There are 3 basic image categories:
- all the Kaldi programs compiled and ready to use stored as danijel3/kaldi-onlinefork
- the above image plus a model, for example danijel3/kaldi-onlinefork:aspire (there can be many different models)
- the web server (which inludes the HTTP and the WebRTC server as described in this repo) stored as danijel3/kaldi-webrtc
To run the server you need the last two images: the model+Kaldi and the webrtc server. They need to be connected to each other through a common network. You can have many Kaldi instances, but need only one webrtc server.
You can use the sample given in the model directory. To prepare the image you only need to create a model subirectory there (so model/model) and put two subdirectories inside:
- model (ie. model/model/model) is for the online acoustic model and contains the following files (usually you get
this folder by running the steps/online/prepare_online_decoding.sh script):
- final.mdl
- tree
- conf subdirectory
- ivectror_extractor subdirectory
- graph is for the language model, ie HCLG transducer, with the following files:
- HCLG.fst
- words.txt
The model configuration files need to be modified so that the paths inside all point to as if the model dir is stored in the root dir, eg:
- /model/model/ivector_extractor/final.mat
- /model/model/conf/splice.conf
- /model/model/conf/online_cmvn.conf
Once you create such a folder, you can build the image by running the following command: docker build -t mymodel .
(You can change the mymodel tag to anything you prefer)
To test the image, you can run it on its own: docker run --rm -p 5050:5050 mymodel
Once it's running you can feed it some data using netcat: nc localhost:5050 < audio.raw
More instructions on how to use the online decoder with netcat is available at the bottom of this document.