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] * 1.5), 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() remove_weight_norm(self.conv_pre) 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 = [[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 @torch.no_grad() def infer(self, x, max_len=None): o = self.dec(x[:,:,:max_len]) return o