video-dubbing / Wav2Lip /wav2lip_train.py
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from os.path import dirname, join, basename, isfile
from tqdm import tqdm
from models import SyncNet_color as SyncNet
from models import Wav2Lip as Wav2Lip
import audio
import torch
from torch import nn
from torch import optim
import torch.backends.cudnn as cudnn
from torch.utils import data as data_utils
import numpy as np
from glob import glob
import os, random, cv2, argparse
from hparams import hparams, get_image_list
parser = argparse.ArgumentParser(description='Code to train the Wav2Lip model without the visual quality discriminator')
parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True, type=str)
parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str)
parser.add_argument('--syncnet_checkpoint_path', help='Load the pre-trained Expert discriminator', required=True, type=str)
parser.add_argument('--checkpoint_path', help='Resume from this checkpoint', default=None, type=str)
args = parser.parse_args()
global_step = 0
global_epoch = 0
use_cuda = torch.cuda.is_available()
print('use_cuda: {}'.format(use_cuda))
syncnet_T = 5
syncnet_mel_step_size = 16
class Dataset(object):
def __init__(self, split):
self.all_videos = get_image_list(args.data_root, split)
def get_frame_id(self, frame):
return int(basename(frame).split('.')[0])
def get_window(self, start_frame):
start_id = self.get_frame_id(start_frame)
vidname = dirname(start_frame)
window_fnames = []
for frame_id in range(start_id, start_id + syncnet_T):
frame = join(vidname, '{}.jpg'.format(frame_id))
if not isfile(frame):
return None
window_fnames.append(frame)
return window_fnames
def read_window(self, window_fnames):
if window_fnames is None: return None
window = []
for fname in window_fnames:
img = cv2.imread(fname)
if img is None:
return None
try:
img = cv2.resize(img, (hparams.img_size, hparams.img_size))
except Exception as e:
return None
window.append(img)
return window
def crop_audio_window(self, spec, start_frame):
if type(start_frame) == int:
start_frame_num = start_frame
else:
start_frame_num = self.get_frame_id(start_frame) # 0-indexing ---> 1-indexing
start_idx = int(80. * (start_frame_num / float(hparams.fps)))
end_idx = start_idx + syncnet_mel_step_size
return spec[start_idx : end_idx, :]
def get_segmented_mels(self, spec, start_frame):
mels = []
assert syncnet_T == 5
start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing
if start_frame_num - 2 < 0: return None
for i in range(start_frame_num, start_frame_num + syncnet_T):
m = self.crop_audio_window(spec, i - 2)
if m.shape[0] != syncnet_mel_step_size:
return None
mels.append(m.T)
mels = np.asarray(mels)
return mels
def prepare_window(self, window):
# 3 x T x H x W
x = np.asarray(window) / 255.
x = np.transpose(x, (3, 0, 1, 2))
return x
def __len__(self):
return len(self.all_videos)
def __getitem__(self, idx):
while 1:
idx = random.randint(0, len(self.all_videos) - 1)
vidname = self.all_videos[idx]
img_names = list(glob(join(vidname, '*.jpg')))
if len(img_names) <= 3 * syncnet_T:
continue
img_name = random.choice(img_names)
wrong_img_name = random.choice(img_names)
while wrong_img_name == img_name:
wrong_img_name = random.choice(img_names)
window_fnames = self.get_window(img_name)
wrong_window_fnames = self.get_window(wrong_img_name)
if window_fnames is None or wrong_window_fnames is None:
continue
window = self.read_window(window_fnames)
if window is None:
continue
wrong_window = self.read_window(wrong_window_fnames)
if wrong_window is None:
continue
try:
wavpath = join(vidname, "audio.wav")
wav = audio.load_wav(wavpath, hparams.sample_rate)
orig_mel = audio.melspectrogram(wav).T
except Exception as e:
continue
mel = self.crop_audio_window(orig_mel.copy(), img_name)
if (mel.shape[0] != syncnet_mel_step_size):
continue
indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name)
if indiv_mels is None: continue
window = self.prepare_window(window)
y = window.copy()
window[:, :, window.shape[2]//2:] = 0.
wrong_window = self.prepare_window(wrong_window)
x = np.concatenate([window, wrong_window], axis=0)
x = torch.FloatTensor(x)
mel = torch.FloatTensor(mel.T).unsqueeze(0)
indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1)
y = torch.FloatTensor(y)
return x, indiv_mels, mel, y
def save_sample_images(x, g, gt, global_step, checkpoint_dir):
x = (x.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
g = (g.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
gt = (gt.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
refs, inps = x[..., 3:], x[..., :3]
folder = join(checkpoint_dir, "samples_step{:09d}".format(global_step))
if not os.path.exists(folder): os.mkdir(folder)
collage = np.concatenate((refs, inps, g, gt), axis=-2)
for batch_idx, c in enumerate(collage):
for t in range(len(c)):
cv2.imwrite('{}/{}_{}.jpg'.format(folder, batch_idx, t), c[t])
logloss = nn.BCELoss()
def cosine_loss(a, v, y):
d = nn.functional.cosine_similarity(a, v)
loss = logloss(d.unsqueeze(1), y)
return loss
device = torch.device("cuda" if use_cuda else "cpu")
syncnet = SyncNet().to(device)
for p in syncnet.parameters():
p.requires_grad = False
recon_loss = nn.L1Loss()
def get_sync_loss(mel, g):
g = g[:, :, :, g.size(3)//2:]
g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1)
# B, 3 * T, H//2, W
a, v = syncnet(mel, g)
y = torch.ones(g.size(0), 1).float().to(device)
return cosine_loss(a, v, y)
def train(device, model, train_data_loader, test_data_loader, optimizer,
checkpoint_dir=None, checkpoint_interval=None, nepochs=None):
global global_step, global_epoch
resumed_step = global_step
while global_epoch < nepochs:
print('Starting Epoch: {}'.format(global_epoch))
running_sync_loss, running_l1_loss = 0., 0.
prog_bar = tqdm(enumerate(train_data_loader))
for step, (x, indiv_mels, mel, gt) in prog_bar:
model.train()
optimizer.zero_grad()
# Move data to CUDA device
x = x.to(device)
mel = mel.to(device)
indiv_mels = indiv_mels.to(device)
gt = gt.to(device)
g = model(indiv_mels, x)
if hparams.syncnet_wt > 0.:
sync_loss = get_sync_loss(mel, g)
else:
sync_loss = 0.
l1loss = recon_loss(g, gt)
loss = hparams.syncnet_wt * sync_loss + (1 - hparams.syncnet_wt) * l1loss
loss.backward()
optimizer.step()
if global_step % checkpoint_interval == 0:
save_sample_images(x, g, gt, global_step, checkpoint_dir)
global_step += 1
cur_session_steps = global_step - resumed_step
running_l1_loss += l1loss.item()
if hparams.syncnet_wt > 0.:
running_sync_loss += sync_loss.item()
else:
running_sync_loss += 0.
if global_step == 1 or global_step % checkpoint_interval == 0:
save_checkpoint(
model, optimizer, global_step, checkpoint_dir, global_epoch)
if global_step == 1 or global_step % hparams.eval_interval == 0:
with torch.no_grad():
average_sync_loss = eval_model(test_data_loader, global_step, device, model, checkpoint_dir)
if average_sync_loss < .75:
hparams.set_hparam('syncnet_wt', 0.01) # without image GAN a lesser weight is sufficient
prog_bar.set_description('L1: {}, Sync Loss: {}'.format(running_l1_loss / (step + 1),
running_sync_loss / (step + 1)))
global_epoch += 1
def eval_model(test_data_loader, global_step, device, model, checkpoint_dir):
eval_steps = 700
print('Evaluating for {} steps'.format(eval_steps))
sync_losses, recon_losses = [], []
step = 0
while 1:
for x, indiv_mels, mel, gt in test_data_loader:
step += 1
model.eval()
# Move data to CUDA device
x = x.to(device)
gt = gt.to(device)
indiv_mels = indiv_mels.to(device)
mel = mel.to(device)
g = model(indiv_mels, x)
sync_loss = get_sync_loss(mel, g)
l1loss = recon_loss(g, gt)
sync_losses.append(sync_loss.item())
recon_losses.append(l1loss.item())
if step > eval_steps:
averaged_sync_loss = sum(sync_losses) / len(sync_losses)
averaged_recon_loss = sum(recon_losses) / len(recon_losses)
print('L1: {}, Sync loss: {}'.format(averaged_recon_loss, averaged_sync_loss))
return averaged_sync_loss
def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch):
checkpoint_path = join(
checkpoint_dir, "checkpoint_step{:09d}.pth".format(global_step))
optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None
torch.save({
"state_dict": model.state_dict(),
"optimizer": optimizer_state,
"global_step": step,
"global_epoch": epoch,
}, checkpoint_path)
print("Saved checkpoint:", checkpoint_path)
def _load(checkpoint_path):
if use_cuda:
checkpoint = torch.load(checkpoint_path)
else:
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
return checkpoint
def load_checkpoint(path, model, optimizer, reset_optimizer=False, overwrite_global_states=True):
global global_step
global global_epoch
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)
if not reset_optimizer:
optimizer_state = checkpoint["optimizer"]
if optimizer_state is not None:
print("Load optimizer state from {}".format(path))
optimizer.load_state_dict(checkpoint["optimizer"])
if overwrite_global_states:
global_step = checkpoint["global_step"]
global_epoch = checkpoint["global_epoch"]
return model
if __name__ == "__main__":
checkpoint_dir = args.checkpoint_dir
# Dataset and Dataloader setup
train_dataset = Dataset('train')
test_dataset = Dataset('val')
train_data_loader = data_utils.DataLoader(
train_dataset, batch_size=hparams.batch_size, shuffle=True,
num_workers=hparams.num_workers)
test_data_loader = data_utils.DataLoader(
test_dataset, batch_size=hparams.batch_size,
num_workers=4)
device = torch.device("cuda" if use_cuda else "cpu")
# Model
model = Wav2Lip().to(device)
print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad)))
optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
lr=hparams.initial_learning_rate)
if args.checkpoint_path is not None:
load_checkpoint(args.checkpoint_path, model, optimizer, reset_optimizer=False)
load_checkpoint(args.syncnet_checkpoint_path, syncnet, None, reset_optimizer=True, overwrite_global_states=False)
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir)
# Train!
train(device, model, train_data_loader, test_data_loader, optimizer,
checkpoint_dir=checkpoint_dir,
checkpoint_interval=hparams.checkpoint_interval,
nepochs=hparams.nepochs)