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| import torch | |
| from rvc.lib.algorithm.commons import fused_add_tanh_sigmoid_multiply | |
| class WaveNet(torch.nn.Module): | |
| """WaveNet residual blocks as used in WaveGlow | |
| Args: | |
| hidden_channels (int): Number of hidden channels. | |
| kernel_size (int): Size of the convolutional kernel. | |
| dilation_rate (int): Dilation rate of the convolution. | |
| n_layers (int): Number of convolutional layers. | |
| gin_channels (int, optional): Number of conditioning channels. Defaults to 0. | |
| p_dropout (float, optional): Dropout probability. Defaults to 0. | |
| """ | |
| def __init__( | |
| self, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0, | |
| p_dropout=0, | |
| ): | |
| super(WaveNet, self).__init__() | |
| assert kernel_size % 2 == 1 | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = (kernel_size,) | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.p_dropout = p_dropout | |
| self.in_layers = torch.nn.ModuleList() | |
| self.res_skip_layers = torch.nn.ModuleList() | |
| self.drop = torch.nn.Dropout(p_dropout) | |
| if gin_channels != 0: | |
| cond_layer = torch.nn.Conv1d( | |
| gin_channels, 2 * hidden_channels * n_layers, 1 | |
| ) | |
| self.cond_layer = torch.nn.utils.parametrizations.weight_norm( | |
| cond_layer, name="weight" | |
| ) | |
| dilations = [dilation_rate**i for i in range(n_layers)] | |
| paddings = [(kernel_size * d - d) // 2 for d in dilations] | |
| for i in range(n_layers): | |
| in_layer = torch.nn.Conv1d( | |
| hidden_channels, | |
| 2 * hidden_channels, | |
| kernel_size, | |
| dilation=dilations[i], | |
| padding=paddings[i], | |
| ) | |
| in_layer = torch.nn.utils.parametrizations.weight_norm( | |
| in_layer, name="weight" | |
| ) | |
| self.in_layers.append(in_layer) | |
| res_skip_channels = ( | |
| hidden_channels if i == n_layers - 1 else 2 * hidden_channels | |
| ) | |
| res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) | |
| res_skip_layer = torch.nn.utils.parametrizations.weight_norm( | |
| res_skip_layer, name="weight" | |
| ) | |
| self.res_skip_layers.append(res_skip_layer) | |
| def forward(self, x, x_mask, g=None, **kwargs): | |
| """Forward pass. | |
| Args: | |
| x (torch.Tensor): Input tensor of shape (batch_size, hidden_channels, time_steps). | |
| x_mask (torch.Tensor): Mask tensor of shape (batch_size, 1, time_steps). | |
| g (torch.Tensor, optional): Conditioning tensor of shape (batch_size, gin_channels, time_steps). | |
| Defaults to None. | |
| """ | |
| output = torch.zeros_like(x) | |
| n_channels_tensor = torch.IntTensor([self.hidden_channels]) | |
| if g is not None: | |
| g = self.cond_layer(g) | |
| for i in range(self.n_layers): | |
| x_in = self.in_layers[i](x) | |
| if g is not None: | |
| cond_offset = i * 2 * self.hidden_channels | |
| g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] | |
| else: | |
| g_l = torch.zeros_like(x_in) | |
| acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) | |
| acts = self.drop(acts) | |
| res_skip_acts = self.res_skip_layers[i](acts) | |
| if i < self.n_layers - 1: | |
| res_acts = res_skip_acts[:, : self.hidden_channels, :] | |
| x = (x + res_acts) * x_mask | |
| output = output + res_skip_acts[:, self.hidden_channels :, :] | |
| else: | |
| output = output + res_skip_acts | |
| return output * x_mask | |
| def remove_weight_norm(self): | |
| """Remove weight normalization from the module.""" | |
| if self.gin_channels != 0: | |
| torch.nn.utils.remove_weight_norm(self.cond_layer) | |
| for l in self.in_layers: | |
| torch.nn.utils.remove_weight_norm(l) | |
| for l in self.res_skip_layers: | |
| torch.nn.utils.remove_weight_norm(l) | |