conformer
The convolutional module from the Conformer paper
Decision gist · record as of 2026-08-14
Yes, if you need the Conformer architecture for a specific project and can accept a frozen, unmaintained codebase. The low install friction and permissive license make it easy to adopt, and the implementation is straightforward enough to fork or patch if needed. However, if you require ongoing maintenance, compatibility updates, or active support, look for an actively maintained alternative or be prepared to maintain a fork yourself.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and einops to be installed; no other system-level dependencies.
- Low friction: pure Python wheel with only torch and einops as runtime dependencies.
- However, the package is abandoned—last release was 2023-05-17 and no commits since then, so expect no maintenance or bug fixes.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this code freely with minimal restrictions, making it suitable for both open and closed projects.
last release 2023-05-17 (1185 days) · last repo commit 2023-05-17 · 437 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 508,777 downloads/mo, #6,276 on PyPI
Alternatives
Verify before relying
pip install conformer
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)
x = torch.randn(1, 1024, 512)
output = block(x)- Whether the implementation matches the exact Conformer paper specification or if there are known deviations.
- Current compatibility with recent PyTorch versions, given the last release was in 2023.
- Whether the TODO items (relative positional encoding, flash attention) have been addressed elsewhere or remain unimplemented.
What it is and what it does
Conformer is a PyTorch library implementing the Conformer architecture from the 2020 paper, which augments transformer models with depthwise convolutions to improve local feature extraction. The package provides three main components: ConformerConvModule (the core convolutional layer), ConformerBlock (a complete transformer block with attention, feed-forward, and convolution), and Conformer (a full stack of multiple blocks). It is primarily designed for speech recognition and audio tasks where capturing local patterns alongside global attention is beneficial.
The library depends only on torch and einops, making installation straightforward. However, the project is no longer maintained—the last commit was in May 2023 and the repository shows no active development. This means bug fixes, compatibility updates with newer PyTorch versions, or feature improvements are unlikely. For production use or long-term projects, you should evaluate whether the frozen implementation meets your needs or if you need an actively maintained alternative.
Use it for
- Building speech recognition models that combine transformer attention with local convolution for improved acoustic modeling.
- Prototyping audio processing pipelines where you need the Conformer architecture as a backbone component.
- Research or educational projects exploring the Conformer paper's approach to augmenting transformers with convolution.
- Adapting existing Conformer implementations into larger custom architectures by using ConformerBlock or ConformerConvModule as building blocks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need the Conformer architecture for a specific project and can accept a frozen, unmaintained codebase.
The low install friction and permissive license make it easy to adopt, and the implementation is straightforward enough to fork or patch if needed. However, if you require ongoing maintenance, compatibility updates, or active support, look for an actively maintained alternative or be prepared to maintain a fork yourself.
Install
conformer on PyPI
Before you install
Low friction: pure Python wheel with only torch and einops as runtime dependencies. However, the package is abandoned—last release was 2023-05-17 and no commits since then, so expect no maintenance or bug fixes.
Requires PyTorch and einops to be installed; no other system-level dependencies.
License in practice
MIT license is permissive; you can use, modify, and distribute this code freely with minimal restrictions, making it suitable for both open and closed projects.
Quickstart
pip install conformer
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)
x = torch.randn(1, 1024, 512)
output = block(x)Verify before relying
- Whether the implementation matches the exact Conformer paper specification or if there are known deviations.
- Current compatibility with recent PyTorch versions, given the last release was in 2023.
- Whether the TODO items (relative positional encoding, flash attention) have been addressed elsewhere or remain unimplemented.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageseinopstorch |
| Maintenance | Abandoned 1,185 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 508,777 / month, #6,276 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: conformer-0.3.2-py3-none-any.whl
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