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| # 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, device): | |
| 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() | |
| bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim) | |
| for parent in [bert, bert_encoder, predictor, decoder, text_encoder]: | |
| for child in parent.children(): | |
| if isinstance(child, nn.RNNBase): | |
| child.flatten_parameters() | |
| model = Munch( | |
| bert=bert.to(device).eval(), | |
| bert_encoder=bert_encoder.to(device).eval(), | |
| predictor=predictor.to(device).eval(), | |
| decoder=decoder.to(device).eval(), | |
| text_encoder=text_encoder.to(device).eval(), | |
| ) | |
| return model | |