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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 | |