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| import torch | |
| import torch.distributions as dist | |
| from torch import nn | |
| from modules.commons.normalizing_flow.glow_modules import Glow | |
| from modules.tts.portaspeech.portaspeech import PortaSpeech | |
| class PortaSpeechFlow(PortaSpeech): | |
| def __init__(self, ph_dict_size, word_dict_size, hparams, out_dims=None): | |
| super().__init__(ph_dict_size, word_dict_size, hparams, out_dims) | |
| cond_hs = 80 | |
| if hparams.get('use_txt_cond', True): | |
| cond_hs = cond_hs + hparams['hidden_size'] | |
| if hparams.get('use_latent_cond', False): | |
| cond_hs = cond_hs + hparams['latent_size'] | |
| if hparams['use_cond_proj']: | |
| self.g_proj = nn.Conv1d(cond_hs, 160, 5, padding=2) | |
| cond_hs = 160 | |
| self.post_flow = Glow( | |
| 80, hparams['post_glow_hidden'], hparams['post_glow_kernel_size'], 1, | |
| hparams['post_glow_n_blocks'], hparams['post_glow_n_block_layers'], | |
| n_split=4, n_sqz=2, | |
| gin_channels=cond_hs, | |
| share_cond_layers=hparams['post_share_cond_layers'], | |
| share_wn_layers=hparams['share_wn_layers'], | |
| sigmoid_scale=hparams['sigmoid_scale'] | |
| ) | |
| self.prior_dist = dist.Normal(0, 1) | |
| def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None, | |
| spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None, | |
| forward_post_glow=True, two_stage=True, global_step=None): | |
| is_training = self.training | |
| train_fvae = not (forward_post_glow and two_stage) | |
| if not train_fvae: | |
| self.eval() | |
| with torch.set_grad_enabled(mode=train_fvae): | |
| ret = super(PortaSpeechFlow, self).forward( | |
| txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, | |
| spk_embed, spk_id, pitch, infer, tgt_mels, global_step) | |
| if (forward_post_glow or not two_stage) and self.hparams['use_post_flow']: | |
| self.run_post_glow(tgt_mels, infer, is_training, ret) | |
| return ret | |
| def run_post_glow(self, tgt_mels, infer, is_training, ret): | |
| x_recon = ret['mel_out'].transpose(1, 2) | |
| g = x_recon | |
| B, _, T = g.shape | |
| if self.hparams.get('use_txt_cond', True): | |
| g = torch.cat([g, ret['decoder_inp'].transpose(1, 2)], 1) | |
| if self.hparams.get('use_latent_cond', False): | |
| g_z = ret['z_p'][:, :, :, None].repeat(1, 1, 1, 4).reshape(B, -1, T) | |
| g = torch.cat([g, g_z], 1) | |
| if self.hparams['use_cond_proj']: | |
| g = self.g_proj(g) | |
| prior_dist = self.prior_dist | |
| if not infer: | |
| if is_training: | |
| self.post_flow.train() | |
| nonpadding = ret['nonpadding'].transpose(1, 2) | |
| y_lengths = nonpadding.sum(-1) | |
| if self.hparams['detach_postflow_input']: | |
| g = g.detach() | |
| tgt_mels = tgt_mels.transpose(1, 2) | |
| z_postflow, ldj = self.post_flow(tgt_mels, nonpadding, g=g) | |
| ldj = ldj / y_lengths / 80 | |
| ret['z_pf'], ret['ldj_pf'] = z_postflow, ldj | |
| ret['postflow'] = -prior_dist.log_prob(z_postflow).mean() - ldj.mean() | |
| if torch.isnan(ret['postflow']): | |
| ret['postflow'] = None | |
| else: | |
| nonpadding = torch.ones_like(x_recon[:, :1, :]) | |
| z_post = prior_dist.sample(x_recon.shape).to(g.device) * self.hparams['noise_scale'] | |
| x_recon, _ = self.post_flow(z_post, nonpadding, g, reverse=True) | |
| ret['mel_out'] = x_recon.transpose(1, 2) | |