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Running
on
Zero
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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"
)
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)
# last one is not necessary
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)
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