import copy import math import torch from torch import nn from torch.nn import functional as F import commons import modules import attentions 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 modules import PQMF, CoMBD, SubBandDiscriminator, TMEncoder from stft import TorchSTFT 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 TextEncoder(nn.Module): def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, tm_last): 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.emb = nn.Embedding(n_vocab, hidden_channels) nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) self.encoder = attentions.Encoder( hidden_channels + tm_last, filter_channels, n_heads, n_layers, kernel_size, p_dropout) self.proj= nn.Conv1d(hidden_channels + tm_last, out_channels * 2, 1) def forward(self, x, x_lengths, torchmoji_hidden): x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] torchmoji_hidden = torchmoji_hidden[:, None].repeat(1, x.size(1), 1) x = torch.cat((x, torchmoji_hidden), dim=-1) 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) 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, gen_istft_n_fft, 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.post_n_fft = gen_istft_n_fft self.conv_post = Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3) self.ups.apply(init_weights) self.reflection_pad = torch.nn.ReflectionPad1d((1, 0)) self.out_proj_x1 = Conv1d(upsample_initial_channel // 4, 1, 7, 1, padding=3) 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 if i == 1: x1 = self.out_proj_x1(x) # elif i == 2: # x2 = self.out_proj_x2(x) x = F.leaky_relu(x) x = self.reflection_pad(x) x = self.conv_post(x) spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) return spec, phase, x1 #, x2 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 MultiScaleDiscriminator(torch.nn.Module): def __init__(self): super(MultiScaleDiscriminator, self).__init__() self.discriminators = nn.ModuleList([ DiscriminatorS(use_spectral_norm=True), DiscriminatorS(), DiscriminatorS(), ]) self.meanpools = nn.ModuleList([ AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2) ]) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): if i != 0: y = self.meanpools[i-1](y) y_hat = self.meanpools[i-1](y_hat) 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 MultiCoMBDiscriminator(torch.nn.Module): def __init__(self, kernels, channels, groups, strides): super(MultiCoMBDiscriminator, self).__init__() self.combd_1 = CoMBD(filters=channels, kernels=kernels[0], groups=groups, strides=strides) self.combd_2 = CoMBD(filters=channels, kernels=kernels[1], groups=groups, strides=strides) self.combd_3 = CoMBD(filters=channels, kernels=kernels[2], groups=groups, strides=strides) self.pqmf_2 = PQMF(N=2, taps=256, cutoff=0.25, beta=10.0) self.pqmf_4 = PQMF(N=4, taps=192, cutoff=0.13, beta=10.0) def forward(self, x, x_hat, x1_hat): y = [] y_hat = [] fmap = [] fmap_hat = [] p3, p3_fmap = self.combd_3(x) y.append(p3) fmap.append(p3_fmap) p3_hat, p3_fmap_hat = self.combd_3(x_hat) y_hat.append(p3_hat) fmap_hat.append(p3_fmap_hat) #x2_ = self.pqmf_2(x)[:, :1, :] # Select first band x1_ = self.pqmf_4(x)[:, :1, :] # Select first band #x2_hat_ = self.pqmf_2(x_hat)[:, :1, :] x1_hat_ = self.pqmf_4(x_hat)[:, :1, :] # p2_, p2_fmap_ = self.combd_2(x2_) # y.append(p2_) # fmap.append(p2_fmap_) # p2_hat_, p2_fmap_hat_ = self.combd_2(x2_hat) # y_hat.append(p2_hat_) # fmap_hat.append(p2_fmap_hat_) p1_, p1_fmap_ = self.combd_1(x1_) y.append(p1_) fmap.append(p1_fmap_) p1_hat_, p1_fmap_hat_ = self.combd_1(x1_hat) y_hat.append(p1_hat_) fmap_hat.append(p1_fmap_hat_) # p2, p2_fmap = self.combd_2(x2_) # y.append(p2) # fmap.append(p2_fmap) # # p2_hat, p2_fmap_hat = self.combd_2(x2_hat_) # y_hat.append(p2_hat) # fmap_hat.append(p2_fmap_hat) p1, p1_fmap = self.combd_1(x1_) y.append(p1) fmap.append(p1_fmap) p1_hat, p1_fmap_hat = self.combd_1(x1_hat_) y_hat.append(p1_hat) fmap_hat.append(p1_fmap_hat) return y, y_hat, fmap, fmap_hat class MultiSubBandDiscriminator(torch.nn.Module): def __init__(self, tkernels, fkernel, tchannels, fchannels, tstrides, fstride, tdilations, fdilations, tsubband, n, m, freq_init_ch): super(MultiSubBandDiscriminator, self).__init__() self.fsbd = SubBandDiscriminator(init_channel=freq_init_ch, channels=fchannels, kernel=fkernel, strides=fstride, dilations=fdilations) self.tsubband1 = tsubband[0] self.tsbd1 = SubBandDiscriminator(init_channel=self.tsubband1, channels=tchannels, kernel=tkernels[0], strides=tstrides[0], dilations=tdilations[0]) self.tsubband2 = tsubband[1] self.tsbd2 = SubBandDiscriminator(init_channel=self.tsubband2, channels=tchannels, kernel=tkernels[1], strides=tstrides[1], dilations=tdilations[1]) self.tsubband3 = tsubband[2] self.tsbd3 = SubBandDiscriminator(init_channel=self.tsubband3, channels=tchannels, kernel=tkernels[2], strides=tstrides[2], dilations=tdilations[2]) self.pqmf_n = PQMF(N=n, taps=256, cutoff=0.03, beta=10.0) self.pqmf_m = PQMF(N=m, taps=256, cutoff=0.1, beta=9.0) def forward(self, x, x_hat): fmap = [] fmap_hat = [] y = [] y_hat = [] # Time analysis xn = self.pqmf_n(x) xn_hat = self.pqmf_n(x_hat) q3, feat_q3 = self.tsbd3(xn[:, :self.tsubband3, :]) q3_hat, feat_q3_hat = self.tsbd3(xn_hat[:, :self.tsubband3, :]) y.append(q3) y_hat.append(q3_hat) fmap.append(feat_q3) fmap_hat.append(feat_q3_hat) q2, feat_q2 = self.tsbd2(xn[:, :self.tsubband2, :]) q2_hat, feat_q2_hat = self.tsbd2(xn_hat[:, :self.tsubband2, :]) y.append(q2) y_hat.append(q2_hat) fmap.append(feat_q2) fmap_hat.append(feat_q2_hat) q1, feat_q1 = self.tsbd1(xn[:, :self.tsubband1, :]) q1_hat, feat_q1_hat = self.tsbd1(xn_hat[:, :self.tsubband1, :]) y.append(q1) y_hat.append(q1_hat) fmap.append(feat_q1) fmap_hat.append(feat_q1_hat) # Frequency analysis xm = self.pqmf_m(x) xm_hat = self.pqmf_m(x_hat) xm = xm.transpose(-2, -1) xm_hat = xm_hat.transpose(-2, -1) q4, feat_q4 = self.fsbd(xm) q4_hat, feat_q4_hat = self.fsbd(xm_hat) y.append(q4) y_hat.append(q4_hat) fmap.append(feat_q4) fmap_hat.append(feat_q4_hat) return y, y_hat, fmap, fmap_hat 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, moji_start_size, moji_enc_sizes, bert_size, bert_final, n_speakers=0, gin_channels=0, use_sdp=True, gen_istft_n_fft=16, gen_istft_hop_size=4, is_quant=False, **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.gen_istft_n_fft = gen_istft_n_fft self.gen_istft_hop_size = gen_istft_hop_size self.bert_size = bert_size self.bert_final = bert_final self.is_quant = is_quant self.use_sdp = use_sdp self.moji_last = moji_enc_sizes[-1] self.final_exp = self.moji_last + self.bert_final self.enc_p = TextEncoder(n_vocab, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, self.moji_last,) self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gin_channels=gin_channels) self.enc_q = PosteriorEncoder(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) self.aligner = modules.AlignmentEncoder(n_mel_channels=80, n_text_channels=hidden_channels, n_att_channels=80, temperature=0.0005) self.satt_module = modules.SelfAttentionModule(n_text_channels=hidden_channels + self.moji_last, n_lm_tokens_channels=self.bert_size) self.stft = TorchSTFT(self.gen_istft_n_fft, hop_length= self.gen_istft_hop_size, win_length=self.gen_istft_n_fft) self.symbol_emb = self.enc_p.emb self.tm_enc = TMEncoder(moji_start_size,moji_enc_sizes) self.satt_enc = nn.Sequential( nn.Linear(hidden_channels + self.moji_last,self.bert_final), nn.ReLU()) if use_sdp: self.dp = StochasticDurationPredictor(hidden_channels + self.final_exp, 192, 3, 0.5, 4, gin_channels=gin_channels) else: self.dp = DurationPredictor(hidden_channels + self.final_exp, 256, 3, 0.5, gin_channels=gin_channels) if n_speakers > 1: self.emb_g = nn.Embedding(n_speakers, gin_channels) @torch.jit.unused def run_aligner(self, text, text_len, text_mask, spect, spect_len, attn_prior,cond): text_emb = self.symbol_emb(text) text_emb = text_emb.permute(0, 2, 1) text_mask = text_mask.permute(0, 2, 1) # [b, 1, mxlen] => [b, mxlen, 1] attn_soft, attn_logprob = self.aligner( spect, text_emb, mask=text_mask == 0, attn_prior=attn_prior,conditioning=cond ) attn_hard = modules.binarize_attention_parallel(attn_soft, text_len, spect_len) attn_hard_dur = attn_hard.sum(2) # assert torch.all(torch.eq(attn_hard_dur.sum(dim=1), spect_len)) # print( return attn_soft, attn_logprob, attn_hard, attn_hard_dur def forward(self, x, x_lengths, y, y_lengths, mel, tm_hidden, bert, bert_lens, sid=None): x_orig = x tm_encoded = self.tm_enc(tm_hidden) # [b, tm_last_size] x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tm_encoded) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = None # bert = [b, tokens, channels], bert_lens = [b] bert_mask = commons.sequence_mask(bert_lens, bert.size(1)) x_inp = x.permute(0, 2, 1) # [b, text_channels, t] -> [b, t, text_channels] x_mask_inp = x_mask.bool().permute(0, 2, 1).squeeze() # [b, 1, t] (float) -> [b, t] (bool) lm_features = self.satt_module( x_inp, bert, bert, q_mask=x_mask_inp, kv_mask=bert_mask == 0 ) lm_features = torch.nan_to_num(lm_features) # prevent nan poisoning lm_encoded = torch.nan_to_num(self.satt_enc(lm_features)) # [b, tokens, bert_final] lm_encoded = lm_encoded.permute(0, 2, 1) # see x_inp but reverse / [b, text_channels, tokens] x = torch.cat((x, lm_encoded), dim=1) # append encoded BERT as channels z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) z_p = self.flow(z, y_mask, g=g) attn_soft, attn_logprob, attn, attn_hard_dur = self.run_aligner(x_orig, x_lengths, x_mask, mel, y_lengths,None,g) w = attn_hard_dur if self.use_sdp: l_length = self.dp(x, x_mask, w, g=g) l_length = l_length / torch.sum(x_mask) else: logw_ = torch.log(w + 1e-6) * x_mask logw = self.dp(x, x_mask, g=g) l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging # 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) spec, phase, x1 = self.dec(z_slice, g=g) o = self.stft.inverse(spec, phase) return o, x1, l_length, attn, attn_logprob, attn_soft, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) def infer(self, x, x_lengths, tm_hidden, bert, bert_lens, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): tm_encoded = self.tm_enc(tm_hidden) # [b, tm_last_size] x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tm_encoded) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = None # bert = [b, tokens, channels], bert_lens = [b] bert_mask = commons.sequence_mask(bert_lens, bert.size(1)) x_inp = x.permute(0, 2, 1) # [b, text_channels, t] -> [b, t, text_channels] x_mask_inp = x_mask.bool().permute(0, 2, 1).squeeze() # [b, 1, t] (float) -> [b, t] (bool) if x_mask_inp.dim() < 2: # for single inference we might accidentally remove [1, t] batch dim x_mask_inp = x_mask_inp.unsqueeze(0) # [t] -> [1, t] lm_features = self.satt_module( x_inp, bert, bert, q_mask=x_mask_inp, kv_mask=bert_mask == 0 ) lm_features = torch.nan_to_num(lm_features) # prevent nan poisoning lm_encoded = torch.nan_to_num(self.satt_enc(lm_features)) # [b, tokens, bert_final] lm_encoded = lm_encoded.permute(0, 2, 1) # see x_inp but reverse / [b, text_channels, tokens] x = torch.cat((x, lm_encoded), dim=1) # append encoded BERT as channels if self.use_sdp: logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) else: logw = self.dp(x, x_mask, g=g) 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) if g is None and self.is_quant: g = torch.FloatTensor([1.0]) spec, phase, x1 = self.dec((z * y_mask)[:,:,:max_len], g=g) o = self.stft.inverse(spec, phase) return o, attn, y_mask, (z, z_p, m_p, logs_p) def infer_nodec(self, x, x_lengths, tm_hidden, bert, bert_lens, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): tm_encoded = self.tm_enc(tm_hidden) # [b, tm_last_size] x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tm_encoded) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = None # bert = [b, tokens, channels], bert_lens = [b] bert_mask = commons.sequence_mask(bert_lens, bert.size(1)) x_inp = x.permute(0, 2, 1) # [b, text_channels, t] -> [b, t, text_channels] x_mask_inp = x_mask.bool().permute(0, 2, 1).squeeze() # [b, 1, t] (float) -> [b, t] (bool) if x_mask_inp.dim() < 2: # for single inference we might accidentally remove [1, t] batch dim x_mask_inp = x_mask_inp.unsqueeze(0) # [t] -> [1, t] lm_features = self.satt_module( x_inp, bert, bert, q_mask=x_mask_inp, kv_mask=bert_mask == 0 ) lm_features = torch.nan_to_num(lm_features) # prevent nan poisoning lm_encoded = torch.nan_to_num(self.satt_enc(lm_features)) # [b, tokens, bert_final] lm_encoded = lm_encoded.permute(0, 2, 1) # see x_inp but reverse / [b, text_channels, tokens] x = torch.cat((x, lm_encoded), dim=1) # append encoded BERT as channels if self.use_sdp: logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) else: logw = self.dp(x, x_mask, g=g) 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) return z, y_mask, max_len, g # HURR DURR COPYING FUNCTIONS LE BAD # TorchScript tracing mysteries def infer_ts(self, x, x_lengths, tm_hidden, bert, bert_lens, length_scale=1,sid=None,noise_scale=.667,noise_scale_w=0.8, max_len=None): tm_encoded = self.tm_enc(tm_hidden) # [b, tm_last_size] x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tm_encoded) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = None # bert = [b, tokens, channels], bert_lens = [b] bert_mask = commons.sequence_mask(bert_lens, bert.size(1)) x_inp = x.permute(0, 2, 1) # [b, text_channels, t] -> [b, t, text_channels] x_mask_inp = x_mask.bool().permute(0, 2, 1).squeeze() # [b, 1, t] (float) -> [b, t] (bool) if x_mask_inp.dim() < 2: # for single inference we might accidentally remove [1, t] batch dim x_mask_inp = x_mask_inp.unsqueeze(0) # [t] -> [1, t] lm_features = self.satt_module( x_inp, bert, bert, q_mask=x_mask_inp, kv_mask=bert_mask == 0 ) lm_features = torch.nan_to_num(lm_features) # prevent nan poisoning lm_encoded = torch.nan_to_num(self.satt_enc(lm_features)) # [b, tokens, bert_final] lm_encoded = lm_encoded.permute(0, 2, 1) # see x_inp but reverse / [b, text_channels, tokens] x = torch.cat((x, lm_encoded), dim=1) # append encoded BERT as channels if self.use_sdp: logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) else: logw = self.dp(x, x_mask, g=g) 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) if g is None and self.is_quant: g = torch.FloatTensor([1.0]) spec, phase, x1 = self.dec((z * y_mask)[:,:,:max_len], g=g) o = self.stft.inverse(spec, phase) return o, attn # HURR DURR COPYING FUNCTIONS LE BAD # TorchScript tracing mysteries def infer_ts_noistft(self, x, x_lengths, tm_hidden, bert, bert_lens, length_scale=1,sid=None,noise_scale=.667,noise_scale_w=0.8, max_len=None): tm_encoded = self.tm_enc(tm_hidden) # [b, tm_last_size] x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tm_encoded) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = None # bert = [b, tokens, channels], bert_lens = [b] bert_mask = commons.sequence_mask(bert_lens, bert.size(1)) x_inp = x.permute(0, 2, 1) # [b, text_channels, t] -> [b, t, text_channels] x_mask_inp = x_mask.bool().permute(0, 2, 1).squeeze() # [b, 1, t] (float) -> [b, t] (bool) if x_mask_inp.dim() < 2: # for single inference we might accidentally remove [1, t] batch dim x_mask_inp = x_mask_inp.unsqueeze(0) # [t] -> [1, t] lm_features = self.satt_module( x_inp, bert, bert, q_mask=x_mask_inp, kv_mask=bert_mask == 0 ) lm_features = torch.nan_to_num(lm_features) # prevent nan poisoning lm_encoded = torch.nan_to_num(self.satt_enc(lm_features)) # [b, tokens, bert_final] lm_encoded = lm_encoded.permute(0, 2, 1) # see x_inp but reverse / [b, text_channels, tokens] x = torch.cat((x, lm_encoded), dim=1) # append encoded BERT as channels if self.use_sdp: logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) else: logw = self.dp(x, x_mask, g=g) 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) if g is None and self.is_quant: g = torch.FloatTensor([1.0]) spec, phase, x1 = self.dec((z * y_mask)[:,:,:max_len], g=g) return spec, phase, x1, attn def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): assert self.n_speakers > 0, "n_speakers have to be larger than 0." g_src = self.emb_g(sid_src).unsqueeze(-1) g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) z_p = self.flow(z, y_mask, g=g_src) z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) o_hat = self.dec(z_hat * y_mask, g=g_tgt) return o_hat, y_mask, (z, z_p, z_hat)