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| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import modules | |
| from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from commons import init_weights, get_padding | |
| from torch.cuda.amp import autocast | |
| import torchaudio | |
| from einops import rearrange | |
| from alias_free_torch import * | |
| import activations | |
| class AMPBlock0(torch.nn.Module): | |
| def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): | |
| super(AMPBlock0, self).__init__() | |
| self.convs1 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], | |
| padding=get_padding(kernel_size, dilation[2]))), | |
| ]) | |
| self.convs1.apply(init_weights) | |
| self.convs2 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| ]) | |
| self.convs2.apply(init_weights) | |
| self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers | |
| self.activations = nn.ModuleList([ | |
| Activation1d( | |
| activation=activations.SnakeBeta(channels, alpha_logscale=True)) | |
| for _ in range(self.num_layers) | |
| ]) | |
| def forward(self, x): | |
| acts1, acts2 = self.activations[::2], self.activations[1::2] | |
| for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): | |
| xt = a1(x) | |
| xt = c1(xt) | |
| xt = a2(xt) | |
| xt = c2(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs1: | |
| remove_weight_norm(l) | |
| for l in self.convs2: | |
| remove_weight_norm(l) | |
| class Generator(torch.nn.Module): | |
| def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): | |
| super(Generator, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)) | |
| resblock = AMPBlock0 | |
| self.resblocks = nn.ModuleList() | |
| for i in range(1): | |
| ch = upsample_initial_channel//(2**(i)) | |
| for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
| self.resblocks.append(resblock(ch, k, d, activation="snakebeta")) | |
| activation_post = activations.SnakeBeta(ch, alpha_logscale=True) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| def forward(self, x, g=None): | |
| x = self.conv_pre(x) | |
| if g is not None: | |
| x = x + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = F.interpolate(x, int(x.shape[-1] * 3), mode='linear') | |
| xs = None | |
| for j in range(self.num_kernels): | |
| if xs is None: | |
| xs = self.resblocks[i*self.num_kernels+j](x) | |
| else: | |
| xs += self.resblocks[i*self.num_kernels+j](x) | |
| x = xs / self.num_kernels | |
| x = self.activation_post(x) | |
| x = self.conv_post(x) | |
| x = torch.tanh(x) | |
| return x | |
| def remove_weight_norm(self): | |
| print('Removing weight norm...') | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| class DiscriminatorP(torch.nn.Module): | |
| def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): | |
| super(DiscriminatorP, self).__init__() | |
| self.period = period | |
| self.use_spectral_norm = use_spectral_norm | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| self.convs = nn.ModuleList([ | |
| norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), | |
| ]) | |
| self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
| def forward(self, x): | |
| fmap = [] | |
| # 1d to 2d | |
| b, c, t = x.shape | |
| if t % self.period != 0: # pad first | |
| n_pad = self.period - (t % self.period) | |
| x = F.pad(x, (0, n_pad), "reflect") | |
| t = t + n_pad | |
| x = x.view(b, c, t // self.period, self.period) | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class DiscriminatorR(torch.nn.Module): | |
| def __init__(self, resolution, use_spectral_norm=False): | |
| super(DiscriminatorR, self).__init__() | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| n_fft, hop_length, win_length = resolution | |
| self.spec_transform = torchaudio.transforms.Spectrogram( | |
| n_fft=n_fft, hop_length=hop_length, win_length=win_length, window_fn=torch.hann_window, | |
| normalized=True, center=False, pad_mode=None, power=None) | |
| self.convs = nn.ModuleList([ | |
| norm_f(nn.Conv2d(2, 32, (3, 9), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(2,1), padding=(2, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(4,1), padding=(4, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))), | |
| ]) | |
| self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1))) | |
| def forward(self, y): | |
| fmap = [] | |
| x = self.spec_transform(y) # [B, 2, Freq, Frames, 2] | |
| x = torch.cat([x.real, x.imag], dim=1) | |
| x = rearrange(x, 'b c w t -> b c t w') | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class MultiPeriodDiscriminator(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| periods = [2,3,5,7,11] | |
| resolutions = [[4096, 1024, 4096], [2048, 512, 2048], [1024, 256, 1024], [512, 128, 512], [256, 64, 256], [128, 32, 128]] | |
| discs = [DiscriminatorR(resolutions[i], use_spectral_norm=use_spectral_norm) for i in range(len(resolutions))] | |
| discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] | |
| self.discriminators = nn.ModuleList(discs) | |
| def forward(self, y, y_hat): | |
| y_d_rs = [] | |
| y_d_gs = [] | |
| fmap_rs = [] | |
| fmap_gs = [] | |
| for i, d in enumerate(self.discriminators): | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_rs.append(fmap_r) | |
| fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| class SynthesizerTrn(nn.Module): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__(self, | |
| spec_channels, | |
| segment_size, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| **kwargs): | |
| super().__init__() | |
| self.spec_channels = spec_channels | |
| self.resblock = resblock | |
| self.resblock_kernel_sizes = resblock_kernel_sizes | |
| self.resblock_dilation_sizes = resblock_dilation_sizes | |
| self.upsample_rates = upsample_rates | |
| self.upsample_initial_channel = upsample_initial_channel | |
| self.upsample_kernel_sizes = upsample_kernel_sizes | |
| self.segment_size = segment_size | |
| self.dec = Generator(1, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes) | |
| def forward(self, x): | |
| y = self.dec(x) | |
| return y | |
| def infer(self, x, max_len=None): | |
| o = self.dec(x[:,:,:max_len]) | |
| return o | |