# https://github.com/yl4579/StyleTTS2/blob/main/models.py from istftnet import Decoder from munch import Munch from plbert import load_plbert from torch.nn.utils import weight_norm, spectral_norm import numpy as np import os import os.path as osp import torch import torch.nn as nn import torch.nn.functional as F class LearnedDownSample(nn.Module): def __init__(self, layer_type, dim_in): super().__init__() self.layer_type = layer_type if self.layer_type == 'none': self.conv = nn.Identity() elif self.layer_type == 'timepreserve': self.conv = spectral_norm(nn.Conv2d(dim_in, dim_in, kernel_size=(3, 1), stride=(2, 1), groups=dim_in, padding=(1, 0))) elif self.layer_type == 'half': self.conv = spectral_norm(nn.Conv2d(dim_in, dim_in, kernel_size=(3, 3), stride=(2, 2), groups=dim_in, padding=1)) else: raise RuntimeError('Got unexpected donwsampletype %s, expected is [none, timepreserve, half]' % self.layer_type) def forward(self, x): return self.conv(x) class LearnedUpSample(nn.Module): def __init__(self, layer_type, dim_in): super().__init__() self.layer_type = layer_type if self.layer_type == 'none': self.conv = nn.Identity() elif self.layer_type == 'timepreserve': self.conv = nn.ConvTranspose2d(dim_in, dim_in, kernel_size=(3, 1), stride=(2, 1), groups=dim_in, output_padding=(1, 0), padding=(1, 0)) elif self.layer_type == 'half': self.conv = nn.ConvTranspose2d(dim_in, dim_in, kernel_size=(3, 3), stride=(2, 2), groups=dim_in, output_padding=1, padding=1) else: raise RuntimeError('Got unexpected upsampletype %s, expected is [none, timepreserve, half]' % self.layer_type) def forward(self, x): return self.conv(x) class DownSample(nn.Module): def __init__(self, layer_type): super().__init__() self.layer_type = layer_type def forward(self, x): if self.layer_type == 'none': return x elif self.layer_type == 'timepreserve': return F.avg_pool2d(x, (2, 1)) elif self.layer_type == 'half': if x.shape[-1] % 2 != 0: x = torch.cat([x, x[..., -1].unsqueeze(-1)], dim=-1) return F.avg_pool2d(x, 2) else: raise RuntimeError('Got unexpected donwsampletype %s, expected is [none, timepreserve, half]' % self.layer_type) class UpSample(nn.Module): def __init__(self, layer_type): super().__init__() self.layer_type = layer_type def forward(self, x): if self.layer_type == 'none': return x elif self.layer_type == 'timepreserve': return F.interpolate(x, scale_factor=(2, 1), mode='nearest') elif self.layer_type == 'half': return F.interpolate(x, scale_factor=2, mode='nearest') else: raise RuntimeError('Got unexpected upsampletype %s, expected is [none, timepreserve, half]' % self.layer_type) class ResBlk(nn.Module): def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), normalize=False, downsample='none'): super().__init__() self.actv = actv self.normalize = normalize self.downsample = DownSample(downsample) self.downsample_res = LearnedDownSample(downsample, dim_in) self.learned_sc = dim_in != dim_out self._build_weights(dim_in, dim_out) def _build_weights(self, dim_in, dim_out): self.conv1 = spectral_norm(nn.Conv2d(dim_in, dim_in, 3, 1, 1)) self.conv2 = spectral_norm(nn.Conv2d(dim_in, dim_out, 3, 1, 1)) if self.normalize: self.norm1 = nn.InstanceNorm2d(dim_in, affine=True) self.norm2 = nn.InstanceNorm2d(dim_in, affine=True) if self.learned_sc: self.conv1x1 = spectral_norm(nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False)) def _shortcut(self, x): if self.learned_sc: x = self.conv1x1(x) if self.downsample: x = self.downsample(x) return x def _residual(self, x): if self.normalize: x = self.norm1(x) x = self.actv(x) x = self.conv1(x) x = self.downsample_res(x) if self.normalize: x = self.norm2(x) x = self.actv(x) x = self.conv2(x) return x def forward(self, x): x = self._shortcut(x) + self._residual(x) return x / np.sqrt(2) # unit variance class LinearNorm(torch.nn.Module): def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): super(LinearNorm, self).__init__() self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) torch.nn.init.xavier_uniform_( self.linear_layer.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) def forward(self, x): return self.linear_layer(x) class Discriminator2d(nn.Module): def __init__(self, dim_in=48, num_domains=1, max_conv_dim=384, repeat_num=4): super().__init__() blocks = [] blocks += [spectral_norm(nn.Conv2d(1, dim_in, 3, 1, 1))] for lid in range(repeat_num): dim_out = min(dim_in*2, max_conv_dim) blocks += [ResBlk(dim_in, dim_out, downsample='half')] dim_in = dim_out blocks += [nn.LeakyReLU(0.2)] blocks += [spectral_norm(nn.Conv2d(dim_out, dim_out, 5, 1, 0))] blocks += [nn.LeakyReLU(0.2)] blocks += [nn.AdaptiveAvgPool2d(1)] blocks += [spectral_norm(nn.Conv2d(dim_out, num_domains, 1, 1, 0))] self.main = nn.Sequential(*blocks) def get_feature(self, x): features = [] for l in self.main: x = l(x) features.append(x) out = features[-1] out = out.view(out.size(0), -1) # (batch, num_domains) return out, features def forward(self, x): out, features = self.get_feature(x) out = out.squeeze() # (batch) return out, features class ResBlk1d(nn.Module): def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2), normalize=False, downsample='none', dropout_p=0.2): super().__init__() self.actv = actv self.normalize = normalize self.downsample_type = downsample self.learned_sc = dim_in != dim_out self._build_weights(dim_in, dim_out) self.dropout_p = dropout_p if self.downsample_type == 'none': self.pool = nn.Identity() else: self.pool = weight_norm(nn.Conv1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1)) def _build_weights(self, dim_in, dim_out): self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_in, 3, 1, 1)) self.conv2 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1)) if self.normalize: self.norm1 = nn.InstanceNorm1d(dim_in, affine=True) self.norm2 = nn.InstanceNorm1d(dim_in, affine=True) if self.learned_sc: self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False)) def downsample(self, x): if self.downsample_type == 'none': return x else: if x.shape[-1] % 2 != 0: x = torch.cat([x, x[..., -1].unsqueeze(-1)], dim=-1) return F.avg_pool1d(x, 2) def _shortcut(self, x): if self.learned_sc: x = self.conv1x1(x) x = self.downsample(x) return x def _residual(self, x): if self.normalize: x = self.norm1(x) x = self.actv(x) x = F.dropout(x, p=self.dropout_p, training=self.training) x = self.conv1(x) x = self.pool(x) if self.normalize: x = self.norm2(x) x = self.actv(x) x = F.dropout(x, p=self.dropout_p, training=self.training) x = self.conv2(x) return x def forward(self, x): x = self._shortcut(x) + self._residual(x) return x / np.sqrt(2) # unit variance class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-5): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): x = x.transpose(1, -1) x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) return x.transpose(1, -1) class TextEncoder(nn.Module): def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)): super().__init__() self.embedding = nn.Embedding(n_symbols, channels) padding = (kernel_size - 1) // 2 self.cnn = nn.ModuleList() for _ in range(depth): self.cnn.append(nn.Sequential( weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)), LayerNorm(channels), actv, nn.Dropout(0.2), )) # self.cnn = nn.Sequential(*self.cnn) self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True) def forward(self, x, input_lengths, m): x = self.embedding(x) # [B, T, emb] x = x.transpose(1, 2) # [B, emb, T] m = m.to(input_lengths.device).unsqueeze(1) x.masked_fill_(m, 0.0) for c in self.cnn: x = c(x) x.masked_fill_(m, 0.0) x = x.transpose(1, 2) # [B, T, chn] input_lengths = input_lengths.cpu().numpy() x = nn.utils.rnn.pack_padded_sequence( x, input_lengths, batch_first=True, enforce_sorted=False) self.lstm.flatten_parameters() x, _ = self.lstm(x) x, _ = nn.utils.rnn.pad_packed_sequence( x, batch_first=True) x = x.transpose(-1, -2) x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]]) x_pad[:, :, :x.shape[-1]] = x x = x_pad.to(x.device) x.masked_fill_(m, 0.0) return x def inference(self, x): x = self.embedding(x) x = x.transpose(1, 2) x = self.cnn(x) x = x.transpose(1, 2) self.lstm.flatten_parameters() x, _ = self.lstm(x) return x def length_to_mask(self, lengths): mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) mask = torch.gt(mask+1, lengths.unsqueeze(1)) return mask class AdaIN1d(nn.Module): def __init__(self, style_dim, num_features): super().__init__() self.norm = nn.InstanceNorm1d(num_features, affine=False) self.fc = nn.Linear(style_dim, num_features*2) def forward(self, x, s): h = self.fc(s) h = h.view(h.size(0), h.size(1), 1) gamma, beta = torch.chunk(h, chunks=2, dim=1) return (1 + gamma) * self.norm(x) + beta class UpSample1d(nn.Module): def __init__(self, layer_type): super().__init__() self.layer_type = layer_type def forward(self, x): if self.layer_type == 'none': return x else: return F.interpolate(x, scale_factor=2, mode='nearest') class AdainResBlk1d(nn.Module): def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2), upsample='none', dropout_p=0.0): super().__init__() self.actv = actv self.upsample_type = upsample self.upsample = UpSample1d(upsample) self.learned_sc = dim_in != dim_out self._build_weights(dim_in, dim_out, style_dim) self.dropout = nn.Dropout(dropout_p) if upsample == 'none': self.pool = nn.Identity() else: self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1)) def _build_weights(self, dim_in, dim_out, style_dim): self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1)) self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1)) self.norm1 = AdaIN1d(style_dim, dim_in) self.norm2 = AdaIN1d(style_dim, dim_out) if self.learned_sc: self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False)) def _shortcut(self, x): x = self.upsample(x) if self.learned_sc: x = self.conv1x1(x) return x def _residual(self, x, s): x = self.norm1(x, s) x = self.actv(x) x = self.pool(x) x = self.conv1(self.dropout(x)) x = self.norm2(x, s) x = self.actv(x) x = self.conv2(self.dropout(x)) return x def forward(self, x, s): out = self._residual(x, s) out = (out + self._shortcut(x)) / np.sqrt(2) return out class AdaLayerNorm(nn.Module): def __init__(self, style_dim, channels, eps=1e-5): super().__init__() self.channels = channels self.eps = eps self.fc = nn.Linear(style_dim, channels*2) def forward(self, x, s): x = x.transpose(-1, -2) x = x.transpose(1, -1) h = self.fc(s) h = h.view(h.size(0), h.size(1), 1) gamma, beta = torch.chunk(h, chunks=2, dim=1) gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1) x = F.layer_norm(x, (self.channels,), eps=self.eps) x = (1 + gamma) * x + beta return x.transpose(1, -1).transpose(-1, -2) class ProsodyPredictor(nn.Module): def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1): super().__init__() self.text_encoder = DurationEncoder(sty_dim=style_dim, d_model=d_hid, nlayers=nlayers, dropout=dropout) self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) self.duration_proj = LinearNorm(d_hid, max_dur) self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) self.F0 = nn.ModuleList() self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) self.N = nn.ModuleList() self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) def forward(self, texts, style, text_lengths, alignment, m): d = self.text_encoder(texts, style, text_lengths, m) batch_size = d.shape[0] text_size = d.shape[1] # predict duration input_lengths = text_lengths.cpu().numpy() x = nn.utils.rnn.pack_padded_sequence( d, input_lengths, batch_first=True, enforce_sorted=False) m = m.to(text_lengths.device).unsqueeze(1) self.lstm.flatten_parameters() x, _ = self.lstm(x) x, _ = nn.utils.rnn.pad_packed_sequence( x, batch_first=True) x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]]) x_pad[:, :x.shape[1], :] = x x = x_pad.to(x.device) duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training)) en = (d.transpose(-1, -2) @ alignment) return duration.squeeze(-1), en def F0Ntrain(self, x, s): x, _ = self.shared(x.transpose(-1, -2)) F0 = x.transpose(-1, -2) for block in self.F0: F0 = block(F0, s) F0 = self.F0_proj(F0) N = x.transpose(-1, -2) for block in self.N: N = block(N, s) N = self.N_proj(N) return F0.squeeze(1), N.squeeze(1) def length_to_mask(self, lengths): mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) mask = torch.gt(mask+1, lengths.unsqueeze(1)) return mask class DurationEncoder(nn.Module): def __init__(self, sty_dim, d_model, nlayers, dropout=0.1): super().__init__() self.lstms = nn.ModuleList() for _ in range(nlayers): self.lstms.append(nn.LSTM(d_model + sty_dim, d_model // 2, num_layers=1, batch_first=True, bidirectional=True, dropout=dropout)) self.lstms.append(AdaLayerNorm(sty_dim, d_model)) self.dropout = dropout self.d_model = d_model self.sty_dim = sty_dim def forward(self, x, style, text_lengths, m): masks = m.to(text_lengths.device) x = x.permute(2, 0, 1) s = style.expand(x.shape[0], x.shape[1], -1) x = torch.cat([x, s], axis=-1) x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0) x = x.transpose(0, 1) input_lengths = text_lengths.cpu().numpy() x = x.transpose(-1, -2) for block in self.lstms: if isinstance(block, AdaLayerNorm): x = block(x.transpose(-1, -2), style).transpose(-1, -2) x = torch.cat([x, s.permute(1, -1, 0)], axis=1) x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0) else: x = x.transpose(-1, -2) x = nn.utils.rnn.pack_padded_sequence( x, input_lengths, batch_first=True, enforce_sorted=False) block.flatten_parameters() x, _ = block(x) x, _ = nn.utils.rnn.pad_packed_sequence( x, batch_first=True) x = F.dropout(x, p=self.dropout, training=self.training) x = x.transpose(-1, -2) x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]]) x_pad[:, :, :x.shape[-1]] = x x = x_pad.to(x.device) return x.transpose(-1, -2) def inference(self, x, style): x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model) style = style.expand(x.shape[0], x.shape[1], -1) x = torch.cat([x, style], axis=-1) src = self.pos_encoder(x) output = self.transformer_encoder(src).transpose(0, 1) return output def length_to_mask(self, lengths): mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) mask = torch.gt(mask+1, lengths.unsqueeze(1)) return mask # https://github.com/yl4579/StyleTTS2/blob/main/utils.py def recursive_munch(d): if isinstance(d, dict): return Munch((k, recursive_munch(v)) for k, v in d.items()) elif isinstance(d, list): return [recursive_munch(v) for v in d] else: return d def build_model(args): args = recursive_munch(args) assert args.decoder.type == 'istftnet', 'Decoder type unknown' decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels, resblock_kernel_sizes = args.decoder.resblock_kernel_sizes, upsample_rates = args.decoder.upsample_rates, upsample_initial_channel=args.decoder.upsample_initial_channel, resblock_dilation_sizes=args.decoder.resblock_dilation_sizes, upsample_kernel_sizes=args.decoder.upsample_kernel_sizes, gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size) text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token) predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout) bert = load_plbert() return Munch( bert=bert, bert_encoder=nn.Linear(bert.config.hidden_size, args.hidden_dim), predictor=predictor, decoder=decoder, text_encoder=text_encoder, )