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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: int,
kernel_size: int,
dilation_rate,
n_layers: int,
gin_channels: int = 0,
p_dropout: int = 0,
):
super().__init__()
assert kernel_size % 2 == 1, "Kernel size must be odd for proper padding."
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.n_channels_tensor = torch.IntTensor([hidden_channels]) # Static tensor
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.drop = torch.nn.Dropout(p_dropout)
# Conditional layer for global conditioning
if gin_channels:
self.cond_layer = torch.nn.utils.parametrizations.weight_norm(
torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1),
name="weight",
)
# Precompute dilations and paddings
dilations = [dilation_rate**i for i in range(n_layers)]
paddings = [(kernel_size * d - d) // 2 for d in dilations]
# Initialize layers
for i in range(n_layers):
self.in_layers.append(
torch.nn.utils.parametrizations.weight_norm(
torch.nn.Conv1d(
hidden_channels,
2 * hidden_channels,
kernel_size,
dilation=dilations[i],
padding=paddings[i],
),
name="weight",
)
)
res_skip_channels = (
hidden_channels if i == n_layers - 1 else 2 * hidden_channels
)
self.res_skip_layers.append(
torch.nn.utils.parametrizations.weight_norm(
torch.nn.Conv1d(hidden_channels, res_skip_channels, 1),
name="weight",
)
)
def forward(self, x, x_mask, g=None):
output = x.clone().zero_()
# Apply conditional layer if global conditioning is provided
g = self.cond_layer(g) if g is not None else None
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
g_l = (
g[
:,
i * 2 * self.hidden_channels : (i + 1) * 2 * self.hidden_channels,
:,
]
if g is not None
else 0
)
# Activation with fused Tanh-Sigmoid
acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, self.n_channels_tensor)
acts = self.drop(acts)
# Residual and skip connections
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:
torch.nn.utils.remove_weight_norm(self.cond_layer)
for layer in self.in_layers:
torch.nn.utils.remove_weight_norm(layer)
for layer in self.res_skip_layers:
torch.nn.utils.remove_weight_norm(layer)
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