# coding: utf-8
from typing import Dict, List, Tuple, Union
import torch
from torch import nn
from torch.cuda.amp.autocast_mode import autocast
from trainer.trainer_utils import get_optimizer, get_scheduler
from TTS.tts.layers.tacotron.capacitron_layers import CapacitronVAE
from TTS.tts.layers.tacotron.gst_layers import GST
from TTS.tts.layers.tacotron.tacotron import Decoder, Encoder, PostCBHG
from TTS.tts.models.base_tacotron import BaseTacotron
from TTS.tts.utils.measures import alignment_diagonal_score
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
from TTS.utils.capacitron_optimizer import CapacitronOptimizer
class Tacotron(BaseTacotron):
"""Tacotron as in https://arxiv.org/abs/1703.10135
It's an autoregressive encoder-attention-decoder-postnet architecture.
Check `TacotronConfig` for the arguments.
Args:
config (TacotronConfig): Configuration for the Tacotron model.
speaker_manager (SpeakerManager): Speaker manager to handle multi-speaker settings. Only use if the model is
a multi-speaker model. Defaults to None.
"""
def __init__(
self,
config: "TacotronConfig",
ap: "AudioProcessor" = None,
tokenizer: "TTSTokenizer" = None,
speaker_manager: SpeakerManager = None,
):
super().__init__(config, ap, tokenizer, speaker_manager)
# pass all config fields to `self`
# for fewer code change
for key in config:
setattr(self, key, config[key])
# set speaker embedding channel size for determining `in_channels` for the connected layers.
# `init_multispeaker` needs to be called once more in training to initialize the speaker embedding layer based
# on the number of speakers infered from the dataset.
if self.use_speaker_embedding or self.use_d_vector_file:
self.init_multispeaker(config)
self.decoder_in_features += self.embedded_speaker_dim # add speaker embedding dim
if self.use_gst:
self.decoder_in_features += self.gst.gst_embedding_dim
if self.use_capacitron_vae:
self.decoder_in_features += self.capacitron_vae.capacitron_VAE_embedding_dim
# embedding layer
self.embedding = nn.Embedding(self.num_chars, 256, padding_idx=0)
self.embedding.weight.data.normal_(0, 0.3)
# base model layers
self.encoder = Encoder(self.encoder_in_features)
self.decoder = Decoder(
self.decoder_in_features,
self.decoder_output_dim,
self.r,
self.memory_size,
self.attention_type,
self.windowing,
self.attention_norm,
self.prenet_type,
self.prenet_dropout,
self.use_forward_attn,
self.transition_agent,
self.forward_attn_mask,
self.location_attn,
self.attention_heads,
self.separate_stopnet,
self.max_decoder_steps,
)
self.postnet = PostCBHG(self.decoder_output_dim)
self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, self.out_channels)
# setup prenet dropout
self.decoder.prenet.dropout_at_inference = self.prenet_dropout_at_inference
# global style token layers
if self.gst and self.use_gst:
self.gst_layer = GST(
num_mel=self.decoder_output_dim,
num_heads=self.gst.gst_num_heads,
num_style_tokens=self.gst.gst_num_style_tokens,
gst_embedding_dim=self.gst.gst_embedding_dim,
)
# Capacitron layers
if self.capacitron_vae and self.use_capacitron_vae:
self.capacitron_vae_layer = CapacitronVAE(
num_mel=self.decoder_output_dim,
encoder_output_dim=self.encoder_in_features,
capacitron_VAE_embedding_dim=self.capacitron_vae.capacitron_VAE_embedding_dim,
speaker_embedding_dim=self.embedded_speaker_dim
if self.use_speaker_embedding and self.capacitron_vae.capacitron_use_speaker_embedding
else None,
text_summary_embedding_dim=self.capacitron_vae.capacitron_text_summary_embedding_dim
if self.capacitron_vae.capacitron_use_text_summary_embeddings
else None,
)
# backward pass decoder
if self.bidirectional_decoder:
self._init_backward_decoder()
# setup DDC
if self.double_decoder_consistency:
self.coarse_decoder = Decoder(
self.decoder_in_features,
self.decoder_output_dim,
self.ddc_r,
self.memory_size,
self.attention_type,
self.windowing,
self.attention_norm,
self.prenet_type,
self.prenet_dropout,
self.use_forward_attn,
self.transition_agent,
self.forward_attn_mask,
self.location_attn,
self.attention_heads,
self.separate_stopnet,
self.max_decoder_steps,
)
def forward( # pylint: disable=dangerous-default-value
self, text, text_lengths, mel_specs=None, mel_lengths=None, aux_input={"speaker_ids": None, "d_vectors": None}
):
"""
Shapes:
text: [B, T_in]
text_lengths: [B]
mel_specs: [B, T_out, C]
mel_lengths: [B]
aux_input: 'speaker_ids': [B, 1] and 'd_vectors':[B, C]
"""
aux_input = self._format_aux_input(aux_input)
outputs = {"alignments_backward": None, "decoder_outputs_backward": None}
inputs = self.embedding(text)
input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
# B x T_in x encoder_in_features
encoder_outputs = self.encoder(inputs)
# sequence masking
encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
# global style token
if self.gst and self.use_gst:
# B x gst_dim
encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
# speaker embedding
if self.use_speaker_embedding or self.use_d_vector_file:
if not self.use_d_vector_file:
# B x 1 x speaker_embed_dim
embedded_speakers = self.speaker_embedding(aux_input["speaker_ids"])[:, None]
else:
# B x 1 x speaker_embed_dim
embedded_speakers = torch.unsqueeze(aux_input["d_vectors"], 1)
encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers)
# Capacitron
if self.capacitron_vae and self.use_capacitron_vae:
# B x capacitron_VAE_embedding_dim
encoder_outputs, *capacitron_vae_outputs = self.compute_capacitron_VAE_embedding(
encoder_outputs,
reference_mel_info=[mel_specs, mel_lengths],
text_info=[inputs, text_lengths]
if self.capacitron_vae.capacitron_use_text_summary_embeddings
else None,
speaker_embedding=embedded_speakers if self.capacitron_vae.capacitron_use_speaker_embedding else None,
)
else:
capacitron_vae_outputs = None
# decoder_outputs: B x decoder_in_features x T_out
# alignments: B x T_in x encoder_in_features
# stop_tokens: B x T_in
decoder_outputs, alignments, stop_tokens = self.decoder(encoder_outputs, mel_specs, input_mask)
# sequence masking
if output_mask is not None:
decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
# B x T_out x decoder_in_features
postnet_outputs = self.postnet(decoder_outputs)
# sequence masking
if output_mask is not None:
postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
# B x T_out x posnet_dim
postnet_outputs = self.last_linear(postnet_outputs)
# B x T_out x decoder_in_features
decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
if self.bidirectional_decoder:
decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
outputs["alignments_backward"] = alignments_backward
outputs["decoder_outputs_backward"] = decoder_outputs_backward
if self.double_decoder_consistency:
decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(
mel_specs, encoder_outputs, alignments, input_mask
)
outputs["alignments_backward"] = alignments_backward
outputs["decoder_outputs_backward"] = decoder_outputs_backward
outputs.update(
{
"model_outputs": postnet_outputs,
"decoder_outputs": decoder_outputs,
"alignments": alignments,
"stop_tokens": stop_tokens,
"capacitron_vae_outputs": capacitron_vae_outputs,
}
)
return outputs
@torch.no_grad()
def inference(self, text_input, aux_input=None):
aux_input = self._format_aux_input(aux_input)
inputs = self.embedding(text_input)
encoder_outputs = self.encoder(inputs)
if self.gst and self.use_gst:
# B x gst_dim
encoder_outputs = self.compute_gst(encoder_outputs, aux_input["style_mel"], aux_input["d_vectors"])
if self.capacitron_vae and self.use_capacitron_vae:
if aux_input["style_text"] is not None:
style_text_embedding = self.embedding(aux_input["style_text"])
style_text_length = torch.tensor([style_text_embedding.size(1)], dtype=torch.int64).to(
encoder_outputs.device
) # pylint: disable=not-callable
reference_mel_length = (
torch.tensor([aux_input["style_mel"].size(1)], dtype=torch.int64).to(encoder_outputs.device)
if aux_input["style_mel"] is not None
else None
) # pylint: disable=not-callable
# B x capacitron_VAE_embedding_dim
encoder_outputs, *_ = self.compute_capacitron_VAE_embedding(
encoder_outputs,
reference_mel_info=[aux_input["style_mel"], reference_mel_length]
if aux_input["style_mel"] is not None
else None,
text_info=[style_text_embedding, style_text_length] if aux_input["style_text"] is not None else None,
speaker_embedding=aux_input["d_vectors"]
if self.capacitron_vae.capacitron_use_speaker_embedding
else None,
)
if self.num_speakers > 1:
if not self.use_d_vector_file:
# B x 1 x speaker_embed_dim
embedded_speakers = self.speaker_embedding(aux_input["speaker_ids"])
# reshape embedded_speakers
if embedded_speakers.ndim == 1:
embedded_speakers = embedded_speakers[None, None, :]
elif embedded_speakers.ndim == 2:
embedded_speakers = embedded_speakers[None, :]
else:
# B x 1 x speaker_embed_dim
embedded_speakers = torch.unsqueeze(aux_input["d_vectors"], 1)
encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers)
decoder_outputs, alignments, stop_tokens = self.decoder.inference(encoder_outputs)
postnet_outputs = self.postnet(decoder_outputs)
postnet_outputs = self.last_linear(postnet_outputs)
decoder_outputs = decoder_outputs.transpose(1, 2)
outputs = {
"model_outputs": postnet_outputs,
"decoder_outputs": decoder_outputs,
"alignments": alignments,
"stop_tokens": stop_tokens,
}
return outputs
def before_backward_pass(self, loss_dict, optimizer) -> None:
# Extracting custom training specific operations for capacitron
# from the trainer
if self.use_capacitron_vae:
loss_dict["capacitron_vae_beta_loss"].backward()
optimizer.first_step()
def train_step(self, batch: Dict, criterion: torch.nn.Module) -> Tuple[Dict, Dict]:
"""Perform a single training step by fetching the right set of samples from the batch.
Args:
batch ([Dict]): A dictionary of input tensors.
criterion ([torch.nn.Module]): Callable criterion to compute model loss.
"""
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_input = batch["mel_input"]
mel_lengths = batch["mel_lengths"]
linear_input = batch["linear_input"]
stop_targets = batch["stop_targets"]
stop_target_lengths = batch["stop_target_lengths"]
speaker_ids = batch["speaker_ids"]
d_vectors = batch["d_vectors"]
aux_input = {"speaker_ids": speaker_ids, "d_vectors": d_vectors}
outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input)
# set the [alignment] lengths wrt reduction factor for guided attention
if mel_lengths.max() % self.decoder.r != 0:
alignment_lengths = (
mel_lengths + (self.decoder.r - (mel_lengths.max() % self.decoder.r))
) // self.decoder.r
else:
alignment_lengths = mel_lengths // self.decoder.r
# compute loss
with autocast(enabled=False): # use float32 for the criterion
loss_dict = criterion(
outputs["model_outputs"].float(),
outputs["decoder_outputs"].float(),
mel_input.float(),
linear_input.float(),
outputs["stop_tokens"].float(),
stop_targets.float(),
stop_target_lengths,
outputs["capacitron_vae_outputs"] if self.capacitron_vae else None,
mel_lengths,
None if outputs["decoder_outputs_backward"] is None else outputs["decoder_outputs_backward"].float(),
outputs["alignments"].float(),
alignment_lengths,
None if outputs["alignments_backward"] is None else outputs["alignments_backward"].float(),
text_lengths,
)
# compute alignment error (the lower the better )
align_error = 1 - alignment_diagonal_score(outputs["alignments"])
loss_dict["align_error"] = align_error
return outputs, loss_dict
def get_optimizer(self) -> List:
if self.use_capacitron_vae:
return CapacitronOptimizer(self.config, self.named_parameters())
return get_optimizer(self.config.optimizer, self.config.optimizer_params, self.config.lr, self)
def get_scheduler(self, optimizer: object):
opt = optimizer.primary_optimizer if self.use_capacitron_vae else optimizer
return get_scheduler(self.config.lr_scheduler, self.config.lr_scheduler_params, opt)
def before_gradient_clipping(self):
if self.use_capacitron_vae:
# Capacitron model specific gradient clipping
model_params_to_clip = []
for name, param in self.named_parameters():
if param.requires_grad:
if name != "capacitron_vae_layer.beta":
model_params_to_clip.append(param)
torch.nn.utils.clip_grad_norm_(model_params_to_clip, self.capacitron_vae.capacitron_grad_clip)
def _create_logs(self, batch, outputs, ap):
postnet_outputs = outputs["model_outputs"]
decoder_outputs = outputs["decoder_outputs"]
alignments = outputs["alignments"]
alignments_backward = outputs["alignments_backward"]
mel_input = batch["mel_input"]
linear_input = batch["linear_input"]
pred_linear_spec = postnet_outputs[0].data.cpu().numpy()
pred_mel_spec = decoder_outputs[0].data.cpu().numpy()
gt_linear_spec = linear_input[0].data.cpu().numpy()
gt_mel_spec = mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"pred_linear_spec": plot_spectrogram(pred_linear_spec, ap, output_fig=False),
"real_linear_spec": plot_spectrogram(gt_linear_spec, ap, output_fig=False),
"pred_mel_spec": plot_spectrogram(pred_mel_spec, ap, output_fig=False),
"real_mel_spec": plot_spectrogram(gt_mel_spec, ap, output_fig=False),
"alignment": plot_alignment(align_img, output_fig=False),
}
if self.bidirectional_decoder or self.double_decoder_consistency:
figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy(), output_fig=False)
# Sample audio
audio = ap.inv_spectrogram(pred_linear_spec.T)
return figures, {"audio": audio}
def train_log(
self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
) -> None: # pylint: disable=no-self-use
figures, audios = self._create_logs(batch, outputs, self.ap)
logger.train_figures(steps, figures)
logger.train_audios(steps, audios, self.ap.sample_rate)
def eval_step(self, batch: dict, criterion: nn.Module):
return self.train_step(batch, criterion)
def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
figures, audios = self._create_logs(batch, outputs, self.ap)
logger.eval_figures(steps, figures)
logger.eval_audios(steps, audios, self.ap.sample_rate)
@staticmethod
def init_from_config(config: "TacotronConfig", samples: Union[List[List], List[Dict]] = None):
"""Initiate model from config
Args:
config (TacotronConfig): Model config.
samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
Defaults to None.
"""
from TTS.utils.audio import AudioProcessor
ap = AudioProcessor.init_from_config(config)
tokenizer, new_config = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config, samples)
return Tacotron(new_config, ap, tokenizer, speaker_manager)