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
|
"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
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.
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 OWNER
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.
*/
"""
import numbers
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import numpy as np
from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d
from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d
from utils.layers.td_shaper import TDShaper
from utils.complexity import _conv1d_flop_count
from models.nns_base import NNSBase
from models.silk_feature_net_pl import SilkFeatureNetPL
from models.silk_feature_net import SilkFeatureNet
from .scale_embedding import ScaleEmbedding
import sys
sys.path.append('../dnntools')
from dnntools.quantization import soft_quant
from dnntools.sparsification import create_sparsifier, mark_for_sparsification
class NoLACE(NNSBase):
""" Non-Linear Adaptive Coding Enhancer """
FRAME_SIZE=80
def __init__(self,
num_features=47,
pitch_embedding_dim=64,
cond_dim=256,
pitch_max=257,
kernel_size=15,
preemph=0.85,
skip=91,
comb_gain_limit_db=-6,
global_gain_limits_db=[-6, 6],
conv_gain_limits_db=[-6, 6],
numbits_range=[50, 650],
numbits_embedding_dim=8,
hidden_feature_dim=64,
partial_lookahead=True,
norm_p=2,
avg_pool_k=4,
pool_after=False,
softquant=False,
sparsify=False,
sparsification_schedule=[100, 1000, 100],
sparsification_density=0.5,
apply_weight_norm=False):
super().__init__(skip=skip, preemph=preemph)
self.num_features = num_features
self.cond_dim = cond_dim
self.pitch_max = pitch_max
self.pitch_embedding_dim = pitch_embedding_dim
self.kernel_size = kernel_size
self.preemph = preemph
self.skip = skip
self.numbits_range = numbits_range
self.numbits_embedding_dim = numbits_embedding_dim
self.hidden_feature_dim = hidden_feature_dim
self.partial_lookahead = partial_lookahead
if isinstance(sparsification_density, numbers.Number):
sparsification_density = 10 * [sparsification_density]
norm = weight_norm if apply_weight_norm else lambda x, name=None: x
# pitch embedding
self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim)
# numbits embedding
self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True)
# feature net
if partial_lookahead:
self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim, softquant=softquant, sparsify=sparsify, sparsification_density=sparsification_density, apply_weight_norm=apply_weight_norm)
else:
self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim)
# comb filters
left_pad = self.kernel_size // 2
right_pad = self.kernel_size - 1 - left_pad
self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm)
self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm)
# spectral shaping
self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm)
# non-linear transforms
self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm)
self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm)
self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm)
# combinators
self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm)
self.af3 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm)
self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm)
# feature transforms
self.post_cf1 = norm(nn.Conv1d(cond_dim, cond_dim, 2))
self.post_cf2 = norm(nn.Conv1d(cond_dim, cond_dim, 2))
self.post_af1 = norm(nn.Conv1d(cond_dim, cond_dim, 2))
self.post_af2 = norm(nn.Conv1d(cond_dim, cond_dim, 2))
self.post_af3 = norm(nn.Conv1d(cond_dim, cond_dim, 2))
if softquant:
self.post_cf1 = soft_quant(self.post_cf1)
self.post_cf2 = soft_quant(self.post_cf2)
self.post_af1 = soft_quant(self.post_af1)
self.post_af2 = soft_quant(self.post_af2)
self.post_af3 = soft_quant(self.post_af3)
if sparsify:
mark_for_sparsification(self.post_cf1, (sparsification_density[4], [8, 4]))
mark_for_sparsification(self.post_cf2, (sparsification_density[5], [8, 4]))
mark_for_sparsification(self.post_af1, (sparsification_density[6], [8, 4]))
mark_for_sparsification(self.post_af2, (sparsification_density[7], [8, 4]))
mark_for_sparsification(self.post_af3, (sparsification_density[8], [8, 4]))
self.sparsifier = create_sparsifier(self, *sparsification_schedule)
def flop_count(self, rate=16000, verbose=False):
frame_rate = rate / self.FRAME_SIZE
# feature net
feature_net_flops = self.feature_net.flop_count(frame_rate)
comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate)
af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate)
shape_flops = self.tdshape1.flop_count(rate) + self.tdshape2.flop_count(rate) + self.tdshape3.flop_count(rate)
feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate)
+ _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate))
if verbose:
print(f"feature net: {feature_net_flops / 1e6} MFLOPS")
print(f"comb filters: {comb_flops / 1e6} MFLOPS")
print(f"adaptive conv: {af_flops / 1e6} MFLOPS")
print(f"feature transforms: {feature_flops / 1e6} MFLOPS")
return feature_net_flops + comb_flops + af_flops + feature_flops + shape_flops
def feature_transform(self, f, layer):
f0 = f.permute(0, 2, 1)
f = F.pad(f0, [1, 0])
if self.residual_in_feature_transform:
f = torch.tanh(layer(f) + f0)
else:
f = torch.tanh(layer(f))
return f.permute(0, 2, 1)
def forward(self, x, features, periods, numbits, debug=False):
periods = periods.squeeze(-1)
pitch_embedding = self.pitch_embedding(periods)
numbits_embedding = self.numbits_embedding(numbits).flatten(2)
full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1)
cf = self.feature_net(full_features)
y = self.cf1(x, cf, periods, debug=debug)
cf = self.feature_transform(cf, self.post_cf1)
y = self.cf2(y, cf, periods, debug=debug)
cf = self.feature_transform(cf, self.post_cf2)
y = self.af1(y, cf, debug=debug)
cf = self.feature_transform(cf, self.post_af1)
y1 = y[:, 0:1, :]
y2 = self.tdshape1(y[:, 1:2, :], cf)
y = torch.cat((y1, y2), dim=1)
y = self.af2(y, cf, debug=debug)
cf = self.feature_transform(cf, self.post_af2)
y1 = y[:, 0:1, :]
y2 = self.tdshape2(y[:, 1:2, :], cf)
y = torch.cat((y1, y2), dim=1)
y = self.af3(y, cf, debug=debug)
cf = self.feature_transform(cf, self.post_af3)
y1 = y[:, 0:1, :]
y2 = self.tdshape3(y[:, 1:2, :], cf)
y = torch.cat((y1, y2), dim=1)
y = self.af4(y, cf, debug=debug)
return y
|
