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# original source takes from https://github.com/jik876/hifi-gan/ | |
# MIT License | |
# | |
# Copyright (c) 2020 Jungil Kong | |
# | |
# Permission is hereby granted, free of charge, to any person obtaining a copy | |
# of this software and associated documentation files (the "Software"), to deal | |
# in the Software without restriction, including without limitation the rights | |
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
# copies of the Software, and to permit persons to whom the Software is | |
# furnished to do so, subject to the following conditions: | |
# | |
# The above copyright notice and this permission notice shall be included in | |
# all copies or substantial portions of the Software. | |
# | |
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
# SOFTWARE. | |
import math | |
import torch | |
import torch.nn.functional as F | |
import torch.nn as nn | |
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
from hifigan_utils import init_weights, get_padding | |
LRELU_SLOPE = 0.1 | |
class GaussianBlurAugmentation(nn.Module): | |
def __init__(self, kernel_size, sigmas, p_blurring): | |
super(GaussianBlurAugmentation, self).__init__() | |
self.kernel_size = kernel_size | |
self.sigmas = sigmas | |
kernels = self.initialize_kernels(kernel_size, sigmas) | |
self.register_buffer("kernels", kernels) | |
self.p_blurring = p_blurring | |
self.conv = F.conv2d | |
def initialize_kernels(self, kernel_size, sigmas): | |
mesh_grids = torch.meshgrid( | |
[torch.arange(size, dtype=torch.float32) for size in kernel_size] | |
) | |
kernels = [] | |
for sigma in sigmas: | |
kernel = 1 | |
sigma = [sigma] * len(kernel_size) | |
for size, std, mgrid in zip(kernel_size, sigma, mesh_grids): | |
mean = (size - 1) / 2 | |
kernel *= ( | |
1 | |
/ (std * math.sqrt(2 * math.pi)) | |
* torch.exp(-(((mgrid - mean) / std) ** 2) / 2) | |
) | |
# Make sure sum of values in gaussian kernel equals 1. | |
kernel = kernel / torch.sum(kernel) | |
# Reshape to depthwise convolutional weight | |
kernel = kernel.view(1, 1, *kernel.size()) | |
kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1)) | |
kernels.append(kernel[None]) | |
kernels = torch.cat(kernels) | |
return kernels | |
def forward(self, x): | |
if torch.rand(1)[0] > self.p_blurring: | |
return x | |
else: | |
i = torch.randint(len(self.kernels), (1,))[0] | |
kernel = self.kernels[i] | |
pad = int((self.kernel_size[0] - 1) / 2) | |
x = F.pad(x[:, None], (pad, pad, pad, pad), mode="reflect") | |
x = self.conv(x, weight=kernel)[:, 0] | |
return x | |
class ResBlock1(torch.nn.Module): | |
__constants__ = ["lrelu_slope"] | |
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): | |
super(ResBlock1, self).__init__() | |
self.h = h | |
self.lrelu_slope = LRELU_SLOPE | |
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, self.lrelu_slope) | |
xt = c1(xt) | |
xt = F.leaky_relu(xt, self.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 ResBlock2(torch.nn.Module): | |
__constants__ = ["lrelu_slope"] | |
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): | |
super(ResBlock2, self).__init__() | |
self.h = h | |
self.convs = 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]), | |
) | |
), | |
] | |
) | |
self.convs.apply(init_weights) | |
self.lrelu_slope = LRELU_SLOPE | |
def forward(self, x): | |
for c in self.convs: | |
xt = F.leaky_relu(x, self.lrelu_slope) | |
xt = c(xt) | |
x = xt + x | |
return x | |
def remove_weight_norm(self): | |
for l in self.convs: | |
remove_weight_norm(l) | |
class Generator(torch.nn.Module): | |
__constants__ = ["lrelu_slope", "num_kernels", "num_upsamples", "p_blur"] | |
def __init__(self, h): | |
super(Generator, self).__init__() | |
self.num_kernels = len(h.resblock_kernel_sizes) | |
self.num_upsamples = len(h.upsample_rates) | |
self.conv_pre = weight_norm( | |
Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3) | |
) | |
self.p_blur = h.gaussian_blur["p_blurring"] | |
self.gaussian_blur_fn = None | |
if self.p_blur > 0.0: | |
self.gaussian_blur_fn = GaussianBlurAugmentation( | |
h.gaussian_blur["kernel_size"], h.gaussian_blur["sigmas"], self.p_blur | |
) | |
else: | |
self.gaussian_blur_fn = nn.Identity() | |
self.lrelu_slope = LRELU_SLOPE | |
resblock = ResBlock1 if h.resblock == "1" else ResBlock2 | |
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)): | |
resblock_list = nn.ModuleList() | |
ch = h.upsample_initial_channel // (2 ** (i + 1)) | |
for j, (k, d) in enumerate( | |
zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) | |
): | |
resblock_list.append(resblock(h, ch, k, d)) | |
self.resblocks.append(resblock_list) | |
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) | |
self.ups.apply(init_weights) | |
self.conv_post.apply(init_weights) | |
def load_state_dict(self, state_dict): | |
new_state_dict = {} | |
for k, v in state_dict.items(): | |
new_k = k | |
if "resblocks" in k: | |
parts = k.split(".") | |
# only do this is the checkpoint type is older | |
if len(parts) == 5: | |
layer = int(parts[1]) | |
new_layer = f"{layer // 3}.{layer % 3}" | |
new_k = f"resblocks.{new_layer}.{'.'.join(parts[2:])}" | |
new_state_dict[new_k] = v | |
super().load_state_dict(new_state_dict) | |
def forward(self, x): | |
if self.p_blur > 0.0: | |
x = self.gaussian_blur_fn(x) | |
x = self.conv_pre(x) | |
for upsample_layer, resblock_group in zip(self.ups, self.resblocks): | |
x = F.leaky_relu(x, self.lrelu_slope) | |
x = upsample_layer(x) | |
xs = torch.zeros(x.shape, dtype=x.dtype, device=x.device) | |
for resblock in resblock_group: | |
xs += resblock(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): | |
print("Removing weight norm...") | |
for l in self.ups: | |
remove_weight_norm(l) | |
for group in self.resblocks: | |
for block in group: | |
block.remove_weight_norm() | |
remove_weight_norm(self.conv_pre) | |
remove_weight_norm(self.conv_post) | |
class DiscriminatorP(torch.nn.Module): | |
__constants__ = ["LRELU_SLOPE"] | |
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): | |
super(DiscriminatorP, self).__init__() | |
self.period = period | |
norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
self.convs = nn.ModuleList( | |
[ | |
norm_f( | |
Conv2d( | |
1, | |
32, | |
(kernel_size, 1), | |
(stride, 1), | |
padding=(get_padding(5, 1), 0), | |
) | |
), | |
norm_f( | |
Conv2d( | |
32, | |
128, | |
(kernel_size, 1), | |
(stride, 1), | |
padding=(get_padding(5, 1), 0), | |
) | |
), | |
norm_f( | |
Conv2d( | |
128, | |
512, | |
(kernel_size, 1), | |
(stride, 1), | |
padding=(get_padding(5, 1), 0), | |
) | |
), | |
norm_f( | |
Conv2d( | |
512, | |
1024, | |
(kernel_size, 1), | |
(stride, 1), | |
padding=(get_padding(5, 1), 0), | |
) | |
), | |
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), | |
] | |
) | |
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
def forward(self, x): | |
fmap = [] | |
# 1d to 2d | |
b, c, t = x.shape | |
if t % self.period != 0: # pad first | |
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) | |
fmap.append(x) | |
x = self.conv_post(x) | |
fmap.append(x) | |
x = torch.flatten(x, 1, -1) | |
return x, fmap | |
class MultiPeriodDiscriminator(torch.nn.Module): | |
def __init__(self): | |
super(MultiPeriodDiscriminator, self).__init__() | |
self.discriminators = nn.ModuleList( | |
[ | |
DiscriminatorP(2), | |
DiscriminatorP(3), | |
DiscriminatorP(5), | |
DiscriminatorP(7), | |
DiscriminatorP(11), | |
] | |
) | |
def forward(self, y, y_hat): | |
y_d_rs = [] | |
y_d_gs = [] | |
fmap_rs = [] | |
fmap_gs = [] | |
for i, d in enumerate(self.discriminators): | |
y_d_r, fmap_r = d(y) | |
y_d_g, fmap_g = d(y_hat) | |
y_d_rs.append(y_d_r) | |
fmap_rs.append(fmap_r) | |
y_d_gs.append(y_d_g) | |
fmap_gs.append(fmap_g) | |
return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
class DiscriminatorS(torch.nn.Module): | |
__constants__ = ["LRELU_SLOPE"] | |
def __init__(self, use_spectral_norm=False): | |
super(DiscriminatorS, self).__init__() | |
norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
self.convs = nn.ModuleList( | |
[ | |
norm_f(Conv1d(1, 128, 15, 1, padding=7)), | |
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), | |
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), | |
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), | |
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), | |
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), | |
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), | |
] | |
) | |
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) | |
def forward(self, x): | |
fmap = [] | |
for l in self.convs: | |
x = l(x) | |
x = F.leaky_relu(x, LRELU_SLOPE) | |
fmap.append(x) | |
x = self.conv_post(x) | |
fmap.append(x) | |
x = torch.flatten(x, 1, -1) | |
return x, fmap | |
class MultiScaleDiscriminator(torch.nn.Module): | |
def __init__(self): | |
super(MultiScaleDiscriminator, self).__init__() | |
self.discriminators = nn.ModuleList( | |
[ | |
DiscriminatorS(use_spectral_norm=True), | |
DiscriminatorS(), | |
DiscriminatorS(), | |
] | |
) | |
self.meanpools = nn.ModuleList( | |
[AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] | |
) | |
def forward(self, y, y_hat): | |
y_d_rs = [] | |
y_d_gs = [] | |
fmap_rs = [] | |
fmap_gs = [] | |
for i, d in enumerate(self.discriminators): | |
if i != 0: | |
y = self.meanpools[i - 1](y) | |
y_hat = self.meanpools[i - 1](y_hat) | |
y_d_r, fmap_r = d(y) | |
y_d_g, fmap_g = d(y_hat) | |
y_d_rs.append(y_d_r) | |
fmap_rs.append(fmap_r) | |
y_d_gs.append(y_d_g) | |
fmap_gs.append(fmap_g) | |
return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
def feature_loss(fmap_r, fmap_g): | |
loss = 0 | |
for dr, dg in zip(fmap_r, fmap_g): | |
for rl, gl in zip(dr, dg): | |
loss += torch.mean(torch.abs(rl - gl)) | |
return loss * 2 | |
def discriminator_loss(disc_real_outputs, disc_generated_outputs): | |
loss = 0 | |
r_losses = [] | |
g_losses = [] | |
for dr, dg in zip(disc_real_outputs, disc_generated_outputs): | |
r_loss = torch.mean((1 - dr) ** 2) | |
g_loss = torch.mean(dg**2) | |
loss += r_loss + g_loss | |
r_losses.append(r_loss.item()) | |
g_losses.append(g_loss.item()) | |
return loss, r_losses, g_losses | |
def generator_loss(disc_outputs): | |
loss = 0 | |
gen_losses = [] | |
for dg in disc_outputs: | |
l = torch.mean((1 - dg) ** 2) | |
gen_losses.append(l) | |
loss += l | |
return loss, gen_losses | |