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