setup_wav2lip.py

import os
import requests
import subprocess
import sys
import gdown
import shutil
from tqdm import tqdm

def download_file(url, destination, use_gdown=False):
    if not os.path.exists(os.path.dirname(destination)):
        os.makedirs(os.path.dirname(destination))
        
    if use_gdown:
        gdown.download(url, destination, quiet=False)
    else:
        response = requests.get(url, stream=True)
        total_size = int(response.headers.get('content-length', 0))
        
        with open(destination, 'wb') as f, tqdm(
            desc=destination,
            total=total_size,
            unit='iB',
            unit_scale=True,
            unit_divisor=1024,
        ) as pbar:
            for data in response.iter_content(chunk_size=1024):
                size = f.write(data)
                pbar.update(size)

def main():
    # Instalar gdown si no está instalado
    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gdown'])

    # Crear directorios necesarios
    directories = [
        'checkpoints',
        'temp_audio',
        'temp_previews',
        'temp_wav2lip',
        'uploads',
        'models',
        'face_detection/detection/sfd',
        'audio_processing'
    ]
    
    for dir_name in directories:
        if not os.path.exists(dir_name):
            os.makedirs(dir_name, exist_ok=True)

    # URLs alternativas para el modelo wav2lip
    model_urls = [
        ('https://drive.google.com/uc?id=1PwHvZJu8Z8AzztRkm8zPh8_6VG2LTJkR', True),
        ('https://huggingface.co/spaces/vumichien/wav2lip/resolve/main/wav2lip_gan.pth', False),
    ]

    # Descargar modelo wav2lip probando diferentes fuentes
    checkpoint_path = 'checkpoints/wav2lip_gan.pth'
    if not os.path.exists(checkpoint_path):
        success = False
        for url, use_gdown in model_urls:
            if download_file(url, checkpoint_path, use_gdown):
                success = True
                break
        
        if not success:
            print("Error: No se pudo descargar el modelo wav2lip_gan.pth")
            print("Por favor, descarga manualmente el modelo desde:")
            print("https://drive.google.com/uc?id=1PwHvZJu8Z8AzztRkm8zPh8_6VG2LTJkR")
            print("Y colócalo en la carpeta 'checkpoints' con el nombre 'wav2lip_gan.pth'")
            return

    # Archivos necesarios de wav2lip con URLs alternativas
    files_to_download = {
        'inference.py': [
            'https://raw.githubusercontent.com/Rudrabha/Wav2Lip/master/inference.py',
            'https://huggingface.co/spaces/vumichien/wav2lip/raw/main/inference.py'
        ],
        'models/wav2lip.py': [
            'https://raw.githubusercontent.com/Rudrabha/Wav2Lip/master/models/wav2lip.py',
            'https://huggingface.co/spaces/vumichien/wav2lip/raw/main/models/wav2lip.py'
        ],
        'face_detection/detection/sfd/sfd_detector.py': [
            'https://raw.githubusercontent.com/Rudrabha/Wav2Lip/master/face_detection/detection/sfd/sfd_detector.py',
            'https://huggingface.co/spaces/vumichien/wav2lip/raw/main/face_detection/detection/sfd/sfd_detector.py'
        ],
        'face_detection/detection/sfd/net_s3fd.py': [
            'https://raw.githubusercontent.com/Rudrabha/Wav2Lip/master/face_detection/detection/sfd/net_s3fd.py',
            'https://huggingface.co/spaces/vumichien/wav2lip/raw/main/face_detection/detection/sfd/net_s3fd.py'
        ],
        'face_detection/api.py': [
            'https://raw.githubusercontent.com/Rudrabha/Wav2Lip/master/face_detection/api.py',
            'https://huggingface.co/spaces/vumichien/wav2lip/raw/main/face_detection/api.py'
        ],
        'audio_processing/audio.py': [
            'https://raw.githubusercontent.com/Rudrabha/Wav2Lip/master/audio.py',
            'https://huggingface.co/spaces/vumichien/wav2lip/raw/main/audio.py'
        ]
    }

    # Crear archivos __init__.py necesarios
    init_files = [
        'models/__init__.py',
        'face_detection/__init__.py',
        'face_detection/detection/__init__.py',
        'face_detection/detection/sfd/__init__.py',
        'audio_processing/__init__.py'
    ]

    for init_file in init_files:
        init_path = os.path.join(os.path.dirname(__file__), init_file)
        if not os.path.exists(init_path):
            os.makedirs(os.path.dirname(init_path), exist_ok=True)
            with open(init_path, 'w') as f:
                pass

    for file_path, urls in files_to_download.items():
        if not os.path.exists(file_path):
            success = False
            for url in urls:
                if download_file(url, file_path):
                    success = True
                    break
            
            if not success:
                print(f"Error: No se pudo descargar {file_path}")
                return

    # Modificar inference.py para usar el módulo audio_processing
    with open('inference.py', 'r') as f:
        content = f.read()
    
    content = content.replace('import audio', 'from audio_processing import audio')
    
    with open('inference.py', 'w') as f:
        f.write(content)

    # Instalar dependencias específicas de wav2lip
    dependencies = [
        'torch==1.8.0',
        'torchvision==0.9.0',
        'torchaudio==0.8.0',
        'opencv-python',
        'numpy',
        'scipy',
        'librosa==0.8.0',
        'face-alignment==1.3.5',
        'audioread==3.0.1',
        'soundfile==0.12.1',
        'resampy==0.4.2',
        'numba==0.57.1',
        'scikit-learn'
    ]

    print("\nInstalando dependencias...")
    for dep in dependencies:
        try:
            subprocess.check_call([sys.executable, '-m', 'pip', 'install', dep])
        except subprocess.CalledProcessError as e:
            print(f"Error instalando {dep}: {str(e)}")
            if "torch" in dep:
                print("Intentando instalar torch con una versión compatible...")
                try:
                    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'torch', 'torchvision', 'torchaudio'])
                except:
                    print("Error instalando torch. Por favor, instálalo manualmente según tu sistema:")
                    print("https://pytorch.org/get-started/locally/")

    print("\nInstalación completada.")
    print("Ahora puedes ejecutar la aplicación con: python app.py")

if __name__ == '__main__':
    main()