mirror of https://github.com/coqui-ai/TTS.git
364 lines
15 KiB
Python
364 lines
15 KiB
Python
# coding: utf-8
|
|
import numpy as np
|
|
import torch
|
|
from torch import nn
|
|
|
|
from TTS.tts.utils.measures import alignment_diagonal_score
|
|
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
|
|
from TTS.tts.layers.tacotron.gst_layers import GST
|
|
from TTS.tts.layers.tacotron.tacotron2 import Decoder, Encoder, Postnet
|
|
from TTS.tts.models.tacotron_abstract import TacotronAbstract
|
|
|
|
|
|
class Tacotron2(TacotronAbstract):
|
|
"""Tacotron2 as in https://arxiv.org/abs/1712.05884
|
|
|
|
It's an autoregressive encoder-attention-decoder-postnet architecture.
|
|
|
|
Args:
|
|
num_chars (int): number of input characters to define the size of embedding layer.
|
|
num_speakers (int): number of speakers in the dataset. >1 enables multi-speaker training and model learns speaker embeddings.
|
|
r (int): initial model reduction rate.
|
|
postnet_output_dim (int, optional): postnet output channels. Defaults to 80.
|
|
decoder_output_dim (int, optional): decoder output channels. Defaults to 80.
|
|
attn_type (str, optional): attention type. Check ```TTS.tts.layers.tacotron.common_layers.init_attn```. Defaults to 'original'.
|
|
attn_win (bool, optional): enable/disable attention windowing.
|
|
It especially useful at inference to keep attention alignment diagonal. Defaults to False.
|
|
attn_norm (str, optional): Attention normalization method. "sigmoid" or "softmax". Defaults to "softmax".
|
|
prenet_type (str, optional): prenet type for the decoder. Defaults to "original".
|
|
prenet_dropout (bool, optional): prenet dropout rate. Defaults to True.
|
|
prenet_dropout_at_inference (bool, optional): use dropout at inference time. This leads to a better quality for
|
|
some models. Defaults to False.
|
|
forward_attn (bool, optional): enable/disable forward attention.
|
|
It is only valid if ```attn_type``` is ```original```. Defaults to False.
|
|
trans_agent (bool, optional): enable/disable transition agent in forward attention. Defaults to False.
|
|
forward_attn_mask (bool, optional): enable/disable extra masking over forward attention. Defaults to False.
|
|
location_attn (bool, optional): enable/disable location sensitive attention.
|
|
It is only valid if ```attn_type``` is ```original```. Defaults to True.
|
|
attn_K (int, optional): Number of attention heads for GMM attention. Defaults to 5.
|
|
separate_stopnet (bool, optional): enable/disable separate stopnet training without only gradient
|
|
flow from stopnet to the rest of the model. Defaults to True.
|
|
bidirectional_decoder (bool, optional): enable/disable bidirectional decoding. Defaults to False.
|
|
double_decoder_consistency (bool, optional): enable/disable double decoder consistency. Defaults to False.
|
|
ddc_r (int, optional): reduction rate for the coarse decoder of double decoder consistency. Defaults to None.
|
|
encoder_in_features (int, optional): input channels for the encoder. Defaults to 512.
|
|
decoder_in_features (int, optional): input channels for the decoder. Defaults to 512.
|
|
speaker_embedding_dim (int, optional): external speaker conditioning vector channels. Defaults to None.
|
|
use_gst (bool, optional): enable/disable Global style token module.
|
|
gst (Coqpit, optional): Coqpit to initialize the GST module. If `None`, GST is disabled. Defaults to None.
|
|
gradual_trainin (List): Gradual training schedule. If None or `[]`, no gradual training is used.
|
|
Defaults to `[]`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
num_chars,
|
|
num_speakers,
|
|
r,
|
|
postnet_output_dim=80,
|
|
decoder_output_dim=80,
|
|
attn_type="original",
|
|
attn_win=False,
|
|
attn_norm="softmax",
|
|
prenet_type="original",
|
|
prenet_dropout=True,
|
|
prenet_dropout_at_inference=False,
|
|
forward_attn=False,
|
|
trans_agent=False,
|
|
forward_attn_mask=False,
|
|
location_attn=True,
|
|
attn_K=5,
|
|
separate_stopnet=True,
|
|
bidirectional_decoder=False,
|
|
double_decoder_consistency=False,
|
|
ddc_r=None,
|
|
encoder_in_features=512,
|
|
decoder_in_features=512,
|
|
speaker_embedding_dim=None,
|
|
use_gst=False,
|
|
gst=None,
|
|
gradual_training=[],
|
|
):
|
|
super().__init__(
|
|
num_chars,
|
|
num_speakers,
|
|
r,
|
|
postnet_output_dim,
|
|
decoder_output_dim,
|
|
attn_type,
|
|
attn_win,
|
|
attn_norm,
|
|
prenet_type,
|
|
prenet_dropout,
|
|
prenet_dropout_at_inference,
|
|
forward_attn,
|
|
trans_agent,
|
|
forward_attn_mask,
|
|
location_attn,
|
|
attn_K,
|
|
separate_stopnet,
|
|
bidirectional_decoder,
|
|
double_decoder_consistency,
|
|
ddc_r,
|
|
encoder_in_features,
|
|
decoder_in_features,
|
|
speaker_embedding_dim,
|
|
use_gst,
|
|
gst,
|
|
gradual_training,
|
|
)
|
|
|
|
# speaker embedding layer
|
|
if self.num_speakers > 1:
|
|
if not self.embeddings_per_sample:
|
|
speaker_embedding_dim = 512
|
|
self.speaker_embedding = nn.Embedding(self.num_speakers, speaker_embedding_dim)
|
|
self.speaker_embedding.weight.data.normal_(0, 0.3)
|
|
|
|
# speaker and gst embeddings is concat in decoder input
|
|
if self.num_speakers > 1:
|
|
self.decoder_in_features += speaker_embedding_dim # add speaker embedding dim
|
|
|
|
# embedding layer
|
|
self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
|
|
|
|
# base model layers
|
|
self.encoder = Encoder(self.encoder_in_features)
|
|
self.decoder = Decoder(
|
|
self.decoder_in_features,
|
|
self.decoder_output_dim,
|
|
r,
|
|
attn_type,
|
|
attn_win,
|
|
attn_norm,
|
|
prenet_type,
|
|
prenet_dropout,
|
|
forward_attn,
|
|
trans_agent,
|
|
forward_attn_mask,
|
|
location_attn,
|
|
attn_K,
|
|
separate_stopnet,
|
|
)
|
|
self.postnet = Postnet(self.postnet_output_dim)
|
|
|
|
# setup prenet dropout
|
|
self.decoder.prenet.dropout_at_inference = prenet_dropout_at_inference
|
|
|
|
# global style token layers
|
|
if self.gst and use_gst:
|
|
self.gst_layer = GST(
|
|
num_mel=decoder_output_dim,
|
|
speaker_embedding_dim=speaker_embedding_dim,
|
|
num_heads=gst.gst_num_heads,
|
|
num_style_tokens=gst.gst_num_style_tokens,
|
|
gst_embedding_dim=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,
|
|
ddc_r,
|
|
attn_type,
|
|
attn_win,
|
|
attn_norm,
|
|
prenet_type,
|
|
prenet_dropout,
|
|
forward_attn,
|
|
trans_agent,
|
|
forward_attn_mask,
|
|
location_attn,
|
|
attn_K,
|
|
separate_stopnet,
|
|
)
|
|
|
|
@staticmethod
|
|
def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
|
|
mel_outputs = mel_outputs.transpose(1, 2)
|
|
mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
|
|
return mel_outputs, mel_outputs_postnet, alignments
|
|
|
|
def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, cond_input=None):
|
|
"""
|
|
Shapes:
|
|
text: [B, T_in]
|
|
text_lengths: [B]
|
|
mel_specs: [B, T_out, C]
|
|
mel_lengths: [B]
|
|
cond_input: 'speaker_ids': [B, 1] and 'x_vectors':[B, C]
|
|
"""
|
|
outputs = {"alignments_backward": None, "decoder_outputs_backward": None}
|
|
# compute mask for padding
|
|
# B x T_in_max (boolean)
|
|
input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
|
|
# B x D_embed x T_in_max
|
|
embedded_inputs = self.embedding(text).transpose(1, 2)
|
|
# B x T_in_max x D_en
|
|
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
|
|
if self.gst and self.use_gst:
|
|
# B x gst_dim
|
|
encoder_outputs = self.compute_gst(encoder_outputs, mel_specs, cond_input["x_vectors"])
|
|
if self.num_speakers > 1:
|
|
if not self.embeddings_per_sample:
|
|
# B x 1 x speaker_embed_dim
|
|
speaker_embeddings = self.speaker_embedding(cond_input["speaker_ids"])[:, None]
|
|
else:
|
|
# B x 1 x speaker_embed_dim
|
|
speaker_embeddings = torch.unsqueeze(cond_input["x_vectors"], 1)
|
|
encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
|
|
|
|
encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
|
|
|
|
# B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
|
|
decoder_outputs, alignments, stop_tokens = self.decoder(encoder_outputs, mel_specs, input_mask)
|
|
# sequence masking
|
|
if mel_lengths is not None:
|
|
decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
|
|
# B x mel_dim x T_out
|
|
postnet_outputs = self.postnet(decoder_outputs)
|
|
postnet_outputs = decoder_outputs + postnet_outputs
|
|
# sequence masking
|
|
if output_mask is not None:
|
|
postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
|
|
# B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
|
|
decoder_outputs, postnet_outputs, alignments = self.shape_outputs(decoder_outputs, postnet_outputs, alignments)
|
|
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, cond_input=None):
|
|
embedded_inputs = self.embedding(text).transpose(1, 2)
|
|
encoder_outputs = self.encoder.inference(embedded_inputs)
|
|
|
|
if self.gst and self.use_gst:
|
|
# B x gst_dim
|
|
encoder_outputs = self.compute_gst(encoder_outputs, cond_input["style_mel"], cond_input["x_vectors"])
|
|
if self.num_speakers > 1:
|
|
if not self.embeddings_per_sample:
|
|
x_vector = self.speaker_embedding(cond_input['speaker_ids'])[:, None]
|
|
x_vector = torch.unsqueeze(x_vector, 0).transpose(1, 2)
|
|
else:
|
|
x_vector = cond_input["x_vectors"]
|
|
|
|
encoder_outputs = self._concat_speaker_embedding(encoder_outputs, x_vector)
|
|
|
|
decoder_outputs, alignments, stop_tokens = self.decoder.inference(encoder_outputs)
|
|
postnet_outputs = self.postnet(decoder_outputs)
|
|
postnet_outputs = decoder_outputs + postnet_outputs
|
|
decoder_outputs, postnet_outputs, alignments = self.shape_outputs(decoder_outputs, postnet_outputs, alignments)
|
|
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"]
|
|
speaker_ids = batch["speaker_ids"]
|
|
x_vectors = batch["x_vectors"]
|
|
|
|
# forward pass model
|
|
outputs = self.forward(
|
|
text_input,
|
|
text_lengths,
|
|
mel_input,
|
|
mel_lengths,
|
|
cond_input={"speaker_ids": speaker_ids, "x_vectors": x_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
|
|
|
|
cond_input = {"speaker_ids": speaker_ids, "x_vectors": x_vectors}
|
|
outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, cond_input)
|
|
|
|
# compute loss
|
|
loss_dict = criterion(
|
|
outputs["model_outputs"],
|
|
outputs["decoder_outputs"],
|
|
mel_input,
|
|
linear_input,
|
|
outputs["stop_tokens"],
|
|
stop_targets,
|
|
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, batch, outputs):
|
|
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_melspectrogram(pred_spec.T)
|
|
return figures, 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)
|