Even more so if they compared it to Kaldi TDNN-LSTM with RNNLM lattice rescoring (test-clean 3.22%, apparently): https://github.com/kaldi-asr/kaldi/blob/master/egs/librispee...
In Facebook open-sources a speech-recognition system and a machine learning librarykaldi-asr
kaldi
Shell2015–2024
kaldi-asr / kaldi
kaldi
kaldi-asr/kaldi is the official location of the Kaldi project.
20Distinct people
29Mentions
0Explicit recommenders
15.5KGitHub stars
What to know before exploring
What it is
kaldi-asr/kaldi is the official location of the Kaldi project.
GitHub
Where it is used
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- alphacep/vosk-api · linked in 3 of the same discussions
- espnet/espnet · linked in 3 of the same discussions
- flashlight/wav2letter · linked in 3 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 6:59:22 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year29 dated mentions
Observed mentions: 2015-08-22 18:56:08 UTC to 2024-10-03 06:47:04 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| 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
4 sourcesKaldi-asr/kaldi: This is now the official location of the Kaldi project
1 points · Open discussionLooks like Kaldi gets 4.27% WER on CV:
https://github.com/kaldi-asr/kaldi/blob/master/egs/commonvoi...
But that's likely trained exclusively on CV audio and using a language model derived from the CV training data so that's also not a fair comparison unless the other engines were trained the same way.
Comparing systems trained on different datasets (with different language models) like this is like comparing apples to oranges. Mixing wildly different CPU and memory requirements into the benchmarks just makes it worse.
It should be fairly straightforward to do an unbiased comparison by training and evaluating with the standard Librispeech split and language model. It might be interesting to see how accuracy improves as the models scale up until they match the resource requirements of the other engines.
That said, the speed and memory usa
Read the rest
ge are impressive and I like the focus on very low resource environments. Seems like it has a lot of potential even if it may not be SOTA.
Sadly, Mozilla's effort is somewhat flawed in its current iteration. There are only very few different sentences (tokens) used as prompts, leading to a lot of repetitions of the same spoken utterance instead of greater coverage of possible utterances, and there is near complete overlap in the sentences spoken in the training set and those in the development and test sets, making it nearly impossible to generalize results from performance evaluations to utterances not contained in the training set. See https://discourse.mozilla.org/t/common-voice-v1-corpus-desig...
Some suggestions for improvements were made in a discussion on the Kaldi Github (https://github.com/kaldi-asr/kaldi/issues/2141), but the bottom line is that it's not clear what the exact use-case of the corpus is. It's not command recognition, there's no particular domain, just
Read the rest
more or less random English sentences collected from CC0 sources. Use cases should drive corpus collection.