Gen_hyperlipsbase_videos.py

from HYPERLIPS import Hyperlips
import argparse
import os



parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using HyperLipsBase or HyperLipsHR models')
parser.add_argument('--checkpoint_path_BASE', type=str,help='Name of saved HyperLipsBase checkpoint to load weights from', default="checkpoints/require_grad_checkpoint_step000169000.pth")
parser.add_argument('--checkpoint_path_HR', type=str,help='Name of saved HyperLipsHR checkpoint to load weights from', default=None)
parser.add_argument('--videos', type=str,
                    help='Filepath of video/image that contains faces to use', default="datasets/MEAD")
parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.',
                    default='hyperlips_base_results')
parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0],
                    help='Padding (top, bottom, left, right). Please adjust to include chin at least')
parser.add_argument('--filter_window', default=None, type=int,
                    help='real window is 2*T+1')
parser.add_argument('--hyper_batch_size', type=int, help='Batch size for hyperlips model(s)', default=128)
parser.add_argument('--resize_factor', default=1, type=int,
                    help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')
parser.add_argument('--img_size', default=128, type=int)
parser.add_argument('--segmentation_path', type=str,
					help='Name of saved checkpoint of segmentation network', default="checkpoints/face_segmentation.pth")
parser.add_argument('--face_enhancement_path', type=str,
					help='Name of saved checkpoint of segmentation network', default=None)#"checkpoints/GFPGANv1.3.pth"
parser.add_argument('--no_faceenhance', default=False, action='store_true',
					help='Prevent using face enhancement')
parser.add_argument('--gpu_id', type=float, help='gpu id (default: 0)',
                    default=0, required=False)
args = parser.parse_args()


def inference_list():
    Hyperlips_executor = Hyperlips(checkpoint_path_BASE=args.checkpoint_path_BASE,
                                    checkpoint_path_HR=args.checkpoint_path_HR,
                                    segmentation_path=args.segmentation_path,
                                    face_enhancement_path = args.face_enhancement_path,
                                    gpu_id = args.gpu_id,
                                    window =args.filter_window,
                                    hyper_batch_size=args.hyper_batch_size,
                                    img_size = args.img_size,
                                    resize_factor = args.resize_factor,
                                    pad = args.pads)
    Hyperlips_executor._HyperlipsLoadModels()
    filelist = os.listdir(args.videos)
    for i in filelist:
        face = args.videos+"/"+i
        audio = args.videos+"/"+i
        outputfile = args.outfile+"/"+i
        Hyperlips_executor._HyperlipsInference(face,audio,outputfile)


if __name__ == '__main__':

    inference_list()