Update app.py

app.py

@@ -1,52 +1,43 @@
+import os
+import re
+import time
 import sys
-import io, os, stat
 import subprocess
-import random
-from zipfile import ZipFile
-import uuid
-import time
-import torch
-import torchaudio
-import time
-# Mantenemos la descarga de MeCab
-os.system('python -m unidic download')
-
-# Mantenemos el acuerdo de CPML
-os.environ["COQUI_TOS_AGREED"] = "1"
-
-import langid
-import base64
-import csv
-from io import StringIO
-import datetime
-import re
-
 import gradio as gr
-from scipy.io.wavfile import write
 from pydub import AudioSegment
-
 from TTS.api import TTS
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 from TTS.utils.generic_utils import get_user_data_dir
+from huggingface_hub import hf_hub_download
 
-HF_TOKEN = os.environ.get("HF_TOKEN")
+# Configuración inicial
+os.environ["COQUI_TOS_AGREED"] = "1"
 
-from huggingface_hub import hf_hub_download
-import os
-from TTS.utils.manage import get_user_data_dir
+def check_and_install(package):
+    try:
+        __import__(package)
+    except ImportError:
+        print(f"{package} no está instalado. Instalando...")
+        subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+# Asegurar que MeCab y UniDic estén instalados
+check_and_install("MeCab")
+check_and_install("unidic-lite")
 
-# Mantenemos la autenticación y descarga del modelo
+# Descargar UniDic
+os.system('python -m unidic download')
+
+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"Downloading {file_name} from {repo_id}")
-    local_file_path = os.path.join(local_dir, file_name)
+    print(f"Descargando {file_name} de {repo_id}")
     hf_hub_download(repo_id=repo_id, filename=file_name, local_dir=local_dir)
 
-# Cargamos configuración y modelo
 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")
@@ -59,90 +50,118 @@ model.load_checkpoint(config, checkpoint_path=checkpoint_path, vocab_path=vocab_
 
 print("Modelo cargado en CPU")
 
-# Mantenemos variables globales y funciones auxiliares
-DEVICE_ASSERT_DETECTED = 0
-DEVICE_ASSERT_PROMPT = None
-DEVICE_ASSERT_LANG = None
-supported_languages = config.languages
+def split_text(text):
+    return re.split(r'(?<=[.!?])\s+', text)
 
-# Función de inferencia usando parámetros predeterminados del archivo de configuración
-def predict(prompt, language, audio_file_pth, mic_file_path, use_mic):
+def predict(prompt, language, reference_audio):
     try:
-        if use_mic:
-            speaker_wav = mic_file_path
-        else:
-            speaker_wav = audio_file_pth
+        if len(prompt) < 2 or len(prompt) > 600:
+            return None, "El texto debe tener entre 2 y 600 caracteres."
 
-        if len(prompt) < 2 or len(prompt) > 200:
-            return None, None, "El texto debe tener entre 2 y 200 caracteres."
+        sentences = split_text(prompt)
 
-        # Usamos los valores de la configuración directamente
-        temperature = getattr(config, "temperature", 0.75)
-        repetition_penalty = getattr(config, "repetition_penalty", 5.0)
-        gpt_cond_len = getattr(config, "gpt_cond_len", 30)
-        gpt_cond_chunk_len = getattr(config, "gpt_cond_chunk_len", 4)
-        max_ref_length = getattr(config, "max_ref_len", 60)
+        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=speaker_wav,
+            audio_path=reference_audio,
             gpt_cond_len=gpt_cond_len,
             gpt_cond_chunk_len=gpt_cond_chunk_len,
             max_ref_length=max_ref_length
         )
 
-        # Medimos el tiempo de inferencia manualmente
         start_time = time.time()
-        out = model.inference(
-            prompt,
-            language,
-            gpt_cond_latent,
-            speaker_embedding,
-            temperature=temperature,
-            repetition_penalty=repetition_penalty,
-        )
+        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
         
-        torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
+        output_path = "output.wav"
+        combined_audio.export(output_path, format="wav")
 
-        # Calculamos las métricas usando el tiempo medido manualmente
-        audio_length = len(out["wav"]) / 24000  # duración del audio en segundos
+        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 gr.make_waveform("output.wav"), "output.wav", metrics_text
+        return output_path, metrics_text
 
     except Exception as e:
         print(f"Error detallado: {str(e)}")
-        return None, None, f"Error: {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)
 
-# Interfaz de Gradio actualizada sin sliders
-with gr.Blocks(theme=gr.themes.Base()) as demo:
-    gr.Markdown("# Sintetizador de Voz XTTS")
-    
     with gr.Row():
-        with gr.Column():
+        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...")
-            language = gr.Dropdown(label="Idioma", choices=supported_languages, value="es")
-            audio_file = gr.Audio(label="Audio de referencia", type="filepath")
-            use_mic = gr.Checkbox(label="Usar micrófono")
-            mic_file = gr.Audio(label="Grabar con micrófono", source="microphone", type="filepath", visible=False)
-            
-            use_mic.change(fn=lambda x: gr.update(visible=x), inputs=[use_mic], outputs=[mic_file])
-            
-            generate_button = gr.Button("Generar voz")
-        
-        with gr.Column():
-            output_audio = gr.Audio(label="Audio generado")
-            waveform = gr.Image(label="Forma de onda")
-            metrics = gr.Textbox(label="Métricas")
-    
+            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, audio_file, mic_file, use_mic],
-        outputs=[waveform, output_audio, metrics]
+        inputs=[input_text, language_selector, reference_audio],
+        outputs=[generated_audio, metrics_output]
     )
 
-demo.launch(debug=True)
+if __name__ == "__main__":
+    demo.launch()