Spaces:
Sleeping
Sleeping
File size: 11,097 Bytes
0883aa1 |
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 |
# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
"""DepthwiseConv2dSubsampling4 and TimeReductionLayer definition."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.wenet_extractor.transformer.subsampling import BaseSubsampling
from typing import Tuple
from modules.wenet_extractor.squeezeformer.conv2d import Conv2dValid
class DepthwiseConv2dSubsampling4(BaseSubsampling):
"""Depthwise Convolutional 2D subsampling (to 1/4 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
pos_enc_class (nn.Module): position encoding class.
dw_stride (int): Whether do depthwise convolution.
input_size (int): filter bank dimension.
"""
def __init__(
self,
idim: int,
odim: int,
pos_enc_class: torch.nn.Module,
dw_stride: bool = False,
input_size: int = 80,
input_dropout_rate: float = 0.1,
init_weights: bool = True,
):
super(DepthwiseConv2dSubsampling4, self).__init__()
self.idim = idim
self.odim = odim
self.pw_conv = nn.Conv2d(
in_channels=idim, out_channels=odim, kernel_size=3, stride=2
)
self.act1 = nn.ReLU()
self.dw_conv = nn.Conv2d(
in_channels=odim,
out_channels=odim,
kernel_size=3,
stride=2,
groups=odim if dw_stride else 1,
)
self.act2 = nn.ReLU()
self.pos_enc = pos_enc_class
self.input_proj = nn.Sequential(
nn.Linear(odim * (((input_size - 1) // 2 - 1) // 2), odim),
nn.Dropout(p=input_dropout_rate),
)
if init_weights:
linear_max = (odim * input_size / 4) ** -0.5
torch.nn.init.uniform_(
self.input_proj.state_dict()["0.weight"], -linear_max, linear_max
)
torch.nn.init.uniform_(
self.input_proj.state_dict()["0.bias"], -linear_max, linear_max
)
self.subsampling_rate = 4
# 6 = (3 - 1) * 1 + (3 - 1) * 2
self.right_context = 6
def forward(
self, x: torch.Tensor, x_mask: torch.Tensor, offset: int = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
x = x.unsqueeze(1) # (b, c=1, t, f)
x = self.pw_conv(x)
x = self.act1(x)
x = self.dw_conv(x)
x = self.act2(x)
b, c, t, f = x.size()
x = x.permute(0, 2, 1, 3)
x = x.contiguous().view(b, t, c * f)
x, pos_emb = self.pos_enc(x, offset)
x = self.input_proj(x)
return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2]
class TimeReductionLayer1D(nn.Module):
"""
Modified NeMo,
Squeezeformer Time Reduction procedure.
Downsamples the audio by `stride` in the time dimension.
Args:
channel (int): input dimension of
MultiheadAttentionMechanism and PositionwiseFeedForward
out_dim (int): Output dimension of the module.
kernel_size (int): Conv kernel size for
depthwise convolution in convolution module
stride (int): Downsampling factor in time dimension.
"""
def __init__(
self, channel: int, out_dim: int, kernel_size: int = 5, stride: int = 2
):
super(TimeReductionLayer1D, self).__init__()
self.channel = channel
self.out_dim = out_dim
self.kernel_size = kernel_size
self.stride = stride
self.padding = max(0, self.kernel_size - self.stride)
self.dw_conv = nn.Conv1d(
in_channels=channel,
out_channels=channel,
kernel_size=kernel_size,
stride=stride,
padding=self.padding,
groups=channel,
)
self.pw_conv = nn.Conv1d(
in_channels=channel,
out_channels=out_dim,
kernel_size=1,
stride=1,
padding=0,
groups=1,
)
self.init_weights()
def init_weights(self):
dw_max = self.kernel_size**-0.5
pw_max = self.channel**-0.5
torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
def forward(
self,
xs,
xs_lens: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
):
xs = xs.transpose(1, 2) # [B, C, T]
xs = xs.masked_fill(mask_pad.eq(0), 0.0)
xs = self.dw_conv(xs)
xs = self.pw_conv(xs)
xs = xs.transpose(1, 2) # [B, T, C]
B, T, D = xs.size()
mask = mask[:, :: self.stride, :: self.stride]
mask_pad = mask_pad[:, :, :: self.stride]
L = mask_pad.size(-1)
# For JIT exporting, we remove F.pad operator.
if L - T < 0:
xs = xs[:, : L - T, :].contiguous()
else:
dummy_pad = torch.zeros(B, L - T, D, device=xs.device)
xs = torch.cat([xs, dummy_pad], dim=1)
xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc")
return xs, xs_lens, mask, mask_pad
class TimeReductionLayer2D(nn.Module):
def __init__(self, kernel_size: int = 5, stride: int = 2, encoder_dim: int = 256):
super(TimeReductionLayer2D, self).__init__()
self.encoder_dim = encoder_dim
self.kernel_size = kernel_size
self.dw_conv = Conv2dValid(
in_channels=encoder_dim,
out_channels=encoder_dim,
kernel_size=(kernel_size, 1),
stride=stride,
valid_trigy=True,
)
self.pw_conv = Conv2dValid(
in_channels=encoder_dim,
out_channels=encoder_dim,
kernel_size=1,
stride=1,
valid_trigx=False,
valid_trigy=False,
)
self.kernel_size = kernel_size
self.stride = stride
self.init_weights()
def init_weights(self):
dw_max = self.kernel_size**-0.5
pw_max = self.encoder_dim**-0.5
torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
def forward(
self,
xs: torch.Tensor,
xs_lens: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
xs = xs.masked_fill(mask_pad.transpose(1, 2).eq(0), 0.0)
xs = xs.unsqueeze(2)
padding1 = self.kernel_size - self.stride
xs = F.pad(xs, (0, 0, 0, 0, 0, padding1, 0, 0), mode="constant", value=0.0)
xs = self.dw_conv(xs.permute(0, 3, 1, 2))
xs = self.pw_conv(xs).permute(0, 3, 2, 1).squeeze(1).contiguous()
tmp_length = xs.size(1)
xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc")
padding2 = max(0, (xs_lens.max() - tmp_length).data.item())
batch_size, hidden = xs.size(0), xs.size(-1)
dummy_pad = torch.zeros(batch_size, padding2, hidden, device=xs.device)
xs = torch.cat([xs, dummy_pad], dim=1)
mask = mask[:, ::2, ::2]
mask_pad = mask_pad[:, :, ::2]
return xs, xs_lens, mask, mask_pad
class TimeReductionLayerStream(nn.Module):
"""
Squeezeformer Time Reduction procedure.
Downsamples the audio by `stride` in the time dimension.
Args:
channel (int): input dimension of
MultiheadAttentionMechanism and PositionwiseFeedForward
out_dim (int): Output dimension of the module.
kernel_size (int): Conv kernel size for
depthwise convolution in convolution module
stride (int): Downsampling factor in time dimension.
"""
def __init__(
self, channel: int, out_dim: int, kernel_size: int = 1, stride: int = 2
):
super(TimeReductionLayerStream, self).__init__()
self.channel = channel
self.out_dim = out_dim
self.kernel_size = kernel_size
self.stride = stride
self.dw_conv = nn.Conv1d(
in_channels=channel,
out_channels=channel,
kernel_size=kernel_size,
stride=stride,
padding=0,
groups=channel,
)
self.pw_conv = nn.Conv1d(
in_channels=channel,
out_channels=out_dim,
kernel_size=1,
stride=1,
padding=0,
groups=1,
)
self.init_weights()
def init_weights(self):
dw_max = self.kernel_size**-0.5
pw_max = self.channel**-0.5
torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
def forward(
self,
xs,
xs_lens: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
):
xs = xs.transpose(1, 2) # [B, C, T]
xs = xs.masked_fill(mask_pad.eq(0), 0.0)
xs = self.dw_conv(xs)
xs = self.pw_conv(xs)
xs = xs.transpose(1, 2) # [B, T, C]
B, T, D = xs.size()
mask = mask[:, :: self.stride, :: self.stride]
mask_pad = mask_pad[:, :, :: self.stride]
L = mask_pad.size(-1)
# For JIT exporting, we remove F.pad operator.
if L - T < 0:
xs = xs[:, : L - T, :].contiguous()
else:
dummy_pad = torch.zeros(B, L - T, D, device=xs.device)
xs = torch.cat([xs, dummy_pad], dim=1)
xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc")
return xs, xs_lens, mask, mask_pad
|