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import math
import torch
from torch import nn
from TTS.vocoder.layers.parallel_wavegan import ResidualBlock
class ParallelWaveganDiscriminator(nn.Module):
"""PWGAN discriminator as in https://arxiv.org/abs/1910.11480.
It classifies each audio window real/fake and returns a sequence
of predictions.
It is a stack of convolutional blocks with dilation.
"""
# pylint: disable=dangerous-default-value
def __init__(
self,
in_channels=1,
out_channels=1,
kernel_size=3,
num_layers=10,
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
):
super().__init__()
assert (kernel_size - 1) % 2 == 0, " [!] does not support even number kernel size."
assert dilation_factor > 0, " [!] dilation factor must be > 0."
self.conv_layers = nn.ModuleList()
conv_in_channels = in_channels
for i in range(num_layers - 1):
if i == 0:
dilation = 1
else:
dilation = i if dilation_factor == 1 else dilation_factor**i
conv_in_channels = conv_channels
padding = (kernel_size - 1) // 2 * dilation
conv_layer = [
nn.Conv1d(
conv_in_channels,
conv_channels,
kernel_size=kernel_size,
padding=padding,
dilation=dilation,
bias=bias,
),
getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
]
self.conv_layers += conv_layer
padding = (kernel_size - 1) // 2
last_conv_layer = nn.Conv1d(conv_in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=bias)
self.conv_layers += [last_conv_layer]
self.apply_weight_norm()
def forward(self, x):
"""
x : (B, 1, T).
Returns:
Tensor: (B, 1, T)
"""
for f in self.conv_layers:
x = f(x)
return x
def apply_weight_norm(self):
def _apply_weight_norm(m):
if isinstance(m, (torch.nn.Conv1d, torch.nn.Conv2d)):
torch.nn.utils.weight_norm(m)
self.apply(_apply_weight_norm)
def remove_weight_norm(self):
def _remove_weight_norm(m):
try:
# print(f"Weight norm is removed from {m}.")
nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
class ResidualParallelWaveganDiscriminator(nn.Module):
# pylint: disable=dangerous-default-value
def __init__(
self,
in_channels=1,
out_channels=1,
kernel_size=3,
num_layers=30,
stacks=3,
res_channels=64,
gate_channels=128,
skip_channels=64,
dropout=0.0,
bias=True,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
):
super().__init__()
assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
self.in_channels = in_channels
self.out_channels = out_channels
self.num_layers = num_layers
self.stacks = stacks
self.kernel_size = kernel_size
self.res_factor = math.sqrt(1.0 / num_layers)
# check the number of num_layers and stacks
assert num_layers % stacks == 0
layers_per_stack = num_layers // stacks
# define first convolution
self.first_conv = nn.Sequential(
nn.Conv1d(in_channels, res_channels, kernel_size=1, padding=0, dilation=1, bias=True),
getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
)
# define residual blocks
self.conv_layers = nn.ModuleList()
for layer in range(num_layers):
dilation = 2 ** (layer % layers_per_stack)
conv = ResidualBlock(
kernel_size=kernel_size,
res_channels=res_channels,
gate_channels=gate_channels,
skip_channels=skip_channels,
aux_channels=-1,
dilation=dilation,
dropout=dropout,
bias=bias,
use_causal_conv=False,
)
self.conv_layers += [conv]
# define output layers
self.last_conv_layers = nn.ModuleList(
[
getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
nn.Conv1d(skip_channels, skip_channels, kernel_size=1, padding=0, dilation=1, bias=True),
getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
nn.Conv1d(skip_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=True),
]
)
# apply weight norm
self.apply_weight_norm()
def forward(self, x):
"""
x: (B, 1, T).
"""
x = self.first_conv(x)
skips = 0
for f in self.conv_layers:
x, h = f(x, None)
skips += h
skips *= self.res_factor
# apply final layers
x = skips
for f in self.last_conv_layers:
x = f(x)
return x
def apply_weight_norm(self):
def _apply_weight_norm(m):
if isinstance(m, (torch.nn.Conv1d, torch.nn.Conv2d)):
torch.nn.utils.weight_norm(m)
self.apply(_apply_weight_norm)
def remove_weight_norm(self):
def _remove_weight_norm(m):
try:
print(f"Weight norm is removed from {m}.")
nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
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