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from os import listdir, path |
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import numpy as np |
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import scipy, cv2, os, sys, argparse, audio |
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import json, subprocess, random, string |
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from tqdm import tqdm |
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from glob import glob |
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import torch, face_detection |
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from models import Wav2Lip |
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import platform |
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parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models') |
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parser.add_argument('--checkpoint_path', type=str, |
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help='Name of saved checkpoint to load weights from', required=True) |
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parser.add_argument('--face', type=str, |
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help='Filepath of video/image that contains faces to use', required=True) |
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parser.add_argument('--audio', type=str, |
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help='Filepath of video/audio file to use as raw audio source', required=True) |
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parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', |
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default='results/result_voice.mp4') |
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parser.add_argument('--static', type=bool, |
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help='If True, then use only first video frame for inference', default=False) |
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parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', |
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default=25., required=False) |
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parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], |
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help='Padding (top, bottom, left, right). Please adjust to include chin at least') |
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parser.add_argument('--face_det_batch_size', type=int, |
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help='Batch size for face detection', default=16) |
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parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128) |
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parser.add_argument('--resize_factor', default=1, type=int, |
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help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p') |
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parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1], |
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help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' |
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'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width') |
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parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], |
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help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.' |
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'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).') |
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parser.add_argument('--rotate', default=False, action='store_true', |
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help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.' |
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'Use if you get a flipped result, despite feeding a normal looking video') |
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parser.add_argument('--nosmooth', default=False, action='store_true', |
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help='Prevent smoothing face detections over a short temporal window') |
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args = parser.parse_args() |
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args.img_size = 96 |
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if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: |
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args.static = True |
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def get_smoothened_boxes(boxes, T): |
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for i in range(len(boxes)): |
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if i + T > len(boxes): |
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window = boxes[len(boxes) - T:] |
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else: |
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window = boxes[i : i + T] |
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boxes[i] = np.mean(window, axis=0) |
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return boxes |
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def face_detect(images): |
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detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, |
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flip_input=False, device=device) |
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batch_size = args.face_det_batch_size |
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while 1: |
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predictions = [] |
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try: |
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for i in tqdm(range(0, len(images), batch_size)): |
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predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size]))) |
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except RuntimeError: |
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if batch_size == 1: |
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raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument') |
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batch_size //= 2 |
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print('Recovering from OOM error; New batch size: {}'.format(batch_size)) |
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continue |
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break |
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results = [] |
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pady1, pady2, padx1, padx2 = args.pads |
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for rect, image in zip(predictions, images): |
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if rect is None: |
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cv2.imwrite('temp/faulty_frame.jpg', image) |
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raise ValueError('Face not detected! Ensure the video contains a face in all the frames.') |
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y1 = max(0, rect[1] - pady1) |
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y2 = min(image.shape[0], rect[3] + pady2) |
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x1 = max(0, rect[0] - padx1) |
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x2 = min(image.shape[1], rect[2] + padx2) |
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results.append([x1, y1, x2, y2]) |
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boxes = np.array(results) |
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if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5) |
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results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)] |
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del detector |
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return results |
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def datagen(frames, mels): |
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img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] |
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if args.box[0] == -1: |
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if not args.static: |
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face_det_results = face_detect(frames) |
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else: |
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face_det_results = face_detect([frames[0]]) |
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else: |
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print('Using the specified bounding box instead of face detection...') |
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y1, y2, x1, x2 = args.box |
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face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames] |
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for i, m in enumerate(mels): |
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idx = 0 if args.static else i%len(frames) |
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frame_to_save = frames[idx].copy() |
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face, coords = face_det_results[idx].copy() |
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face = cv2.resize(face, (args.img_size, args.img_size)) |
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img_batch.append(face) |
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mel_batch.append(m) |
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frame_batch.append(frame_to_save) |
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coords_batch.append(coords) |
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if len(img_batch) >= args.wav2lip_batch_size: |
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img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) |
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img_masked = img_batch.copy() |
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img_masked[:, args.img_size//2:] = 0 |
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img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. |
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mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) |
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yield img_batch, mel_batch, frame_batch, coords_batch |
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img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] |
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if len(img_batch) > 0: |
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img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) |
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img_masked = img_batch.copy() |
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img_masked[:, args.img_size//2:] = 0 |
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img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. |
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mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) |
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yield img_batch, mel_batch, frame_batch, coords_batch |
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mel_step_size = 16 |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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print('Using {} for inference.'.format(device)) |
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def _load(checkpoint_path): |
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if device == 'cuda': |
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checkpoint = torch.load(checkpoint_path) |
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else: |
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checkpoint = torch.load(checkpoint_path, |
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map_location=lambda storage, loc: storage) |
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return checkpoint |
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def load_model(path): |
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model = Wav2Lip() |
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print("Load checkpoint from: {}".format(path)) |
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checkpoint = _load(path) |
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s = checkpoint["state_dict"] |
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new_s = {} |
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for k, v in s.items(): |
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new_s[k.replace('module.', '')] = v |
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model.load_state_dict(new_s) |
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model = model.to(device) |
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return model.eval() |
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def main(): |
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if not os.path.isfile(args.face): |
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raise ValueError('--face argument must be a valid path to video/image file') |
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elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: |
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full_frames = [cv2.imread(args.face)] |
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fps = args.fps |
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else: |
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video_stream = cv2.VideoCapture(args.face) |
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fps = video_stream.get(cv2.CAP_PROP_FPS) |
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print('Reading video frames...') |
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full_frames = [] |
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while 1: |
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still_reading, frame = video_stream.read() |
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if not still_reading: |
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video_stream.release() |
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break |
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if args.resize_factor > 1: |
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frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor)) |
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if args.rotate: |
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frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE) |
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y1, y2, x1, x2 = args.crop |
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if x2 == -1: x2 = frame.shape[1] |
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if y2 == -1: y2 = frame.shape[0] |
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frame = frame[y1:y2, x1:x2] |
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full_frames.append(frame) |
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print ("Number of frames available for inference: "+str(len(full_frames))) |
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if not args.audio.endswith('.wav'): |
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print('Extracting raw audio...') |
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command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav') |
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subprocess.call(command, shell=True) |
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args.audio = 'temp/temp.wav' |
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wav = audio.load_wav(args.audio, 16000) |
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mel = audio.melspectrogram(wav) |
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print(mel.shape) |
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if np.isnan(mel.reshape(-1)).sum() > 0: |
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raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again') |
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mel_chunks = [] |
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mel_idx_multiplier = 80./fps |
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i = 0 |
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while 1: |
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start_idx = int(i * mel_idx_multiplier) |
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if start_idx + mel_step_size > len(mel[0]): |
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mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:]) |
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break |
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mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size]) |
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i += 1 |
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print("Length of mel chunks: {}".format(len(mel_chunks))) |
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full_frames = full_frames[:len(mel_chunks)] |
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batch_size = args.wav2lip_batch_size |
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gen = datagen(full_frames.copy(), mel_chunks) |
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for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, |
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total=int(np.ceil(float(len(mel_chunks))/batch_size)))): |
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if i == 0: |
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model = load_model(args.checkpoint_path) |
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print ("Model loaded") |
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frame_h, frame_w = full_frames[0].shape[:-1] |
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out = cv2.VideoWriter('temp/result.avi', |
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cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h)) |
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img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device) |
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mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device) |
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with torch.no_grad(): |
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pred = model(mel_batch, img_batch) |
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pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. |
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for p, f, c in zip(pred, frames, coords): |
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y1, y2, x1, x2 = c |
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p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1)) |
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f[y1:y2, x1:x2] = p |
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out.write(f) |
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out.release() |
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command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile) |
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subprocess.call(command, shell=platform.system() != 'Windows') |
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if __name__ == '__main__': |
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main() |
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