-
Notifications
You must be signed in to change notification settings - Fork 2.2k
/
conformer_encoder.py
222 lines (195 loc) · 8.11 KB
/
conformer_encoder.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
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from nemo.collections.asr.parts.conformer_modules import ConformerEncoderBlock
from nemo.collections.asr.parts.multi_head_attention import PositionalEncoding, RelPositionalEncoding
from nemo.collections.asr.parts.subsampling import ConvSubsampling
from nemo.core.classes.common import typecheck
from nemo.core.classes.exportable import Exportable
from nemo.core.classes.module import NeuralModule
from nemo.core.neural_types import AcousticEncodedRepresentation, LengthsType, NeuralType, SpectrogramType
__all__ = ['ConformerEncoder']
class ConformerEncoder(NeuralModule, Exportable):
"""
The encoder for ASR model of Conformer.
Based on this paper:
'Conformer: Convolution-augmented Transformer for Speech Recognition' by Anmol Gulati et al.
https://arxiv.org/abs/2005.08100
Args:
feat_in (int): the size of feature channels
n_layers (int): number of layers of ConformerBlock
d_model (int): the hidden size of the model
feat_out (int): the size of the output features
Defaults to -1 (means feat_out is d_model)
subsampling (str): the method of subsampling, choices=['vggnet', 'striding']
subsampling_factor (int): the subsampling factor which should be power of 2
Defaults to 4.
subsampling_conv_channels (int): the size of the convolutions in the subsampling module
Defaults to 64.
ff_expansion_factor (int): the expansion factor in feed forward layers
Defaults to 4.
self_attention_model (str): type of the attention layer and positional encoding
choices=['rel_pos', 'abs_pos'].
pos_emb_max_len (int): the maximum length of positional embeddings
Defaulst to 5000
n_heads (int): number of heads in multi-headed attention layers
Defaults to 4.
xscaling (bool): enables scaling the inputs to the multi-headed attention layers by sqrt(d_model)
Defaults to True.
conv_kernel_size (int): the size of the convolutions in the convolutional modules
Defaults to 31.
dropout (float): the dropout rate used in all layers except the attention layers
Defaults to 0.1.
dropout_emb (float): the dropout rate used for the positional embeddings
Defaults to 0.1.
dropout_att (float): the dropout rate used for the attention layer
Defaults to 0.0.
"""
def _prepare_for_export(self):
Exportable._prepare_for_export(self)
def input_example(self):
"""
Generates input examples for tracing etc.
Returns:
A tuple of input examples.
"""
input_example = torch.randn(16, self._feat_in, 256).to(next(self.parameters()).device)
return tuple([input_example])
@property
def input_types(self):
"""Returns definitions of module input ports.
"""
return OrderedDict(
{
"audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()),
"length": NeuralType(tuple('B'), LengthsType()),
}
)
@property
def output_types(self):
"""Returns definitions of module output ports.
"""
return OrderedDict(
{
"outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()),
"encoded_lengths": NeuralType(tuple('B'), LengthsType()),
}
)
def __init__(
self,
feat_in,
n_layers,
d_model,
feat_out=-1,
subsampling='vggnet',
subsampling_factor=4,
subsampling_conv_channels=64,
ff_expansion_factor=4,
self_attention_model='rel_pos',
pos_emb_max_len=5000,
n_heads=4,
xscaling=True,
conv_kernel_size=31,
dropout=0.1,
dropout_emb=0.1,
dropout_att=0.0,
):
super().__init__()
d_ff = d_model * ff_expansion_factor
self.d_model = d_model
self.scale = math.sqrt(self.d_model)
if xscaling:
self.xscale = math.sqrt(d_model)
else:
self.xscale = None
if subsampling:
self.pre_encode = ConvSubsampling(
subsampling=subsampling,
subsampling_factor=subsampling_factor,
feat_in=feat_in,
feat_out=d_model,
conv_channels=subsampling_conv_channels,
activation=nn.ReLU(),
)
self._feat_out = d_model
else:
self._feat_out = d_model
self.pre_encode = nn.Linear(feat_in, d_model)
if self_attention_model == "rel_pos":
self.pos_enc = RelPositionalEncoding(
d_model=d_model,
dropout_rate=dropout,
max_len=pos_emb_max_len,
xscale=self.xscale,
dropout_emb_rate=dropout_emb,
)
elif self_attention_model == "abs_pos":
self.pos_enc = PositionalEncoding(
d_model=d_model, dropout_rate=dropout, max_len=pos_emb_max_len, reverse=False, xscale=self.xscale
)
else:
raise ValueError(f"Not valid self_attention_model: '{self_attention_model}'!")
self.layers = nn.ModuleList()
for i in range(n_layers):
layer = ConformerEncoderBlock(
d_model=d_model,
d_ff=d_ff,
conv_kernel_size=conv_kernel_size,
self_attention_model=self_attention_model,
n_heads=n_heads,
dropout=dropout,
dropout_att=dropout_att,
)
self.layers.append(layer)
if feat_out > 0 and feat_out != self.output_dim:
self.out_proj = nn.Linear(self.feat_out, feat_out)
self._feat_out = feat_out
else:
self.out_proj = None
self._feat_out = d_model
@typecheck()
def forward(self, audio_signal, length):
audio_signal = torch.transpose(audio_signal, 1, 2)
if isinstance(self.pre_encode, ConvSubsampling):
audio_signal, length = self.pre_encode(audio_signal, length)
else:
audio_signal = self.embed(audio_signal)
audio_signal, pos_emb = self.pos_enc(audio_signal)
bs, xmax, idim = audio_signal.size()
# Create the self-attention and padding masks
pad_mask = self.make_pad_mask(length, max_time=xmax, device=audio_signal.device)
xx_mask = pad_mask.unsqueeze(1).repeat([1, xmax, 1])
xx_mask = xx_mask & xx_mask.transpose(1, 2)
pad_mask = (~pad_mask).unsqueeze(2)
for lth, layer in enumerate(self.layers):
audio_signal = layer(x=audio_signal, att_mask=xx_mask, pos_emb=pos_emb, pad_mask=pad_mask,)
if self.out_proj is not None:
audio_signal = self.out_proj(audio_signal)
audio_signal = torch.transpose(audio_signal, 1, 2)
return audio_signal, length
@staticmethod
def make_pad_mask(seq_lens, max_time, device=None):
"""Make masking for padding."""
bs = seq_lens.size(0)
seq_range = torch.arange(0, max_time, dtype=torch.int32)
seq_range_expand = seq_range.unsqueeze(0).expand(bs, max_time)
seq_lens = seq_lens.type(seq_range_expand.dtype).to(seq_range_expand.device)
seq_length_expand = seq_lens.unsqueeze(-1)
mask = seq_range_expand < seq_length_expand
if device:
mask = mask.to(device)
return mask