|
|
|
import torch |
|
from torch import nn |
|
from torch.nn import functional as F |
|
|
|
LRELU_SLOPE = 0.1 |
|
|
|
|
|
class DiscriminatorP(torch.nn.Module): |
|
"""HiFiGAN Periodic Discriminator |
|
|
|
Takes every Pth value from the input waveform and applied a stack of convoluations. |
|
|
|
Note: |
|
if `period` is 2 |
|
`waveform = [1, 2, 3, 4, 5, 6 ...] --> [1, 3, 5 ... ] --> convs -> score, feat` |
|
|
|
Args: |
|
x (Tensor): input waveform. |
|
|
|
Returns: |
|
[Tensor]: discriminator scores per sample in the batch. |
|
[List[Tensor]]: list of features from each convolutional layer. |
|
|
|
Shapes: |
|
x: [B, 1, T] |
|
""" |
|
|
|
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
|
super().__init__() |
|
self.period = period |
|
get_padding = lambda k, d: int((k * d - d) / 2) |
|
norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm |
|
self.convs = nn.ModuleList( |
|
[ |
|
norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
|
norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
|
norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
|
norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
|
norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), |
|
] |
|
) |
|
self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
|
|
|
def forward(self, x): |
|
""" |
|
Args: |
|
x (Tensor): input waveform. |
|
|
|
Returns: |
|
[Tensor]: discriminator scores per sample in the batch. |
|
[List[Tensor]]: list of features from each convolutional layer. |
|
|
|
Shapes: |
|
x: [B, 1, T] |
|
""" |
|
feat = [] |
|
|
|
|
|
b, c, t = x.shape |
|
if t % self.period != 0: |
|
n_pad = self.period - (t % self.period) |
|
x = F.pad(x, (0, n_pad), "reflect") |
|
t = t + n_pad |
|
x = x.view(b, c, t // self.period, self.period) |
|
|
|
for l in self.convs: |
|
x = l(x) |
|
x = F.leaky_relu(x, LRELU_SLOPE) |
|
feat.append(x) |
|
x = self.conv_post(x) |
|
feat.append(x) |
|
x = torch.flatten(x, 1, -1) |
|
|
|
return x, feat |
|
|
|
|
|
class MultiPeriodDiscriminator(torch.nn.Module): |
|
"""HiFiGAN Multi-Period Discriminator (MPD) |
|
Wrapper for the `PeriodDiscriminator` to apply it in different periods. |
|
Periods are suggested to be prime numbers to reduce the overlap between each discriminator. |
|
""" |
|
|
|
def __init__(self, use_spectral_norm=False): |
|
super().__init__() |
|
self.discriminators = nn.ModuleList( |
|
[ |
|
DiscriminatorP(2, use_spectral_norm=use_spectral_norm), |
|
DiscriminatorP(3, use_spectral_norm=use_spectral_norm), |
|
DiscriminatorP(5, use_spectral_norm=use_spectral_norm), |
|
DiscriminatorP(7, use_spectral_norm=use_spectral_norm), |
|
DiscriminatorP(11, use_spectral_norm=use_spectral_norm), |
|
] |
|
) |
|
|
|
def forward(self, x): |
|
""" |
|
Args: |
|
x (Tensor): input waveform. |
|
|
|
Returns: |
|
[List[Tensor]]: list of scores from each discriminator. |
|
[List[List[Tensor]]]: list of list of features from each discriminator's each convolutional layer. |
|
|
|
Shapes: |
|
x: [B, 1, T] |
|
""" |
|
scores = [] |
|
feats = [] |
|
for _, d in enumerate(self.discriminators): |
|
score, feat = d(x) |
|
scores.append(score) |
|
feats.append(feat) |
|
return scores, feats |
|
|
|
|
|
class DiscriminatorS(torch.nn.Module): |
|
"""HiFiGAN Scale Discriminator. |
|
It is similar to `MelganDiscriminator` but with a specific architecture explained in the paper. |
|
|
|
Args: |
|
use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm. |
|
|
|
""" |
|
|
|
def __init__(self, use_spectral_norm=False): |
|
super().__init__() |
|
norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm |
|
self.convs = nn.ModuleList( |
|
[ |
|
norm_f(nn.Conv1d(1, 128, 15, 1, padding=7)), |
|
norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)), |
|
norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)), |
|
norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)), |
|
norm_f(nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)), |
|
norm_f(nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), |
|
norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)), |
|
] |
|
) |
|
self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1)) |
|
|
|
def forward(self, x): |
|
""" |
|
Args: |
|
x (Tensor): input waveform. |
|
|
|
Returns: |
|
Tensor: discriminator scores. |
|
List[Tensor]: list of features from the convolutiona layers. |
|
""" |
|
feat = [] |
|
for l in self.convs: |
|
x = l(x) |
|
x = F.leaky_relu(x, LRELU_SLOPE) |
|
feat.append(x) |
|
x = self.conv_post(x) |
|
feat.append(x) |
|
x = torch.flatten(x, 1, -1) |
|
return x, feat |
|
|
|
|
|
class MultiScaleDiscriminator(torch.nn.Module): |
|
"""HiFiGAN Multi-Scale Discriminator. |
|
It is similar to `MultiScaleMelganDiscriminator` but specially tailored for HiFiGAN as in the paper. |
|
""" |
|
|
|
def __init__(self): |
|
super().__init__() |
|
self.discriminators = nn.ModuleList( |
|
[ |
|
DiscriminatorS(use_spectral_norm=True), |
|
DiscriminatorS(), |
|
DiscriminatorS(), |
|
] |
|
) |
|
self.meanpools = nn.ModuleList([nn.AvgPool1d(4, 2, padding=2), nn.AvgPool1d(4, 2, padding=2)]) |
|
|
|
def forward(self, x): |
|
""" |
|
Args: |
|
x (Tensor): input waveform. |
|
|
|
Returns: |
|
List[Tensor]: discriminator scores. |
|
List[List[Tensor]]: list of list of features from each layers of each discriminator. |
|
""" |
|
scores = [] |
|
feats = [] |
|
for i, d in enumerate(self.discriminators): |
|
if i != 0: |
|
x = self.meanpools[i - 1](x) |
|
score, feat = d(x) |
|
scores.append(score) |
|
feats.append(feat) |
|
return scores, feats |
|
|
|
|
|
class HifiganDiscriminator(nn.Module): |
|
"""HiFiGAN discriminator wrapping MPD and MSD.""" |
|
|
|
def __init__(self): |
|
super().__init__() |
|
self.mpd = MultiPeriodDiscriminator() |
|
self.msd = MultiScaleDiscriminator() |
|
|
|
def forward(self, x): |
|
""" |
|
Args: |
|
x (Tensor): input waveform. |
|
|
|
Returns: |
|
List[Tensor]: discriminator scores. |
|
List[List[Tensor]]: list of list of features from each layers of each discriminator. |
|
""" |
|
scores, feats = self.mpd(x) |
|
scores_, feats_ = self.msd(x) |
|
return scores + scores_, feats + feats_ |
|
|
