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 modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork
from modules.parallel_wavegan.models.source import SourceModuleHnNSF
import numpy as np
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 apply_weight_norm(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
weight_norm(m)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
class ResBlock1(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, 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 ResBlock2(torch.nn.Module):
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)
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, 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 Conv1d1x1(Conv1d):
"""1x1 Conv1d with customized initialization."""
def __init__(self, in_channels, out_channels, bias):
"""Initialize 1x1 Conv1d module."""
super(Conv1d1x1, self).__init__(in_channels, out_channels,
kernel_size=1, padding=0,
dilation=1, bias=bias)
class HifiGanGenerator(torch.nn.Module):
def __init__(self, h, c_out=1):
super(HifiGanGenerator, self).__init__()
self.h = h
self.num_kernels = len(h['resblock_kernel_sizes'])
self.num_upsamples = len(h['upsample_rates'])
if h['use_pitch_embed']:
self.harmonic_num = 8
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates']))
self.m_source = SourceModuleHnNSF(
sampling_rate=h['audio_sample_rate'],
harmonic_num=self.harmonic_num)
self.noise_convs = nn.ModuleList()
self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3))
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'])):
c_cur = h['upsample_initial_channel'] // (2 ** (i + 1))
self.ups.append(weight_norm(
ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2)))
if h['use_pitch_embed']:
if i + 1 < len(h['upsample_rates']):
stride_f0 = np.prod(h['upsample_rates'][i + 1:])
self.noise_convs.append(Conv1d(
1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
else:
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
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, c_out, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x, f0=None):
if f0 is not None:
# harmonic-source signal, noise-source signal, uv flag
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)
har_source, noi_source, uv = self.m_source(f0)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
if f0 is not None:
x_source = self.noise_convs[i](har_source)
x = x + x_source
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):
print('Removing weight norm...')
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)
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1):
super(DiscriminatorP, self).__init__()
self.use_cond = use_cond
if use_cond:
from utils.hparams import hparams
t = hparams['hop_size']
self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
c_in = 2
self.period = period
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv2d(c_in, 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, mel):
fmap = []
if self.use_cond:
x_mel = self.cond_net(mel)
x = torch.cat([x_mel, x], 1)
# 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, use_cond=False, c_in=1):
super(MultiPeriodDiscriminator, self).__init__()
self.discriminators = nn.ModuleList([
DiscriminatorP(2, use_cond=use_cond, c_in=c_in),
DiscriminatorP(3, use_cond=use_cond, c_in=c_in),
DiscriminatorP(5, use_cond=use_cond, c_in=c_in),
DiscriminatorP(7, use_cond=use_cond, c_in=c_in),
DiscriminatorP(11, use_cond=use_cond, c_in=c_in),
])
def forward(self, y, y_hat, mel=None):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y, mel)
y_d_g, fmap_g = d(y_hat, mel)
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):
def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1):
super(DiscriminatorS, self).__init__()
self.use_cond = use_cond
if use_cond:
t = np.prod(upsample_rates)
self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
c_in = 2
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv1d(c_in, 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, mel):
if self.use_cond:
x_mel = self.cond_net(mel)
x = torch.cat([x_mel, x], 1)
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, use_cond=False, c_in=1):
super(MultiScaleDiscriminator, self).__init__()
from utils.hparams import hparams
self.discriminators = nn.ModuleList([
DiscriminatorS(use_spectral_norm=True, use_cond=use_cond,
upsample_rates=[4, 4, hparams['hop_size'] // 16],
c_in=c_in),
DiscriminatorS(use_cond=use_cond,
upsample_rates=[4, 4, hparams['hop_size'] // 32],
c_in=c_in),
DiscriminatorS(use_cond=use_cond,
upsample_rates=[4, 4, hparams['hop_size'] // 64],
c_in=c_in),
])
self.meanpools = nn.ModuleList([
AvgPool1d(4, 2, padding=1),
AvgPool1d(4, 2, padding=1)
])
def forward(self, y, y_hat, mel=None):
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, mel)
y_d_g, fmap_g = d(y_hat, mel)
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):
r_losses = 0
g_losses = 0
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg ** 2)
r_losses += r_loss
g_losses += g_loss
r_losses = r_losses / len(disc_real_outputs)
g_losses = g_losses / len(disc_real_outputs)
return r_losses, g_losses
def cond_discriminator_loss(outputs):
loss = 0
for dg in outputs:
g_loss = torch.mean(dg ** 2)
loss += g_loss
loss = loss / len(outputs)
return loss
def generator_loss(disc_outputs):
loss = 0
for dg in disc_outputs:
l = torch.mean((1 - dg) ** 2)
loss += l
loss = loss / len(disc_outputs)
return loss