import math import random from typing import Any import torch from lightning import LightningModule import diff_ttsg.utils.monotonic_align as monotonic_align from diff_ttsg import utils from diff_ttsg.models.components.diffusion import Diffusion, Diffusion_Motion from diff_ttsg.models.components.text_encoder import (MuMotionEncoder, TextEncoder) from diff_ttsg.utils.model import (denormalize, duration_loss, fix_len_compatibility, generate_path, sequence_mask) from diff_ttsg.utils.utils import plot_tensor log = utils.get_pylogger(__name__) class Diff_TTSG(LightningModule): def __init__( self, n_vocab, n_spks, spk_emb_dim, n_enc_channels, filter_channels, filter_channels_dp, n_heads, n_enc_layers, enc_kernel, enc_dropout, window_size, n_feats, n_motions, dec_dim, beta_min, beta_max, pe_scale, mu_motion_encoder_params, motion_reduction_factor, motion_decoder_channels, data_statistics, out_size, only_speech=False, encoder_type="default", optimizer=None ): super(Diff_TTSG, self).__init__() self.save_hyperparameters(logger=False) self.n_vocab = n_vocab self.n_spks = n_spks self.spk_emb_dim = spk_emb_dim self.n_enc_channels = n_enc_channels self.filter_channels = filter_channels self.filter_channels_dp = filter_channels_dp self.n_heads = n_heads self.n_enc_layers = n_enc_layers self.enc_kernel = enc_kernel self.enc_dropout = enc_dropout self.window_size = window_size self.n_feats = n_feats self.n_motions = n_motions self.dec_dim = dec_dim self.beta_min = beta_min self.beta_max = beta_max self.pe_scale = pe_scale self.generate_motion = not only_speech self.motion_reduction_factor = motion_reduction_factor self.out_size = out_size self.mu_diffusion_channels = motion_decoder_channels if n_spks > 1: self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim) self.encoder = TextEncoder(n_vocab, n_feats, n_enc_channels, filter_channels, filter_channels_dp, n_heads, n_enc_layers, enc_kernel, enc_dropout, window_size, encoder_type=encoder_type) self.decoder = Diffusion(n_feats, dec_dim, n_spks, spk_emb_dim, beta_min, beta_max, pe_scale) if self.generate_motion: self.motion_prior_loss = mu_motion_encoder_params.pop('prior_loss', True) self.mu_motion_encoder = MuMotionEncoder( input_channels=n_feats, output_channels=n_motions, **mu_motion_encoder_params ) self.decoder_motion = Diffusion_Motion( in_channels=n_motions, motion_decoder_channels=motion_decoder_channels, beta_min=beta_min, beta_max=beta_max, ) self.update_data_statistics(data_statistics) def update_data_statistics(self, data_statistics): if data_statistics is None: data_statistics = { 'mel_mean': 0.0, 'mel_std': 1.0, 'motion_mean': 0.0, 'motion_std': 1.0, } self.register_buffer('mel_mean', torch.tensor(data_statistics['mel_mean'])) self.register_buffer('mel_std', torch.tensor(data_statistics['mel_std'])) self.register_buffer('motion_mean', torch.tensor(data_statistics['motion_mean'])) self.register_buffer('motion_std', torch.tensor(data_statistics['motion_std'])) @torch.inference_mode() def synthesise(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, length_scale=1.0): """ Generates mel-spectrogram from text. Returns: 1. encoder outputs 2. decoder outputs 3. generated alignment Args: x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. x_lengths (torch.Tensor): lengths of texts in batch. n_timesteps (int): number of steps to use for reverse diffusion in decoder. temperature (float, optional): controls variance of terminal distribution. stoc (bool, optional): flag that adds stochastic term to the decoder sampler. Usually, does not provide synthesis improvements. length_scale (float, optional): controls speech pace. Increase value to slow down generated speech and vice versa. """ if isinstance(n_timesteps, dict): n_timestep_mel = n_timesteps['mel'] n_timestep_motion = n_timesteps['motion'] else: n_timestep_mel = n_timesteps n_timestep_motion = n_timesteps if isinstance(temperature, dict): temperature_mel = temperature['mel'] temperature_motion = temperature['motion'] else: temperature_mel = temperature temperature_motion = temperature if self.n_spks > 1: # Get speaker embedding spk = self.spk_emb(spk) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk) w = torch.exp(logw) * x_mask w_ceil = torch.ceil(w) * length_scale y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = int(y_lengths.max()) y_max_length_ = fix_len_compatibility(y_max_length) # Using obtained durations `w` construct alignment map `attn` y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) # Align encoded text and get mu_y mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) encoder_outputs = mu_y[:, :, :y_max_length] # Sample latent representation from terminal distribution N(mu_y, I) z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature_mel # Generate sample by performing reverse dynamics decoder_outputs = self.decoder(z, y_mask, mu_y, n_timestep_mel, stoc, spk) decoder_outputs = decoder_outputs[:, :, :y_max_length] if self.generate_motion: mu_y_motion = mu_y[:, :, ::self.motion_reduction_factor] y_motion_mask = y_mask[:, :, ::self.motion_reduction_factor] mu_y_motion = self.mu_motion_encoder(mu_y_motion, y_motion_mask) encoder_outputs_motion = mu_y_motion[:, :, :y_max_length] # sample latent representation from terminal distribution N(mu_y_motion, I) z_motion = mu_y_motion + torch.randn_like(mu_y_motion, device=mu_y_motion.device) / temperature_motion # Generate sample by performing reverse dynamics decoder_outputs_motion = self.decoder_motion(z_motion, y_motion_mask, mu_y_motion, n_timestep_motion, stoc, spk) decoder_outputs_motion = decoder_outputs_motion[:, :, :y_max_length] else: decoder_outputs_motion = None encoder_outputs_motion = None return { 'encoder_outputs_mel': encoder_outputs, 'decoder_outputs_mel': decoder_outputs, 'encoder_outputs_motion': encoder_outputs_motion, 'decoder_outputs_motion': decoder_outputs_motion, 'attn': attn[:, :, :y_max_length], 'mel': denormalize(decoder_outputs, self.mel_mean, self.mel_std), 'motion': denormalize(decoder_outputs_motion, self.motion_mean, self.motion_std) if self.generate_motion else None, } def forward(self, x, x_lengths, y, y_lengths, y_motion, spk=None, out_size=None): """ Computes 3 losses: 1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS). 2. prior loss: loss between mel-spectrogram and encoder outputs. 3. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder. Args: x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. x_lengths (torch.Tensor): lengths of texts in batch. y (torch.Tensor): batch of corresponding mel-spectrograms. y_lengths (torch.Tensor): lengths of mel-spectrograms in batch. out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained. Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size. """ if self.n_spks > 1: # Get speaker embedding spk = self.spk_emb(spk) # Get encoder_outputs `mu_x` and log-scaled token durations `logw` mu_x, logw, x_mask = self.encoder(x, x_lengths, spk) y_max_length = y.shape[-1] y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask) attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) # Use MAS to find most likely alignment `attn` between text and mel-spectrogram with torch.no_grad(): const = -0.5 * math.log(2 * math.pi) * self.n_feats factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device) y_square = torch.matmul(factor.transpose(1, 2), y ** 2) y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y) mu_square = torch.sum(factor * (mu_x ** 2), 1).unsqueeze(-1) log_prior = y_square - y_mu_double + mu_square + const attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1)) attn = attn.detach() # Compute loss between predicted log-scaled durations and those obtained from MAS logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask dur_loss = duration_loss(logw, logw_, x_lengths) # Cut a small segment of mel-spectrogram in order to increase batch size if not isinstance(out_size, type(None)): max_offset = (y_lengths - out_size).clamp(0) # cut a random segment of size `out_size` from each sample in batch max_offset: [758, 160, 773] offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy())) # offset ranges for each sample in batch offset_ranges: [(0, 758), (0, 160), (0, 773)] out_offset = torch.LongTensor([ torch.tensor(random.choice(range(start, end)) if end > start else 0) for start, end in offset_ranges ]).to(y_lengths) attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device) y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device) if self.generate_motion: y_motion_cut = torch.zeros(y_motion.shape[0], self.n_motions, out_size, dtype=y_motion.dtype, device=y_motion.device) y_cut_lengths = [] for i, (y_, out_offset_) in enumerate(zip(y, out_offset)): y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0) y_cut_lengths.append(y_cut_length) cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper] if self.generate_motion: y_motion_cut[i, :, :y_cut_length] = y_motion[i, :, cut_lower:cut_upper] attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper] y_cut_lengths = torch.LongTensor(y_cut_lengths) y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask) attn = attn_cut y = y_cut if self.generate_motion: y_motion = y_motion_cut y_mask = y_cut_mask # Align encoded text with mel-spectrogram and get mu_y segment mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) mu_y = mu_y.transpose(1, 2) # Compute loss of score-based decoder diff_loss, xt = self.decoder.compute_loss(y, y_mask, mu_y, spk) if self.generate_motion: # Reduce motion features mu_y_motion = mu_y[:, :, ::self.motion_reduction_factor] y_motion_mask = y_mask[:, :, ::self.motion_reduction_factor] y_motion = y_motion[:, :, ::self.motion_reduction_factor] mu_y_motion = self.mu_motion_encoder(mu_y_motion, y_motion_mask) diff_loss_motion, xt_motion = self.decoder_motion.compute_loss(y_motion, y_motion_mask, mu_y_motion, spk) else: diff_loss_motion = 0 # Compute loss between aligned encoder outputs and mel-spectrogram prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask) prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats) if self.generate_motion and self.motion_prior_loss: prior_loss_motion = torch.sum(0.5 * ((y_motion - mu_y_motion) ** 2 + math.log(2 * math.pi)) * y_motion_mask) prior_loss_motion = prior_loss_motion / (torch.sum(y_motion_mask) * self.n_motions) else: prior_loss_motion = 0 return dur_loss, prior_loss + prior_loss_motion, diff_loss + diff_loss_motion def configure_optimizers(self) -> Any: optimizer = self.hparams.optimizer(params=self.parameters()) return {'optimizer': optimizer} def get_losses(self, batch): pass x, x_lengths = batch['x'], batch['x_lengths'] y, y_lengths = batch['y'], batch['y_lengths'] y_motion = batch['y_motion'] dur_loss, prior_loss, diff_loss = self(x, x_lengths, y, y_lengths, y_motion, out_size=self.out_size) return { 'dur_loss': dur_loss, 'prior_loss': prior_loss, 'diff_loss': diff_loss, } def training_step(self, batch: Any, batch_idx: int): loss_dict = self.get_losses(batch) self.log('step', float(self.global_step), on_step=True, on_epoch=True, logger=True, sync_dist=True) self.log('sub_loss/train_dur_loss', loss_dict['dur_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True) self.log('sub_loss/train_prior_loss', loss_dict['prior_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True) self.log('sub_loss/train_diff_loss', loss_dict['diff_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True) total_loss = sum(loss_dict.values()) self.log('loss/train', total_loss, on_step=True, on_epoch=True, logger=True, prog_bar=True, sync_dist=True) return {'loss': total_loss, 'log': loss_dict } def validation_step(self, batch: Any, batch_idx: int): loss_dict = self.get_losses(batch) self.log('sub_loss/val_dur_loss', loss_dict['dur_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True) self.log('sub_loss/val_prior_loss', loss_dict['prior_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True) self.log('sub_loss/val_diff_loss', loss_dict['diff_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True) total_loss = sum(loss_dict.values()) self.log('loss/val', total_loss, on_step=True, on_epoch=True, logger=True, prog_bar=True, sync_dist=True) return total_loss def on_validation_end(self) -> None: if self.trainer.is_global_zero: one_batch = next(iter(self.trainer.val_dataloaders)) if self.current_epoch == 0: log.debug("Plotting original samples") for i in range(4): y = one_batch['y'][i].unsqueeze(0).to(self.device) y_motion = one_batch['y_motion'][i].unsqueeze(0).to(self.device) self.logger.experiment.add_image(f'original/mel_{i}', plot_tensor(y.squeeze().cpu()), self.current_epoch, dataformats='HWC') if self.generate_motion: self.logger.experiment.add_image(f'original/mel_{i}', plot_tensor(y_motion.squeeze().cpu()), self.current_epoch, dataformats='HWC') log.debug(f'Synthesising...') for i in range(4): x = one_batch['x'][i].unsqueeze(0).to(self.device) x_lengths = one_batch['x_lengths'][i].unsqueeze(0).to(self.device) output = self.synthesise(x, x_lengths, n_timesteps=20) y_enc, y_dec = output['encoder_outputs_mel'], output['decoder_outputs_mel'] y_motion_enc, y_motion_dec, attn = output['encoder_outputs_motion'], output['decoder_outputs_motion'], output['attn'] self.logger.experiment.add_image(f'generated_enc/{i}', plot_tensor(y_enc.squeeze().cpu()), self.current_epoch, dataformats='HWC') self.logger.experiment.add_image(f'generated_dec/{i}', plot_tensor(y_dec.squeeze().cpu()), self.current_epoch, dataformats='HWC') if self.generate_motion: self.logger.experiment.add_image(f'generated_enc_motion/{i}', plot_tensor(y_motion_enc.squeeze().cpu()), self.current_epoch, dataformats='HWC') self.logger.experiment.add_image(f'generated_dec_motion/{i}', plot_tensor(y_motion_dec.squeeze().cpu()), self.current_epoch, dataformats='HWC') self.logger.experiment.add_image(f'alignment/{i}', plot_tensor(attn.squeeze().cpu()), self.current_epoch, dataformats='HWC')