# coding: utf-8
from typing import Dict, Tuple
import torch
from coqpit import Coqpit
from torch import nn
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.visual import plot_alignment, plot_spectrogram
from TTS.utils.audio import AudioProcessor
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.
"""
def __init__(self, config: Coqpit):
super().__init__(config)
chars, self.config = self.get_characters(config)
config.num_chars = self.num_chars = len(chars)
# 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
# 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,
)
# 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)
# 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,
}
)
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.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 train_step(self, batch, criterion):
"""Perform a single training step by fetching the right set if samples from the batch.
Args:
batch ([type]): [description]
criterion ([type]): [description]
"""
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"]
# forward pass model
outputs = self.forward(
text_input,
text_lengths,
mel_input,
mel_lengths,
aux_input={"speaker_ids": speaker_ids, "d_vectors": d_vectors},
)
# 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
aux_input = {"speaker_ids": speaker_ids, "d_vectors": d_vectors}
outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input)
# compute loss
loss_dict = criterion(
outputs["model_outputs"],
outputs["decoder_outputs"],
mel_input,
linear_input,
outputs["stop_tokens"],
stop_targets,
stop_target_lengths,
mel_lengths,
outputs["decoder_outputs_backward"],
outputs["alignments"],
alignment_lengths,
outputs["alignments_backward"],
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 train_log(self, ap: AudioProcessor, batch: dict, outputs: dict) -> Tuple[Dict, Dict]:
postnet_outputs = outputs["model_outputs"]
alignments = outputs["alignments"]
alignments_backward = outputs["alignments_backward"]
mel_input = batch["mel_input"]
pred_spec = postnet_outputs[0].data.cpu().numpy()
gt_spec = mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
"ground_truth": plot_spectrogram(gt_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
train_audio = ap.inv_spectrogram(pred_spec.T)
return figures, {"audio": train_audio}
def eval_step(self, batch, criterion):
return self.train_step(batch, criterion)
def eval_log(self, ap, batch, outputs):
return self.train_log(ap, batch, outputs)