| import math |
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
|
|
| from modules.encodec import SConv1d |
|
|
| from . import commons |
| LRELU_SLOPE = 0.1 |
|
|
| class LayerNorm(nn.Module): |
| def __init__(self, channels, eps=1e-5): |
| super().__init__() |
| self.channels = channels |
| self.eps = eps |
|
|
| self.gamma = nn.Parameter(torch.ones(channels)) |
| self.beta = nn.Parameter(torch.zeros(channels)) |
|
|
| def forward(self, x): |
| x = x.transpose(1, -1) |
| x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) |
| return x.transpose(1, -1) |
|
|
|
|
| class ConvReluNorm(nn.Module): |
| def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): |
| super().__init__() |
| self.in_channels = in_channels |
| self.hidden_channels = hidden_channels |
| self.out_channels = out_channels |
| self.kernel_size = kernel_size |
| self.n_layers = n_layers |
| self.p_dropout = p_dropout |
| assert n_layers > 1, "Number of layers should be larger than 0." |
|
|
| self.conv_layers = nn.ModuleList() |
| self.norm_layers = nn.ModuleList() |
| self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) |
| self.norm_layers.append(LayerNorm(hidden_channels)) |
| self.relu_drop = nn.Sequential( |
| nn.ReLU(), |
| nn.Dropout(p_dropout)) |
| for _ in range(n_layers - 1): |
| self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) |
| self.norm_layers.append(LayerNorm(hidden_channels)) |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) |
| self.proj.weight.data.zero_() |
| self.proj.bias.data.zero_() |
|
|
| def forward(self, x, x_mask): |
| x_org = x |
| for i in range(self.n_layers): |
| x = self.conv_layers[i](x * x_mask) |
| x = self.norm_layers[i](x) |
| x = self.relu_drop(x) |
| x = x_org + self.proj(x) |
| return x * x_mask |
|
|
|
|
| class DDSConv(nn.Module): |
| """ |
| Dialted and Depth-Separable Convolution |
| """ |
|
|
| def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): |
| super().__init__() |
| self.channels = channels |
| self.kernel_size = kernel_size |
| self.n_layers = n_layers |
| self.p_dropout = p_dropout |
|
|
| self.drop = nn.Dropout(p_dropout) |
| self.convs_sep = nn.ModuleList() |
| self.convs_1x1 = nn.ModuleList() |
| self.norms_1 = nn.ModuleList() |
| self.norms_2 = nn.ModuleList() |
| for i in range(n_layers): |
| dilation = kernel_size ** i |
| padding = (kernel_size * dilation - dilation) // 2 |
| self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, |
| groups=channels, dilation=dilation, padding=padding |
| )) |
| self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) |
| self.norms_1.append(LayerNorm(channels)) |
| self.norms_2.append(LayerNorm(channels)) |
|
|
| def forward(self, x, x_mask, g=None): |
| if g is not None: |
| x = x + g |
| for i in range(self.n_layers): |
| y = self.convs_sep[i](x * x_mask) |
| y = self.norms_1[i](y) |
| y = F.gelu(y) |
| y = self.convs_1x1[i](y) |
| y = self.norms_2[i](y) |
| y = F.gelu(y) |
| y = self.drop(y) |
| x = x + y |
| return x * x_mask |
|
|
|
|
| class WN(torch.nn.Module): |
| def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, causal=False): |
| super(WN, self).__init__() |
| conv1d_type = SConv1d |
| 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 = nn.Dropout(p_dropout) |
|
|
| if gin_channels != 0: |
| self.cond_layer = conv1d_type(gin_channels, 2 * hidden_channels * n_layers, 1, norm='weight_norm') |
|
|
| for i in range(n_layers): |
| dilation = dilation_rate ** i |
| padding = int((kernel_size * dilation - dilation) / 2) |
| in_layer = conv1d_type(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, |
| padding=padding, norm='weight_norm', causal=causal) |
| self.in_layers.append(in_layer) |
|
|
| |
| if i < n_layers - 1: |
| res_skip_channels = 2 * hidden_channels |
| else: |
| res_skip_channels = hidden_channels |
|
|
| res_skip_layer = conv1d_type(hidden_channels, res_skip_channels, 1, norm='weight_norm', causal=causal) |
| self.res_skip_layers.append(res_skip_layer) |
|
|
| def forward(self, x, x_mask, g=None, **kwargs): |
| 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 = commons.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): |
| 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) |
