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游雁

2023-11-16 4ace5a95b052d338947fc88809a440ccd55cf6b4

 funasr/modules/subsampling.py

@@ -358,7 +358,8 @@
        """
        x = x.transpose(1, 2)  # (b, d ,t)
        x = self.pad_fn(x)
        x = F.relu(self.conv(x))
        #x = F.relu(self.conv(x))
        x = F.leaky_relu(self.conv(x), negative_slope=0.)
        x = x.transpose(1, 2)  # (b, t ,d)

        if x_len is None:
@@ -427,6 +428,7 @@
        conv_size: Union[int, Tuple],
        subsampling_factor: int = 4,
        vgg_like: bool = True,
        conv_kernel_size: int = 3,
        output_size: Optional[int] = None,
    ) -> None:
        """Construct a ConvInput object."""
@@ -436,14 +438,14 @@
                conv_size1, conv_size2 = conv_size

                self.conv = torch.nn.Sequential(
                    torch.nn.Conv2d(1, conv_size1, 3, stride=1, padding=1),
                    torch.nn.Conv2d(1, conv_size1, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.Conv2d(conv_size1, conv_size1, 3, stride=1, padding=1),
                    torch.nn.Conv2d(conv_size1, conv_size1, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.MaxPool2d((1, 2)),
                    torch.nn.Conv2d(conv_size1, conv_size2, 3, stride=1, padding=1),
                    torch.nn.Conv2d(conv_size1, conv_size2, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.Conv2d(conv_size2, conv_size2, 3, stride=1, padding=1),
                    torch.nn.Conv2d(conv_size2, conv_size2, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.MaxPool2d((1, 2)),
                )
@@ -462,14 +464,14 @@
                kernel_1 = int(subsampling_factor / 2)

                self.conv = torch.nn.Sequential(
                    torch.nn.Conv2d(1, conv_size1, 3, stride=1, padding=1),
                    torch.nn.Conv2d(1, conv_size1, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.Conv2d(conv_size1, conv_size1, 3, stride=1, padding=1),
                    torch.nn.Conv2d(conv_size1, conv_size1, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.MaxPool2d((kernel_1, 2)),
                    torch.nn.Conv2d(conv_size1, conv_size2, 3, stride=1, padding=1),
                    torch.nn.Conv2d(conv_size1, conv_size2, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.Conv2d(conv_size2, conv_size2, 3, stride=1, padding=1),
                    torch.nn.Conv2d(conv_size2, conv_size2, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
                    torch.nn.ReLU(),
                    torch.nn.MaxPool2d((2, 2)),
                )
@@ -487,14 +489,14 @@
                self.conv = torch.nn.Sequential(
                    torch.nn.Conv2d(1, conv_size, 3, [1,2], [1,0]),
                    torch.nn.ReLU(),
                    torch.nn.Conv2d(conv_size, conv_size, 3, [1,2], [1,0]),
                    torch.nn.Conv2d(conv_size, conv_size, conv_kernel_size, [1,2], [1,0]),
                    torch.nn.ReLU(),
                )

                output_proj = conv_size * (((input_size - 1) // 2 - 1) // 2)

                self.subsampling_factor = subsampling_factor
                self.kernel_2 = 3
                self.kernel_2 = conv_kernel_size
                self.stride_2 = 1

                self.create_new_mask = self.create_new_conv2d_mask