| |
| from istftnet import AdaIN1d, Decoder |
| from munch import Munch |
| from pathlib import Path |
| from plbert import load_plbert |
| from torch.nn.utils import weight_norm, spectral_norm |
| import json |
| 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 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 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.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True) |
|
|
| def forward(self, x, input_lengths, m): |
| x = self.embedding(x) |
| x = x.transpose(1, 2) |
| 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) |
|
|
| x = nn.utils.rnn.pack_padded_sequence( |
| x, input_lengths.cpu(), 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) |
| |
| for c in self.cnn: |
| x = c(x) |
| |
| x = x.transpose(1, 2) |
|
|
| |
| x, _ = self.lstm(x) |
|
|
| x = x.transpose(-1, -2) |
|
|
| 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 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] |
| |
| |
| input_lengths = text_lengths |
| 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: torch.Tensor, s: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: |
| x1 = x.transpose(-1, -2) |
| x2, _temp = self.shared(x1) |
|
|
| F0 = x2.transpose(-1, -2) |
| for block in self.F0: |
| F0 = block(F0, s) |
| F0 = self.F0_proj(F0) |
|
|
| N = x2.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], dim=-1) |
| x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0) |
| |
| x = x.transpose(0, 1) |
| 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)], dim=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, text_lengths.cpu(), 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: torch.Tensor, style: torch.Tensor) -> torch.Tensor: |
| |
| x = x.permute(2, 0, 1) |
| s = style.expand(x.shape[0], x.shape[1], -1) |
| x = torch.cat([x, s], axis=-1) |
| |
| x = x.transpose(0, 1) |
| 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)], dim=1) |
| else: |
| x = x.transpose(-1, -2) |
|
|
| |
| x, _ = block(x) |
|
|
| x = F.dropout(x, p=self.dropout, training=self.training) |
| x = x.transpose(-1, -2) |
| return x.transpose(-1, -2) |
| |
| 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 |
|
|
| |
| 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(path: str, device: str): |
| config = Path(__file__).parent / 'config.json' |
| assert config.exists(), f'Config path incorrect: config.json not found at {config}' |
| with open(config, 'r') as r: |
| args = recursive_munch(json.load(r)) |
| assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}' |
| 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(), |
| ) |
| for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items(): |
| assert key in model, key |
| try: |
| model[key].load_state_dict(state_dict) |
| except: |
| state_dict = {k[7:]: v for k, v in state_dict.items()} |
| model[key].load_state_dict(state_dict, strict=False) |
| return model |
|
|
