| 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: |
| |
| 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) |
| |
| 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) |
| 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 |
|
|
