espnet2.enh.layers.tcn.DepthwiseSeparableConv

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class espnet2.enh.layers.tcn.DepthwiseSeparableConv(in_channels, out_channels, skip_channels, kernel_size, stride, padding, dilation, norm_type='gLN', causal=False)

Bases: Module

Depthwise Separable Convolution Layer.

This module implements depthwise separable convolution, which consists of a depthwise convolution followed by a pointwise convolution. The depthwise convolution applies a single filter to each input channel, while the pointwise convolution combines the outputs of the depthwise convolution.

skip_conv

A convolutional layer for skip connections if skip_channels is not None.

• Type: nn.Conv1d or None

• Parameters:

  • in_channels (int) – Number of input channels.
  • out_channels (int) – Number of output channels.
  • skip_channels (int or None) – Number of channels for skip connection.
  • kernel_size (int) – Size of the convolution kernel.
  • stride (int) – Stride of the convolution.
  • padding (int) – Padding added to both sides of the input.
  • dilation (int) – Dilation factor for the convolution.
  • norm_type (str) – Type of normalization to apply (e.g., ‘gLN’, ‘cLN’).
  • causal (bool) – Whether to use causal convolution.

• Returns: Output tensor after pointwise convolution. skip_out (torch.Tensor or None): Output tensor for skip connection

  if skip_channels is not None.

• Return type: res_out (torch.Tensor)

####### Examples

>>> conv = DepthwiseSeparableConv(
...     in_channels=16,
...     out_channels=32,
...     skip_channels=8,
...     kernel_size=3,
...     stride=1,
...     padding=1,
...     dilation=1,
...     norm_type='gLN',
...     causal=False
... )
>>> x = torch.randn(10, 16, 50)  # Batch size of 10, 16 channels, length 50
>>> res_out, skip_out = conv(x)
>>> res_out.shape
torch.Size([10, 32, 50])
>>> skip_out.shape
torch.Size([10, 8, 50])

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

Perform the forward pass of the temporal convolutional network.

This method takes an input tensor representing a mixture of audio signals and processes it through the network to estimate masks for separating the individual audio sources.

• Parameters:mixture_w – A tensor of shape [M, N, K], where: M (int): The batch size. N (int): The number of input channels (filters). K (int): The length of the input sequences.
• Returns: A tensor of shape [M, C, N, K], representing the estimated masks for the C speakers, where:

  C (int): The number of speakers.

• Return type: est_mask
• Raises:ValueError – If an unsupported mask non-linear function is specified.

####### Examples

>>> model = TemporalConvNet(N=64, B=32, H=128, P=3, X=4, R=2, C=2)
>>> mixture = torch.randn(10, 64, 100)  # Example input tensor
>>> masks = model.forward(mixture)
>>> print(masks.shape)  # Output: torch.Size([10, 2, 64, 100])