# coding:utf-8 import os import os.path as osp import copy import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from Utils.ASR.models import ASRCNN from Utils.JDC.model import JDCNet from Modules.diffusion.sampler import KDiffusion, LogNormalDistribution from Modules.diffusion.modules import Transformer1d, StyleTransformer1d from Modules.diffusion.diffusion import AudioDiffusionConditional from Modules.discriminators import ( MultiPeriodDiscriminator, MultiResSpecDiscriminator, WavLMDiscriminator, ) from munch import Munch import yaml 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, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from commons import init_weights, get_padding 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 / math.sqrt(2) # unit variance class StyleEncoder(nn.Module): def __init__(self, dim_in=48, style_dim=48, max_conv_dim=384): super().__init__() blocks = [] blocks += [spectral_norm(nn.Conv2d(1, dim_in, 3, 1, 1))] repeat_num = 4 for _ 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.AdaptiveAvgPool2d(1)] blocks += [nn.LeakyReLU(0.2)] self.shared = nn.Sequential(*blocks) self.unshared = nn.Linear(dim_out, style_dim) def forward(self, x): h = self.shared(x) h = h.view(h.size(0), -1) s = self.unshared(h) return s 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 / math.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)) / math.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)) * math.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 def load_F0_models(path): # load F0 model F0_model = JDCNet(num_class=1, seq_len=192) params = torch.load(path, map_location="cpu")["net"] F0_model.load_state_dict(params) _ = F0_model.train() return F0_model def load_ASR_models(ASR_MODEL_PATH, ASR_MODEL_CONFIG): # load ASR model def _load_config(path): with open(path) as f: config = yaml.safe_load(f) model_config = config["model_params"] return model_config def _load_model(model_config, model_path): model = ASRCNN(**model_config) params = torch.load(model_path, map_location="cpu")["model"] model.load_state_dict(params) return model asr_model_config = _load_config(ASR_MODEL_CONFIG) asr_model = _load_model(asr_model_config, ASR_MODEL_PATH) _ = asr_model.train() return asr_model def build_model(args, text_aligner, pitch_extractor, bert): assert args.decoder.type in ["istftnet", "hifigan"], "Decoder type unknown" if args.decoder.type == "istftnet": from Modules.istftnet import Decoder 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, ) else: from Modules.hifigan import Decoder 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, ) 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, ) style_encoder = StyleEncoder( dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim ) # acoustic style encoder predictor_encoder = StyleEncoder( dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim ) # prosodic style encoder # define diffusion model if args.multispeaker: transformer = StyleTransformer1d( channels=args.style_dim * 2, context_embedding_features=bert.config.hidden_size, context_features=args.style_dim * 2, **args.diffusion.transformer ) else: transformer = Transformer1d( channels=args.style_dim * 2, context_embedding_features=bert.config.hidden_size, **args.diffusion.transformer ) diffusion = AudioDiffusionConditional( in_channels=1, embedding_max_length=bert.config.max_position_embeddings, embedding_features=bert.config.hidden_size, embedding_mask_proba=args.diffusion.embedding_mask_proba, # Conditional dropout of batch elements, channels=args.style_dim * 2, context_features=args.style_dim * 2, ) diffusion.diffusion = KDiffusion( net=diffusion.unet, sigma_distribution=LogNormalDistribution( mean=args.diffusion.dist.mean, std=args.diffusion.dist.std ), sigma_data=args.diffusion.dist.sigma_data, # a placeholder, will be changed dynamically when start training diffusion model dynamic_threshold=0.0, ) diffusion.diffusion.net = transformer diffusion.unet = transformer nets = Munch( bert=bert, bert_encoder=nn.Linear(bert.config.hidden_size, args.hidden_dim), predictor=predictor, decoder=decoder, text_encoder=text_encoder, predictor_encoder=predictor_encoder, style_encoder=style_encoder, diffusion=diffusion, text_aligner=text_aligner, pitch_extractor=pitch_extractor, mpd=MultiPeriodDiscriminator(), msd=MultiResSpecDiscriminator(), # slm discriminator head wd=WavLMDiscriminator( args.slm.hidden, args.slm.nlayers, args.slm.initial_channel ), ) return nets def load_checkpoint(model, optimizer, path, load_only_params=True, ignore_modules=[]): state = torch.load(path, map_location="cpu") params = state["net"] for key in model: if key in params and key not in ignore_modules: print("%s loaded" % key) model[key].load_state_dict(params[key], strict=False) _ = [model[key].eval() for key in model] if not load_only_params: epoch = state["epoch"] iters = state["iters"] optimizer.load_state_dict(state["optimizer"]) else: epoch = 0 iters = 0 return model, optimizer, epoch, iters class TextEncoderOpenVoice(nn.Module): def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout): 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, filter_channels, n_heads, n_layers, kernel_size, p_dropout) self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, x, x_lengths): x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] 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 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 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 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, tau=1.0): 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) * tau * 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, 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 layer in self.ups: remove_weight_norm(layer) for layer in self.resblocks: layer.remove_weight_norm() 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, layernorm=True): 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) 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) if layernorm: self.layernorm = nn.LayerNorm(self.spec_channels) else: self.layernorm = None def forward(self, inputs, mask=None): N = inputs.size(0) out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] if self.layernorm is not None: out = self.layernorm(out) 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)) 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 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 SynthesizerTrn(nn.Module): """ Synthesizer for Training """ def __init__( self, n_vocab, spec_channels, 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=256, gin_channels=256, **kwargs ): super().__init__() 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.n_speakers = n_speakers if n_speakers == 0: self.ref_enc = ReferenceEncoder(spec_channels, gin_channels) else: self.enc_p = TextEncoderOpenVoice(n_vocab, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) self.emb_g = nn.Embedding(n_speakers, gin_channels) def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., sdp_ratio=0.2, max_len=None): x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: g = None logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * sdp_ratio \ + self.dp(x, x_mask, g=g) * (1 - sdp_ratio) 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) o = self.dec((z * y_mask)[:,:,:max_len], g=g) return o, attn, y_mask, (z, z_p, m_p, logs_p) def voice_conversion(self, y, y_lengths, sid_src, sid_tgt, tau=1.0): g_src = sid_src g_tgt = sid_tgt z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src, tau=tau) 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)