julius

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  "deprecated": 0,
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  "desc": "Two-pass large vocabulary continuous speech recognition engine",
  "disable_reason": null,
  "disabled": 0,
  "enrichment_fetched_at": "2026-06-20T23:39:07+00:00",
  "first_seen": "2026-06-20T23:34:18+00:00",
  "full_name": "julius",
  "github_default_branch": "master",
  "github_last_commit_at": "2025-06-16T07:10:03Z",
  "github_readme_excerpt": "Julius: Open-Source Large Vocabulary Continuous Speech Recognition Engine\n==========================================================================\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2530396.svg)](https://doi.org/10.5281/zenodo.2530396)\n\nCopyright (c) 1991-2020 [Kawahara Lab., Kyoto University](http://sap.ist.i.kyoto-u.ac.jp/)  \nCopyright (c) 2005-2020 [Julius project team, Lee Lab., Nagoya Institute of Technology](http://www.slp.nitech.ac.jp/)  \nCopyright (c) 1997-2000 Information-technology Promotion Agency, Japan  \nCopyright (c) 2000-2005 Shikano Lab., Nara Institute of Science and Technology  \n\n# About Julius\n\n\"Julius\" is a high-performance, small-footprint large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. Based on word N-gram and context-dependent HMM, it can perform real-time decoding on various computers and devices from micro-computer to cloud server. The algorithm is based on 2-pass tree-trellis search, which fully incorporates major decoding techniques such as tree-organized lexicon, 1-best / word-pair context approximation, rank/score pruning, N-gram factoring, cross-word context dependency handling, enveloped beam search, Gaussian pruning, Gaussian selection, etc. Besides search efficiency, it is also modularized to be independent from model structures, and wide variety of HMM structures are supported such as shared-state triphones and tied-mixture models, with any number of mixtures, states, or phone sets. It also can run multi-instance recognition, running dictation, grammar-based recognition or isolated word recognition simultaneously in a single thread.  Standard formats are adopted for the models to cope with other speech / language modeling toolkit such as HTK, SRILM, etc.  Recent version also supports Deep Neural Network (DNN) based real-time decoding.\n\nThe main platform is Linux and other Unix-based system, as well as Windows, Mac, Androids and other platforms.",
  "github_repo": "julius-speech/julius",
  "github_stars": 1926,
  "github_topics": [
    "audio-processing",
    "recognition",
    "speech",
    "speech-recognition"
  ],
  "homepage": "https://github.com/julius-speech/julius",
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  "homepage_title": null,
  "installs_30d": 6,
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  "last_seen": "2026-06-20T23:34:18+00:00",
  "license": "BSD-3-Clause",
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  "name": "julius",
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  "optional_dependencies": [],
  "rank_30d": 9992,
  "rank_365d": 10374,
  "rank_90d": 11334,
  "raw_hash": "fc440595fb72ab0c",
  "recommended_dependencies": [],
  "revision": 0,
  "ruby_source_path": "Formula/j/julius.rb",
  "tap": "homebrew/core",
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  "version_head": null,
  "version_stable": "4.6",
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}