app.py

import torch
import gradio as gr
from transformers import AutoProcessor, MusicgenForConditionalGeneration
from TTS.api import TTS
import librosa
import numpy as np

processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
model_musicgen = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
model_tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cpu")

def generate_music(text_prompt, audio_prompt=None, tts_text=None):
    inputs = processor(text=text_prompt, padding=True, return_tensors="pt")
    max_duration_sec = 600
    min_duration_sec = 30
    max_new_tokens = int(max_duration_sec * model_musicgen.config.audio_length // model_musicgen.config.generation_length)
    min_new_tokens = int(min_duration_sec * model_musicgen.config.audio_length // model_musicgen.config.generation_length)
    audio_values = model_musicgen.generate(**inputs.to("cpu"), max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens)
    music = audio_values.cpu().numpy()[0]

    if audio_prompt is not None and tts_text is not None:
        cloned_audio = model_tts.tts_with_vc(tts_text, speaker_wav=audio_prompt, language="es")

        if model_tts.synthesizer.output_sample_rate != 44100:
            cloned_audio, _ = librosa.resample(cloned_audio, model_tts.synthesizer.output_sample_rate, 44100)

        try:
            music_np = librosa.util.buf_to_float(music, n_bytes=2)

            if len(music_np) > len(cloned_audio):
                padding = np.zeros(len(music_np) - len(cloned_audio))
                cloned_audio = np.concatenate([cloned_audio, padding])
            elif len(cloned_audio) > len(music_np):
                padding = np.zeros(len(cloned_audio) - len(music_np))
                music_np = np.concatenate([music_np, padding])

            combined_audio = music_np + cloned_audio
            return (44100, combined_audio)
        except ImportError:
            print("Error: Se requiere librosa para combinar audio. Instale con 'pip install librosa'")
            return (44100, music)

    return (44100, music)

iface = gr.Interface(
    fn=generate_music,
    inputs=[
        gr.Textbox(label="Descripción de la música"),
        gr.Audio(source="microphone", type="filepath", label="Audio de voz (Opcional)", optional=True),
        gr.Textbox(label="Texto para clonar la voz (Opcional)", optional=True),
    ],
    outputs=gr.Audio(label="Música generada", type="numpy"),
    title="Generador de Música con MusicGen y XTTS",
    description="Introduce una descripción de la música que deseas generar y opcionalmente un audio de voz para clonar con texto.",
)

iface.launch()