Home » Is there any decent speech recognition software for Linux?

Is there any decent speech recognition software for Linux?


Right now I’m experimenting with using KDE connect in combination with Google speech recognition on my android smartphone.

KDE connect allows you to use your android device as an input device for your Linux computer (there are also some other features). You need to install the KDE connect app from the Google play store on your smartphone/tablet and install both kdeconnect and indicator-kdeconnect on your Linux computer. For Ubuntu systems the install goes as follows:

sudo add-apt-repository ppa:vikoadi/ppa
sudo apt update
sudo apt install kdeconnect indicator-kdeconnect

The downside of this installation is that it installs a bunch of KDE packages that you don’t need if you don’t use the KDE desktop environment.

Once you pair your android device with your computer (they have to be on the same network) you can use the android keyboard and then click/press on the mic to use Google speech recognition. As you talk, text will start to appear where ever your cursor is active on your Linux computer.

As for the results, they are a bit mixed for me as I’m currently writing some technical astrophysics document and Google speech recognition is struggling with the jargon that you don’t typically read. Also forget about it figuring out punctuation or proper capitalization.

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It supports 7+ languages.

First you convert the file to the required format and then you recognize it:

ffmpeg -i file.mp3 -ar 16000 -ac 1 file.wav

Then install vosk-api with pip:

pip3 install vosk

Then use these steps:

git clone https://github.com/alphacep/vosk-api
cd vosk-api/python/example
wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
unzip vosk-model-small-en-us-0.3.zip
mv vosk-model-small-en-us-0.3 model
python3 ./test_simple.py test.wav  > result.json

The result is stored in JSON format.

The same directory also contains an SRT subtitle output example, which is more human readable and can be directly useful to people with that use case:

python3 -m pip install srt
python3 ./test_srt.py test.wav

The sections below show some testing I did with it.

test.wav case study

The test.wav example given in the repository says in perfect American English accent and perfect sound quality three sentences which I transcribe as:

one zero zero zero one
nine oh two one oh
zero one eight zero three

The “nine oh two one oh” is said very fast, but still clear. The “z” of the before last “zero” sounds a bit like an “s”.

The SRT generated above reads:

00:00:00,870 --> 00:00:02,610
what zero zero zero one

00:00:03,930 --> 00:00:04,950
no no to uno

00:00:06,240 --> 00:00:08,010
cyril one eight zero three

so we can see that several mistakes were made, presumably in part because we have the understanding that all words are numbers to help us.

Next I also tried with the vosk-model-en-us-aspire-0.2 which was a 1.4GB download compared to 36MB of vosk-model-small-en-us-0.3 and is listed at https://alphacephei.com/vosk/models:

mv model model.vosk-model-small-en-us-0.3
wget https://alphacephei.com/vosk/models/vosk-model-en-us-aspire-0.2.zip
unzip vosk-model-en-us-aspire-0.2.zip
mv vosk-model-en-us-aspire-0.2 model

and the result was:

00:00:00,840 --> 00:00:02,610
one zero zero zero one

00:00:04,026 --> 00:00:04,980
i know what you window

00:00:06,270 --> 00:00:07,980
serial one eight zero three

which got one more word correct.

IBM “Think” Speech case study

Now let’s have some fun, shall we. From https://en.wikipedia.org/wiki/Think_(IBM) (public domain in USA):

wget https://upload.wikimedia.org/wikipedia/commons/4/49/Think_Thomas_J_Watson_Sr.ogg
ffmpeg -i Think_Thomas_J_Watson_Sr.ogg -ar 16000 -ac 1 think.wav
time python3 ./test_srt.py think.wav > think.srt

The sound quality is not great, with a lot of microphone hissing noise due to the technology of the time. The speech is however very clear and paused. The recording is 28 seconds long, and the wav file is 900KB large.

Conversion took 32 seconds. Sample output of the three first sentences:

00:00:00,299 --> 00:00:01,650
and we must study

00:00:02,761 --> 00:00:05,549
reading listening name scott

00:00:06,300 --> 00:00:08,820
observing and thank you

and the Wikipedia transcription for the same segment reads:

00:00:00,518 --> 00:00:02,513
And we must study

00:00:02,613 --> 00:00:08,492
through reading, listening, discussing, observing, and thinking.

“We choose to go to the Moon” case study

https://en.wikipedia.org/wiki/We_choose_to_go_to_the_Moon (public domain)

OK, one more fun one. This audio has good sound quality, with occasional approval screams by the crowd, and a slight echo of the venue:

wget -O moon.ogv https://upload.wikimedia.org/wikipedia/commons/1/16/President_Kennedy%27s_Speech_at_Rice_University.ogv
ffmpeg -i moon.ogv -ss 09:12 -to 09:29 -q:a 0 -map a -ar 16000 -ac 1 moon.wav
time python3 ./test_srt.py moon.wav > moon.srt

Audio duration: 17s, wav file size 532K, conversion time 22s, output:

00:00:01,410 –> 00:00:16,800
we choose to go to the moon in this decade and do the other things not because they are easy but because they are hard because that goal will serve to organize and measure the best of our energies and skills

and the corresponding Wikipedia captions:

00:09:06,310 --> 00:09:18,900
We choose to go to the moon in this decade and do the other things,

00:09:18,900 --> 00:09:22,550
not because they are easy, but because they are hard,

00:09:22,550 --> 00:09:30,000
because that goal will serve to organize and measure the best of our energies and skills,

Perfect except for a missing “the” and punctuation!

Tested on vosk-api 7af3e9a334fbb9557f2a41b97ba77b9745e120b3, Ubuntu 20.04, Lenovo ThinkPad P51.

This answer is based on https://askubuntu.com/a/423849/52975 by Nikolay Shmyrev with additions by me.

Dictation wrapper

https://github.com/ideasman42/nerd-dictation (wrapper for VOSK-API)

Try nerd-dictation, it’s a simple way to access VOSK-API, which is a high quality offline, open-source speech to text engine.

See demo video.

full disclosure, I couldn’t find any solutions that suited my use case, so I wrote this small utility to scratch my own itch.

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