| import torch |
| from programs.applio_code.rvc.lib.algorithm.commons import ( |
| fused_add_tanh_sigmoid_multiply_no_jit, |
| 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) |
|
|
| |
| is_zluda = x.device.type == "cuda" and torch.cuda.get_device_name().endswith( |
| "[ZLUDA]" |
| ) |
|
|
| 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) |
|
|
| |
| if is_zluda: |
| acts = fused_add_tanh_sigmoid_multiply_no_jit( |
| x_in, g_l, n_channels_tensor |
| ) |
| else: |
| 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) |
|
|
