|
import torch |
|
import torch.nn as nn |
|
import torch.nn.functional as F |
|
from torch.nn import Conv1d, ConvTranspose1d |
|
from torch.nn.utils import weight_norm, remove_weight_norm |
|
|
|
LRELU_SLOPE = 0.1 |
|
|
|
|
|
def init_weights(m, mean=0.0, std=0.01): |
|
classname = m.__class__.__name__ |
|
if classname.find("Conv") != -1: |
|
m.weight.data.normal_(mean, std) |
|
|
|
|
|
def get_padding(kernel_size, dilation=1): |
|
return int((kernel_size * dilation - dilation) / 2) |
|
|
|
|
|
class ResBlock(torch.nn.Module): |
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): |
|
super(ResBlock, self).__init__() |
|
self.h = h |
|
self.convs1 = nn.ModuleList( |
|
[ |
|
weight_norm( |
|
Conv1d( |
|
channels, |
|
channels, |
|
kernel_size, |
|
1, |
|
dilation=dilation[0], |
|
padding=get_padding(kernel_size, dilation[0]), |
|
) |
|
), |
|
weight_norm( |
|
Conv1d( |
|
channels, |
|
channels, |
|
kernel_size, |
|
1, |
|
dilation=dilation[1], |
|
padding=get_padding(kernel_size, dilation[1]), |
|
) |
|
), |
|
weight_norm( |
|
Conv1d( |
|
channels, |
|
channels, |
|
kernel_size, |
|
1, |
|
dilation=dilation[2], |
|
padding=get_padding(kernel_size, dilation[2]), |
|
) |
|
), |
|
] |
|
) |
|
self.convs1.apply(init_weights) |
|
|
|
self.convs2 = nn.ModuleList( |
|
[ |
|
weight_norm( |
|
Conv1d( |
|
channels, |
|
channels, |
|
kernel_size, |
|
1, |
|
dilation=1, |
|
padding=get_padding(kernel_size, 1), |
|
) |
|
), |
|
weight_norm( |
|
Conv1d( |
|
channels, |
|
channels, |
|
kernel_size, |
|
1, |
|
dilation=1, |
|
padding=get_padding(kernel_size, 1), |
|
) |
|
), |
|
weight_norm( |
|
Conv1d( |
|
channels, |
|
channels, |
|
kernel_size, |
|
1, |
|
dilation=1, |
|
padding=get_padding(kernel_size, 1), |
|
) |
|
), |
|
] |
|
) |
|
self.convs2.apply(init_weights) |
|
|
|
def forward(self, x): |
|
for c1, c2 in zip(self.convs1, self.convs2): |
|
xt = F.leaky_relu(x, LRELU_SLOPE) |
|
xt = c1(xt) |
|
xt = F.leaky_relu(xt, LRELU_SLOPE) |
|
xt = c2(xt) |
|
x = xt + x |
|
return x |
|
|
|
def remove_weight_norm(self): |
|
for l in self.convs1: |
|
remove_weight_norm(l) |
|
for l in self.convs2: |
|
remove_weight_norm(l) |
|
|
|
|
|
class Generator(torch.nn.Module): |
|
def __init__(self, h): |
|
super(Generator, self).__init__() |
|
self.h = h |
|
self.num_kernels = len(h.resblock_kernel_sizes) |
|
self.num_upsamples = len(h.upsample_rates) |
|
self.conv_pre = weight_norm( |
|
Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3) |
|
) |
|
resblock = ResBlock |
|
|
|
self.ups = nn.ModuleList() |
|
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): |
|
self.ups.append( |
|
weight_norm( |
|
ConvTranspose1d( |
|
h.upsample_initial_channel // (2**i), |
|
h.upsample_initial_channel // (2 ** (i + 1)), |
|
k, |
|
u, |
|
padding=(k - u) // 2, |
|
) |
|
) |
|
) |
|
|
|
self.resblocks = nn.ModuleList() |
|
for i in range(len(self.ups)): |
|
ch = h.upsample_initial_channel // (2 ** (i + 1)) |
|
for j, (k, d) in enumerate( |
|
zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) |
|
): |
|
self.resblocks.append(resblock(h, ch, k, d)) |
|
|
|
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) |
|
self.ups.apply(init_weights) |
|
self.conv_post.apply(init_weights) |
|
|
|
def forward(self, x): |
|
x = self.conv_pre(x) |
|
for i in range(self.num_upsamples): |
|
x = F.leaky_relu(x, LRELU_SLOPE) |
|
x = self.ups[i](x) |
|
xs = None |
|
for j in range(self.num_kernels): |
|
if xs is None: |
|
xs = self.resblocks[i * self.num_kernels + j](x) |
|
else: |
|
xs += self.resblocks[i * self.num_kernels + j](x) |
|
x = xs / self.num_kernels |
|
x = F.leaky_relu(x) |
|
x = self.conv_post(x) |
|
x = torch.tanh(x) |
|
|
|
return x |
|
|
|
def remove_weight_norm(self): |
|
|
|
for l in self.ups: |
|
remove_weight_norm(l) |
|
for l in self.resblocks: |
|
l.remove_weight_norm() |
|
remove_weight_norm(self.conv_pre) |
|
remove_weight_norm(self.conv_post) |
|
|
