# \[ANN\] TIDIGITSRecipe.jl: the first Julia-flavoured speech recognition recipe!

**URL:** <https://discourse.julialang.org/t/ann-tidigitsrecipe-jl-the-first-julia-flavoured-speech-recognition-recipe/56070>\
**Category:** Package Announcements\
**Tags:** audio\
**Created:** [February 26, 2021, 8:53am UTC](https://discourse.julialang.org/t/ann-tidigitsrecipe-jl-the-first-julia-flavoured-speech-recognition-recipe/56070 "2021-02-26T08:53:41Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![nantonel](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nantonel/32/2889_2.png) [@nantonel](https://discourse.julialang.org/u/nantonel)\
**Post date:** [February 26, 2021, 8:53am UTC](https://discourse.julialang.org/t/ann-tidigitsrecipe-jl-the-first-julia-flavoured-speech-recognition-recipe/56070/1 "2021-02-26T08:53:41Z")

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I’m happy to announce [TIDIGITSRecipe](https://github.com/idiap/TIDIGITSRecipe.jl) an entirely Julia-flavoured automatic-speech-recognition (ASR) recipe!

The repository comes with a live demo where you can use your own voice to recognise spoken digits in English. I’d be happy if some of you could try it out and give me some feedback.

This repo is not a package but a set of scripts that can be used to train an ASR system.  
The following packages are used (among others):

- [Flux](https://github.com/FluxML/Flux.jl) as ML library
- [HMMGradients](https://github.com/idiap/HMMGradients.jl) for maximum likelihood training
- [FiniteStateTransducers](https://github.com/idiap/FiniteStateTransducers.jl) for WFST compositions

As far as I know I think this is the first attempt of a completely Julia-based ASR toolkit. Of course TIDIGITS is a relatively simple ASR problem. Hopefully this can be a first-step towards more challenging and exciting datasets!