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import torch
from torch.nn.utils.parametrizations import spectral_norm, weight_norm
from rvc.lib.algorithm.commons import get_padding
from rvc.lib.algorithm.residuals import LRELU_SLOPE
class MultiPeriodDiscriminator(torch.nn.Module):
"""
Multi-period discriminator.
This class implements a multi-period discriminator, which is used to
discriminate between real and fake audio signals. The discriminator
is composed of a series of convolutional layers that are applied to
the input signal at different periods.
Args:
use_spectral_norm (bool): Whether to use spectral normalization.
Defaults to False.
"""
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11, 17]
self.discriminators = torch.nn.ModuleList(
[DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+ [DiscriminatorP(p, use_spectral_norm=use_spectral_norm) for p in periods]
)
def forward(self, y, y_hat):
"""
Forward pass of the multi-period discriminator.
Args:
y (torch.Tensor): Real audio signal.
y_hat (torch.Tensor): Fake audio signal.
"""
y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
for d in self.discriminators:
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class MultiPeriodDiscriminatorV2(torch.nn.Module):
"""
Multi-period discriminator V2.
This class implements a multi-period discriminator V2, which is used
to discriminate between real and fake audio signals. The discriminator
is composed of a series of convolutional layers that are applied to
the input signal at different periods.
Args:
use_spectral_norm (bool): Whether to use spectral normalization.
Defaults to False.
"""
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
periods = [2, 3, 5, 7, 11, 17, 23, 37]
self.discriminators = torch.nn.ModuleList(
[DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+ [DiscriminatorP(p, use_spectral_norm=use_spectral_norm) for p in periods]
)
def forward(self, y, y_hat):
"""
Forward pass of the multi-period discriminator V2.
Args:
y (torch.Tensor): Real audio signal.
y_hat (torch.Tensor): Fake audio signal.
"""
y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
for d in self.discriminators:
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class DiscriminatorS(torch.nn.Module):
"""
Discriminator for the short-term component.
This class implements a discriminator for the short-term component
of the audio signal. The discriminator is composed of a series of
convolutional layers that are applied to the input signal.
"""
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = spectral_norm if use_spectral_norm else weight_norm
self.convs = torch.nn.ModuleList(
[
norm_f(torch.nn.Conv1d(1, 16, 15, 1, padding=7)),
norm_f(torch.nn.Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(torch.nn.Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(torch.nn.Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(torch.nn.Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(torch.nn.Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(torch.nn.Conv1d(1024, 1, 3, 1, padding=1))
self.lrelu = torch.nn.LeakyReLU(LRELU_SLOPE)
def forward(self, x):
"""
Forward pass of the discriminator.
Args:
x (torch.Tensor): Input audio signal.
"""
fmap = []
for conv in self.convs:
x = self.lrelu(conv(x))
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorP(torch.nn.Module):
"""
Discriminator for the long-term component.
This class implements a discriminator for the long-term component
of the audio signal. The discriminator is composed of a series of
convolutional layers that are applied to the input signal at a given
period.
Args:
period (int): Period of the discriminator.
kernel_size (int): Kernel size of the convolutional layers.
Defaults to 5.
stride (int): Stride of the convolutional layers. Defaults to 3.
use_spectral_norm (bool): Whether to use spectral normalization.
Defaults to False.
"""
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
norm_f = spectral_norm if use_spectral_norm else weight_norm
in_channels = [1, 32, 128, 512, 1024]
out_channels = [32, 128, 512, 1024, 1024]
self.convs = torch.nn.ModuleList(
[
norm_f(
torch.nn.Conv2d(
in_ch,
out_ch,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
)
for in_ch, out_ch in zip(in_channels, out_channels)
]
)
self.conv_post = norm_f(torch.nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
self.lrelu = torch.nn.LeakyReLU(LRELU_SLOPE)
def forward(self, x):
"""
Forward pass of the discriminator.
Args:
x (torch.Tensor): Input audio signal.
"""
fmap = []
b, c, t = x.shape
if t % self.period != 0:
n_pad = self.period - (t % self.period)
x = torch.nn.functional.pad(x, (0, n_pad), "reflect")
x = x.view(b, c, -1, self.period)
for conv in self.convs:
x = self.lrelu(conv(x))
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
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