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import copy |
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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 functional as F |
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import commons |
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import modules |
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d |
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm |
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from commons import init_weights, get_padding |
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class ResidualCouplingBlock(nn.Module): |
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def __init__(self, |
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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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n_flows=4, |
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gin_channels=0): |
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super().__init__() |
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self.channels = 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.n_flows = n_flows |
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self.gin_channels = gin_channels |
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self.flows = nn.ModuleList() |
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for i in range(n_flows): |
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self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) |
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self.flows.append(modules.Flip()) |
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def forward(self, x, x_mask, g=None, reverse=False): |
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if not reverse: |
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for flow in self.flows: |
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x, _ = flow(x, x_mask, g=g, reverse=reverse) |
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else: |
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for flow in reversed(self.flows): |
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x = flow(x, x_mask, g=g, reverse=reverse) |
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return x |
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class Encoder(nn.Module): |
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def __init__(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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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(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) |
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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(x.dtype) |
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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__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): |
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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(initial_channel, upsample_initial_channel, 7, 1, padding=3) |
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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(weight_norm( |
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ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), |
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k, u, padding=(k-u)//2))) |
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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(zip(resblock_kernel_sizes, resblock_dilation_sizes)): |
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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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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), |
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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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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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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 + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] |
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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 SpeakerEncoder(torch.nn.Module): |
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def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): |
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super(SpeakerEncoder, self).__init__() |
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self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) |
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self.linear = nn.Linear(model_hidden_size, model_embedding_size) |
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self.relu = nn.ReLU() |
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def forward(self, mels): |
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self.lstm.flatten_parameters() |
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_, (hidden, _) = self.lstm(mels) |
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embeds_raw = self.relu(self.linear(hidden[-1])) |
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return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) |
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def compute_partial_slices(self, total_frames, partial_frames, partial_hop): |
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mel_slices = [] |
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for i in range(0, total_frames-partial_frames, partial_hop): |
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mel_range = torch.arange(i, i+partial_frames) |
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mel_slices.append(mel_range) |
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return mel_slices |
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def embed_utterance(self, mel, partial_frames=128, partial_hop=64): |
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mel_len = mel.size(1) |
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last_mel = mel[:,-partial_frames:] |
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if mel_len > partial_frames: |
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mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) |
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mels = list(mel[:,s] for s in mel_slices) |
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mels.append(last_mel) |
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mels = torch.stack(tuple(mels), 0).squeeze(1) |
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with torch.no_grad(): |
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partial_embeds = self(mels) |
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embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) |
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else: |
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with torch.no_grad(): |
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embed = self(last_mel) |
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return embed |
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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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def __init__(self, |
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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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gin_channels, |
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ssl_dim, |
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use_spk, |
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**kwargs): |
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super().__init__() |
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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.gin_channels = gin_channels |
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self.ssl_dim = ssl_dim |
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self.use_spk = use_spk |
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self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16) |
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self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) |
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self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) |
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) |
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if not self.use_spk: |
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self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels) |
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def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None): |
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if c_lengths == None: |
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c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) |
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if spec_lengths == None: |
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spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device) |
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if not self.use_spk: |
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g = self.enc_spk(mel.transpose(1,2)) |
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g = g.unsqueeze(-1) |
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_, m_p, logs_p, _ = self.enc_p(c, c_lengths) |
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z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) |
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z_p = self.flow(z, spec_mask, g=g) |
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z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size) |
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o = self.dec(z_slice, g=g) |
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return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
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def infer(self, c, g=None, mel=None, c_lengths=None): |
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if c_lengths == None: |
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c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) |
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if not self.use_spk: |
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g = self.enc_spk.embed_utterance(mel.transpose(1,2)) |
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g = g.unsqueeze(-1) |
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z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths) |
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z = self.flow(z_p, c_mask, g=g, reverse=True) |
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o = self.dec(z * c_mask, g=g) |
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return o |
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