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demo_EDTalk_A_using_predefined_exp_weights.py
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demo_EDTalk_A_using_predefined_exp_weights.py
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import os, sys
import torch
import torch.nn as nn
from networks.generator import Generator
from networks.audio_encoder import Audio2Lip
import argparse
import numpy as np
import torchvision
import os
from PIL import Image
from tqdm import tqdm
from torchvision import transforms
import torch.nn.functional as F
from networks.utils import check_package_installed
from moviepy.editor import *
def load_image(filename, size):
img = Image.open(filename).convert('RGB')
img = img.resize((size, size))
img = np.asarray(img)
img = np.transpose(img, (2, 0, 1)) # 3 x 256 x 256
return img / 255.0
def img_preprocessing(img_path, size):
img = load_image(img_path, size) # [0, 1]
img = torch.from_numpy(img).unsqueeze(0).float() # [0, 1]
imgs_norm = (img - 0.5) * 2.0 # [-1, 1]
return imgs_norm
def vid_preprocessing(vid_path):
vid_dict = torchvision.io.read_video(vid_path, pts_unit='sec')
vid = vid_dict[0].permute(0, 3, 1, 2).unsqueeze(0)
fps = vid_dict[2]['video_fps']
vid_norm = (vid / 255.0 - 0.5) * 2.0 # [-1, 1]
transform = transforms.Compose([
transforms.Resize((256, 256)),
])
resized_frames = torch.stack([transform(frame) for frame in vid_norm[0]], dim=0).unsqueeze(0)
return resized_frames, fps
def save_video(vid_target_recon, save_path, fps):
vid = vid_target_recon.permute(0, 2, 3, 4, 1)
vid = vid.clamp(-1, 1).cpu()
vid = ((vid - vid.min()) / (vid.max() - vid.min()) * 255).type('torch.ByteTensor')
torchvision.io.write_video(save_path, vid[0], fps=fps)
import audio
def parse_audio_length(audio_length, sr, fps):
bit_per_frames = sr / fps
num_frames = int(audio_length / bit_per_frames)
audio_length = int(num_frames * bit_per_frames)
return audio_length, num_frames
def crop_pad_audio(wav, audio_length):
if len(wav) > audio_length:
wav = wav[:audio_length]
elif len(wav) < audio_length:
wav = np.pad(wav, [0, audio_length - len(wav)], mode='constant', constant_values=0)
return wav
def get_mel(audio_path):
wav = audio.load_wav(audio_path, 16000)
wav_length, num_frames = parse_audio_length(len(wav), 16000, 25)
wav = crop_pad_audio(wav, wav_length)
orig_mel = audio.melspectrogram(wav).T
spec = orig_mel.copy() # nframes 80
indiv_mels = []
fps = 25
syncnet_mel_step_size = 16
for i in range(num_frames):
start_frame_num = i-2
start_idx = int(80. * (start_frame_num / float(fps)))
end_idx = start_idx + syncnet_mel_step_size
seq = list(range(start_idx, end_idx))
seq = [ min(max(item, 0), orig_mel.shape[0]-1) for item in seq ]
m = spec[seq, :]
indiv_mels.append(m.T)
indiv_mels = np.asarray(indiv_mels) # T 80 16
indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1).unsqueeze(0).cuda()
source_audio_feature = indiv_mels.type(torch.FloatTensor).cuda()
mel_input = source_audio_feature # bs T 1 80 16
bs = mel_input.shape[0]
T = mel_input.shape[1]
audiox = mel_input.view(-1, 1, 80, 16) # bs*T 1 80 16
return audiox, bs, T
def audio_preprocessing(wav_path):
source_audio_feature, bs, T = get_mel(wav_path)
return source_audio_feature, bs, T
class Demo(nn.Module):
def __init__(self, args):
super(Demo, self).__init__()
self.args = args
model_path = args.model_path
audio2lip_model_path = args.audio2lip_model_path
print('==> loading model')
self.audio2lip = Audio2Lip().cuda()
weight = torch.load(audio2lip_model_path, map_location=lambda storage, loc: storage)['audio2lip']
self.audio2lip.load_state_dict(weight)
self.audio2lip.eval()
self.gen = Generator(args.size, args.latent_dim_style, args.latent_dim_lip, args.latent_dim_pose, args.latent_dim_exp, args.channel_multiplier).cuda()
weight = torch.load(model_path, map_location=lambda storage, loc: storage)['gen']
self.gen.load_state_dict(weight)
self.gen.eval()
print('==> loading data')
# print(args.need_crop_source_img)
if args.need_crop_source_img:
from data_preprocess.crop_image2 import crop_image
print('==> croping source_img')
crop_path = os.path.join(os.path.dirname(args.source_path), 'crop_'+os.path.basename(args.source_path))
try:
crop_image(args.source_path, crop_path)
if os.path.exists(crop_path):
args.source_path = crop_path
except:
print('==> crop image failed, use original source for animate')
if args.need_crop_pose_video:
print('==> croping pose_video')
crop_video_path = os.path.join(os.path.dirname(args.pose_driving_path), 'crop_'+os.path.basename(args.pose_driving_path))
crop_cmd = f"python data_preprocess/crop_video.py --inp {args.pose_driving_path} --outp {crop_video_path}"
os.system(crop_cmd)
args.pose_driving_path = crop_video_path
self.img_source = img_preprocessing(args.source_path, args.size).cuda()
self.audio, self.bs, self.T = audio_preprocessing(args.audio_driving_path)
if args.audio_driving_path.endswith(('.mp4', '.avi', '.mov', '.mkv')):
print("Warning: The provided audio_driving_path is in video format. Please provide an audio file.")
self.audio_path = args.audio_driving_path
self.exp_vid_target = np.load(os.path.join('ckpts/predefined_exp_weights', args.exp_type+'.npy'))
self.exp_vid_target = torch.from_numpy(self.exp_vid_target).cuda()
self.save_path = args.save_path
self.pose_vid_target, self.fps = vid_preprocessing(args.pose_driving_path)
self.pose_vid_target = self.pose_vid_target.cuda()
def run(self):
print('==> running')
with torch.no_grad():
# self.save_path = args.save_path
os.makedirs(os.path.dirname(self.save_path), exist_ok=True)
vid_target_recon = []
h_start = None
self.lip_vid_target = self.audio2lip(self.audio, self.bs, self.T)[0]
self.lip_vid_target = conv_feat(self.lip_vid_target, k_size=3, sigma=1) # torch.Size([372, 500])
len_pose = self.pose_vid_target.shape[1]
for i in tqdm(range(self.lip_vid_target.size(0))):
img_target_lip = self.lip_vid_target[i:i+1]
if i>=len_pose:
img_target_pose = self.pose_vid_target[:, -1, :, :, :]
else:
img_target_pose = self.pose_vid_target[:, i, :, :, :]
img_recon = self.gen.test_EDTalk_A_use_exp_weight(self.img_source, img_target_lip, img_target_pose, self.exp_vid_target, h_start)
vid_target_recon.append(img_recon.unsqueeze(2))
vid_target_recon = torch.cat(vid_target_recon, dim=2)
temp_path = self.save_path.replace('.mp4','_temp.mp4')
save_video(vid_target_recon, temp_path, self.fps)
cmd = r'ffmpeg -y -i "%s" -i "%s" -vcodec copy "%s"' % (temp_path, self.audio_path, self.save_path)
os.system(cmd)
os.remove(temp_path)
if args.face_sr and check_package_installed('gfpgan'):
from face_sr.face_enhancer import enhancer_list
import imageio
temp_512_path = self.save_path.replace('.mp4','_512.mp4')
# Super-resolution
imageio.mimsave(temp_512_path + '.tmp.mp4', enhancer_list(self.save_path, method='gfpgan', bg_upsampler=None), fps=float(25), codec='libx264')
# Merge audio and video
video_clip = VideoFileClip(temp_512_path + '.tmp.mp4')
audio_clip = AudioFileClip(self.save_path)
final_clip = video_clip.set_audio(audio_clip)
final_clip.write_videofile(temp_512_path, codec='libx264', audio_codec='aac')
os.remove(temp_512_path + '.tmp.mp4')
def conv_feat(features, k_size, weight=None, sigma=1.0):
c = features.shape[1] # torch.Size([101, 500])
if weight is None:
pad = k_size // 2
k = np.zeros(k_size).astype(np.float)
for x in range(-pad, k_size-pad):
k[x+pad] = np.exp(-x**2 / (2 * (sigma ** 2)))
k = k / k.sum()
print(k) # [0.27406862 0.45186276 0.27406862]
else:
k_size = len(weight)
k = np.array(weight)
pad = k_size // 2
print(k)
k = torch.from_numpy(k).to(features.device).float().unsqueeze(0).unsqueeze(0)
k = k.repeat(c, 1, 1)
features = features.unsqueeze(0).permute(0, 2, 1) # [1, 512, n]
features = F.conv1d(features, k, padding=pad, groups=c)
features = features.permute(0, 2, 1).squeeze(0)
return features
if __name__ == '__main__':
# training params
parser = argparse.ArgumentParser()
parser.add_argument("--size", type=int, default=256)
parser.add_argument("--channel_multiplier", type=int, default=1)
parser.add_argument("--model", type=str, choices=['vox', 'taichi', 'ted'], default='vox')
parser.add_argument("--latent_dim_style", type=int, default=512)
parser.add_argument("--latent_dim_lip", type=int, default=20)
parser.add_argument("--latent_dim_pose", type=int, default=6)
parser.add_argument("--latent_dim_exp", type=int, default=10)
parser.add_argument("--source_path", type=str, default='test_data/identity_source.jpg')
parser.add_argument("--audio_driving_path", type=str, default='test_data/mouth_source.wav')
parser.add_argument("--pose_driving_path", type=str, default='test_data/pose_source1.mp4')
parser.add_argument("--exp_type", type=str, default='angry', help='expression you want to generate, choose from [angry, contempt, disgusted, fear, happy, sad, surprised]') # ['angry', 'contempt', 'disgusted', 'fear', 'happy', 'sad', 'surprised']
parser.add_argument("--save_path", type=str, default='res/demo_EDTalk_A_using_weights.mp4')
parser.add_argument("--audio2lip_model_path", type=str, default='ckpts/Audio2Lip.pt')
parser.add_argument("--model_path", type=str, default='ckpts/EDTalk.pt')
parser.add_argument('--face_sr', action='store_true', help='Face super-resolution (Optional). Please install GFPGAN first')
parser.add_argument("--need_crop_source_img", action='store_true', help='crop input source_img. Please download shape_predictor_68_face_landmarks.dat and put it in ./data_preprocess first')
parser.add_argument("--need_crop_pose_video", action='store_true', help='crop input pose_driving video.')
args = parser.parse_args()
demo = Demo(args)
demo.run()