kaldi-asr/kaldi

Analysis updated 2026-06-24

★ 15,391ShellAudience · researcherComplexity · 5/5Setup · hard

Mindmap

mindmap
  root((kaldi))
    Inputs
      Audio recordings
      Training datasets
      Acoustic models
    Outputs
      Transcripts
      Speaker IDs
      Trained models
    Use Cases
      Research ASR systems
      Speaker verification
      Custom transcription
    Tech Stack
      C++
      Shell
      CUDA
      Python

mindmap root((kaldi)) Inputs Audio recordings Training datasets Acoustic models Outputs Transcripts Speaker IDs Trained models Use Cases Research ASR systems Speaker verification Custom transcription Tech Stack C++ Shell CUDA Python

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What do people build with it?

USE CASE 1

Train a custom acoustic model for a low-resource language with your own dataset

USE CASE 2

Build a speaker verification system that confirms who is calling a hotline

USE CASE 3

Reproduce a published ASR research result using one of the example recipes in egs

What is it built with?

C++ShellCUDAPython

How does it compare?

	kaldi-asr/kaldi	tteck/proxmox	cisofy/lynis
Stars	15,391	15,174	15,644
Language	Shell	Shell	Shell
Setup difficulty	hard	moderate	easy
Complexity	5/5	2/5	2/5
Audience	researcher	ops devops	ops devops

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1day+

Compile from source on Linux or macOS, CUDA recommended for training and the egs recipes need large datasets.

In plain English

Kaldi is a speech recognition toolkit, software that converts spoken audio into text. It is aimed at researchers and engineers working on automatic speech recognition (ASR) problems and is one of the established toolkits in the field for building and experimenting with speech recognition systems. The project also covers speaker identification and speaker verification, which involves determining who is speaking rather than what they said. The toolkit is written primarily in C++ and is designed to run on UNIX-based systems including various Linux distributions, macOS (Darwin), and Cygwin, with separate Windows installation instructions also available. It can take advantage of CUDA-capable GPUs for faster processing. Kaldi includes example system builds (called "egs") that let you run complete speech recognition pipelines on standard datasets to get started. It supports cross-compilation to other platforms including Android and Web Assembly (for in-browser execution using the emscripten toolchain). The project provides documentation on its own website covering both usage and the underlying techniques, along with a Doxygen code reference for developers. Community support is available through mailing lists for both users and developers. Contributors are expected to follow the Google C++ Style Guide with a few project-specific exceptions noted in the documentation.

Copy-paste prompts

Prompt 1

Walk me through the Kaldi recipe in egs/wsj from data prep to decoding

Prompt 2

Set up Kaldi on Ubuntu with CUDA and run the mini_librispeech recipe end to end

Prompt 3

Compare Kaldi to Whisper for an ASR research project and explain when Kaldi still wins

Prompt 4

Cross-compile Kaldi to WebAssembly using emscripten and run inference in a browser tab

Prompt 5

Adapt a Kaldi recipe to train on a custom 50-hour dataset of medical dictation

Frequently asked questions

What is kaldi?

Established speech recognition toolkit in C++ that converts spoken audio into text. Also handles speaker identification and verification, with GPU support.

What language is kaldi written in?

Mainly Shell. The stack also includes C++, Shell, CUDA.

How hard is kaldi to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is kaldi for?

Mainly researcher.

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