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TTS/vocoder/layers/parallel_wavegan.py

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+import torch
+from torch.nn import functional as F
+
+
+class ResidualBlock(torch.nn.Module):
+    """Residual block module in WaveNet."""
+
+    def __init__(
+        self,
+        kernel_size=3,
+        res_channels=64,
+        gate_channels=128,
+        skip_channels=64,
+        aux_channels=80,
+        dropout=0.0,
+        dilation=1,
+        bias=True,
+        use_causal_conv=False,
+    ):
+        super().__init__()
+        self.dropout = dropout
+        # no future time stamps available
+        if use_causal_conv:
+            padding = (kernel_size - 1) * dilation
+        else:
+            assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+            padding = (kernel_size - 1) // 2 * dilation
+        self.use_causal_conv = use_causal_conv
+
+        # dilation conv
+        self.conv = torch.nn.Conv1d(
+            res_channels, gate_channels, kernel_size, padding=padding, dilation=dilation, bias=bias
+        )
+
+        # local conditioning
+        if aux_channels > 0:
+            self.conv1x1_aux = torch.nn.Conv1d(aux_channels, gate_channels, 1, bias=False)
+        else:
+            self.conv1x1_aux = None
+
+        # conv output is split into two groups
+        gate_out_channels = gate_channels // 2
+        self.conv1x1_out = torch.nn.Conv1d(gate_out_channels, res_channels, 1, bias=bias)
+        self.conv1x1_skip = torch.nn.Conv1d(gate_out_channels, skip_channels, 1, bias=bias)
+
+    def forward(self, x, c):
+        """
+        x: B x D_res x T
+        c: B x D_aux x T
+        """
+        residual = x
+        x = F.dropout(x, p=self.dropout, training=self.training)
+        x = self.conv(x)
+
+        # remove future time steps if use_causal_conv conv
+        x = x[:, :, : residual.size(-1)] if self.use_causal_conv else x
+
+        # split into two part for gated activation
+        splitdim = 1
+        xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim)
+
+        # local conditioning
+        if c is not None:
+            assert self.conv1x1_aux is not None
+            c = self.conv1x1_aux(c)
+            ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim)
+            xa, xb = xa + ca, xb + cb
+
+        x = torch.tanh(xa) * torch.sigmoid(xb)
+
+        # for skip connection
+        s = self.conv1x1_skip(x)
+
+        # for residual connection
+        x = (self.conv1x1_out(x) + residual) * (0.5**2)
+
+        return x, s