import math import torch from torch import nn from torch.nn import functional as F import commons import modules import attentions import monotonic_align from torch.nn import Conv1d, ConvTranspose1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from commons import init_weights, get_padding from text import symbols, num_tones, num_languages class DurationDiscriminator(nn.Module): # vits2 def __init__( self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 ): super().__init__() self.in_channels = in_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.gin_channels = gin_channels self.drop = nn.Dropout(p_dropout) self.conv_1 = nn.Conv1d( in_channels, filter_channels, kernel_size, padding=kernel_size // 2 ) self.norm_1 = modules.LayerNorm(filter_channels) self.conv_2 = nn.Conv1d( filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 ) self.norm_2 = modules.LayerNorm(filter_channels) self.dur_proj = nn.Conv1d(1, filter_channels, 1) self.LSTM = nn.LSTM( 2 * filter_channels, filter_channels, batch_first=True, bidirectional=True ) if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, in_channels, 1) self.output_layer = nn.Sequential( nn.Linear(2 * filter_channels, 1), nn.Sigmoid() ) def forward_probability(self, x, dur): dur = self.dur_proj(dur) x = torch.cat([x, dur], dim=1) x = x.transpose(1, 2) x, _ = self.LSTM(x) output_prob = self.output_layer(x) return output_prob def forward(self, x, x_mask, dur_r, dur_hat, g=None): x = torch.detach(x) if g is not None: g = torch.detach(g) x = x + self.cond(g) x = self.conv_1(x * x_mask) x = torch.relu(x) x = self.norm_1(x) x = self.drop(x) x = self.conv_2(x * x_mask) x = torch.relu(x) x = self.norm_2(x) x = self.drop(x) output_probs = [] for dur in [dur_r, dur_hat]: output_prob = self.forward_probability(x, dur) output_probs.append(output_prob) return output_probs class TransformerCouplingBlock(nn.Module): def __init__( self, channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, n_flows=4, gin_channels=0, share_parameter=False, ): super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.n_layers = n_layers self.n_flows = n_flows self.gin_channels = gin_channels self.flows = nn.ModuleList() self.wn = ( attentions.FFT( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow=True, gin_channels=self.gin_channels, ) if share_parameter else None ) for i in range(n_flows): self.flows.append( modules.TransformerCouplingLayer( channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels=self.gin_channels, ) ) 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 StochasticDurationPredictor(nn.Module): def __init__( self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0, ): super().__init__() filter_channels = in_channels # it needs to be removed from future version. self.in_channels = in_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.n_flows = n_flows self.gin_channels = gin_channels self.log_flow = modules.Log() self.flows = nn.ModuleList() self.flows.append(modules.ElementwiseAffine(2)) for i in range(n_flows): self.flows.append( modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) ) self.flows.append(modules.Flip()) self.post_pre = nn.Conv1d(1, filter_channels, 1) self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) self.post_convs = modules.DDSConv( filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout ) self.post_flows = nn.ModuleList() self.post_flows.append(modules.ElementwiseAffine(2)) for i in range(4): self.post_flows.append( modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3) ) self.post_flows.append(modules.Flip()) self.pre = nn.Conv1d(in_channels, filter_channels, 1) self.proj = nn.Conv1d(filter_channels, filter_channels, 1) self.convs = modules.DDSConv( filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout ) if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, filter_channels, 1) def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): x = torch.detach(x) x = self.pre(x) if g is not None: g = torch.detach(g) x = x + self.cond(g) x = self.convs(x, x_mask) x = self.proj(x) * x_mask if not reverse: flows = self.flows assert w is not None logdet_tot_q = 0 h_w = self.post_pre(w) h_w = self.post_convs(h_w, x_mask) h_w = self.post_proj(h_w) * x_mask e_q = ( torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask ) z_q = e_q for flow in self.post_flows: z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) logdet_tot_q += logdet_q z_u, z1 = torch.split(z_q, [1, 1], 1) u = torch.sigmoid(z_u) * x_mask z0 = (w - u) * x_mask logdet_tot_q += torch.sum( (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2] ) logq = ( torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - logdet_tot_q ) logdet_tot = 0 z0, logdet = self.log_flow(z0, x_mask) logdet_tot += logdet z = torch.cat([z0, z1], 1) for flow in flows: z, logdet = flow(z, x_mask, g=x, reverse=reverse) logdet_tot = logdet_tot + logdet nll = ( torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - logdet_tot ) return nll + logq # [b] else: flows = list(reversed(self.flows)) flows = flows[:-2] + [flows[-1]] # remove a useless vflow z = ( torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale ) for flow in flows: z = flow(z, x_mask, g=x, reverse=reverse) z0, z1 = torch.split(z, [1, 1], 1) logw = z0 return logw class DurationPredictor(nn.Module): def __init__( self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 ): super().__init__() self.in_channels = in_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.gin_channels = gin_channels self.drop = nn.Dropout(p_dropout) self.conv_1 = nn.Conv1d( in_channels, filter_channels, kernel_size, padding=kernel_size // 2 ) self.norm_1 = modules.LayerNorm(filter_channels) self.conv_2 = nn.Conv1d( filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 ) self.norm_2 = modules.LayerNorm(filter_channels) self.proj = nn.Conv1d(filter_channels, 1, 1) if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, in_channels, 1) def forward(self, x, x_mask, g=None): x = torch.detach(x) if g is not None: g = torch.detach(g) x = x + self.cond(g) x = self.conv_1(x * x_mask) x = torch.relu(x) x = self.norm_1(x) x = self.drop(x) x = self.conv_2(x * x_mask) x = torch.relu(x) x = self.norm_2(x) x = self.drop(x) x = self.proj(x * x_mask) return x * x_mask class Bottleneck(nn.Sequential): def __init__(self, in_dim, hidden_dim): c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False) c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False) super().__init__(*[c_fc1, c_fc2]) class Block(nn.Module): def __init__(self, in_dim, hidden_dim) -> None: super().__init__() self.norm = nn.LayerNorm(in_dim) self.mlp = MLP(in_dim, hidden_dim) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x + self.mlp(self.norm(x)) return x class MLP(nn.Module): def __init__(self, in_dim, hidden_dim): super().__init__() self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False) self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False) self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False) def forward(self, x: torch.Tensor): x = F.silu(self.c_fc1(x)) * self.c_fc2(x) x = self.c_proj(x) return x class TextEncoder(nn.Module): def __init__( self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, gin_channels=0, ): super().__init__() self.n_vocab = n_vocab self.out_channels = out_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.gin_channels = gin_channels self.emb = nn.Embedding(len(symbols), hidden_channels) nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) self.tone_emb = nn.Embedding(num_tones, hidden_channels) nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5) self.language_emb = nn.Embedding(num_languages, hidden_channels) nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5) self.bert_proj = nn.Conv1d(1024, hidden_channels, 1) # Remove emo_vq since it's not working well. self.style_proj = nn.Linear(256, hidden_channels) self.encoder = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, gin_channels=self.gin_channels, ) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, x, x_lengths, tone, language, bert, style_vec, g=None): bert_emb = self.bert_proj(bert).transpose(1, 2) style_emb = self.style_proj(style_vec.unsqueeze(1)) x = ( self.emb(x) + self.tone_emb(tone) + self.language_emb(language) + bert_emb + style_emb ) * math.sqrt( self.hidden_channels ) # [b, t, h] x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( x.dtype ) x = self.encoder(x * x_mask, x_mask, g=g) stats = self.proj(x) * x_mask m, logs = torch.split(stats, self.out_channels, dim=1) return x, m, logs, x_mask 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 PosteriorEncoder(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 layer in self.ups: remove_weight_norm(layer) for layer in self.resblocks: layer.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 is 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 layer in self.convs: x = layer(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 is 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 layer in self.convs: x = layer(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 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, modules.LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) x = torch.flatten(x, 1, -1) return x class ReferenceEncoder(nn.Module): """ inputs --- [N, Ty/r, n_mels*r] mels outputs --- [N, ref_enc_gru_size] """ def __init__(self, spec_channels, gin_channels=0): super().__init__() self.spec_channels = spec_channels ref_enc_filters = [32, 32, 64, 64, 128, 128] K = len(ref_enc_filters) filters = [1] + ref_enc_filters convs = [ weight_norm( nn.Conv2d( in_channels=filters[i], out_channels=filters[i + 1], kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), ) ) for i in range(K) ] self.convs = nn.ModuleList(convs) # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501 out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) self.gru = nn.GRU( input_size=ref_enc_filters[-1] * out_channels, hidden_size=256 // 2, batch_first=True, ) self.proj = nn.Linear(128, gin_channels) def forward(self, inputs, mask=None): N = inputs.size(0) out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] for conv in self.convs: out = conv(out) # out = wn(out) out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] T = out.size(1) N = out.size(0) out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] self.gru.flatten_parameters() memory, out = self.gru(out) # out --- [1, N, 128] return self.proj(out.squeeze(0)) def calculate_channels(self, L, kernel_size, stride, pad, n_convs): for i in range(n_convs): L = (L - kernel_size + 2 * pad) // stride + 1 return L class SynthesizerTrn(nn.Module): """ Synthesizer for Training """ def __init__( self, n_vocab, 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, n_speakers=256, gin_channels=256, use_sdp=True, n_flow_layer=4, n_layers_trans_flow=6, flow_share_parameter=False, use_transformer_flow=True, **kwargs ): super().__init__() self.n_vocab = n_vocab 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.n_speakers = n_speakers self.gin_channels = gin_channels self.n_layers_trans_flow = n_layers_trans_flow self.use_spk_conditioned_encoder = kwargs.get( "use_spk_conditioned_encoder", True ) self.use_sdp = use_sdp self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False) self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01) self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6) self.current_mas_noise_scale = self.mas_noise_scale_initial if self.use_spk_conditioned_encoder and gin_channels > 0: self.enc_gin_channels = gin_channels self.enc_p = TextEncoder( n_vocab, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, gin_channels=self.enc_gin_channels, ) 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 = PosteriorEncoder( spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels, ) if use_transformer_flow: self.flow = TransformerCouplingBlock( inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels, share_parameter=flow_share_parameter, ) else: self.flow = ResidualCouplingBlock( inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels, ) self.sdp = StochasticDurationPredictor( hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels ) self.dp = DurationPredictor( hidden_channels, 256, 3, 0.5, gin_channels=gin_channels ) if n_speakers >= 1: self.emb_g = nn.Embedding(n_speakers, gin_channels) else: self.ref_enc = ReferenceEncoder(spec_channels, gin_channels) def forward( self, x, x_lengths, y, y_lengths, sid, tone, language, bert, style_vec, ): if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) x, m_p, logs_p, x_mask = self.enc_p( x, x_lengths, tone, language, bert, style_vec, g=g ) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) z_p = self.flow(z, y_mask, g=g) with torch.no_grad(): # negative cross-entropy s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] neg_cent1 = torch.sum( -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True ) # [b, 1, t_s] neg_cent2 = torch.matmul( -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] neg_cent3 = torch.matmul( z_p.transpose(1, 2), (m_p * s_p_sq_r) ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] neg_cent4 = torch.sum( -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True ) # [b, 1, t_s] neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 if self.use_noise_scaled_mas: epsilon = ( torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale ) neg_cent = neg_cent + epsilon attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) attn = ( monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)) .unsqueeze(1) .detach() ) w = attn.sum(2) l_length_sdp = self.sdp(x, x_mask, w, g=g) l_length_sdp = l_length_sdp / torch.sum(x_mask) logw_ = torch.log(w + 1e-6) * x_mask logw = self.dp(x, x_mask, g=g) # logw_sdp = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=1.0) l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum( x_mask ) # for averaging # l_length_sdp += torch.sum((logw_sdp - logw_) ** 2, [1, 2]) / torch.sum(x_mask) l_length = l_length_dp + l_length_sdp # expand prior m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) z_slice, ids_slice = commons.rand_slice_segments( z, y_lengths, self.segment_size ) o = self.dec(z_slice, g=g) return ( o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_), # , logw_sdp), g, ) def infer( self, x, x_lengths, sid, tone, language, bert, style_vec, noise_scale=0.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0, y=None, ): # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) # g = self.gst(y) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) x, m_p, logs_p, x_mask = self.enc_p( x, x_lengths, tone, language, bert, style_vec, g=g ) logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * ( sdp_ratio ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio) w = torch.exp(logw) * x_mask * length_scale w_ceil = torch.ceil(w) y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to( x_mask.dtype ) attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) attn = commons.generate_path(w_ceil, attn_mask) m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose( 1, 2 ) # [b, t', t], [b, t, d] -> [b, d, t'] logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose( 1, 2 ) # [b, t', t], [b, t, d] -> [b, d, t'] z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale z = self.flow(z_p, y_mask, g=g, reverse=True) o = self.dec((z * y_mask)[:, :, :max_len], g=g) return o, attn, y_mask, (z, z_p, m_p, logs_p)