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Wav2Lip/checkpoints/README.md

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+Place all your checkpoints (.pth files) here. 

Wav2Lip/inference.py

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+from os import listdir, path
+import numpy as np
+import scipy, cv2, os, sys, argparse, audio
+import json, subprocess, random, string
+from tqdm import tqdm
+from glob import glob
+import torch, face_detection
+from models import Wav2Lip
+import platform
+
+parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')
+
+parser.add_argument('--checkpoint_path', type=str, 
+					help='Name of saved checkpoint to load weights from', required=True)
+
+parser.add_argument('--face', type=str, 
+					help='Filepath of video/image that contains faces to use', required=True)
+parser.add_argument('--audio', type=str, 
+					help='Filepath of video/audio file to use as raw audio source', required=True)
+parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', 
+								default='results/result_voice.mp4')
+
+parser.add_argument('--static', type=bool, 
+					help='If True, then use only first video frame for inference', default=False)
+parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', 
+					default=25., required=False)
+
+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('--face_det_batch_size', type=int, 
+					help='Batch size for face detection', default=16)
+parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip 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('--crop', nargs='+', type=int, default=[0, -1, 0, -1], 
+					help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' 
+					'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')
+
+parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], 
+					help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.'
+					'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).')
+
+parser.add_argument('--rotate', default=False, action='store_true',
+					help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.'
+					'Use if you get a flipped result, despite feeding a normal looking video')
+
+parser.add_argument('--nosmooth', default=False, action='store_true',
+					help='Prevent smoothing face detections over a short temporal window')
+
+args = parser.parse_args()
+args.img_size = 96
+
+if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+	args.static = True
+
+def get_smoothened_boxes(boxes, T):
+	for i in range(len(boxes)):
+		if i + T > len(boxes):
+			window = boxes[len(boxes) - T:]
+		else:
+			window = boxes[i : i + T]
+		boxes[i] = np.mean(window, axis=0)
+	return boxes
+
+def face_detect(images):
+	detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, 
+											flip_input=False, device=device)
+
+	batch_size = args.face_det_batch_size
+	
+	while 1:
+		predictions = []
+		try:
+			for i in tqdm(range(0, len(images), batch_size)):
+				predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
+		except RuntimeError:
+			if batch_size == 1: 
+				raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
+			batch_size //= 2
+			print('Recovering from OOM error; New batch size: {}'.format(batch_size))
+			continue
+		break
+
+	results = []
+	pady1, pady2, padx1, padx2 = args.pads
+	for rect, image in zip(predictions, images):
+		if rect is None:
+			cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected.
+			raise ValueError('Face not detected! Ensure the video contains a face in all the frames.')
+
+		y1 = max(0, rect[1] - pady1)
+		y2 = min(image.shape[0], rect[3] + pady2)
+		x1 = max(0, rect[0] - padx1)
+		x2 = min(image.shape[1], rect[2] + padx2)
+		
+		results.append([x1, y1, x2, y2])
+
+	boxes = np.array(results)
+	if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5)
+	results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
+
+	del detector
+	return results 
+
+def datagen(frames, mels):
+	img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
+
+	if args.box[0] == -1:
+		if not args.static:
+			face_det_results = face_detect(frames) # BGR2RGB for CNN face detection
+		else:
+			face_det_results = face_detect([frames[0]])
+	else:
+		print('Using the specified bounding box instead of face detection...')
+		y1, y2, x1, x2 = args.box
+		face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]
+
+	for i, m in enumerate(mels):
+		idx = 0 if args.static else i%len(frames)
+		frame_to_save = frames[idx].copy()
+		face, coords = face_det_results[idx].copy()
+
+		face = cv2.resize(face, (args.img_size, args.img_size))
+			
+		img_batch.append(face)
+		mel_batch.append(m)
+		frame_batch.append(frame_to_save)
+		coords_batch.append(coords)
+
+		if len(img_batch) >= args.wav2lip_batch_size:
+			img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+
+			img_masked = img_batch.copy()
+			img_masked[:, args.img_size//2:] = 0
+
+			img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
+			mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+			yield img_batch, mel_batch, frame_batch, coords_batch
+			img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
+
+	if len(img_batch) > 0:
+		img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+
+		img_masked = img_batch.copy()
+		img_masked[:, args.img_size//2:] = 0
+
+		img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
+		mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+		yield img_batch, mel_batch, frame_batch, coords_batch
+
+mel_step_size = 16
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print('Using {} for inference.'.format(device))
+
+def _load(checkpoint_path):
+	if device == 'cuda':
+		checkpoint = torch.load(checkpoint_path)
+	else:
+		checkpoint = torch.load(checkpoint_path,
+								map_location=lambda storage, loc: storage)
+	return checkpoint
+
+def load_model(path):
+	model = Wav2Lip()
+	print("Load checkpoint from: {}".format(path))
+	checkpoint = _load(path)
+	s = checkpoint["state_dict"]
+	new_s = {}
+	for k, v in s.items():
+		new_s[k.replace('module.', '')] = v
+	model.load_state_dict(new_s)
+
+	model = model.to(device)
+	return model.eval()
+
+def main():
+	if not os.path.isfile(args.face):
+		raise ValueError('--face argument must be a valid path to video/image file')
+
+	elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+		full_frames = [cv2.imread(args.face)]
+		fps = args.fps
+
+	else:
+		video_stream = cv2.VideoCapture(args.face)
+		fps = video_stream.get(cv2.CAP_PROP_FPS)
+
+		print('Reading video frames...')
+
+		full_frames = []
+		while 1:
+			still_reading, frame = video_stream.read()
+			if not still_reading:
+				video_stream.release()
+				break
+			if args.resize_factor > 1:
+				frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor))
+
+			if args.rotate:
+				frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
+
+			y1, y2, x1, x2 = args.crop
+			if x2 == -1: x2 = frame.shape[1]
+			if y2 == -1: y2 = frame.shape[0]
+
+			frame = frame[y1:y2, x1:x2]
+
+			full_frames.append(frame)
+
+	print ("Number of frames available for inference: "+str(len(full_frames)))
+
+	if not args.audio.endswith('.wav'):
+		print('Extracting raw audio...')
+		command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')
+
+		subprocess.call(command, shell=True)
+		args.audio = 'temp/temp.wav'
+
+	wav = audio.load_wav(args.audio, 16000)
+	mel = audio.melspectrogram(wav)
+	print(mel.shape)
+
+	if np.isnan(mel.reshape(-1)).sum() > 0:
+		raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
+
+	mel_chunks = []
+	mel_idx_multiplier = 80./fps 
+	i = 0
+	while 1:
+		start_idx = int(i * mel_idx_multiplier)
+		if start_idx + mel_step_size > len(mel[0]):
+			mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+			break
+		mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+		i += 1
+
+	print("Length of mel chunks: {}".format(len(mel_chunks)))
+
+	full_frames = full_frames[:len(mel_chunks)]
+
+	batch_size = args.wav2lip_batch_size
+	gen = datagen(full_frames.copy(), mel_chunks)
+
+	for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, 
+											total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
+		if i == 0:
+			model = load_model(args.checkpoint_path)
+			print ("Model loaded")
+
+			frame_h, frame_w = full_frames[0].shape[:-1]
+			out = cv2.VideoWriter('temp/result.avi', 
+									cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
+
+		img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+		mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+
+		with torch.no_grad():
+			pred = model(mel_batch, img_batch)
+
+		pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+		
+		for p, f, c in zip(pred, frames, coords):
+			y1, y2, x1, x2 = c
+			p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
+
+			f[y1:y2, x1:x2] = p
+			out.write(f)
+
+	out.release()
+
+	command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile)
+	subprocess.call(command, shell=platform.system() != 'Windows')
+
+if __name__ == '__main__':
+	main()

Wav2Lip/results/README.md

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+Generated results will be placed in this folder by default.