| import math | |
| import torch | |
| import numpy as np | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
| from lib.infer_pack import modules, attentions, commons | |
| from torch.nn.utils import weight_norm, remove_weight_norm | |
| from lib.infer_pack.commons import init_weights, get_padding | |
| from lib.infer_pack.commons import init_weights, sequence_mask | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from lib.infer_pack.modules import ResidualCouplingLayer, WN, ResBlock1, ResBlock2, LRELU_SLOPE | |
| class TextEncoder(nn.Module): | |
| def __init__( | |
| self, | |
| input_dim, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| f0=True, | |
| ): | |
| super().__init__() | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.emb_phone = nn.Linear(input_dim, hidden_channels) | |
| self.lrelu = nn.LeakyReLU(0.1, inplace=True) | |
| if f0:self.emb_pitch = nn.Embedding(256, hidden_channels) | |
| self.encoder = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, phone, pitch, lengths): | |
| x = self.emb_phone(phone) + self.emb_pitch(pitch) if pitch is not None else self.emb_phone(phone) | |
| x *= math.sqrt(self.hidden_channels) | |
| x = self.lrelu(x) | |
| x = torch.transpose(x, 1, -1) | |
| x_mask = torch.unsqueeze(sequence_mask(lengths, x.size(2)), 1).to(x.dtype) | |
| x = self.encoder(x * x_mask, x_mask) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return m, logs, x_mask | |
| class TextEncoder768(nn.Module): | |
| def __init__( | |
| self, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| f0=True, | |
| ): | |
| super().__init__() | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.emb_phone = nn.Linear(768, hidden_channels) | |
| self.lrelu = nn.LeakyReLU(0.1, inplace=True) | |
| if f0 == True:self.emb_pitch = nn.Embedding(256, hidden_channels) | |
| self.encoder = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, phone, pitch, lengths): | |
| if pitch is None: x = self.emb_phone(phone) | |
| else: x = self.emb_phone(phone) + self.emb_pitch(pitch) | |
| x = x * math.sqrt(self.hidden_channels) | |
| x = self.lrelu(x) | |
| x = torch.transpose(x, 1, -1) | |
| x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(x.dtype) | |
| x = self.encoder(x * x_mask, x_mask) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return m, logs, x_mask | |
| class ResidualCouplingBlock(nn.Module): | |
| def __init__( | |
| self, | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| n_flows=4, | |
| gin_channels=0, | |
| ): | |
| super().__init__() | |
| self.channels = channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.n_flows = n_flows | |
| self.gin_channels = gin_channels | |
| self.flows = nn.ModuleList() | |
| for _ in range(n_flows): | |
| self.flows.append( | |
| modules.ResidualCouplingLayer( | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=gin_channels, | |
| mean_only=True, | |
| ) | |
| ) | |
| self.flows.append(modules.Flip()) | |
| def forward(self, x, x_mask, g=None, reverse=False): | |
| if not reverse: | |
| for flow in self.flows: x, _ = flow(x, x_mask, g=g, reverse=reverse) | |
| else: | |
| for flow in reversed(self.flows): x = flow(x, x_mask, g=g, reverse=reverse) | |
| return x | |
| def remove_weight_norm(self): | |
| for i in range(self.n_flows): | |
| self.flows[i * 2].remove_weight_norm() | |
| class PosteriorEncoder(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| out_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0, | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.pre = nn.Conv1d(in_channels, hidden_channels, 1) | |
| self.enc = modules.WN( | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=gin_channels, | |
| ) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, g=None): | |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( | |
| x.dtype | |
| ) | |
| x = self.pre(x) * x_mask | |
| x = self.enc(x, x_mask, g=g) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask | |
| return z, m, logs, x_mask | |
| def remove_weight_norm(self): | |
| self.enc.remove_weight_norm() | |
| class Generator(torch.nn.Module): | |
| def __init__( | |
| self, | |
| initial_channel, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| gin_channels=0, | |
| ): | |
| super(Generator, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = Conv1d( | |
| initial_channel, upsample_initial_channel, 7, 1, padding=3 | |
| ) | |
| resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| self.ups.append( | |
| weight_norm( | |
| ConvTranspose1d( | |
| upsample_initial_channel // (2**i), | |
| upsample_initial_channel // (2 ** (i + 1)), | |
| k, | |
| u, | |
| padding=(k - u) // 2, | |
| ) | |
| ) | |
| ) | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = upsample_initial_channel // (2 ** (i + 1)) | |
| for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): self.resblocks.append(resblock(ch, k, d)) | |
| self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.ups.apply(init_weights) | |
| if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| def forward(self, x, g=None): | |
| x = self.conv_pre(x) | |
| if g is not None: x = x + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, modules.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() | |
| class SineGen(torch.nn.Module): | |
| def __init__( | |
| self, | |
| samp_rate, | |
| harmonic_num=0, | |
| sine_amp=0.1, | |
| noise_std=0.003, | |
| voiced_threshold=0, | |
| ): | |
| super(SineGen, self).__init__() | |
| self.sine_amp = sine_amp | |
| self.noise_std = noise_std | |
| self.harmonic_num = harmonic_num | |
| self.dim = self.harmonic_num + 1 | |
| self.sampling_rate = samp_rate | |
| self.voiced_threshold = voiced_threshold | |
| def _f02uv(self, f0): | |
| uv = torch.ones_like(f0) | |
| uv = uv * (f0 > self.voiced_threshold) | |
| return uv | |
| def forward(self, f0, upp): | |
| with torch.no_grad(): | |
| f0 = f0[:, None].transpose(1, 2) | |
| f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) | |
| f0_buf[:, :, 0] = f0[:, :, 0] | |
| for idx in np.arange(self.harmonic_num): f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2) | |
| rad_values = (f0_buf / self.sampling_rate) % 1 | |
| rand_ini = torch.rand(f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device) | |
| rand_ini[:, 0] = 0 | |
| rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini | |
| tmp_over_one = torch.cumsum(rad_values, 1) | |
| tmp_over_one *= upp | |
| tmp_over_one = F.interpolate(tmp_over_one.transpose(2, 1), scale_factor=upp, mode="linear", align_corners=True).transpose(2, 1) | |
| rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode="nearest").transpose(2, 1) | |
| tmp_over_one %= 1 | |
| tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 | |
| cumsum_shift = torch.zeros_like(rad_values) | |
| cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 | |
| sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi) | |
| sine_waves = sine_waves * self.sine_amp | |
| uv = self._f02uv(f0) | |
| uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode="nearest").transpose(2, 1) | |
| noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 | |
| noise = noise_amp * torch.randn_like(sine_waves) | |
| sine_waves = sine_waves * uv + noise | |
| return sine_waves, uv, noise | |
| class SourceModuleHnNSF(torch.nn.Module): | |
| def __init__( | |
| self, | |
| sampling_rate, | |
| harmonic_num=0, | |
| sine_amp=0.1, | |
| add_noise_std=0.003, | |
| voiced_threshod=0, | |
| is_half=True, | |
| ): | |
| super(SourceModuleHnNSF, self).__init__() | |
| self.sine_amp = sine_amp | |
| self.noise_std = add_noise_std | |
| self.is_half = is_half | |
| self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod) | |
| self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) | |
| self.l_tanh = torch.nn.Tanh() | |
| def forward(self, x, upp=None): | |
| sine_wavs, uv, _ = self.l_sin_gen(x, upp) | |
| if self.is_half: sine_wavs = sine_wavs.half() | |
| sine_merge = self.l_tanh(self.l_linear(sine_wavs)) | |
| return sine_merge, None, None | |
| class GeneratorNSF(torch.nn.Module): | |
| def __init__( | |
| self, | |
| initial_channel, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| gin_channels, | |
| sr, | |
| is_half=False, | |
| ): | |
| super(GeneratorNSF, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) | |
| self.m_source = SourceModuleHnNSF(sampling_rate=sr, harmonic_num=0, is_half=is_half) | |
| self.noise_convs = nn.ModuleList() | |
| self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) | |
| resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| c_cur = upsample_initial_channel // (2 ** (i + 1)) | |
| self.ups.append(weight_norm(ConvTranspose1d(upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2,))) | |
| if i + 1 < len(upsample_rates): | |
| stride_f0 = np.prod(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 = upsample_initial_channel // (2 ** (i + 1)) | |
| for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): self.resblocks.append(resblock(ch, k, d)) | |
| self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.ups.apply(init_weights) | |
| if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| self.upp = np.prod(upsample_rates) | |
| def forward(self, x, f0, g=None): | |
| har_source, noi_source, uv = self.m_source(f0, self.upp) | |
| har_source = har_source.transpose(1, 2) | |
| x = self.conv_pre(x) | |
| if g is not None: x = x + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| x = self.ups[i](x) | |
| 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): | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| sr2sr = {"32k": 32000,"40k": 40000,"48k": 48000,} | |
| class SynthesizerTrnMsNSFsidM(nn.Module): | |
| def __init__( | |
| self, | |
| spec_channels, | |
| segment_size, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| spk_embed_dim, | |
| gin_channels, | |
| sr, | |
| version, | |
| **kwargs | |
| ): | |
| super().__init__() | |
| if type(sr) == type("strr"): sr = sr2sr[sr] | |
| self.spec_channels = spec_channels | |
| self.inter_channels = inter_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.resblock = resblock | |
| self.resblock_kernel_sizes = resblock_kernel_sizes | |
| self.resblock_dilation_sizes = resblock_dilation_sizes | |
| self.upsample_rates = upsample_rates | |
| self.upsample_initial_channel = upsample_initial_channel | |
| self.upsample_kernel_sizes = upsample_kernel_sizes | |
| self.segment_size = segment_size | |
| self.gin_channels = gin_channels | |
| self.spk_embed_dim = spk_embed_dim | |
| if version == "v1": self.enc_p = TextEncoder(inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) | |
| else: self.enc_p = TextEncoder768(inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) | |
| self.dec = GeneratorNSF(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels, sr=sr, is_half=kwargs["is_half"]) | |
| self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
| self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels) | |
| self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) | |
| self.speaker_map = None | |
| def remove_weight_norm(self): | |
| self.dec.remove_weight_norm() | |
| self.flow.remove_weight_norm() | |
| self.enc_q.remove_weight_norm() | |
| def construct_spkmixmap(self, n_speaker): | |
| self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels)) | |
| for i in range(n_speaker): self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]])) | |
| self.speaker_map = self.speaker_map.unsqueeze(0) | |
| def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None): | |
| if self.speaker_map is not None: | |
| g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) | |
| g = g * self.speaker_map | |
| g = torch.sum(g, dim=1) | |
| g = g.transpose(0, -1).transpose(0, -2).squeeze(0) | |
| else: | |
| g = g.unsqueeze(0) | |
| g = self.emb_g(g).transpose(1, 2) | |
| m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) | |
| z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask | |
| z = self.flow(z_p, x_mask, g=g, reverse=True) | |
| return self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) | |
| class MultiPeriodDiscriminator(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| periods = [2, 3, 5, 7, 11, 17] | |
| discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] | |
| discs += [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] | |
| self.discriminators = nn.ModuleList(discs) | |
| def forward(self, y, y_hat): | |
| y_d_rs = [] | |
| y_d_gs = [] | |
| fmap_rs = [] | |
| fmap_gs = [] | |
| for d in self.discriminators: | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_rs.append(fmap_r) | |
| fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| class MultiPeriodDiscriminatorV2(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminatorV2, self).__init__() | |
| periods = [2, 3, 5, 7, 11, 17, 23, 37] | |
| discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] | |
| discs += [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] | |
| self.discriminators = nn.ModuleList(discs) | |
| def forward(self, y, y_hat): | |
| y_d_rs = [] | |
| y_d_gs = [] | |
| fmap_rs = [] | |
| fmap_gs = [] | |
| for d in self.discriminators: | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_rs.append(fmap_r) | |
| 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): | |
| super(DiscriminatorS, self).__init__() | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| self.convs = nn.ModuleList( | |
| [ | |
| norm_f(Conv1d(1, 16, 15, 1, padding=7)), | |
| norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), | |
| norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), | |
| norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), | |
| norm_f(Conv1d(1024, 1024, 41, 4, groups=256, 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, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class DiscriminatorP(torch.nn.Module): | |
| def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): | |
| super(DiscriminatorP, self).__init__() | |
| self.period = period | |
| self.use_spectral_norm = use_spectral_norm | |
| 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(kernel_size, 1), 0),)), | |
| norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)), | |
| norm_f(Conv2d( 128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)), | |
| norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0),)), | |
| norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0),)), | |
| ] | |
| ) | |
| self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
| def forward(self, x): | |
| fmap = [] | |
| 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, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap |