Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,835 Bytes
c968fc3 |
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 |
# 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}
# }
#
"""SqueezeformerEncoderLayer definition."""
import torch
import torch.nn as nn
from typing import Optional, Tuple
class SqueezeformerEncoderLayer(nn.Module):
"""Encoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
instance can be used as the argument.
feed_forward1 (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
conv_module (torch.nn.Module): Convolution module instance.
`ConvlutionModule` instance can be used as the argument.
feed_forward2 (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: use layer_norm after each sub-block.
"""
def __init__(
self,
size: int,
self_attn: torch.nn.Module,
feed_forward1: Optional[nn.Module] = None,
conv_module: Optional[nn.Module] = None,
feed_forward2: Optional[nn.Module] = None,
normalize_before: bool = False,
dropout_rate: float = 0.1,
concat_after: bool = False,
):
super(SqueezeformerEncoderLayer, self).__init__()
self.size = size
self.self_attn = self_attn
self.layer_norm1 = nn.LayerNorm(size)
self.ffn1 = feed_forward1
self.layer_norm2 = nn.LayerNorm(size)
self.conv_module = conv_module
self.layer_norm3 = nn.LayerNorm(size)
self.ffn2 = feed_forward2
self.layer_norm4 = nn.LayerNorm(size)
self.normalize_before = normalize_before
self.dropout = nn.Dropout(dropout_rate)
self.concat_after = concat_after
if concat_after:
self.concat_linear = nn.Linear(size + size, size)
else:
self.concat_linear = nn.Identity()
def forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
pos_emb: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
# self attention module
residual = x
if self.normalize_before:
x = self.layer_norm1(x)
x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
if self.concat_after:
x_concat = torch.cat((x, x_att), dim=-1)
x = residual + self.concat_linear(x_concat)
else:
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.layer_norm1(x)
# ffn module
residual = x
if self.normalize_before:
x = self.layer_norm2(x)
x = self.ffn1(x)
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.layer_norm2(x)
# conv module
new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
residual = x
if self.normalize_before:
x = self.layer_norm3(x)
x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.layer_norm3(x)
# ffn module
residual = x
if self.normalize_before:
x = self.layer_norm4(x)
x = self.ffn2(x)
# we do not use dropout here since it is inside feed forward function
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.layer_norm4(x)
return x, mask, new_att_cache, new_cnn_cache
|