|
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" |
|
) |
|
|
|
for i in range(n_layers): |
|
dilation = dilation_rate**i |
|
padding = int((kernel_size * dilation - dilation) / 2) |
|
in_layer = torch.nn.Conv1d( |
|
hidden_channels, |
|
2 * hidden_channels, |
|
kernel_size, |
|
dilation=dilation, |
|
padding=padding, |
|
) |
|
in_layer = torch.nn.utils.parametrizations.weight_norm( |
|
in_layer, name="weight" |
|
) |
|
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 = 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) |
|
|
