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| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import Conv1d, Conv2d, ConvTranspose1d | |
| from torch.nn import functional as F | |
| from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm | |
| from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
| import attentions | |
| import commons | |
| import modules | |
| from commons import get_padding, init_weights | |
| from flow import ResidualCouplingBlock | |
| class PriorEncoder(nn.Module): | |
| def __init__( | |
| self, | |
| n_vocab, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| ): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| 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 = nn.Embedding(n_vocab, hidden_channels) | |
| nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) | |
| self.pre_attn_encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers // 2, | |
| kernel_size, | |
| p_dropout, | |
| ) | |
| self.post_attn_encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers - n_layers // 2, | |
| kernel_size, | |
| p_dropout, | |
| ) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, y_lengths, attn): | |
| x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( | |
| x.dtype | |
| ) | |
| x = self.pre_attn_encoder(x * x_mask, x_mask) | |
| y = torch.einsum("bht,blt->bhl", x, attn) | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( | |
| y.dtype | |
| ) | |
| y = self.post_attn_encoder(y * y_mask, y_mask) | |
| stats = self.proj(y) * y_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return y, m, logs, y_mask | |
| 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 | |
| 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 j, (k, d) in enumerate( | |
| 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): | |
| print("Removing weight norm...") | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| 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 = [] | |
| # 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, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| 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 MultiPeriodDiscriminator(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| periods = [2, 3, 5, 7, 11] | |
| discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] | |
| discs = 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 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) | |
| 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 SynthesizerTrn(nn.Module): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__( | |
| self, | |
| n_vocab, | |
| 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, | |
| n_speakers=0, | |
| gin_channels=0, | |
| **kwargs | |
| ): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| 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.n_speakers = n_speakers | |
| self.gin_channels = gin_channels | |
| self.enc_p = PriorEncoder( | |
| n_vocab, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| ) | |
| self.dec = Generator( | |
| inter_channels, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| gin_channels=gin_channels, | |
| ) | |
| 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, 2, 4, gin_channels=gin_channels | |
| ) | |
| if n_speakers > 1: | |
| self.emb_g = nn.Embedding(n_speakers, gin_channels) | |
| def forward(self, x, x_lengths, attn, y, y_lengths, sid=None): | |
| x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, y_lengths, attn=attn) | |
| if self.n_speakers > 0: | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| else: | |
| g = None | |
| z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) | |
| z_p = self.flow(z, y_mask, g=g) | |
| z_slice, ids_slice = commons.rand_slice_segments( | |
| z, y_lengths, self.segment_size | |
| ) | |
| o = self.dec(z_slice, g=g) | |
| l_length = None | |
| return ( | |
| o, | |
| l_length, | |
| attn, | |
| ids_slice, | |
| x_mask, | |
| y_mask, | |
| (z, z_p, m_p, logs_p, m_q, logs_q), | |
| ) | |
| def infer( | |
| self, | |
| x, | |
| x_lengths, | |
| y_lengths, | |
| attn, | |
| sid=None, | |
| noise_scale=1, | |
| max_len=None, | |
| ): | |
| x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, y_lengths, attn=attn) | |
| if self.n_speakers > 0: | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| else: | |
| g = None | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, attn.shape[1]), 1).to( | |
| x_mask.dtype | |
| ) | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| z = self.flow(z_p, y_mask, g=g, reverse=True) | |
| o = self.dec((z * y_mask)[:, :, :max_len], g=g) | |
| return o, attn, y_mask, (z, z_p, m_p, logs_p) | |
| class DurationNet(torch.nn.Module): | |
| def __init__(self, vocab_size: int, dim: int, num_layers=2): | |
| super().__init__() | |
| self.embed = torch.nn.Embedding(vocab_size, embedding_dim=dim) | |
| self.rnn = torch.nn.GRU( | |
| dim, | |
| dim, | |
| num_layers=num_layers, | |
| batch_first=True, | |
| bidirectional=True, | |
| dropout=0.2, | |
| ) | |
| self.proj = torch.nn.Linear(2 * dim, 1) | |
| def forward(self, token, lengths): | |
| x = self.embed(token) | |
| lengths = lengths.long().cpu() | |
| x = pack_padded_sequence( | |
| x, lengths=lengths, batch_first=True, enforce_sorted=False | |
| ) | |
| x, _ = self.rnn(x) | |
| x, _ = pad_packed_sequence(x, batch_first=True, total_length=token.shape[1]) | |
| x = self.proj(x) | |
| x = torch.nn.functional.softplus(x) | |
| return x | |