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Vosk is a speech recognition toolkit. The best things in Vosk are:

1. Supports 8 languages - English, German, French, Spanish, Portuguese, Chinese, Russian, Vietnamese. More is coming.
2. Works offline even on lightweight devices - Raspberry Pi, Android, iOS
3. Installs with simple pip3 install vosk
4. Portable models per language is just only 50Mb but there are much bigger server models available.
5. Provides streaming API for the best user experience (unlike popular speech-recognition python package)
6. There bindings for different prograbmming languages too - java/csharp/javascript etc.
7. Allows quick reconfiguration of vocabulary for best accuracy.
8. Supports speaker identification beside simple speech recognition

cd android
gradle build

Please note that medium blog post about 64-bit is not relevant anymore, the script builds x86, arm64 and armv7 libraries automatically without any modifications.

For example of Android application using Vosk-API check https://github.com/alphacep/kaldi-android-demo project

Available on request. Drop as a mail at [email protected].

The easiest way to install vosk api is with pip. You do not have to compile anything. We currently support only Linux on x86_64 and Raspberry Pi. Other systems (windows, mac) will come soon.

Make sure you have newer pip and python:

• Python version >= 3.4
• pip version >= 19.0

Uprade python and pip if needed. Then install vosk on Linux with a simple command

pip3 install vosk

We also provide a websocket server and grpc server which can be used in telephony and other applications. With bigger models adapted for 8khz audio it provides more accuracy.

The server is installed with docker and can run with a single command:

docker run -d -p 2700:2700 alphacep/kaldi-en:latest

For details see https://github.com/alphacep/vosk-server

If you still want to build from scratch, you can compile Kaldi and Vosk yourself. The compilation is straightforward but might be a little confusing for newbie. In case you want to follow this, please watch the errors.

git clone -b lookahead --single-branch https://github.com/alphacep/kaldi
cd kaldi/tools
make

install all dependencies and repeat make if needed

extras/install_openblas.sh
cd ../src
./configure --mathlib=OPENBLAS --shared --use-cuda=no
make -j 10

Then build the python module

export KALDI_ROOT=<KALDI_ROOT>
cd python
python3 setup.py install

Run like this:

cd vosk-api/python/example
wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
tar xf alphacep-model-android-en-us-0.3.tar.gz 
mv alphacep-model-android-en-us-0.3 model-en
python3 ./test_simple.py test.wav

To run with your audio file make sure it has proper format - PCM 16khz 16bit mono, otherwise decoding will not work.

You can find other examples of using a microphone, decoding with a fixed small vocabulary or speaker identification setup in python/example subfolder

Or Java

cd java && KALDI_ROOT=<KALDI_ROOT> make
wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
tar xf alphacep-model-android-en-us-0.3.tar.gz 
mv alphacep-model-android-en-us-0.3 model
make run

Or C#

cd csharp && KALDI_ROOT=<KALDI_ROOT> make
wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz
tar xf alphacep-model-android-en-us-0.3.tar.gz 
mv alphacep-model-android-en-us-0.3 model
mono test.exe

For information about models see the documentation on available models.

If you have any questions, feel free to

• Post an issue here on github
• Send us an e-mail at [email protected]
• Join our group dedicated to speech recognition on Telegram @speech_recognition