mirror of https://github.com/coqui-ai/TTS.git
192 lines
9.5 KiB
Python
192 lines
9.5 KiB
Python
import torch
|
|
from torch import nn
|
|
|
|
from mozilla_voice_tts.tts.layers.gst_layers import GST
|
|
from mozilla_voice_tts.tts.layers.tacotron2 import Decoder, Encoder, Postnet
|
|
from mozilla_voice_tts.tts.models.tacotron_abstract import TacotronAbstract
|
|
|
|
# TODO: match function arguments with tacotron
|
|
class Tacotron2(TacotronAbstract):
|
|
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,
|
|
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,
|
|
gst=False,
|
|
gst_embedding_dim=512,
|
|
gst_num_heads=4,
|
|
gst_style_tokens=10):
|
|
super(Tacotron2,
|
|
self).__init__(num_chars, num_speakers, r, postnet_output_dim,
|
|
decoder_output_dim, attn_type, attn_win,
|
|
attn_norm, prenet_type, prenet_dropout,
|
|
forward_attn, trans_agent, forward_attn_mask,
|
|
location_attn, attn_K, separate_stopnet,
|
|
bidirectional_decoder, double_decoder_consistency,
|
|
ddc_r, gst)
|
|
|
|
# init layer dims
|
|
speaker_embedding_dim = 512 if num_speakers > 1 else 0
|
|
gst_embedding_dim = gst_embedding_dim if self.gst else 0
|
|
decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
|
|
encoder_in_features = 512 if num_speakers > 1 else 512
|
|
proj_speaker_dim = 80 if num_speakers > 1 else 0
|
|
# base layers
|
|
self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
|
|
|
|
# speaker embedding layer
|
|
if num_speakers > 1:
|
|
self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
|
|
self.speaker_embedding.weight.data.normal_(0, 0.3)
|
|
self.encoder = Encoder(encoder_in_features)
|
|
self.decoder = Decoder(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)
|
|
|
|
# global style token layers
|
|
if self.gst:
|
|
self.gst_layer = GST(num_mel=80,
|
|
num_heads=gst_num_heads,
|
|
num_style_tokens=gst_style_tokens,
|
|
embedding_dim=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(
|
|
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, speaker_ids=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.num_speakers > 1:
|
|
embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
|
|
embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
|
|
if hasattr(self, 'gst'):
|
|
# B x gst_dim
|
|
encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, mel_specs)
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_gst, embedded_speakers], dim=-1)
|
|
else:
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
|
|
else:
|
|
if hasattr(self, 'gst'):
|
|
# B x gst_dim
|
|
encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, mel_specs)
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_gst], dim=-1)
|
|
|
|
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)
|
|
return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
|
|
if self.double_decoder_consistency:
|
|
decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
|
|
return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
|
|
return decoder_outputs, postnet_outputs, alignments, stop_tokens
|
|
|
|
@torch.no_grad()
|
|
def inference(self, text, speaker_ids=None, style_mel=None):
|
|
embedded_inputs = self.embedding(text).transpose(1, 2)
|
|
encoder_outputs = self.encoder.inference(embedded_inputs)
|
|
|
|
if self.num_speakers > 1:
|
|
embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
|
|
embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
|
|
if hasattr(self, 'gst'):
|
|
# B x gst_dim
|
|
encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_gst, embedded_speakers], dim=-1)
|
|
else:
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
|
|
else:
|
|
if hasattr(self, 'gst'):
|
|
# B x gst_dim
|
|
encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_gst], dim=-1)
|
|
|
|
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)
|
|
return decoder_outputs, postnet_outputs, alignments, stop_tokens
|
|
|
|
def inference_truncated(self, text, speaker_ids=None, style_mel=None):
|
|
"""
|
|
Preserve model states for continuous inference
|
|
"""
|
|
embedded_inputs = self.embedding(text).transpose(1, 2)
|
|
encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
|
|
|
|
if self.num_speakers > 1:
|
|
embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
|
|
embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
|
|
if hasattr(self, 'gst'):
|
|
# B x gst_dim
|
|
encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_gst, embedded_speakers], dim=-1)
|
|
else:
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
|
|
else:
|
|
if hasattr(self, 'gst'):
|
|
# B x gst_dim
|
|
encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
|
|
encoder_outputs = torch.cat([encoder_outputs, embedded_gst], dim=-1)
|
|
|
|
mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
|
|
encoder_outputs)
|
|
mel_outputs_postnet = self.postnet(mel_outputs)
|
|
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
|
|
mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
|
|
mel_outputs, mel_outputs_postnet, alignments)
|
|
return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
|