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| """VGG2L definition for transformer-transducer.""" | |
| import torch | |
| class VGG2L(torch.nn.Module): | |
| """VGG2L module for transformer-transducer encoder.""" | |
| def __init__(self, idim, odim): | |
| """Construct a VGG2L object. | |
| Args: | |
| idim (int): dimension of inputs | |
| odim (int): dimension of outputs | |
| """ | |
| super(VGG2L, self).__init__() | |
| self.vgg2l = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, 64, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(64, 64, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.MaxPool2d((3, 2)), | |
| torch.nn.Conv2d(64, 128, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(128, 128, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.MaxPool2d((2, 2)), | |
| ) | |
| self.output = torch.nn.Linear(128 * ((idim // 2) // 2), odim) | |
| def forward(self, x, x_mask): | |
| """VGG2L forward for x. | |
| Args: | |
| x (torch.Tensor): input torch (B, T, idim) | |
| x_mask (torch.Tensor): (B, 1, T) | |
| Returns: | |
| x (torch.Tensor): input torch (B, sub(T), attention_dim) | |
| x_mask (torch.Tensor): (B, 1, sub(T)) | |
| """ | |
| x = x.unsqueeze(1) | |
| x = self.vgg2l(x) | |
| b, c, t, f = x.size() | |
| x = self.output(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| if x_mask is None: | |
| return x, None | |
| else: | |
| x_mask = self.create_new_mask(x_mask, x) | |
| return x, x_mask | |
| def create_new_mask(self, x_mask, x): | |
| """Create a subsampled version of x_mask. | |
| Args: | |
| x_mask (torch.Tensor): (B, 1, T) | |
| x (torch.Tensor): (B, sub(T), attention_dim) | |
| Returns: | |
| x_mask (torch.Tensor): (B, 1, sub(T)) | |
| """ | |
| x_t1 = x_mask.size(2) - (x_mask.size(2) % 3) | |
| x_mask = x_mask[:, :, :x_t1][:, :, ::3] | |
| x_t2 = x_mask.size(2) - (x_mask.size(2) % 2) | |
| x_mask = x_mask[:, :, :x_t2][:, :, ::2] | |
| return x_mask | |