inference.py

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))

		# Create lists to store frames and their processing status
		processed_frames = []
		silent_mel = np.zeros_like(mel_batch[0])
		
		# Keep track of silent period
		in_silent_period = False
		first_silent_idx = None
		
		# First pass: mark frames for processing
		for j, mel_frame in enumerate(mel_batch):
			# Calculate global frame index based on batch position
			global_frame_idx = i * batch_size + j
			if not audio.is_dialog(mel_frame, frame_idx=global_frame_idx, fps=fps):
				if not in_silent_period:
					# Start of silent period - mark first frame for processing with silent mel
					processed_frames.append((frames[j], j, coords[j], True))  # True indicates silent frame
					mel_batch[j] = silent_mel
					first_silent_idx = j
					in_silent_period = True
				else:
					# During silent period - will be replaced with first silent frame's prediction
					processed_frames.append((frames[j], None, coords[j], True))
			else:
				# Speech frame - process normally
				processed_frames.append((frames[j], j, coords[j], False))
				in_silent_period = False
				first_silent_idx = None

		# Process frames through the model
		valid_indices = [idx for _, idx, _, _ in processed_frames if idx is not None]
		
		if valid_indices:  # If we have frames to process
			valid_img_batch = img_batch[valid_indices]
			valid_mel_batch = mel_batch[valid_indices]

			img_batch = torch.FloatTensor(np.transpose(valid_img_batch, (0, 3, 1, 2))).to(device)
			mel_batch = torch.FloatTensor(np.transpose(valid_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.
			
			# Write all frames
			pred_idx = 0
			silent_frame = None
			
			for k, (frame, idx, coords, is_silent) in enumerate(processed_frames):
				y1, y2, x1, x2 = coords
				
				if idx is not None:
					# This is a processed frame
					p = cv2.resize(pred[pred_idx].astype(np.uint8), (x2 - x1, y2 - y1))
					if is_silent:
						# Store the first silent frame prediction
						silent_frame = p.copy()
					
					# Calculate dimensions for cropped region
					face_height = y2 - y1
					face_width = x2 - x1
					
					# Calculate 20% from bottom (was 30% before)
					start_y = y2 - int(face_height * 0.39)
					
					# Calculate 60% of width, centered
					width_reduction = int(face_width * 0.2)
					new_x1 = x1 + width_reduction
					new_x2 = x2 - width_reduction
					
					# Calculate exact heights
					source_height = int(face_height * 0.39) # Adjusted to match start_y change
					source_start = face_height - source_height
					
					# Apply cropped prediction to frame
					frame[start_y:y2, new_x1:new_x2] = p[source_start:, width_reduction:-width_reduction]
					
					pred_idx += 1

					# Save every 10th frame for validation
					if k % 10 == 0:
						cv2.imwrite(f'temp/frame_{k}.jpg', frame)
				else:
					# Use the stored silent frame for silent periods
					face_height = y2 - y1
					face_width = x2 - x1
					
					start_y = y2 - int(face_height * 0.39)
					width_reduction = int(face_width * 0.2)
					new_x1 = x1 + width_reduction
					new_x2 = x2 - width_reduction

					source_height = int(face_height * 0.39)
					source_start = face_height - source_height
					
					# Resize the silent frame first
					resized_silent = cv2.resize(silent_frame, (x2 - x1, y2 - y1))
					frame[start_y:y2, new_x1:new_x2] = resized_silent[source_start:, width_reduction:-width_reduction]
				
				out.write(frame)

	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__':
	#this should make a static result
	main()