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import torch
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from torch import nn
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from torch.nn import functional as F
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class Conv2d(nn.Module):
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def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.conv_block = nn.Sequential(
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nn.Conv2d(cin, cout, kernel_size, stride, padding),
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nn.BatchNorm2d(cout)
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)
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self.act = nn.ReLU()
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self.residual = residual
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def forward(self, x):
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out = self.conv_block(x)
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if self.residual:
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out += x
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return self.act(out)
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class AudioEncoder(nn.Module):
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def __init__(self, wav2lip_checkpoint, device):
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super(AudioEncoder, self).__init__()
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self.audio_encoder = nn.Sequential(
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Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
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Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
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Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
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Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
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Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
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Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
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Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
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wav2lip_state_dict = torch.load(wav2lip_checkpoint, map_location=torch.device(device))['state_dict']
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state_dict = self.audio_encoder.state_dict()
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for k,v in wav2lip_state_dict.items():
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if 'audio_encoder' in k:
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state_dict[k.replace('module.audio_encoder.', '')] = v
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self.audio_encoder.load_state_dict(state_dict)
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def forward(self, audio_sequences):
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B = audio_sequences.size(0)
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audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
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audio_embedding = self.audio_encoder(audio_sequences)
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dim = audio_embedding.shape[1]
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audio_embedding = audio_embedding.reshape((B, -1, dim, 1, 1))
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return audio_embedding.squeeze(-1).squeeze(-1)
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