forked from pyrasis/Korean-FastSpeech2-Pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
modules.py
180 lines (141 loc) · 6.47 KB
/
modules.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
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
import copy
import math
import hparams as hp
import utils
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class VarianceAdaptor(nn.Module):
""" Variance Adaptor """
def __init__(self):
super(VarianceAdaptor, self).__init__()
self.duration_predictor = VariancePredictor()
self.length_regulator = LengthRegulator()
self.pitch_predictor = VariancePredictor()
self.energy_predictor = VariancePredictor()
self.energy_embedding_producer = Conv(1, hp.encoder_hidden, kernel_size=9, bias=False, padding=4)
self.pitch_embedding_producer = Conv(1, hp.encoder_hidden, kernel_size=9, bias=False, padding=4)
def forward(self, x, src_mask, mel_mask=None, duration_target=None, pitch_target=None, energy_target=None, max_len=None, dur_pitch_energy_aug=None, f0_stat=None, energy_stat=None):
log_duration_prediction = self.duration_predictor(x, src_mask)
pitch_prediction = self.pitch_predictor(x, src_mask)
if pitch_target is not None:
pitch_embedding = self.pitch_embedding_producer(pitch_target.unsqueeze(2))
else:
pitch_prediction = utils.de_norm(pitch_prediction, mean=f0_stat[0], std=f0_stat[1]) * dur_pitch_energy_aug[1]
pitch_prediction = utils.standard_norm_torch(pitch_prediction, mean=f0_stat[0], std=f0_stat[1])
pitch_embedding = self.pitch_embedding_producer(pitch_prediction.unsqueeze(2))
energy_prediction = self.energy_predictor(x, src_mask)
if energy_target is not None:
energy_embedding = self.energy_embedding_producer(energy_target.unsqueeze(2))
else:
energy_prediction = utils.de_norm(energy_prediction, mean=energy_stat[0], std=energy_stat[1]) * dur_pitch_energy_aug[2]
energy_prediction = utils.standard_norm_torch(energy_prediction, mean=energy_stat[0], std=energy_stat[1])
energy_embedding = self.energy_embedding_producer(energy_prediction.unsqueeze(2))
x = x + pitch_embedding + energy_embedding
if duration_target is not None:
x, mel_len = self.length_regulator(x, duration_target, max_len)
else:
duration_rounded = torch.clamp(torch.round(torch.exp(log_duration_prediction)-hp.log_offset) * dur_pitch_energy_aug[0], min=0)
x, mel_len = self.length_regulator(x, duration_rounded, max_len)
mel_mask = utils.get_mask_from_lengths(mel_len)
return x, log_duration_prediction, pitch_prediction, energy_prediction, mel_len, mel_mask
class LengthRegulator(nn.Module):
""" Length Regulator """
def __init__(self):
super(LengthRegulator, self).__init__()
def LR(self, x, duration, max_len):
output = list()
mel_len = list()
for batch, expand_target in zip(x, duration):
expanded = self.expand(batch, expand_target)
output.append(expanded)
mel_len.append(expanded.shape[0])
if max_len is not None:
output = utils.pad(output, max_len)
else:
output = utils.pad(output)
return output, torch.LongTensor(mel_len).to(device)
def expand(self, batch, predicted):
out = list()
for i, vec in enumerate(batch):
expand_size = predicted[i].item()
out.append(vec.expand(int(expand_size), -1))
out = torch.cat(out, 0)
return out
def forward(self, x, duration, max_len):
output, mel_len = self.LR(x, duration, max_len)
return output, mel_len
class VariancePredictor(nn.Module):
""" Duration, Pitch and Energy Predictor """
def __init__(self):
super(VariancePredictor, self).__init__()
self.input_size = hp.encoder_hidden
self.filter_size = hp.variance_predictor_filter_size
self.kernel = hp.variance_predictor_kernel_size
self.conv_output_size = hp.variance_predictor_filter_size
self.dropout = hp.variance_predictor_dropout
self.conv_layer = nn.Sequential(OrderedDict([
("conv1d_1", Conv(self.input_size,
self.filter_size,
kernel_size=self.kernel,
padding=(self.kernel-1)//2)),
("relu_1", nn.ReLU()),
("layer_norm_1", nn.LayerNorm(self.filter_size)),
("dropout_1", nn.Dropout(self.dropout)),
("conv1d_2", Conv(self.filter_size,
self.filter_size,
kernel_size=self.kernel,
padding=1)),
("relu_2", nn.ReLU()),
("layer_norm_2", nn.LayerNorm(self.filter_size)),
("dropout_2", nn.Dropout(self.dropout))
]))
self.linear_layer = nn.Linear(self.conv_output_size, 1)
def forward(self, encoder_output, mask):
out = self.conv_layer(encoder_output)
out = self.linear_layer(out)
out = out.squeeze(-1)
if mask is not None:
out = out.masked_fill(mask, 0.)
return out
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
dilation=1,
bias=True,
w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channels: dimension of output
:param kernel_size: size of kernel
:param stride: size of stride
:param padding: size of padding
:param dilation: dilation rate
:param bias: boolean. if True, bias is included.
:param w_init: str. weight inits with xavier initialization.
"""
super(Conv, self).__init__()
self.conv = nn.Conv1d(in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias)
def forward(self, x):
x = x.contiguous().transpose(1, 2)
x = self.conv(x)
x = x.contiguous().transpose(1, 2)
return x