import copy import math from typing import List import torch from torch import nn from torch.nn import functional as F from module import commons from module import modules from module import attentions from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from module.commons import init_weights, get_padding from module.mrte_model import MRTE from module.quantize import ResidualVectorQuantizer from text import symbols from torch.cuda.amp import autocast 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, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, latent_channels=192, ): super().__init__() 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.latent_channels = latent_channels self.ssl_proj = nn.Conv1d(768, hidden_channels, 1) self.encoder_ssl = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers // 2, kernel_size, p_dropout, ) self.encoder_text = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout ) self.text_embedding = nn.Embedding(len(symbols), hidden_channels) self.mrte = MRTE() self.encoder2 = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers // 2, kernel_size, p_dropout, ) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, y, y_lengths, text, text_lengths, ge, test=None): y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( y.dtype ) y = self.ssl_proj(y * y_mask) * y_mask y = self.encoder_ssl(y * y_mask, y_mask) text_mask = torch.unsqueeze( commons.sequence_mask(text_lengths, text.size(1)), 1 ).to(y.dtype) if test == 1: text[:, :] = 0 text = self.text_embedding(text).transpose(1, 2) text = self.encoder_text(text * text_mask, text_mask) y = self.mrte(y, y_mask, text, text_mask, ge) y = self.encoder2(y * y_mask, y_mask) stats = self.proj(y) * y_mask m, logs = torch.split(stats, self.out_channels, dim=1) return y, m, logs, y_mask def extract_latent(self, x): x = self.ssl_proj(x) quantized, codes, commit_loss, quantized_list = self.quantizer(x) return codes.transpose(0, 1) def decode_latent(self, codes, y_mask, refer, refer_mask, ge): quantized = self.quantizer.decode(codes) y = self.vq_proj(quantized) * y_mask y = self.encoder_ssl(y * y_mask, y_mask) y = self.mrte(y, y_mask, refer, refer_mask, ge) y = self.encoder2(y * y_mask, y_mask) stats = self.proj(y) * y_mask m, logs = torch.split(stats, self.out_channels, dim=1) return y, m, logs, y_mask, quantized 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): if g != None: g = g.detach() 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 WNEncoder(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, 1) self.norm = modules.LayerNorm(out_channels) 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) out = self.proj(x) * x_mask out = self.norm(out) return out 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 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)]) 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): 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)).unsqueeze(-1) 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 Quantizer_module(torch.nn.Module): def __init__(self, n_e, e_dim): super(Quantizer_module, self).__init__() self.embedding = nn.Embedding(n_e, e_dim) self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e) def forward(self, x): d = ( torch.sum(x**2, 1, keepdim=True) + torch.sum(self.embedding.weight**2, 1) - 2 * torch.matmul(x, self.embedding.weight.T) ) min_indicies = torch.argmin(d, 1) z_q = self.embedding(min_indicies) return z_q, min_indicies class Quantizer(torch.nn.Module): def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160): super(Quantizer, self).__init__() assert embed_dim % n_code_groups == 0 self.quantizer_modules = nn.ModuleList( [ Quantizer_module(n_codes, embed_dim // n_code_groups) for _ in range(n_code_groups) ] ) self.n_code_groups = n_code_groups self.embed_dim = embed_dim def forward(self, xin): # B, C, T B, C, T = xin.shape xin = xin.transpose(1, 2) x = xin.reshape(-1, self.embed_dim) x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1) min_indicies = [] z_q = [] for _x, m in zip(x, self.quantizer_modules): _z_q, _min_indicies = m(_x) z_q.append(_z_q) min_indicies.append(_min_indicies) # B * T, z_q = torch.cat(z_q, -1).reshape(xin.shape) loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean( (z_q - xin.detach()) ** 2 ) z_q = xin + (z_q - xin).detach() z_q = z_q.transpose(1, 2) codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups) return z_q, loss, codes.transpose(1, 2) def embed(self, x): # idx: N, 4, T x = x.transpose(1, 2) x = torch.split(x, 1, 2) ret = [] for q, embed in zip(x, self.quantizer_modules): q = embed.embedding(q.squeeze(-1)) ret.append(q) ret = torch.cat(ret, -1) return ret.transpose(1, 2) # N, C, T class CodePredictor(nn.Module): def __init__( self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, n_q=8, dims=1024, ssl_dim=768, ): super().__init__() 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.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1) self.ref_enc = modules.MelStyleEncoder( ssl_dim, style_vector_dim=hidden_channels ) self.encoder = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout ) self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1) self.n_q = n_q self.dims = dims def forward(self, x, x_mask, refer, codes, infer=False): x = x.detach() x = self.vq_proj(x * x_mask) * x_mask g = self.ref_enc(refer, x_mask) x = x + g x = self.encoder(x * x_mask, x_mask) x = self.out_proj(x * x_mask) * x_mask logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose( 2, 3 ) target = codes[1:].transpose(0, 1) if not infer: logits = logits.reshape(-1, self.dims) target = target.reshape(-1) loss = torch.nn.functional.cross_entropy(logits, target) return loss else: _, top10_preds = torch.topk(logits, 10, dim=-1) correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1) top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item() print("Top-10 Accuracy:", top3_acc, "%") pred_codes = torch.argmax(logits, dim=-1) acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item() print("Top-1 Accuracy:", acc, "%") return pred_codes.transpose(0, 1) 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, n_speakers=0, gin_channels=0, use_sdp=True, semantic_frame_rate=None, freeze_quantizer=None, **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.n_speakers = n_speakers self.gin_channels = gin_channels self.use_sdp = use_sdp self.enc_p = TextEncoder( inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, ) 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, ) self.flow = ResidualCouplingBlock( inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels ) self.ref_enc = modules.MelStyleEncoder( spec_channels, style_vector_dim=gin_channels ) ssl_dim = 768 assert semantic_frame_rate in ["25hz", "50hz"] self.semantic_frame_rate = semantic_frame_rate if semantic_frame_rate == "25hz": self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2) else: self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1) self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024) if freeze_quantizer: self.ssl_proj.requires_grad_(False) self.quantizer.requires_grad_(False) # self.enc_p.text_embedding.requires_grad_(False) # self.enc_p.encoder_text.requires_grad_(False) # self.enc_p.mrte.requires_grad_(False) def forward(self, ssl, y, y_lengths, text, text_lengths): y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( y.dtype ) ge = self.ref_enc(y * y_mask, y_mask) with autocast(enabled=False): ssl = self.ssl_proj(ssl) quantized, codes, commit_loss, quantized_list = self.quantizer( ssl, layers=[0] ) if self.semantic_frame_rate == "25hz": quantized = F.interpolate( quantized, size=int(quantized.shape[-1] * 2), mode="nearest" ) x, m_p, logs_p, y_mask = self.enc_p( quantized, y_lengths, text, text_lengths, ge ) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge) z_p = self.flow(z, y_mask, g=ge) z_slice, ids_slice = commons.rand_slice_segments( z, y_lengths, self.segment_size ) o = self.dec(z_slice, g=ge) return ( o, commit_loss, ids_slice, y_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), quantized, ) def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5): y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( y.dtype ) ge = self.ref_enc(y * y_mask, y_mask) ssl = self.ssl_proj(ssl) quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0]) if self.semantic_frame_rate == "25hz": quantized = F.interpolate( quantized, size=int(quantized.shape[-1] * 2), mode="nearest" ) x, m_p, logs_p, y_mask = self.enc_p( quantized, y_lengths, text, text_lengths, ge, test=test ) z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale z = self.flow(z_p, y_mask, g=ge, reverse=True) o = self.dec((z * y_mask)[:, :, :], g=ge) return o, y_mask, (z, z_p, m_p, logs_p) @torch.no_grad() def decode(self, codes, text, refer, noise_scale=0.5): ge = None if refer is not None: refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device) refer_mask = torch.unsqueeze( commons.sequence_mask(refer_lengths, refer.size(2)), 1 ).to(refer.dtype) ge = self.ref_enc(refer * refer_mask, refer_mask) y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device) text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) quantized = self.quantizer.decode(codes) if self.semantic_frame_rate == "25hz": quantized = F.interpolate( quantized, size=int(quantized.shape[-1] * 2), mode="nearest" ) x, m_p, logs_p, y_mask = self.enc_p( quantized, y_lengths, text, text_lengths, ge ) z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale z = self.flow(z_p, y_mask, g=ge, reverse=True) o = self.dec((z * y_mask)[:, :, :], g=ge) return o @torch.no_grad() def batched_decode(self, codes, y_lengths, text, text_lengths, refer, noise_scale=0.5): ge = None if refer is not None: refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device) refer_mask = torch.unsqueeze( commons.sequence_mask(refer_lengths, refer.size(2)), 1 ).to(refer.dtype) ge = self.ref_enc(refer * refer_mask, refer_mask) # y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, codes.size(2)), 1).to( # codes.dtype # ) y_lengths = (y_lengths * 2).long().to(codes.device) text_lengths = text_lengths.long().to(text.device) # y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device) # text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) # 假设padding之后再decode没有问题, 影响未知，但听起来好像没问题？ quantized = self.quantizer.decode(codes) if self.semantic_frame_rate == "25hz": quantized = F.interpolate( quantized, size=int(quantized.shape[-1] * 2), mode="nearest" ) x, m_p, logs_p, y_mask = self.enc_p( quantized, y_lengths, text, text_lengths, ge ) z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale z = self.flow(z_p, y_mask, g=ge, reverse=True) z_masked = (z * y_mask)[:, :, :] # 串行。把padding部分去掉再decode o_list:List[torch.Tensor] = [] for i in range(z_masked.shape[0]): z_slice = z_masked[i, :, :y_lengths[i]].unsqueeze(0) o = self.dec(z_slice, g=ge)[0, 0, :].detach() o_list.append(o) # 并行（会有问题）。先decode，再把padding的部分去掉 # o = self.dec(z_masked, g=ge) # upsample_rate = int(math.prod(self.upsample_rates)) # o_lengths = y_lengths*upsample_rate # o_list = [o[i, 0, :idx].detach() for i, idx in enumerate(o_lengths)] return o_list def extract_latent(self, x): ssl = self.ssl_proj(x) quantized, codes, commit_loss, quantized_list = self.quantizer(ssl) return codes.transpose(0, 1)