Implementation of the convolutional module from the Conformer paper, for improving the local inductive bias in Transformers.
$ pip install conformerThe Conformer convolutional module, the main novelty of the paper
import torch
from conformer import ConformerConvModule
layer = ConformerConvModule(
dim = 512,
causal = False, # auto-regressive or not - 1d conv will be made causal with padding if so
expansion_factor = 2, # what multiple of the dimension to expand for the depthwise convolution
kernel_size = 31, # kernel size, 17 - 31 was said to be optimal
dropout = 0. # dropout at the very end
)
x = torch.randn(1, 1024, 512)
x = layer(x) + x1 Conformer Block
import torch
from conformer import ConformerBlock
block = ConformerBlock(
dim = 512,
dim_head = 64,
heads = 8,
ff_mult = 4,
conv_expansion_factor = 2,
conv_kernel_size = 31,
attn_dropout = 0.,
ff_dropout = 0.,
conv_dropout = 0.
)
x = torch.randn(1, 1024, 512)
block(x) # (1, 1024, 512)Conformer - just multiple ConformerBlock from above
import torch
from conformer import Conformer
conformer = Conformer(
dim = 512,
depth = 12, # 12 blocks
dim_head = 64,
heads = 8,
ff_mult = 4,
conv_expansion_factor = 2,
conv_kernel_size = 31,
attn_dropout = 0.,
ff_dropout = 0.,
conv_dropout = 0.
)
x = torch.randn(1, 1024, 512)
conformer(x) # (1, 1024, 512)- switch to a better relative positional encoding. shaw's is dated
- flash attention with a better RPE
@misc{gulati2020conformer,
title = {Conformer: Convolution-augmented Transformer for Speech Recognition},
author = {Anmol Gulati and James Qin and Chung-Cheng Chiu and Niki Parmar and Yu Zhang and Jiahui Yu and Wei Han and Shibo Wang and Zhengdong Zhang and Yonghui Wu and Ruoming Pang},
year = {2020},
eprint = {2005.08100},
archivePrefix = {arXiv},
primaryClass = {eess.AS}
}