-
Notifications
You must be signed in to change notification settings - Fork 530
/
glow_old.py
233 lines (196 loc) · 8.93 KB
/
glow_old.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
import copy
import torch
from glow import Invertible1x1Conv, remove
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a+input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class WN(torch.nn.Module):
"""
This is the WaveNet like layer for the affine coupling. The primary difference
from WaveNet is the convolutions need not be causal. There is also no dilation
size reset. The dilation only doubles on each layer
"""
def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
kernel_size):
super(WN, self).__init__()
assert(kernel_size % 2 == 1)
assert(n_channels % 2 == 0)
self.n_layers = n_layers
self.n_channels = n_channels
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.cond_layers = torch.nn.ModuleList()
start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
start = torch.nn.utils.weight_norm(start, name='weight')
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
for i in range(n_layers):
dilation = 2 ** i
padding = int((kernel_size*dilation - dilation)/2)
in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
dilation=dilation, padding=padding)
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
self.in_layers.append(in_layer)
cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels, 1)
cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
self.cond_layers.append(cond_layer)
# last one is not necessary
if i < n_layers - 1:
res_skip_channels = 2*n_channels
else:
res_skip_channels = n_channels
res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
self.res_skip_layers.append(res_skip_layer)
def forward(self, forward_input):
audio, spect = forward_input
audio = self.start(audio)
for i in range(self.n_layers):
acts = fused_add_tanh_sigmoid_multiply(
self.in_layers[i](audio),
self.cond_layers[i](spect),
torch.IntTensor([self.n_channels]))
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
audio = res_skip_acts[:,:self.n_channels,:] + audio
skip_acts = res_skip_acts[:,self.n_channels:,:]
else:
skip_acts = res_skip_acts
if i == 0:
output = skip_acts
else:
output = skip_acts + output
return self.end(output)
class WaveGlow(torch.nn.Module):
def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
n_early_size, WN_config):
super(WaveGlow, self).__init__()
self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
n_mel_channels,
1024, stride=256)
assert(n_group % 2 == 0)
self.n_flows = n_flows
self.n_group = n_group
self.n_early_every = n_early_every
self.n_early_size = n_early_size
self.WN = torch.nn.ModuleList()
self.convinv = torch.nn.ModuleList()
n_half = int(n_group/2)
# Set up layers with the right sizes based on how many dimensions
# have been output already
n_remaining_channels = n_group
for k in range(n_flows):
if k % self.n_early_every == 0 and k > 0:
n_half = n_half - int(self.n_early_size/2)
n_remaining_channels = n_remaining_channels - self.n_early_size
self.convinv.append(Invertible1x1Conv(n_remaining_channels))
self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
self.n_remaining_channels = n_remaining_channels # Useful during inference
def forward(self, forward_input):
return None
"""
forward_input[0] = audio: batch x time
forward_input[1] = upsamp_spectrogram: batch x n_cond_channels x time
"""
"""
spect, audio = forward_input
# Upsample spectrogram to size of audio
spect = self.upsample(spect)
assert(spect.size(2) >= audio.size(1))
if spect.size(2) > audio.size(1):
spect = spect[:, :, :audio.size(1)]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
output_audio = []
s_list = []
s_conv_list = []
for k in range(self.n_flows):
if k%4 == 0 and k > 0:
output_audio.append(audio[:,:self.n_multi,:])
audio = audio[:,self.n_multi:,:]
# project to new basis
audio, s = self.convinv[k](audio)
s_conv_list.append(s)
n_half = int(audio.size(1)/2)
if k%2 == 0:
audio_0 = audio[:,:n_half,:]
audio_1 = audio[:,n_half:,:]
else:
audio_1 = audio[:,:n_half,:]
audio_0 = audio[:,n_half:,:]
output = self.nn[k]((audio_0, spect))
s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = torch.exp(s)*audio_1 + b
s_list.append(s)
if k%2 == 0:
audio = torch.cat([audio[:,:n_half,:], audio_1],1)
else:
audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
output_audio.append(audio)
return torch.cat(output_audio,1), s_list, s_conv_list
"""
def infer(self, spect, sigma=1.0):
spect = self.upsample(spect)
# trim conv artifacts. maybe pad spec to kernel multiple
time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
spect = spect[:, :, :-time_cutoff]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
if spect.type() == 'torch.cuda.HalfTensor':
audio = torch.cuda.HalfTensor(spect.size(0),
self.n_remaining_channels,
spect.size(2)).normal_()
else:
audio = torch.cuda.FloatTensor(spect.size(0),
self.n_remaining_channels,
spect.size(2)).normal_()
audio = torch.autograd.Variable(sigma*audio)
for k in reversed(range(self.n_flows)):
n_half = int(audio.size(1)/2)
if k%2 == 0:
audio_0 = audio[:,:n_half,:]
audio_1 = audio[:,n_half:,:]
else:
audio_1 = audio[:,:n_half,:]
audio_0 = audio[:,n_half:,:]
output = self.WN[k]((audio_0, spect))
s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = (audio_1 - b)/torch.exp(s)
if k%2 == 0:
audio = torch.cat([audio[:,:n_half,:], audio_1],1)
else:
audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
audio = self.convinv[k](audio, reverse=True)
if k%4 == 0 and k > 0:
if spect.type() == 'torch.cuda.HalfTensor':
z = torch.cuda.HalfTensor(spect.size(0),
self.n_early_size,
spect.size(2)).normal_()
else:
z = torch.cuda.FloatTensor(spect.size(0),
self.n_early_size,
spect.size(2)).normal_()
audio = torch.cat((sigma*z, audio),1)
return audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
@staticmethod
def remove_weightnorm(model):
waveglow = model
for WN in waveglow.WN:
WN.start = torch.nn.utils.remove_weight_norm(WN.start)
WN.in_layers = remove(WN.in_layers)
WN.cond_layers = remove(WN.cond_layers)
WN.res_skip_layers = remove(WN.res_skip_layers)
return waveglow