Kaldi has 4.14% WER (95.86% accuracy) on the same test dataset (test-clean) [1] using a model that runs faster than real time on CPU. You would have to make the model smaller to run it in real time on a RasPi3, but according to this [2], you can get decent WERs for read speech even then.
[1] https://github.com/kaldi-asr/kaldi/blob/master/egs/librispee...
[2] https://groups.google.com/d/msg/kaldi-help/Pr6jPH1Qshg/kn8df...
In Initial Release of Mozilla’s Open Source Speech Recognition Model and Voice Datakaldi-asr
kaldi
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kaldi-asr / kaldi
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kaldi-asr/kaldi is the official location of the Kaldi project.
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Observed mentions: 2015-08-22 18:56:08 UTC to 2024-10-03 06:47:04 UTC.
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 2 | 2 | 0 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 5 | 1 | 4 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 7 | 0 | 7 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 4 | 1 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 7 | 0 | 7 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 2 | 1 | 1 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 0 | 1 |
From the project README
An excerpt from the repository, fetched Nov 12, 2026.
Read the project’s introduction
Kaldi Speech Recognition Toolkit
To build the toolkit: see ./INSTALL. These instructions are valid for UNIX
systems including various flavors of Linux; Darwin; and Cygwin (has not been
tested on more "exotic" varieties of UNIX). For Windows installation
instructions (excluding Cygwin), see windows/INSTALL.
To run the example system builds, see egs/README.txt
If you encounter problems (and you probably will), please do not hesitate to
contact the developers (see below). In addition to specific questions, please
let us know if there are specific aspects of the project that you feel could be
improved, that you find confusing, etc., and which missing features you most
wish it had.
Kaldi information channels
For HOT news about Kaldi see the project site.
Documentation of Kaldi:
Info about the project, description of techniques, tutorial for C++ coding.
Doxygen reference of the C++ code.
Kaldi forums and mailing lists:
We have two different lists
User list kaldi-help
Developer list kaldi-developers:
To sign up to any of those mailing lists, go to
http://kaldi-asr.org/forums.html:
Development pattern for contributors
Create a personal fork
of the main Kaldi repository in GitHub.
Make your changes in a named branch different from master, e.g. you create
a branch my-awesome-feature.
Generate a pull request
through the Web interface of GitHub.
As a general rule, please follow Google C++ Style Guide.
There are a few exceptions in Kaldi.
You can use the Google's cpplint.py
to verify that your code is free of basic mistakes.
Platform specific notes
Fedora 41 (and later)
In order to build it on Fedora 41 using the libraries that are provided by the distro, you need to install the development libraries and dependencies with
sudo dnf install lapack-devel openfst-devel
then build the package as follows:
cmake -S ./ -Bbuild/Release -DFETCHCONTENT_FULLY_DISCONNECTED=ON -DBuildForFedora=ON
cmake --build /home/gerhard/workspace/kaldi/build/Release
PowerPC 64bits little-endian (ppc64le)
Kaldi is expected to work out of the box in RHEL >= 7 and Ubuntu >= 16.04 with
OpenBLAS, ATLAS, or CUDA.
CUDA drivers for ppc64le can be found at https://developer.nvidia.com/cuda-downloads.
An IBM Redbook is
available as a guide to install and configure CUDA.
Android
Kaldi supports cross compiling for Android using Android NDK, clang++ and
OpenBLAS.
See this blog post
for details.
Web Assembly
Kaldi supports cross compiling for Web Assembly for in-browser execution
using emscripten and OpenBLAS
See this repo
for a step-by-step description of the build process.
Read the full README on GitHubWhat Hacker News says
7 sourcesDon't forget Kaldi!
https://github.com/kaldi-asr/kaldi
In Initial Release of Mozilla’s Open Source Speech Recognition Model and Voice DataOpen source speech recognition on Github:
1. speech-to-text-wavenet (https://github.com/buriburisuri/speech-to-text-wavenet)
2. kaldi(https://github.com/kaldi-asr/kaldi)
3. Speech recognition module for Python (https://github.com/Uberi/speech_recognition)
4. DeepSpeech(https://github.com/mozilla/DeepSpeech)
5. Natural Language Processing Tasks and References(https://github.com/Kyubyong/nlp_tasks)
FYI.
In Speech Recognition Is Not Solvedsee https://groups.google.com/d/msg/kaldi-developers/jakcCUK5lKY...
It seems to be a heavily compressed version of the ASpIRE challenge example model (https://github.com/kaldi-asr/kaldi/tree/master/egs/aspire/).
In Show HN: Serverless speech recognition with WebAssemblyThe 8.5% in this file is what you'd compare to Microsoft and IBM's recent ~5% results.
https://github.com/kaldi-asr/kaldi/blob/master/egs/fisher_sw...
Kaldi hasn't been in first place on that dataset recently, but it was a few years ago.
On other more researchy datasets (eg. for distant speakers or languages other than English), the best system is often based on Kaldi.
In Speech Recognition Is Not SolvedI too would be interested in pointers to the leading open source options.
Just yesterday there was a Show HN built with the https://github.com/kaldi-asr/kaldi project, emscripten-ized: https://news.ycombinator.com/item?id=15534531
In Speech Recognition Is Not SolvedIn this thread:
The Zork game, written in WebAssembly, using serverless (offline) speech recognition by using the Kaldi Speech Recognition Toolkit. Kaldi is written in C++ which then (i guess) is compiled into WebAssembly via Emscripten.
https://github.com/kaldi-asr/kaldi
Very very interesting.
Additional comment: When I saw the Amazon Alexa API (i was at a small meeting which included defining your own "utterances", i was dissapointed that the Alexa required internet connection to perform the speech recognition, even moreso when the interaction with it was not so sophisticated (after all, it is geared to understanding predefined commands).
In Show HN: Serverless speech recognition with WebAssembly