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import math |
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import torch |
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from torch import nn |
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from torch.nn import Conv1d, Conv2d, ConvTranspose1d |
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from torch.nn import functional as F |
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from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm |
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from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence |
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import attentions |
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import commons |
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import modules |
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from commons import get_padding, init_weights |
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from flow import ResidualCouplingBlock |
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class PriorEncoder(nn.Module): |
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def __init__( |
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self, |
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n_vocab, |
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out_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout, |
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): |
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super().__init__() |
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self.n_vocab = n_vocab |
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self.out_channels = out_channels |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.emb = nn.Embedding(n_vocab, hidden_channels) |
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) |
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self.pre_attn_encoder = attentions.Encoder( |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers // 2, |
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kernel_size, |
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p_dropout, |
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) |
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self.post_attn_encoder = attentions.Encoder( |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers - n_layers // 2, |
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kernel_size, |
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p_dropout, |
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) |
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
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def forward(self, x, x_lengths, y_lengths, attn): |
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x = self.emb(x) * math.sqrt(self.hidden_channels) |
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x = torch.transpose(x, 1, -1) |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( |
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x.dtype |
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) |
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x = self.pre_attn_encoder(x * x_mask, x_mask) |
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y = torch.einsum("bht,blt->bhl", x, attn) |
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( |
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y.dtype |
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) |
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y = self.post_attn_encoder(y * y_mask, y_mask) |
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stats = self.proj(y) * y_mask |
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m, logs = torch.split(stats, self.out_channels, dim=1) |
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return y, m, logs, y_mask |
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class PosteriorEncoder(nn.Module): |
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def __init__( |
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self, |
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in_channels, |
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out_channels, |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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gin_channels=0, |
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): |
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super().__init__() |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.hidden_channels = hidden_channels |
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self.kernel_size = kernel_size |
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self.dilation_rate = dilation_rate |
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self.n_layers = n_layers |
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self.gin_channels = gin_channels |
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
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self.enc = modules.WN( |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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gin_channels=gin_channels, |
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) |
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
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def forward(self, x, x_lengths, g=None): |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( |
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x.dtype |
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) |
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x = self.pre(x) * x_mask |
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x = self.enc(x, x_mask, g=g) |
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stats = self.proj(x) * x_mask |
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m, logs = torch.split(stats, self.out_channels, dim=1) |
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask |
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return z, m, logs, x_mask |
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class Generator(torch.nn.Module): |
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def __init__( |
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self, |
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initial_channel, |
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resblock, |
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resblock_kernel_sizes, |
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resblock_dilation_sizes, |
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upsample_rates, |
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upsample_initial_channel, |
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upsample_kernel_sizes, |
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gin_channels=0, |
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): |
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super(Generator, self).__init__() |
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self.num_kernels = len(resblock_kernel_sizes) |
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self.num_upsamples = len(upsample_rates) |
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self.conv_pre = Conv1d( |
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initial_channel, upsample_initial_channel, 7, 1, padding=3 |
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) |
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resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 |
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self.ups = nn.ModuleList() |
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): |
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self.ups.append( |
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weight_norm( |
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ConvTranspose1d( |
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upsample_initial_channel // (2**i), |
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upsample_initial_channel // (2 ** (i + 1)), |
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k, |
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u, |
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padding=(k - u) // 2, |
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) |
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) |
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) |
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self.resblocks = nn.ModuleList() |
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for i in range(len(self.ups)): |
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ch = upsample_initial_channel // (2 ** (i + 1)) |
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for j, (k, d) in enumerate( |
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zip(resblock_kernel_sizes, resblock_dilation_sizes) |
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): |
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self.resblocks.append(resblock(ch, k, d)) |
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) |
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self.ups.apply(init_weights) |
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if gin_channels != 0: |
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) |
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def forward(self, x, g=None): |
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x = self.conv_pre(x) |
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if g is not None: |
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x = x + self.cond(g) |
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for i in range(self.num_upsamples): |
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x = F.leaky_relu(x, modules.LRELU_SLOPE) |
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x = self.ups[i](x) |
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xs = None |
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for j in range(self.num_kernels): |
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if xs is None: |
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xs = self.resblocks[i * self.num_kernels + j](x) |
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else: |
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xs += self.resblocks[i * self.num_kernels + j](x) |
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x = xs / self.num_kernels |
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x = F.leaky_relu(x) |
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x = self.conv_post(x) |
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x = torch.tanh(x) |
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return x |
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def remove_weight_norm(self): |
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print("Removing weight norm...") |
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for l in self.ups: |
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remove_weight_norm(l) |
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for l in self.resblocks: |
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l.remove_weight_norm() |
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class DiscriminatorP(torch.nn.Module): |
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
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super(DiscriminatorP, self).__init__() |
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self.period = period |
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self.use_spectral_norm = use_spectral_norm |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList( |
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[ |
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norm_f( |
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Conv2d( |
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1, |
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32, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(kernel_size, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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32, |
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128, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(kernel_size, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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128, |
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512, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(kernel_size, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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512, |
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1024, |
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(kernel_size, 1), |
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(stride, 1), |
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padding=(get_padding(kernel_size, 1), 0), |
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) |
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), |
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norm_f( |
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Conv2d( |
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1024, |
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1024, |
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(kernel_size, 1), |
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1, |
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padding=(get_padding(kernel_size, 1), 0), |
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) |
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), |
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] |
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) |
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
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def forward(self, x): |
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fmap = [] |
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b, c, t = x.shape |
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if t % self.period != 0: |
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n_pad = self.period - (t % self.period) |
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x = F.pad(x, (0, n_pad), "reflect") |
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t = t + n_pad |
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x = x.view(b, c, t // self.period, self.period) |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, modules.LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class DiscriminatorS(torch.nn.Module): |
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def __init__(self, use_spectral_norm=False): |
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super(DiscriminatorS, self).__init__() |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList( |
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[ |
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norm_f(Conv1d(1, 16, 15, 1, padding=7)), |
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), |
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), |
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), |
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), |
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), |
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] |
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) |
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) |
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def forward(self, x): |
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fmap = [] |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, modules.LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class MultiPeriodDiscriminator(torch.nn.Module): |
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def __init__(self, use_spectral_norm=False): |
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super(MultiPeriodDiscriminator, self).__init__() |
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periods = [2, 3, 5, 7, 11] |
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discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] |
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discs = discs + [ |
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DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods |
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] |
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self.discriminators = nn.ModuleList(discs) |
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def forward(self, y, y_hat): |
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y_d_rs = [] |
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y_d_gs = [] |
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fmap_rs = [] |
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fmap_gs = [] |
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for i, d in enumerate(self.discriminators): |
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y_d_r, fmap_r = d(y) |
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y_d_g, fmap_g = d(y_hat) |
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y_d_rs.append(y_d_r) |
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y_d_gs.append(y_d_g) |
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fmap_rs.append(fmap_r) |
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fmap_gs.append(fmap_g) |
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
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class SynthesizerTrn(nn.Module): |
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""" |
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Synthesizer for Training |
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""" |
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|
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def __init__( |
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self, |
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n_vocab, |
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spec_channels, |
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segment_size, |
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inter_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout, |
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resblock, |
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resblock_kernel_sizes, |
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resblock_dilation_sizes, |
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upsample_rates, |
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upsample_initial_channel, |
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upsample_kernel_sizes, |
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n_speakers=0, |
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gin_channels=0, |
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**kwargs |
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): |
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super().__init__() |
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self.n_vocab = n_vocab |
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self.spec_channels = spec_channels |
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self.inter_channels = inter_channels |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.resblock = resblock |
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self.resblock_kernel_sizes = resblock_kernel_sizes |
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self.resblock_dilation_sizes = resblock_dilation_sizes |
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self.upsample_rates = upsample_rates |
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self.upsample_initial_channel = upsample_initial_channel |
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self.upsample_kernel_sizes = upsample_kernel_sizes |
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self.segment_size = segment_size |
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self.n_speakers = n_speakers |
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self.gin_channels = gin_channels |
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|
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self.enc_p = PriorEncoder( |
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n_vocab, |
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inter_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout, |
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) |
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self.dec = Generator( |
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inter_channels, |
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resblock, |
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resblock_kernel_sizes, |
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resblock_dilation_sizes, |
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upsample_rates, |
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upsample_initial_channel, |
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upsample_kernel_sizes, |
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gin_channels=gin_channels, |
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) |
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self.enc_q = PosteriorEncoder( |
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spec_channels, |
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inter_channels, |
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hidden_channels, |
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5, |
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1, |
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16, |
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gin_channels=gin_channels, |
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) |
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self.flow = ResidualCouplingBlock( |
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inter_channels, hidden_channels, 5, 2, 4, gin_channels=gin_channels |
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) |
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|
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if n_speakers > 1: |
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self.emb_g = nn.Embedding(n_speakers, gin_channels) |
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|
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def forward(self, x, x_lengths, attn, y, y_lengths, sid=None): |
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x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, y_lengths, attn=attn) |
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if self.n_speakers > 0: |
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g = self.emb_g(sid).unsqueeze(-1) |
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else: |
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g = None |
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z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
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z_p = self.flow(z, y_mask, g=g) |
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|
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z_slice, ids_slice = commons.rand_slice_segments( |
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z, y_lengths, self.segment_size |
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) |
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o = self.dec(z_slice, g=g) |
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l_length = None |
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return ( |
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o, |
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l_length, |
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attn, |
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ids_slice, |
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x_mask, |
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y_mask, |
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(z, z_p, m_p, logs_p, m_q, logs_q), |
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) |
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|
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def infer( |
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self, |
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x, |
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x_lengths, |
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y_lengths, |
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attn, |
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sid=None, |
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noise_scale=1, |
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max_len=None, |
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): |
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x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, y_lengths, attn=attn) |
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if self.n_speakers > 0: |
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g = self.emb_g(sid).unsqueeze(-1) |
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else: |
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g = None |
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|
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, attn.shape[1]), 1).to( |
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x_mask.dtype |
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) |
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale |
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z = self.flow(z_p, y_mask, g=g, reverse=True) |
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o = self.dec((z * y_mask)[:, :, :max_len], g=g) |
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return o, attn, y_mask, (z, z_p, m_p, logs_p) |
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|
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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( |
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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 |
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) |
|
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 |
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|
