Spaces:
Sleeping
Sleeping
File size: 5,848 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 |
# 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}
# }
#
"""ConvolutionModule definition."""
from typing import Tuple
import torch
from torch import nn
class ConvolutionModule(nn.Module):
"""ConvolutionModule in Conformer model."""
def __init__(
self,
channels: int,
kernel_size: int = 15,
activation: nn.Module = nn.ReLU(),
norm: str = "batch_norm",
causal: bool = False,
bias: bool = True,
stride: int = 1,
):
"""Construct an ConvolutionModule object.
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernel size of conv layers.
causal (int): Whether use causal convolution or not
stride (int): Stride Convolution, for efficient Conformer
"""
super().__init__()
self.pointwise_conv1 = nn.Conv1d(
channels,
2 * channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
# self.lorder is used to distinguish if it's a causal convolution,
# if self.lorder > 0: it's a causal convolution, the input will be
# padded with self.lorder frames on the left in forward.
# else: it's a symmetrical convolution
if causal:
padding = 0
self.lorder = kernel_size - 1
else:
# kernel_size should be an odd number for none causal convolution
assert (kernel_size - 1) % 2 == 0
padding = (kernel_size - 1) // 2
self.lorder = 0
self.depthwise_conv = nn.Conv1d(
channels,
channels,
kernel_size,
stride=stride, # for depthwise_conv in StrideConv
padding=padding,
groups=channels,
bias=bias,
)
assert norm in ["batch_norm", "layer_norm"]
if norm == "batch_norm":
self.use_layer_norm = False
self.norm = nn.BatchNorm1d(channels)
else:
self.use_layer_norm = True
self.norm = nn.LayerNorm(channels)
self.pointwise_conv2 = nn.Conv1d(
channels,
channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.activation = activation
self.stride = stride
def forward(
self,
x: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
cache: torch.Tensor = torch.zeros((0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute convolution module.
Args:
x (torch.Tensor): Input tensor (#batch, time, channels).
mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
(0, 0, 0) means fake mask.
cache (torch.Tensor): left context cache, it is only
used in causal convolution (#batch, channels, cache_t),
(0, 0, 0) meas fake cache.
Returns:
torch.Tensor: Output tensor (#batch, time, channels).
"""
# exchange the temporal dimension and the feature dimension
x = x.transpose(1, 2) # (#batch, channels, time)
# mask batch padding
if mask_pad.size(2) > 0: # time > 0
x.masked_fill_(~mask_pad, 0.0)
if self.lorder > 0:
if cache.size(2) == 0: # cache_t == 0
x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
else:
# When export ONNX，the first cache is not None but all-zero,
# cause shape error in residual block,
# eg. cache14 + x9 = 23, 23-7+1=17 != 9
cache = cache[:, :, -self.lorder :]
assert cache.size(0) == x.size(0) # equal batch
assert cache.size(1) == x.size(1) # equal channel
x = torch.cat((cache, x), dim=2)
assert x.size(2) > self.lorder
new_cache = x[:, :, -self.lorder :]
else:
# It's better we just return None if no cache is requried,
# However, for JIT export, here we just fake one tensor instead of
# None.
new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
# GLU mechanism
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
# 1D Depthwise Conv
x = self.depthwise_conv(x)
if self.use_layer_norm:
x = x.transpose(1, 2)
x = self.activation(self.norm(x))
if self.use_layer_norm:
x = x.transpose(1, 2)
x = self.pointwise_conv2(x)
# mask batch padding
if mask_pad.size(2) > 0: # time > 0
if mask_pad.size(2) != x.size(2):
mask_pad = mask_pad[:, :, :: self.stride]
x.masked_fill_(~mask_pad, 0.0)
return x.transpose(1, 2), new_cache
|