Vosk is a speech recognition toolkit. The best things in Vosk are:
- Supports 9 languages - English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese. More to come.
- Works offline, even on lightweight devices - Raspberry Pi, Android, iOS
- Installs with simple
pip3 install vosk - Portable per-language models are only 50Mb each, but there are much bigger server models available.
- Provides streaming API for the best user experience (unlike popular speech-recognition python packages)
- There are bindings for different programming languages, too - java/csharp/javascript etc.
- Allows quick reconfiguration of vocabulary for best accuracy.
- 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 contact@alphacephei.com.
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
Please note that some platforms are not fully supported by pip, for example you have install on arm64 from released wheels:
pip3 install https://github.com/alphacep/vosk-api/releases/download/0.3.7/vosk-0.3.7-cp37-cp37m-linux_aarch64.whl
Also please note that vosk requires libgfortran which might be missing, you might need to install it package manager.
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 --user --single-version-externally-managed --root=/
Run like this:
cd vosk-api/python/example
wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
unzip alphacep-model-android-en-us-0.3.zip
mv alphacep-model-android-en-us-0.3 model
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://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
unzip alphacep-model-android-en-us-0.3.zip
mv alphacep-model-android-en-us-0.3 model
make run
Or C#
cd csharp && KALDI_ROOT=<KALDI_ROOT> make
wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.3.zip
unzip alphacep-model-android-en-us-0.3.zip
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 contact@alphacephei.com
- Join our group dedicated to speech recognition on Telegram @speech_recognition