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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 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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from torch.cuda.amp import autocast |
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import torchaudio |
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from einops import rearrange |
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from alias_free_torch import * |
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import activations |
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class AMPBlock0(torch.nn.Module): |
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): |
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super(AMPBlock0, self).__init__() |
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self.convs1 = nn.ModuleList([ |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], |
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padding=get_padding(kernel_size, dilation[0]))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], |
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padding=get_padding(kernel_size, dilation[1]))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], |
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padding=get_padding(kernel_size, dilation[2]))), |
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]) |
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self.convs1.apply(init_weights) |
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self.convs2 = nn.ModuleList([ |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, |
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padding=get_padding(kernel_size, 1))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, |
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padding=get_padding(kernel_size, 1))), |
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, |
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padding=get_padding(kernel_size, 1))), |
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]) |
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self.convs2.apply(init_weights) |
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self.num_layers = len(self.convs1) + len(self.convs2) |
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self.activations = nn.ModuleList([ |
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Activation1d( |
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activation=activations.SnakeBeta(channels, alpha_logscale=True)) |
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for _ in range(self.num_layers) |
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]) |
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def forward(self, x): |
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acts1, acts2 = self.activations[::2], self.activations[1::2] |
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for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): |
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xt = a1(x) |
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xt = c1(xt) |
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xt = a2(xt) |
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xt = c2(xt) |
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x = xt + x |
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return x |
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def remove_weight_norm(self): |
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for l in self.convs1: |
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remove_weight_norm(l) |
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for l in self.convs2: |
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remove_weight_norm(l) |
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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 = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)) |
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resblock = AMPBlock0 |
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self.resblocks = nn.ModuleList() |
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for i in range(1): |
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ch = upsample_initial_channel//(2**(i)) |
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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, activation="snakebeta")) |
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activation_post = activations.SnakeBeta(ch, alpha_logscale=True) |
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self.activation_post = Activation1d(activation=activation_post) |
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) |
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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.interpolate(x, int(x.shape[-1] * 1.5), mode='linear') |
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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 = self.activation_post(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.resblocks: |
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l.remove_weight_norm() |
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remove_weight_norm(self.conv_pre) |
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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 DiscriminatorR(torch.nn.Module): |
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def __init__(self, resolution, use_spectral_norm=False): |
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super(DiscriminatorR, self).__init__() |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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n_fft, hop_length, win_length = resolution |
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self.spec_transform = torchaudio.transforms.Spectrogram( |
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n_fft=n_fft, hop_length=hop_length, win_length=win_length, window_fn=torch.hann_window, |
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normalized=True, center=False, pad_mode=None, power=None) |
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self.convs = nn.ModuleList([ |
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norm_f(nn.Conv2d(2, 32, (3, 9), padding=(1, 4))), |
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norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), |
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norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(2,1), padding=(2, 4))), |
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norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(4,1), padding=(4, 4))), |
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norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))), |
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]) |
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self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1))) |
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def forward(self, y): |
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fmap = [] |
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x = self.spec_transform(y) |
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x = torch.cat([x.real, x.imag], dim=1) |
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x = rearrange(x, 'b c w t -> b c t w') |
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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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resolutions = [[2048, 512, 2048], [1024, 256, 1024], [512, 128, 512], [256, 64, 256], [128, 32, 128]] |
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discs = [DiscriminatorR(resolutions[i], use_spectral_norm=use_spectral_norm) for i in range(len(resolutions))] |
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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 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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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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**kwargs): |
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super().__init__() |
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self.spec_channels = spec_channels |
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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.dec = Generator(1, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes) |
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def forward(self, x): |
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y = self.dec(x) |
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return y |
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@torch.no_grad() |
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def infer(self, x, max_len=None): |
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o = self.dec(x[:,:,:max_len]) |
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return o |
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