update requirements

requirements.txt

@@ -15,6 +15,9 @@ scipy
 tb-nightly
 yapf
 realesrgan
-
 ffmpeg
-gradio
+gradio==4.1.2
+ffmpy
+flask_ngrok2
+flask_ngrok
+opencv-python

wav2lip/inference.py

@@ -10,271 +10,281 @@ 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('--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('--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('--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('--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')
+                    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')
+                    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
+    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
+    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 
+    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 = [], [], [], []
+    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]
+    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()
+    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)
+        face = cv2.resize(face, (args.img_size, args.img_size))
 
-		if len(img_batch) >= args.wav2lip_batch_size:
-			img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+        img_batch.append(face)
+        mel_batch.append(m)
+        frame_batch.append(frame_to_save)
+        coords_batch.append(coords)
 
-			img_masked = img_batch.copy()
-			img_masked[:, args.img_size//2:] = 0
+        if len(img_batch) >= args.wav2lip_batch_size:
+            img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
 
-			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])
+            img_masked = img_batch.copy()
+            img_masked[:, args.img_size // 2:] = 0
 
-			yield img_batch, mel_batch, frame_batch, coords_batch
-			img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
+            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])
 
-	if len(img_batch) > 0:
-		img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+            yield img_batch, mel_batch, frame_batch, coords_batch
+            img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
 
-		img_masked = img_batch.copy()
-		img_masked[:, args.img_size//2:] = 0
+    if len(img_batch) > 0:
+        img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
 
-		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])
+        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
 
-		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
+    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()
+    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')
+    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)
 
-	elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
-		full_frames = [cv2.imread(args.face)]
-		fps = args.fps
+        print('Reading video frames...')
 
-	else:
-		video_stream = cv2.VideoCapture(args.face)
-		fps = video_stream.get(cv2.CAP_PROP_FPS)
+        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))
 
-		print('Reading video frames...')
+            if args.rotate:
+                frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
 
-		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))
+            y1, y2, x1, x2 = args.crop
+            if x2 == -1: x2 = frame.shape[1]
+            if y2 == -1: y2 = frame.shape[0]
 
-			if args.rotate:
-				frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
+            frame = frame[y1:y2, x1:x2]
 
-			y1, y2, x1, x2 = args.crop
-			if x2 == -1: x2 = frame.shape[1]
-			if y2 == -1: y2 = frame.shape[0]
+            full_frames.append(frame)
 
-			frame = frame[y1:y2, x1:x2]
+    print("Number of frames available for inference: " + str(len(full_frames)))
 
-			full_frames.append(frame)
+    if not args.audio.endswith('.wav'):
+        print('Extracting raw audio...')
+        command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')
 
-	print ("Number of frames available for inference: "+str(len(full_frames)))
+        subprocess.call(command, shell=True)
+        args.audio = 'temp/temp.wav'
 
-	if not args.audio.endswith('.wav'):
-		print('Extracting raw audio...')
-		command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')
+    wav = audio.load_wav(args.audio, 16000)
+    mel = audio.melspectrogram(wav)
+    print(mel.shape)
 
-		subprocess.call(command, shell=True)
-		args.audio = 'temp/temp.wav'
+    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')
 
-	wav = audio.load_wav(args.audio, 16000)
-	mel = audio.melspectrogram(wav)
-	print(mel.shape)
+    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
 
-	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')
+    print("Length of mel chunks: {}".format(len(mel_chunks)))
 
-	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
+    full_frames = full_frames[:len(mel_chunks)]
 
-	print("Length of mel chunks: {}".format(len(mel_chunks)))
+    batch_size = args.wav2lip_batch_size
+    gen = datagen(full_frames.copy(), mel_chunks)
 
-	full_frames = full_frames[:len(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")
 
-	batch_size = args.wav2lip_batch_size
-	gen = datagen(full_frames.copy(), mel_chunks)
+            frame_h, frame_w = full_frames[0].shape[:-1]
+            out = cv2.VideoWriter('/tmp/result.avi',
+                                  cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
 
-	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")
+        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)
 
-			frame_h, frame_w = full_frames[0].shape[:-1]
-			out = cv2.VideoWriter('/tmp/result.avi',
-									cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
+        with torch.no_grad():
+            pred = model(mel_batch, img_batch)
 
-		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)
+        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
 
-		with torch.no_grad():
-			pred = model(mel_batch, img_batch)
+        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))
 
-		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)
 
-			f[y1:y2, x1:x2] = p
-			out.write(f)
+    out.release()
 
-	out.release()
+    command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, '/tmp/result.avi', args.outfile)
+    subprocess.call(command, shell=platform.system() != 'Windows')
 
-	command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, '/tmp/result.avi', args.outfile)
-	subprocess.call(command, shell=platform.system() != 'Windows')
 
 if __name__ == '__main__':
-	main()
+    main()