This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020. For HD commercial model, please try out Sync Labs
Traceback (most recent call last):
File "wav2lip_train.py", line 372, in
train(device, model, train_data_loader, test_data_loader, optimizer,
File "wav2lip_train.py", line 223, in train
sync_loss = get_sync_loss(mel, g)
File "wav2lip_train.py", line 196, in get_sync_loss
a, v = syncnet(mel, g)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, kwargs)
File "/root/wav2lip_288x288/models/syncnetv2.py", line 63, in forward
face_embedding = self.face_encoder(face_sequences)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, *kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(input, kwargs)
File "/root/wav2lip_288x288/models/conv2.py", line 17, in forward
out = self.conv_block(x)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, *kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(input, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py", line 168, in forward
return F.batch_norm(
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py", line 2419, in batch_norm
_verify_batch_size(input.size())
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py", line 2387, in _verify_batch_size
raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512, 1, 1])
Traceback (most recent call last): File "wav2lip_train.py", line 372, in
train(device, model, train_data_loader, test_data_loader, optimizer,
File "wav2lip_train.py", line 223, in train
sync_loss = get_sync_loss(mel, g)
File "wav2lip_train.py", line 196, in get_sync_loss
a, v = syncnet(mel, g)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, kwargs)
File "/root/wav2lip_288x288/models/syncnetv2.py", line 63, in forward
face_embedding = self.face_encoder(face_sequences)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, *kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(input, kwargs)
File "/root/wav2lip_288x288/models/conv2.py", line 17, in forward
out = self.conv_block(x)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, *kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(input, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py", line 168, in forward
return F.batch_norm(
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py", line 2419, in batch_norm
_verify_batch_size(input.size())
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py", line 2387, in _verify_batch_size
raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512, 1, 1])