This repository has been archived by the owner on Sep 29, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 237
/
model.py
609 lines (507 loc) · 23.1 KB
/
model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
"""
BSD 3-Clause License
Copyright (c) 2018, NVIDIA Corporation
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from math import sqrt
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
from training.tacotron2_model.layers import ConvNorm, LinearNorm
from training.tacotron2_model.utils import to_gpu, get_mask_from_lengths, get_x
class LocationLayer(nn.Module):
def __init__(self, attention_n_filters, attention_kernel_size, attention_dim):
super(LocationLayer, self).__init__()
padding = int((attention_kernel_size - 1) / 2)
self.location_conv = ConvNorm(
2, attention_n_filters, kernel_size=attention_kernel_size, padding=padding, bias=False, stride=1, dilation=1
)
self.location_dense = LinearNorm(attention_n_filters, attention_dim, bias=False, w_init_gain="tanh")
def forward(self, attention_weights_cat):
processed_attention = self.location_conv(attention_weights_cat)
processed_attention = processed_attention.transpose(1, 2)
processed_attention = self.location_dense(processed_attention)
return processed_attention
class Attention(nn.Module):
def __init__(
self,
attention_rnn_dim,
embedding_dim,
attention_dim,
attention_location_n_filters,
attention_location_kernel_size,
):
super(Attention, self).__init__()
self.query_layer = LinearNorm(attention_rnn_dim, attention_dim, bias=False, w_init_gain="tanh")
self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False, w_init_gain="tanh")
self.v = LinearNorm(attention_dim, 1, bias=False)
self.location_layer = LocationLayer(attention_location_n_filters, attention_location_kernel_size, attention_dim)
self.score_mask_value = -float("inf")
def get_alignment_energies(self, query, processed_memory, attention_weights_cat):
"""
PARAMS
------
query: decoder output (batch, n_mel_channels * n_frames_per_step)
processed_memory: processed encoder outputs (B, T_in, attention_dim)
attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
RETURNS
-------
alignment (batch, max_time)
"""
processed_query = self.query_layer(query.unsqueeze(1))
processed_attention_weights = self.location_layer(attention_weights_cat)
energies = self.v(torch.tanh(processed_query + processed_attention_weights + processed_memory))
energies = energies.squeeze(-1)
return energies
def forward(self, attention_hidden_state, memory, processed_memory, attention_weights_cat, mask):
"""
PARAMS
------
attention_hidden_state: attention rnn last output
memory: encoder outputs
processed_memory: processed encoder outputs
attention_weights_cat: previous and cummulative attention weights
mask: binary mask for padded data
"""
alignment = self.get_alignment_energies(attention_hidden_state, processed_memory, attention_weights_cat)
if mask is not None:
alignment.data.masked_fill_(mask, self.score_mask_value)
attention_weights = F.softmax(alignment, dim=1)
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
attention_context = attention_context.squeeze(1)
return attention_context, attention_weights
class Prenet(nn.Module):
def __init__(self, in_dim, sizes):
super(Prenet, self).__init__()
in_sizes = [in_dim] + sizes[:-1]
self.layers = nn.ModuleList(
[LinearNorm(in_size, out_size, bias=False) for (in_size, out_size) in zip(in_sizes, sizes)]
)
def forward(self, x):
for linear in self.layers:
x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
return x
class Postnet(nn.Module):
"""Postnet
- Five 1-d convolution with 512 channels and kernel size 5
"""
def __init__(self, n_mel_channels, postnet_embedding_dim, postnet_kernel_size, postnet_n_convolutions):
super(Postnet, self).__init__()
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
ConvNorm(
n_mel_channels,
postnet_embedding_dim,
kernel_size=postnet_kernel_size,
stride=1,
padding=int((postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain="tanh",
),
nn.BatchNorm1d(postnet_embedding_dim),
)
)
for i in range(1, postnet_n_convolutions - 1):
self.convolutions.append(
nn.Sequential(
ConvNorm(
postnet_embedding_dim,
postnet_embedding_dim,
kernel_size=postnet_kernel_size,
stride=1,
padding=int((postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain="tanh",
),
nn.BatchNorm1d(postnet_embedding_dim),
)
)
self.convolutions.append(
nn.Sequential(
ConvNorm(
postnet_embedding_dim,
n_mel_channels,
kernel_size=postnet_kernel_size,
stride=1,
padding=int((postnet_kernel_size - 1) / 2),
dilation=1,
w_init_gain="linear",
),
nn.BatchNorm1d(n_mel_channels),
)
)
def forward(self, x):
for i in range(len(self.convolutions) - 1):
x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
return x
class Encoder(nn.Module):
"""Encoder module:
- Three 1-d convolution banks
- Bidirectional LSTM
"""
def __init__(self, encoder_kernel_size, encoder_n_convolutions, encoder_embedding_dim):
super(Encoder, self).__init__()
convolutions = []
for _ in range(encoder_n_convolutions):
conv_layer = nn.Sequential(
ConvNorm(
encoder_embedding_dim,
encoder_embedding_dim,
kernel_size=encoder_kernel_size,
stride=1,
padding=int((encoder_kernel_size - 1) / 2),
dilation=1,
w_init_gain="relu",
),
nn.BatchNorm1d(encoder_embedding_dim),
)
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
self.lstm = nn.LSTM(
encoder_embedding_dim, int(encoder_embedding_dim / 2), 1, batch_first=True, bidirectional=True
)
def forward(self, x, input_lengths):
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
x = x.transpose(1, 2)
# pytorch tensor are not reversible, hence the conversion
input_lengths = input_lengths.cpu().numpy()
x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
return outputs
def inference(self, x):
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
x = x.transpose(1, 2)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
return outputs
class Decoder(nn.Module):
def __init__(
self,
n_mel_channels,
n_frames_per_step,
encoder_embedding_dim,
attention_dim,
attention_rnn_dim,
attention_location_n_filters,
attention_location_kernel_size,
decoder_rnn_dim,
prenet_dim,
max_decoder_steps,
gate_threshold,
p_attention_dropout,
p_decoder_dropout,
):
super(Decoder, self).__init__()
self.n_mel_channels = n_mel_channels
self.n_frames_per_step = n_frames_per_step
self.encoder_embedding_dim = encoder_embedding_dim
self.attention_rnn_dim = attention_rnn_dim
self.decoder_rnn_dim = decoder_rnn_dim
self.prenet_dim = prenet_dim
self.max_decoder_steps = max_decoder_steps
self.gate_threshold = gate_threshold
self.p_attention_dropout = p_attention_dropout
self.p_decoder_dropout = p_decoder_dropout
self.prenet = Prenet(n_mel_channels * n_frames_per_step, [prenet_dim, prenet_dim])
self.attention_rnn = nn.LSTMCell(prenet_dim + encoder_embedding_dim, attention_rnn_dim)
self.attention_layer = Attention(
attention_rnn_dim,
encoder_embedding_dim,
attention_dim,
attention_location_n_filters,
attention_location_kernel_size,
)
self.decoder_rnn = nn.LSTMCell(attention_rnn_dim + encoder_embedding_dim, decoder_rnn_dim, 1)
self.linear_projection = LinearNorm(decoder_rnn_dim + encoder_embedding_dim, n_mel_channels * n_frames_per_step)
self.gate_layer = LinearNorm(decoder_rnn_dim + encoder_embedding_dim, 1, bias=True, w_init_gain="sigmoid")
def get_go_frame(self, memory):
"""Gets all zeros frames to use as first decoder input
PARAMS
------
memory: decoder outputs
RETURNS
-------
decoder_input: all zeros frames
"""
B = memory.size(0)
decoder_input = Variable(memory.data.new(B, self.n_mel_channels * self.n_frames_per_step).zero_())
return decoder_input
def initialize_decoder_states(self, memory, mask):
"""Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory
PARAMS
------
memory: Encoder outputs
mask: Mask for padded data if training, expects None for inference
"""
B = memory.size(0)
MAX_TIME = memory.size(1)
self.attention_hidden = Variable(memory.data.new(B, self.attention_rnn_dim).zero_())
self.attention_cell = Variable(memory.data.new(B, self.attention_rnn_dim).zero_())
self.decoder_hidden = Variable(memory.data.new(B, self.decoder_rnn_dim).zero_())
self.decoder_cell = Variable(memory.data.new(B, self.decoder_rnn_dim).zero_())
self.attention_weights = Variable(memory.data.new(B, MAX_TIME).zero_())
self.attention_weights_cum = Variable(memory.data.new(B, MAX_TIME).zero_())
self.attention_context = Variable(memory.data.new(B, self.encoder_embedding_dim).zero_())
self.memory = memory
self.processed_memory = self.attention_layer.memory_layer(memory)
self.mask = mask
def parse_decoder_inputs(self, decoder_inputs):
"""Prepares decoder inputs, i.e. mel outputs
PARAMS
------
decode encoder_kernel_size=5,
encoder_n_convolutions=3,
encoder_embedding_dim=512,r_inputs: inputs used for teacher-forced training, i.e. mel-specs
RETURNS
-------
inputs: processed decoder inputs
"""
# (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
decoder_inputs = decoder_inputs.transpose(1, 2)
decoder_inputs = decoder_inputs.view(
decoder_inputs.size(0), int(decoder_inputs.size(1) / self.n_frames_per_step), -1
)
# (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
decoder_inputs = decoder_inputs.transpose(0, 1)
return decoder_inputs
def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
"""Prepares decoder outputs for output
PARAMS
------
mel_outputs:
gate_outputs: gate output energies
alignments:
RETURNS
-------
mel_outputs:
gate_outpust: gate output energies
alignments:
"""
# (T_out, B) -> (B, T_out)
alignments = torch.stack(alignments).transpose(0, 1)
# (T_out, B) -> (B, T_out)
gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
gate_outputs = gate_outputs.contiguous()
# (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
# decouple frames per step
mel_outputs = mel_outputs.view(mel_outputs.size(0), -1, self.n_mel_channels)
# (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
mel_outputs = mel_outputs.transpose(1, 2)
return mel_outputs, gate_outputs, alignments
def decode(self, decoder_input):
"""Decoder step using stored states, attention and memory
PARAMS
------
decoder_input: previous mel output
RETURNS
-------
mel_output:
gate_output: gate output energies
attention_weights:
"""
cell_input = torch.cat((decoder_input, self.attention_context), -1)
self.attention_hidden, self.attention_cell = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell)
)
self.attention_hidden = F.dropout(self.attention_hidden, self.p_attention_dropout, self.training)
attention_weights_cat = torch.cat(
(self.attention_weights.unsqueeze(1), self.attention_weights_cum.unsqueeze(1)), dim=1
)
self.attention_context, self.attention_weights = self.attention_layer(
self.attention_hidden, self.memory, self.processed_memory, attention_weights_cat, self.mask
)
self.attention_weights_cum += self.attention_weights
decoder_input = torch.cat((self.attention_hidden, self.attention_context), -1)
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
decoder_input, (self.decoder_hidden, self.decoder_cell)
)
self.decoder_hidden = F.dropout(self.decoder_hidden, self.p_decoder_dropout, self.training)
decoder_hidden_attention_context = torch.cat((self.decoder_hidden, self.attention_context), dim=1)
decoder_output = self.linear_projection(decoder_hidden_attention_context)
gate_prediction = self.gate_layer(decoder_hidden_attention_context)
return decoder_output, gate_prediction, self.attention_weights
def forward(self, memory, decoder_inputs, memory_lengths, device):
"""Decoder forward pass for training
PARAMS
------
memory: Encoder outputs
decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
memory_lengths: Encoder output lengths for attention masking.
RETURNS
-------
mel_outputs: mel outputs from the decoder
gate_outputs: gate outputs from the decoder
alignments: sequence of attention weights from the decoder
"""
decoder_input = self.get_go_frame(memory).unsqueeze(0)
decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
decoder_inputs = self.prenet(decoder_inputs)
self.initialize_decoder_states(memory, mask=~get_mask_from_lengths(memory_lengths, device))
mel_outputs, gate_outputs, alignments = [], [], []
while len(mel_outputs) < decoder_inputs.size(0) - 1:
decoder_input = decoder_inputs[len(mel_outputs)]
mel_output, gate_output, attention_weights = self.decode(decoder_input)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output.squeeze(1)]
alignments += [attention_weights]
mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(mel_outputs, gate_outputs, alignments)
return mel_outputs, gate_outputs, alignments
def inference(self, memory, max_decoder_steps=None):
"""Decoder inference
PARAMS
------
memory: Encoder outputs
RETURNS
-------
mel_outputs: mel outputs from the decoder
gate_outputs: gate outputs from the decoder
alignments: sequence of attention weights from the decoder
"""
if not max_decoder_steps:
# Use default max decoder steps if not given
max_decoder_steps = self.max_decoder_steps
decoder_input = self.get_go_frame(memory)
self.initialize_decoder_states(memory, mask=None)
mel_outputs, gate_outputs, alignments = [], [], []
while True:
decoder_input = self.prenet(decoder_input)
mel_output, gate_output, alignment = self.decode(decoder_input)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output]
alignments += [alignment]
if torch.sigmoid(gate_output.data) > self.gate_threshold:
break
elif len(mel_outputs) == max_decoder_steps:
raise Exception(
"Warning! Reached max decoder steps. Either the model is low quality or the given sentence is too short/long"
)
decoder_input = mel_output
mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(mel_outputs, gate_outputs, alignments)
return mel_outputs, gate_outputs, alignments
class Tacotron2(nn.Module):
def __init__(
self,
mask_padding=True,
fp16_run=False,
n_mel_channels=80,
n_symbols=148,
symbols_embedding_dim=512,
encoder_kernel_size=5,
encoder_n_convolutions=3,
encoder_embedding_dim=512,
attention_rnn_dim=1024,
attention_dim=128,
attention_location_n_filters=32,
attention_location_kernel_size=31,
decoder_rnn_dim=1024,
prenet_dim=256,
max_decoder_steps=1000,
gate_threshold=0.5,
p_attention_dropout=0.1,
p_decoder_dropout=0.1,
postnet_embedding_dim=512,
postnet_kernel_size=5,
postnet_n_convolutions=5,
):
super(Tacotron2, self).__init__()
self.mask_padding = mask_padding
self.fp16_run = fp16_run
self.n_mel_channels = n_mel_channels
self.n_frames_per_step = 1
self.embedding = nn.Embedding(n_symbols, symbols_embedding_dim)
std = sqrt(2.0 / (n_symbols + symbols_embedding_dim))
val = sqrt(3.0) * std # uniform bounds for std
self.embedding.weight.data.uniform_(-val, val)
self.encoder = Encoder(encoder_kernel_size, encoder_n_convolutions, encoder_embedding_dim)
self.decoder = Decoder(
n_mel_channels,
self.n_frames_per_step,
encoder_embedding_dim,
attention_dim,
attention_rnn_dim,
attention_location_n_filters,
attention_location_kernel_size,
decoder_rnn_dim,
prenet_dim,
max_decoder_steps,
gate_threshold,
p_attention_dropout,
p_decoder_dropout,
)
self.postnet = Postnet(n_mel_channels, postnet_embedding_dim, postnet_kernel_size, postnet_n_convolutions)
def parse_batch(self, batch):
text_padded, input_lengths, mel_padded, gate_padded, output_lengths = batch
text_padded = to_gpu(text_padded).long()
input_lengths = to_gpu(input_lengths).long()
max_len = torch.max(input_lengths.data).item()
mel_padded = to_gpu(mel_padded).float()
gate_padded = to_gpu(gate_padded).float()
output_lengths = to_gpu(output_lengths).long()
return ((text_padded, input_lengths, mel_padded, max_len, output_lengths), (mel_padded, gate_padded))
def parse_output(self, outputs, output_lengths, mask_size, alignment_mask_size, device):
if self.mask_padding:
mask = ~get_mask_from_lengths(output_lengths, device, mask_size)
mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1))
mask = mask.permute(1, 0, 2)
outputs[0].data.masked_fill_(mask, 0.0)
outputs[1].data.masked_fill_(mask, 0.0)
outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies
if outputs[3].size(2) != alignment_mask_size:
outputs[3] = nn.ConstantPad1d((0, alignment_mask_size - outputs[3].size(2)), 0)(outputs[3])
return outputs
def forward(self, inputs, mask_size, alignment_mask_size):
text_inputs, text_lengths, mels, output_lengths = get_x(inputs)
device = text_inputs.device
text_lengths, output_lengths = text_lengths.data, output_lengths.data
embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
mel_outputs, gate_outputs, alignments = self.decoder(
encoder_outputs, mels, memory_lengths=text_lengths, device=device
)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
return self.parse_output(
[mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
output_lengths,
mask_size,
alignment_mask_size,
device,
)
def inference(self, inputs, max_decoder_steps=None):
embedded_inputs = self.embedding(inputs).transpose(1, 2)
encoder_outputs = self.encoder.inference(embedded_inputs)
mel_outputs, gate_outputs, alignments = self.decoder.inference(encoder_outputs, max_decoder_steps)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
return [mel_outputs, mel_outputs_postnet, gate_outputs, alignments]