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')