mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
303 lines
11 KiB
Python
303 lines
11 KiB
Python
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from torch.nn import Conv1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, spectral_norm
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from vocoder.fregan.utils import get_padding
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from vocoder.fregan.stft_loss import stft
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from vocoder.fregan.dwt import DWT_1D
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LRELU_SLOPE = 0.1
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class SpecDiscriminator(nn.Module):
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"""docstring for Discriminator."""
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def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
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super(SpecDiscriminator, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.fft_size = fft_size
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self.shift_size = shift_size
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self.win_length = win_length
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self.window = getattr(torch, window)(win_length)
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self.discriminators = nn.ModuleList([
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norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))),
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])
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self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
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def forward(self, y):
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fmap = []
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with torch.no_grad():
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y = y.squeeze(1)
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y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device()))
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y = y.unsqueeze(1)
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for i, d in enumerate(self.discriminators):
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y = d(y)
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y = F.leaky_relu(y, LRELU_SLOPE)
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fmap.append(y)
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y = self.out(y)
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fmap.append(y)
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return torch.flatten(y, 1, -1), fmap
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class MultiResSpecDiscriminator(torch.nn.Module):
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def __init__(self,
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fft_sizes=[1024, 2048, 512],
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hop_sizes=[120, 240, 50],
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win_lengths=[600, 1200, 240],
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window="hann_window"):
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super(MultiResSpecDiscriminator, self).__init__()
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self.discriminators = nn.ModuleList([
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SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
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SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
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SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)
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])
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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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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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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 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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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.dwt1d = DWT_1D()
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self.dwt_conv1 = norm_f(Conv1d(2, 1, 1))
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self.dwt_proj1 = norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0)))
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self.dwt_conv2 = norm_f(Conv1d(4, 1, 1))
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self.dwt_proj2 = norm_f(Conv2d(1, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0)))
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self.dwt_conv3 = norm_f(Conv1d(8, 1, 1))
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self.dwt_proj3 = norm_f(Conv2d(1, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0)))
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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(5, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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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# DWT 1
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x_d1_high1, x_d1_low1 = self.dwt1d(x)
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x_d1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1))
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# 1d to 2d
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b, c, t = x_d1.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x_d1 = F.pad(x_d1, (0, n_pad), "reflect")
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t = t + n_pad
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x_d1 = x_d1.view(b, c, t // self.period, self.period)
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x_d1 = self.dwt_proj1(x_d1)
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# DWT 2
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x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1)
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x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1)
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x_d2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))
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# 1d to 2d
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b, c, t = x_d2.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x_d2 = F.pad(x_d2, (0, n_pad), "reflect")
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t = t + n_pad
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x_d2 = x_d2.view(b, c, t // self.period, self.period)
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x_d2 = self.dwt_proj2(x_d2)
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# DWT 3
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x_d3_high1, x_d3_low1 = self.dwt1d(x_d2_high1)
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x_d3_high2, x_d3_low2 = self.dwt1d(x_d2_low1)
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x_d3_high3, x_d3_low3 = self.dwt1d(x_d2_high2)
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x_d3_high4, x_d3_low4 = self.dwt1d(x_d2_low2)
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x_d3 = self.dwt_conv3(
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torch.cat([x_d3_high1, x_d3_low1, x_d3_high2, x_d3_low2, x_d3_high3, x_d3_low3, x_d3_high4, x_d3_low4],
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dim=1))
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# 1d to 2d
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b, c, t = x_d3.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x_d3 = F.pad(x_d3, (0, n_pad), "reflect")
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t = t + n_pad
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x_d3 = x_d3.view(b, c, t // self.period, self.period)
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x_d3 = self.dwt_proj3(x_d3)
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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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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i = 0
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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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if i == 0:
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x = torch.cat([x, x_d1], dim=2)
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elif i == 1:
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x = torch.cat([x, x_d2], dim=2)
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elif i == 2:
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x = torch.cat([x, x_d3], dim=2)
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else:
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x = x
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i = i + 1
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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 ResWiseMultiPeriodDiscriminator(torch.nn.Module):
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def __init__(self):
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super(ResWiseMultiPeriodDiscriminator, self).__init__()
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self.discriminators = nn.ModuleList([
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DiscriminatorP(2),
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DiscriminatorP(3),
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DiscriminatorP(5),
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DiscriminatorP(7),
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DiscriminatorP(11),
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])
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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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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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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 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.dwt1d = DWT_1D()
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self.dwt_conv1 = norm_f(Conv1d(2, 128, 15, 1, padding=7))
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self.dwt_conv2 = norm_f(Conv1d(4, 128, 41, 2, padding=20))
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self.convs = nn.ModuleList([
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norm_f(Conv1d(1, 128, 15, 1, padding=7)),
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norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
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norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
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norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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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# DWT 1
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x_d1_high1, x_d1_low1 = self.dwt1d(x)
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x_d1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1))
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# DWT 2
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x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1)
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x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1)
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x_d2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))
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i = 0
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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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if i == 0:
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x = torch.cat([x, x_d1], dim=2)
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if i == 1:
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x = torch.cat([x, x_d2], dim=2)
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i = i + 1
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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 ResWiseMultiScaleDiscriminator(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(ResWiseMultiScaleDiscriminator, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.dwt1d = DWT_1D()
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self.dwt_conv1 = norm_f(Conv1d(2, 1, 1))
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self.dwt_conv2 = norm_f(Conv1d(4, 1, 1))
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self.discriminators = nn.ModuleList([
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DiscriminatorS(use_spectral_norm=True),
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DiscriminatorS(),
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DiscriminatorS(),
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])
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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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# DWT 1
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y_hi, y_lo = self.dwt1d(y)
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y_1 = self.dwt_conv1(torch.cat([y_hi, y_lo], dim=1))
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x_d1_high1, x_d1_low1 = self.dwt1d(y_hat)
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y_hat_1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1))
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# DWT 2
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x_d2_high1, x_d2_low1 = self.dwt1d(y_hi)
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x_d2_high2, x_d2_low2 = self.dwt1d(y_lo)
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y_2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))
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x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1)
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x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1)
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y_hat_2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))
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for i, d in enumerate(self.discriminators):
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if i == 1:
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y = y_1
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y_hat = y_hat_1
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if i == 2:
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y = y_2
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y_hat = y_hat_2
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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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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
|
||
|
|
|
||
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|