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_source = torch.nn.functional.relu(x_source) tmp_shape = x_source.shape[1] x_source = torch.nn.functional.layer_norm(x_source.transpose(1, -1), (tmp_shape, )).transpose(1, -1) x = x + x_source xs = None for j in range(self.num_kernels): xs_ = self.resblocks[i * self.num_kernels + j](x) if xs is None: xs = xs_ else: xs += xs_ 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