import torch import torch.nn.functional as F import torch.nn as nn from torch.nn import Conv1d, AvgPool1d, Conv2d from torch.nn.utils import weight_norm, spectral_norm from .utils import get_padding LRELU_SLOPE = 0.1 def stft(x, fft_size, hop_size, win_length, window): """Perform STFT and convert to magnitude spectrogram. Args: x (Tensor): Input signal tensor (B, T). fft_size (int): FFT size. hop_size (int): Hop size. win_length (int): Window length. window (str): Window function type. Returns: Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). """ x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True) real = x_stft[..., 0] imag = x_stft[..., 1] return torch.abs(x_stft).transpose(2, 1) class SpecDiscriminator(nn.Module): """docstring for Discriminator.""" def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): super(SpecDiscriminator, self).__init__() norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.fft_size = fft_size self.shift_size = shift_size self.win_length = win_length self.window = getattr(torch, window)(win_length) self.discriminators = nn.ModuleList([ norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))), ]) self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) def forward(self, y): fmap = [] y = y.squeeze(1) y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device())) y = y.unsqueeze(1) for i, d in enumerate(self.discriminators): y = d(y) y = F.leaky_relu(y, LRELU_SLOPE) fmap.append(y) y = self.out(y) fmap.append(y) return torch.flatten(y, 1, -1), fmap class MultiResSpecDiscriminator(torch.nn.Module): def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window="hann_window"): super(MultiResSpecDiscriminator, self).__init__() self.discriminators = nn.ModuleList([ SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window) ]) 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) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs 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 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(5, 1), 0))), norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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, 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): super(MultiPeriodDiscriminator, self).__init__() self.discriminators = nn.ModuleList([ DiscriminatorP(2), DiscriminatorP(3), DiscriminatorP(5), DiscriminatorP(7), DiscriminatorP(11), ]) 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) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs class WavLMDiscriminator(nn.Module): """docstring for Discriminator.""" def __init__(self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False): super(WavLMDiscriminator, self).__init__() norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.pre = norm_f(Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)) self.convs = nn.ModuleList([ norm_f(nn.Conv1d(initial_channel, initial_channel * 2, kernel_size=5, padding=2)), norm_f(nn.Conv1d(initial_channel * 2, initial_channel * 4, kernel_size=5, padding=2)), norm_f(nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)), ]) self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1)) def forward(self, x): x = self.pre(x) fmap = [] for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) x = torch.flatten(x, 1, -1) return x