README.md

Conformer

Implementation of the convolutional module from the Conformer paper, for improving the local inductive bias in Transformers.

Install

$ pip install conformer

Usage

The 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) + x

1 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)

Todo

• switch to a better relative positional encoding. shaw's is dated
• flash attention with a better RPE

Citations

@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}
}