# Copyright (c) 2022 NVIDIA CORPORATION. # Licensed under the MIT license. # Adapted from https://github.com/jik876/hifi-gan under the MIT license. # LICENSE is in incl_licenses directory. import json import os import torch, torch.utils.data import tortoise.models.activations as activations from torch.nn import Conv1d, ConvTranspose1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from tortoise.models.alias_free_torch import * from librosa.filters import mel as librosa_mel_fn LRELU_SLOPE = 0.1 class AMPBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): super(AMPBlock1, self).__init__() self.h = h 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 if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) else: raise NotImplementedError( "activation incorrectly specified. check the config file and look for 'activation'.") 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 AMPBlock2(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): super(AMPBlock2, self).__init__() self.h = h self.convs = 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]))) ]) self.convs.apply(init_weights) self.num_layers = len(self.convs) # total number of conv layers if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) else: raise NotImplementedError( "activation incorrectly specified. check the config file and look for 'activation'.") def forward(self, x): for c, a in zip(self.convs, self.activations): xt = a(x) xt = c(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs: remove_weight_norm(l) class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self class BigVGAN(nn.Module): # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. def __init__(self, config=None, data=None): super(BigVGAN, self).__init__() """ with open(os.path.join(os.path.dirname(__file__), 'config.json'), 'r') as f: data = f.read() """ if config and data is None: with open(config, 'r') as f: data = f.read() jsonConfig = json.loads(data) elif data is not None: if isinstance(data, str): jsonConfig = json.loads(data) else: jsonConfig = data else: raise Exception("no config specified") global h h = AttrDict(jsonConfig) self.mel_channel = h.num_mels self.noise_dim = h.n_fft self.hop_length = h.hop_size self.num_kernels = len(h.resblock_kernel_sizes) self.num_upsamples = len(h.upsample_rates) # pre conv self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 # transposed conv-based upsamplers. does not apply anti-aliasing self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): self.ups.append(nn.ModuleList([ weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2)) ])) # residual blocks using anti-aliased multi-periodicity composition modules (AMP) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = h.upsample_initial_channel // (2 ** (i + 1)) for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): self.resblocks.append(resblock(h, ch, k, d, activation=h.activation)) # post conv if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale) self.activation_post = Activation1d(activation=activation_post) elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale) self.activation_post = Activation1d(activation=activation_post) else: raise NotImplementedError( "activation incorrectly specified. check the config file and look for 'activation'.") self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) # weight initialization for i in range(len(self.ups)): self.ups[i].apply(init_weights) self.conv_post.apply(init_weights) def forward(self,x, c): # pre conv x = self.conv_pre(x) for i in range(self.num_upsamples): # upsampling for i_up in range(len(self.ups[i])): x = self.ups[i][i_up](x) # AMP blocks 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 # post conv 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.ups: for l_i in l: remove_weight_norm(l_i) for l in self.resblocks: l.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) def inference(self, c, z=None): # pad input mel with zeros to cut artifact # see https://github.com/seungwonpark/melgan/issues/8 zero = torch.full((c.shape[0], h.num_mels, 10), -11.5129).to(c.device) mel = torch.cat((c, zero), dim=2) if z is None: z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device) audio = self.forward(mel, z) audio = audio[:, :, :-(self.hop_length * 10)] audio = audio.clamp(min=-1, max=1) return audio def eval(self, inference=False): super(BigVGAN, self).eval() # don't remove weight norm while validation in training loop if inference: self.remove_weight_norm() class DiscriminatorP(nn.Module): def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False): super(DiscriminatorP, self).__init__() self.period = period self.d_mult = h.discriminator_channel_mult norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv2d(1, int(32 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(32 * self.d_mult), int(128 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(128 * self.d_mult), int(512 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(512 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(1024 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), 1, padding=(2, 0))), ]) self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 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(nn.Module): def __init__(self, h): super(MultiPeriodDiscriminator, self).__init__() self.mpd_reshapes = h.mpd_reshapes print("mpd_reshapes: {}".format(self.mpd_reshapes)) discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes] self.discriminators = nn.ModuleList(discriminators) 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 DiscriminatorR(nn.Module): def __init__(self, cfg, resolution): super().__init__() self.resolution = resolution assert len(self.resolution) == 3, \ "MRD layer requires list with len=3, got {}".format(self.resolution) self.lrelu_slope = LRELU_SLOPE norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm if hasattr(cfg, "mrd_use_spectral_norm"): print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm)) norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm self.d_mult = cfg.discriminator_channel_mult if hasattr(cfg, "mrd_channel_mult"): print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult)) self.d_mult = cfg.mrd_channel_mult self.convs = nn.ModuleList([ norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))), norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 3), padding=(1, 1))), ]) self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1))) def forward(self, x): fmap = [] x = self.spectrogram(x) x = x.unsqueeze(1) for l in self.convs: x = l(x) x = F.leaky_relu(x, self.lrelu_slope) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap def spectrogram(self, x): n_fft, hop_length, win_length = self.resolution x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect') x = x.squeeze(1) x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True) x = torch.view_as_real(x) # [B, F, TT, 2] mag = torch.norm(x, p=2, dim=-1) # [B, F, TT] return mag class MultiResolutionDiscriminator(nn.Module): def __init__(self, cfg, debug=False): super().__init__() self.resolutions = cfg.resolutions assert len(self.resolutions) == 3, \ "MRD requires list of list with len=3, each element having a list with len=3. got {}". \ format(self.resolutions) self.discriminators = nn.ModuleList( [DiscriminatorR(cfg, resolution) for resolution in self.resolutions] ) 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(x=y) y_d_g, fmap_g = d(x=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 def get_mel(x): return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax) def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): if torch.min(y) < -1.: print('min value is ', torch.min(y)) if torch.max(y) > 1.: print('max value is ', torch.max(y)) global mel_basis, hann_window if fmax not in mel_basis: mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') y = y.squeeze(1) # complex tensor as default, then use view_as_real for future pytorch compatibility spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) spec = torch.view_as_real(spec) spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) spec = torch.nn.utils.spectral_normalize_torch(spec) return spec def feature_loss(fmap_r, fmap_g): loss = 0 for dr, dg in zip(fmap_r, fmap_g): for rl, gl in zip(dr, dg): loss += torch.mean(torch.abs(rl - gl)) return loss * 2 def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) def get_padding(kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) def discriminator_loss(disc_real_outputs, disc_generated_outputs): loss = 0 r_losses = [] g_losses = [] for dr, dg in zip(disc_real_outputs, disc_generated_outputs): r_loss = torch.mean((1 - dr) ** 2) g_loss = torch.mean(dg ** 2) loss += (r_loss + g_loss) r_losses.append(r_loss.item()) g_losses.append(g_loss.item()) return loss, r_losses, g_losses def generator_loss(disc_outputs): loss = 0 gen_losses = [] for dg in disc_outputs: l = torch.mean((1 - dg) ** 2) gen_losses.append(l) loss += l return loss, gen_losses if __name__ == '__main__': model = BigVGAN() c = torch.randn(3, 100, 10) z = torch.randn(3, 64, 10) print(c.shape) y = model(c, z) print(y.shape) assert y.shape == torch.Size([3, 1, 2560]) pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) print(pytorch_total_params)