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| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| class Conv2d(nn.Module): | |
| def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self.conv_block = nn.Sequential( | |
| nn.Conv2d(cin, cout, kernel_size, stride, padding), | |
| nn.BatchNorm2d(cout) | |
| ) | |
| self.act = nn.ReLU() | |
| self.residual = residual | |
| def forward(self, x): | |
| out = self.conv_block(x) | |
| if self.residual: | |
| out += x | |
| return self.act(out) | |
| class AudioEncoder(nn.Module): | |
| def __init__(self, wav2lip_checkpoint, device): | |
| super(AudioEncoder, self).__init__() | |
| self.audio_encoder = nn.Sequential( | |
| Conv2d(1, 32, kernel_size=3, stride=1, padding=1), | |
| Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), | |
| Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(64, 128, kernel_size=3, stride=3, padding=1), | |
| Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), | |
| Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), | |
| Conv2d(256, 512, kernel_size=3, stride=1, padding=0), | |
| Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) | |
| #### load the pre-trained audio_encoder, we do not need to load wav2lip model here. | |
| # wav2lip_state_dict = torch.load(wav2lip_checkpoint, map_location=torch.device(device))['state_dict'] | |
| # state_dict = self.audio_encoder.state_dict() | |
| # for k,v in wav2lip_state_dict.items(): | |
| # if 'audio_encoder' in k: | |
| # state_dict[k.replace('module.audio_encoder.', '')] = v | |
| # self.audio_encoder.load_state_dict(state_dict) | |
| def forward(self, audio_sequences): | |
| # audio_sequences = (B, T, 1, 80, 16) | |
| B = audio_sequences.size(0) | |
| audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0) | |
| audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 | |
| dim = audio_embedding.shape[1] | |
| audio_embedding = audio_embedding.reshape((B, -1, dim, 1, 1)) | |
| return audio_embedding.squeeze(-1).squeeze(-1) #B seq_len+1 512 | |