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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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from .commons import sequence_mask, rand_slice_segments
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from .modules import ResidualCouplingLayer, WN, Flip, ResBlock1, ResBlock2, LRELU_SLOPE
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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(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
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self.flows.append(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 = 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(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 = ResBlock1 if resblock == '1' else 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, 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, 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, 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 = 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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