import torch from torch import nn from torch.nn import functional as F from nnet import commons from nnet import modules from torch.nn import Conv1d, ConvTranspose1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from nnet.commons import init_weights, get_padding class ResidualCouplingBlock(nn.Module): def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0): super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.n_flows = n_flows self.gin_channels = gin_channels self.flows = nn.ModuleList() for i in range(n_flows): self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) self.flows.append(modules.Flip()) def forward(self, x, x_mask, g=None, reverse=False): if not reverse: for flow in self.flows: x, _ = flow(x, x_mask, g=g, reverse=reverse) else: for flow in reversed(self.flows): x = flow(x, x_mask, g=g, reverse=reverse) return x class Encoder(nn.Module): def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.gin_channels = gin_channels self.pre = nn.Conv1d(in_channels, hidden_channels, 1) self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, x, x_lengths, g=None): x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) x = self.pre(x) * x_mask x = self.enc(x, x_mask, g=g) stats = self.proj(x) * x_mask m, logs = torch.split(stats, self.out_channels, dim=1) z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask return z, m, logs, x_mask 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 = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): self.ups.append(weight_norm( ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), k, u, padding=(k-u)//2))) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = upsample_initial_channel//(2**(i+1)) for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): self.resblocks.append(resblock(ch, k, d)) self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) self.ups.apply(init_weights) 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.leaky_relu(x, modules.LRELU_SLOPE) x = self.ups[i](x) 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 = F.leaky_relu(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: remove_weight_norm(l) 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 DiscriminatorS(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(DiscriminatorS, self).__init__() norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv1d(1, 16, 15, 1, padding=7)), norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), ]) self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) def forward(self, x): fmap = [] 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] discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] 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 SpeakerEncoder(torch.nn.Module): def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): super(SpeakerEncoder, self).__init__() self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) self.linear = nn.Linear(model_hidden_size, model_embedding_size) self.relu = nn.ReLU() def forward(self, mels): self.lstm.flatten_parameters() _, (hidden, _) = self.lstm(mels) embeds_raw = self.relu(self.linear(hidden[-1])) return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) def compute_partial_slices(self, total_frames, partial_frames, partial_hop): mel_slices = [] for i in range(0, total_frames-partial_frames, partial_hop): mel_range = torch.arange(i, i+partial_frames) mel_slices.append(mel_range) return mel_slices def embed_utterance(self, mel, partial_frames=128, partial_hop=64): mel_len = mel.size(1) last_mel = mel[:,-partial_frames:] if mel_len > partial_frames: mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) mels = list(mel[:,s] for s in mel_slices) mels.append(last_mel) mels = torch.stack(tuple(mels), 0).squeeze(1) with torch.no_grad(): partial_embeds = self(mels) embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) #embed = embed / torch.linalg.norm(embed, 2) else: with torch.no_grad(): embed = self(last_mel) return embed class SynthesizerTrn(nn.Module): """ Synthesizer for Training """ def __init__(self, spec_channels, segment_size, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels, ssl_dim, use_spk, **kwargs): super().__init__() self.spec_channels = spec_channels self.inter_channels = inter_channels self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout 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.gin_channels = gin_channels self.ssl_dim = ssl_dim self.use_spk = use_spk self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16) self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) if not self.use_spk: self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels) def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None): if c_lengths == None: c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) if spec_lengths == None: spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device) if not self.use_spk: g = self.enc_spk(mel.transpose(1,2)) g = g.unsqueeze(-1) _, m_p, logs_p, _ = self.enc_p(c, c_lengths) z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) z_p = self.flow(z, spec_mask, g=g) z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size) o = self.dec(z_slice, g=g) return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q) def infer(self, c, g=None, mel=None, c_lengths=None): if c_lengths == None: c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) if not self.use_spk: g = self.enc_spk.embed_utterance(mel.transpose(1,2)) g = g.unsqueeze(-1) z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths) z = self.flow(z_p, c_mask, g=g, reverse=True) o = self.dec(z * c_mask, g=g) return o