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import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from packaging import version
is_pytorch2_1 = version.parse(torch.__version__) >= version.parse("2.1.0")
if is_pytorch2_1:
from torch.nn.utils.parametrizations import spectral_norm, weight_norm
else:
from torch.nn.utils.parametrizations import spectral_norm
from torch.nn.utils import weight_norm
from .commons import get_padding
from .residuals import LRELU_SLOPE
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(
self,
use_spectral_norm: bool = False,
checkpointing: bool = False,
version: str = "v2",
):
super().__init__()
if version == "v1":
periods = [2, 3, 5, 7, 11, 17]
resolutions = []
elif version == "v2":
periods = [2, 3, 5, 7, 11, 17, 23, 37]
resolutions = []
elif version == "v3":
periods = [2, 3, 5, 7, 11]
resolutions = [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]]
self.checkpointing = checkpointing
self.discriminators = torch.nn.ModuleList(
[DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+ [DiscriminatorP(p, use_spectral_norm=use_spectral_norm) for p in periods]
+ [
DiscriminatorR(r, use_spectral_norm=use_spectral_norm)
for r in resolutions
]
)
def forward(self, y, y_hat):
y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
for d in self.discriminators:
if self.training and self.checkpointing:
y_d_r, fmap_r = checkpoint(d, y, use_reentrant=False)
y_d_g, fmap_g = checkpoint(d, y_hat, use_reentrant=False)
else:
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):
def __init__(self, use_spectral_norm: bool = False):
super().__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):
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):
def __init__(
self,
period: int,
kernel_size: int = 5,
stride: int = 3,
use_spectral_norm: bool = False,
):
super().__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]
strides = [3, 3, 3, 3, 1]
self.convs = torch.nn.ModuleList(
[
norm_f(
torch.nn.Conv2d(
in_ch,
out_ch,
(kernel_size, 1),
(s, 1),
padding=(get_padding(kernel_size, 1), 0),
)
)
for in_ch, out_ch, s in zip(in_channels, out_channels, strides)
]
)
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):
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
class DiscriminatorR(torch.nn.Module):
def __init__(self, resolution, use_spectral_norm=False):
super().__init__()
self.resolution = resolution
self.lrelu_slope = 0.1
norm_f = spectral_norm if use_spectral_norm else weight_norm
self.convs = torch.nn.ModuleList(
[
norm_f(
torch.nn.Conv2d(
1,
32,
(3, 9),
padding=(1, 4),
)
),
norm_f(
torch.nn.Conv2d(
32,
32,
(3, 9),
stride=(1, 2),
padding=(1, 4),
)
),
norm_f(
torch.nn.Conv2d(
32,
32,
(3, 9),
stride=(1, 2),
padding=(1, 4),
)
),
norm_f(
torch.nn.Conv2d(
32,
32,
(3, 9),
stride=(1, 2),
padding=(1, 4),
)
),
norm_f(
torch.nn.Conv2d(
32,
32,
(3, 3),
padding=(1, 1),
)
),
]
)
self.conv_post = norm_f(torch.nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
def forward(self, x):
fmap = []
x = self.spectrogram(x).unsqueeze(1)
for layer in self.convs:
x = F.leaky_relu(layer(x), self.lrelu_slope)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
return torch.flatten(x, 1, -1), fmap
def spectrogram(self, x):
n_fft, hop_length, win_length = self.resolution
pad = int((n_fft - hop_length) / 2)
x = F.pad(
x,
(pad, pad),
mode="reflect",
).squeeze(1)
x = torch.stft(
x,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=torch.ones(win_length, device=x.device),
center=False,
return_complex=True,
)
mag = torch.norm(torch.view_as_real(x), p=2, dim=-1)
return mag
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