config.json", "model.pth", "vocab.json"]
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for file_name in files_to_download:
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print(f"Downloading {file_name} from {repo_id}")
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local_file_path = os.path.join(local_dir, file_name)
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hf_hub_download(repo_id=repo_id, filename=file_name, local_dir=local_dir)
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# Cargamos configuración y modelo
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config_path = os.path.join(local_dir, "config.json")
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checkpoint_path = os.path.join(local_dir, "model.pth")
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vocab_path = os.path.join(local_dir, "vocab.json")
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config = XttsConfig()
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config.load_json(config_path)
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model = Xtts.init_from_config(config)
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model.load_checkpoint(config, checkpoint_path=checkpoint_path, vocab_path=vocab_path, eval=True, use_deepspeed=False)
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print("Modelo cargado en CPU")
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# Mantenemos variables globales y funciones auxiliares
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DEVICE_ASSERT_DETECTED = 0
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DEVICE_ASSERT_PROMPT = None
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DEVICE_ASSERT_LANG = None
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supported_languages = config.languages
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# Función de inferencia usando parámetros predeterminados del archivo de configuración
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def predict(prompt, language, audio_file_pth, mic_file_path, use_mic):
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try:
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if use_mic:
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speaker_wav = mic_file_path
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else:
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speaker_wav = audio_file_pth
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if len(prompt) < 2 or len(prompt) > 200:
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return None, None, "El texto debe tener entre 2 y 200 caracteres."
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# Usamos los valores de la configuración directamente
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temperature = getattr(config, "temperature", 0.75)
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repetition_penalty = getattr(config, "repetition_penalty", 5.0)
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gpt_cond_len = getattr(config, "gpt_cond_len", 30)
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gpt_cond_chunk_len = getattr(config, "gpt_cond_chunk_len", 4)
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max_ref_length = getattr(config, "max_ref_len", 60)
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gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
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audio_path=speaker_wav,
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gpt_cond_len=gpt_cond_len,
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gpt_cond_chunk_len=gpt_cond_chunk_len,
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max_ref_length=max_ref_length
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)
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# Medimos el tiempo de inferencia manualmente
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start_time = time.time()
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out = model.inference(
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prompt,
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language,
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gpt_cond_latent,
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speaker_embedding,
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temperature=temperature,
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repetition_penalty=repetition_penalty,
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)
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inference_time = time.time() - start_time
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torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
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# Calculamos las métricas usando el tiempo medido manualmente
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audio_length = len(out["wav"]) / 24000 # duración del audio en segundos
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real_time_factor = inference_time / audio_length
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metrics_text = f"Tiempo de generación: {inference_time:.2f} segundos\n"
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metrics_text += f"Factor de tiempo real: {real_time_factor:.2f}"
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return gr.make_waveform("output.wav"), "output.wav", metrics_text
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except Exception as e:
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print(f"Error detallado: {str(e)}")
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return None, None, f"Error: {str(e)}"
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# Interfaz de Gradio actualizada sin sliders
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with gr.Blocks(theme=gr.themes.Base()) as demo:
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gr.Markdown("# Sintetizador de Voz XTTS")
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(label="Texto a sintetizar", placeholder="Escribe aquí el texto que quieres convertir a voz...")
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language = gr.Dropdown(label="Idioma", choices=supported_languages, value="es")
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audio_file = gr.Audio(label="Audio de referencia", type="filepath")
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use_mic = gr.Checkbox(label="Usar micrófono")
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mic_file = gr.Audio(label="Grabar con micrófono", source="microphone", type="filepath", visible=False)
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use_mic.change(fn=lambda x: gr.update(visible=x), inputs=[use_mic], outputs=[mic_file])
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generate_button = gr.Button("Generar voz")
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with gr.Column():
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output_audio = gr.Audio(label="Audio generado")
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waveform = gr.Image(label="Forma de onda")
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metrics = gr.Textbox(label="Métricas")
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generate_button.click(
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predict,
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inputs=[input_text, language, audio_file, mic_file, use_mic],
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outputs=[waveform, output_audio, metrics]
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)
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demo.launch(debug=True)
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