Vosk Speech Recognition Toolkit

Vosk is an offline open source speech recognition toolkit. It enables speech recognition models for 18 languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian.

Vosk models are small (50 Mb) but provide continuous large vocabulary transcription, zero-latency response with streaming API, reconfigurable vocabulary and speaker identification.

Speech recognition bindings implemented for various programming languages like Python, Java, Node.JS, C#, C++ and others.

Vosk supplies speech recognition for chatbots, smart home appliances, virtual assistants. It can also create subtitles for movies, transcription for lectures and interviews.

Vosk scales from small devices like Raspberry Pi or Android smartphone to big clusters.

Documentation

For installation instructions, examples and documentation visit Vosk Website.

Test the functionality implemented in this repository using C scripts

1. Clone the ETS fork of the Vosk API repository https://github.com/EducationalTestingService/vosk-api.

2. If you are using your own custom ASR model, copy the model into the model directory in the structure shared in the section Model structure on VOSK-API website. Alternatively, you can download one of the open-source ASR models available on Vosk-API website, uncompress the downloaded zip file and, rename the containing directory to model. NOTE: To be able to extract phoneme labels and timestamps, you need to include the phones.txt file that was used during ASR model buildiong in the model/graph directory, so Vosk's open source models may not work out of the box.

3. Add the audio file to be used to test the C test script under c and rename the file to test.wav to use the script as is.

4. The docker image for building Kaldi and the Python wheels is located in the cloned repository under travis.

   Build and run the docker image as follows:

   docker build --file Dockerfile.manylinux --tag alphacep/kaldi-manylinux:latest .
   docker run -ti -v $(pwd)/..:/io alphacep/kaldi-manylinux /bin/bash
   

5. Inside the docker container under the io/src folder run KALDI_ROOT=/opt/kaldi make all to compile your changes.

6. Go to the io/c folder ln -s ../model . to symlink the model directory you created in step 2 inside this folder. Next, run KALDI_ROOT=/opt/kaldi make all to compile the C scripts. This should create an executable for the C script. To test the custom functionalities built by ETS team, run the command ./test_phone_results. This should print out the expected results for the script.