app.py

import gradio as gr
import edge_tts
import asyncio
import tempfile
import os
from pydub import AudioSegment
from pydub.playback import play
import math

# Función para obtener las voces disponibles
async def get_voices():
    voices = await edge_tts.list_voices()
    return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}

# Función principal de conversión de texto a voz
async def text_to_speech(text, voice, rate, pitch):
    if not text.strip():
        return None, "Please enter text to convert."
    if not voice:
        return None, "Please select a voice."
    
    voice_short_name = voice.split(" - ")[0]
    rate_str = f"{rate:+d}%"
    pitch_str = f"{pitch:+d}Hz"
    communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    return tmp_path, None

# Función para agregar el fondo musical al speech
def add_background_music(speech_file, background_music_file, output_file):
    # Cargar los archivos de audio
    speech = AudioSegment.from_mp3(speech_file)
    background_music = AudioSegment.from_mp3(background_music_file)

    # Ajustar el volumen del fondo musical al 15%
    background_music = background_music - 16  # Reducción aproximada para 15%

    # Repetir el fondo musical si es más corto que el speech
    if len(background_music) < len(speech):
        repetitions = math.ceil(len(speech) / len(background_music))
        background_music = background_music * repetitions

    # Cortar el fondo musical para que coincida con la duración del speech
    background_music = background_music[:len(speech)]

    # Superponer el speech y el fondo musical
    final_audio = speech.overlay(background_music)

    # Exportar el audio resultante
    final_audio.export(output_file, format="mp3")
    print(f"Archivo generado exitosamente: {output_file}")

# Interfaz Gradio
async def tts_interface(text, voice, rate, pitch, background_music):
    # Generar el speech
    speech_file, warning = await text_to_speech(text, voice, rate, pitch)
    if warning:
        return None, None, gr.Warning(warning)
    
    # Verificar si se proporcionó un archivo de fondo musical
    if background_music and background_music != "":
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
            output_file = tmp_file.name
            add_background_music(speech_file, background_music, output_file)
        # Eliminar el archivo temporal del speech original
        os.remove(speech_file)
        return output_file, None, None
    
    # Si no hay fondo musical, devolver el speech original
    return speech_file, None, None

async def create_demo():
    voices = await get_voices()
    
    description = """
    Convert text to speech with audio background to 15% volumen, perfect for audiobooks or youtube videos ! using Microsoft Edge TTS. Adjust speech rate and pitch: 0 is default, positive values increase, negative values decrease.
    
    """
    
    demo = gr.Interface(
        fn=tts_interface,
        inputs=[
            gr.Textbox(label="Input Text", lines=5),
            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Voice", value=""),
            gr.Slider(minimum=-50, maximum=50, value=0, label="Speech Rate Adjustment (%)", step=1),
            gr.Slider(minimum=-20, maximum=20, value=0, label="Pitch Adjustment (Hz)", step=1),
            gr.Audio(label="Background Music", type="filepath")  # Sin el argumento 'optional'
        ],
        outputs=[
            gr.Audio(label="Generated Audio", type="filepath"),
            gr.Image(label="Visualization", visible=False),
            gr.Markdown(label="Warning", visible=False)
        ],
        title="Edge TTS Text-to-Speech",
        description=description,
        article="Experience the power of Edge TTS for text-to-speech conversion, and explore our advanced Text-to-Video Converter for even more creative possibilities!",
        analytics_enabled=False,
        allow_flagging="manual",
        api_name=None
    )
    return demo

async def main():
    demo = await create_demo()
    demo.queue(default_concurrency_limit=5)
    demo.launch(show_api=False)

if __name__ == "__main__":
    asyncio.run(main())