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Silvius Backend

For the silvius client, see .

This is an essentially unmodified clone of Tanel Alumae's kaldi-gstreamer-server. In short, to run a Silvius server, first compile Kaldi with ./configure --shared. This can be an unmodified version of Kaldi. Then set KALDI_ROOT, clone and compile gst-kaldi-nnet2-online, and set the environment variable GST_PLUGIN_PATH to point at its src/ directory. Finally, run one master server and at least one worker. For more detailed instructions, see the INSTALL file.

The built-in Tedlium example can be used as an uncustomized model. A customized model for silvius v1.0 is currently available here: This will be downloaded by the script in models/.

To build customized language models, clone silvius-kaldi and compile Kaldi normally. My only additions are new scripts in the egs/ directory, so you may want to merge with Kaldi master first. Then go to egs/tedlium/silvius and execute This downloads a pre-trained Tedlium online nnet2 model. It reads corpus.txt and mixes it with the Cantab LM (, then converts to G.fst (local/, then compiles a new HCLG.fst (mix-hclg.fst). Finally, it makes a new directory under model/ (the first time it creates directory 1, then directory 2, etc). Just replace corpus.txt with new 1-grams to customize the model differently.

This model can be copied directly to the server's directory and referred to with silvius-tedlium.yaml. I set up a symlink tedlium-latest to point at the current model directory so I would not have to keep changing the config file. The only other step which must be made is to convert the config files to use local paths, i.e. search and replace in the model/conf/* files (sed -i):

sed -i 's|/export/a12/...|test/models/english/tedlium-N|' \

That's it. I recommend mixing language models on a server in the background as it takes significant RAM.

Kaldi GStreamer server

GitHub license Code Climate

This is a real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framework and implemented in Python.


  • Full duplex communication based on websockets: speech goes in, partial hypotheses come out (think of Android's voice typing)
  • Very scalable: the server consists of a master component and workers; one worker is needed per concurrent recognition session; workers can be started and stopped independently of the master on remote machines
  • Can do speech segmentation, i.e., a long speech signal is broken into shorter segments based on silences
  • Supports arbitrarily long speech input (e.g., you can stream live speech into it)
  • Supports Kaldi's GMM and "online DNN" models
  • Supports rescoring of the recognition lattice with a large language model
  • Supports persisting the acoustic model adaptation state between requests
  • Supports unlimited set of audio codecs (actually only those supported by GStreamer)
  • Supports rewriting raw recognition results using external programs (can be used for converting words to numbers, etc)
  • Python, Java, Javascript clients are available

English demo that uses the server:

Estonian demo:


  • 2016-11-28: Server now supports serving requests using SSL. SSL is automatically turned on when the certfile and keyfile command line arguments are specified.

  • 2016-10-14: Support for nnet3 (including 'chain') models, thanks to @yifan! Not tested very carefully. Set the decoder->nnet-mode property to 3 to use nnet3 models.

  • 2016-10-06: added a sample conf for Librispeech models and the corresponding model download script (thanks to @skoocda)

  • 2015-12-04: added a link to the Dockerfile.

  • 2015-06-30: server now uses the recently added "full final results" functionality of gst-kaldi-nnet2-online. Full results can include things like n-best hypotheses, word and phone alignment information, and possibly other things in the future. You have to upgrade gst-kaldi-nnet2-online (when using this plugin instead of the GMM-based Kaldi GStreamer plugin) prior to using this. Also added a sample full results post-processing script (see sample_english_nnet2.yaml on how to use it).



Building Kaldi and all the other packages required by this software can be quite complicated. Instead of building all the prerequisites manually, one could use the Dockerfile created by José Eduardo Silva:


Python 2.7 with the following packages:

NB!: The server doesn't work quite correctly with ws4py 0.3.5 because of a bug I reported here: Lawouach/WebSocket-for-Python#152. Use ws4py 0.3.2 instead. To install ws4py 0.3.2 using pip, run:

pip install ws4py==0.3.2

In addition, you need Python 2.x bindings for gobject-introspection libraries, provided by the python-gi package on Debian and Ubuntu.


Download and compile Kaldi ( Also compile the online extensions (make ext) and the Kaldi GStreamer plugin (see README in Kaldi's src/gst-plugin directory).

Acoustic and language models for Kaldi

You need GMM-HMM-based acoustic and n-gram language models (actually their FST cascade) for your language.

Working (but not very accurate) recognition models are available for English and Estonian in the test/models/ directory. English models are based on Voxforge acoustic models and the CMU Sphinx 2013 general English trigram language model ( The language models were heavily pruned so that the resulting FST cascade would be less than the 100 MB GitHub file size limit.

Update: the server also supports Kaldi's new "online2" online decoder that uses DNN-based acoustic models with i-vector input. See below on how to use it. According to experiments on two Estonian online decoding setups, the DNN-based models result in about 20% (or more) relatively less errors than GMM-based models (e.g., WER dropped from 13% to 9%).

Running the server

Running the master server

The following starts the main server on localhost:8888

python kaldigstserver/ --port=8888

Running workers

The master server doesn't perform speech recognition itself, it simply delegates client recognition requests to workers. You need one worker per recognition session. So, the number of running workers should be at least the number of potential concurrent recognition sessions. Good thing is that workers are fully independent and do not even have to be running on the same machine, thus offering practically unlimited parallelness.

There are two decoders that a worker can use: based on the Kaldi onlinegmmdecodefaster GStreamer plugin or based on the newer kaldinnet2onlinedecoder plugin. The first one supports GMM models, the latter one needs "online2" DNN-based models with i-vector input.

To run a worker, first write a configuration file. A sample configuration that uses the English GMM-HMM models that come with this project is available in sample_worker.yaml. A sample worker that uses "online2" DNN-based models is in sample_english_nnet2.yaml.

Using the 'onlinegmmdecodefaster' based worker

Before starting a worker, make sure that the GST plugin path includes Kaldi's src/gst-plugin directory (which should contain the file, something like:

export GST_PLUGIN_PATH=~/tools/kaldi-trunk/src/gst-plugin

Test if it worked:

gst-inspect-1.0 onlinegmmdecodefaster

The latter should print out information about the Kaldi's GStreamer plugin.

Now, you can start a worker:

python kaldigstserver/ -u ws://localhost:8888/worker/ws/speech -c sample_worker.yaml

The -u ws://localhost:8890/worker/ws/speech argument specifies the address of the main server that the worker should connect to. Make sure you are using the same port as in the server invocation.

You can start any number of worker processes, just use the same command to start the next workers.

It might be a good idea to use supervisord to start and stop the main server and several workers. A sample supervisord configuration file is in etc/english-supervisord.conf.

Server usage

A sample implementation of the client is in kaldigstserver/

If you started the server/worker as described above, you should be able to test the installation by invoking:

python kaldigstserver/ -r 32000 test/data/english_test.raw

Expected output:


Expected output when using using the DNN-based online models based on Fisher:

one two or three you fall five six seven eight. yeah.

The -r 32000 in the last command tells the client to send audio to the server at 32000 bytes per second. The raw sample audio file uses a sample rate of 16k with a 16-bit encoding which results in a byterate of 32000.

You can also send ogg audio:

python kaldigstserver/ -r 4800 test/data/english_test.ogg

The rate in the last command is 4800. The bit rate of the ogg file is 37.5k, which results in a byte rate of 4800.

Using the 'kaldinnet2onlinedecoder' based worker

The DNN-based online decoder requires a newer GStreamer plugin that is not in the Kaldi codebase and has to be compiled seperately. It's available at Clone it, e.g., under ~/tools/gst-kaldi-nnet2-online. Follow the instuctions and compile it. This should result in a file ~/tools/gst-kaldi-nnet2-online/src/

Also, download the DNN-based models for English, trained on the TEDLIUM speech corpus and combined with a generic English language model provided by Cantab Research. Run the under test/models to download the models (attention, 1.5 GB):

cd test/models 
cd ../../

Before starting a worker, make sure that the GST plugin path includes the path where the library you compiled earlier resides, something like:

export GST_PLUGIN_PATH=~/tools/gst-kaldi-nnet2-online/src

Test if it worked:

gst-inspect-1.0 kaldinnet2onlinedecoder

The latter should print out information about the new Kaldi's GStreamer plugin.

Now, you can start a worker:

python kaldigstserver/ -u ws://localhost:8888/worker/ws/speech -c sample_english_nnet2.yaml

As the acoustic models are trained on TED data, we also test on TED data. The file test/data/bill_gates-TED.mp3 contains about one minute of a TED talk by Bill Gates. It's encoded as 64 kb MP3, so let's send it to the server at 64*1024/8=8192 bytes per second:

python kaldigstserver/ -r 8192 test/data/bill_gates-TED.mp3

Recognized words should start appearing at the terminal. The final result should be something like:

when i was a kid the disaster we worry about most was a nuclear war. that's why we had a bear like this down our basement filled with cans of food and water. nuclear attack came we were supposed to go downstairs hunker down and eat out of that barrel. today the greatest risk of global catastrophe. don't look like this instead it looks like this. if anything kills over ten million people in the next few decades it's most likely to be a highly infectious virus rather than a war. not missiles that microbes now part of the reason for this is that we have invested a huge amount in nuclear deterrence we've actually invested very little in a system to stop an epidemic. we're not ready for the next epidemic.

Compare that to the original transcript in test/data/bill_gates-TED.txt:

When I was a kid, the disaster we worried about most was a nuclear war. That's why we had a barrel like this down in our basement, filled with cans of food and water. When the nuclear attack came, we were supposed to go downstairs, hunker down, and eat out of that barrel. Today the greatest risk of global catastrophe doesn't look like this. Instead, it looks like this. If anything kills over 10 million people in the next few decades, it's most likely to be a highly infectious virus rather than a war. Not missiles, but microbes. Now, part of the reason for this is that we've invested a huge amount in nuclear deterrents. But we've actually invested very little in a system to stop an epidemic. We're not ready for the next epidemic.

Retrieving and sending adaptation state

If you use the 'kaldinnet2onlinedecoder' based worker, you can retrieve the adaptation state after the decoding session finishes, and send the previously retrieved adaptation state when starting a new session.

The 'kaldinnet2onlinedecoder' worker always sends the adaptation state encoded in a JSON container once the session ends. Client can store it in a file. This is functionality is implemented by the Assuming that you started the server and a worker as in the last example, you can do:

python kaldigstserver/ -r 32000 --save-adaptation-state adaptation-state.json test/data/english_test.wav

The adaptation-state.json file will contain something like this:

{"type": "string+gzip+base64", "value": "eJxlvUuPdEmSHbavXx...", "time": "2014-11-14T11:08:49"}

As you can see, the adaptation state is not human-readable, it's actually gzipped and base64-encoded text data.

To start another decoding session using the saved adaptation state, you can do something like this:

python kaldigstserver/ -r 32000 --send-adaptation-state adaptation-state.json test/data/english_test.wav

Alternative usage through a HTTP API

One can also use the server through a very simple HTTP-based API. This allows to simply send audio via a PUT or POST request to http://server:port/client/dynamic/recognize and read the JSON ouput. Note that the JSON output is differently structured than the output of the websocket-based API. This interface is compatible to the one implemented by

The HTTP API supports chunked transfer encoding which means that server can read and decode an audio stream before it is complete.


Send audio to server:

 curl  -T test/data/english_test.wav  "http://localhost:8888/client/dynamic/recognize"


{"status": 0, "hypotheses": [{"utterance": "one two or three you fall five six seven eight. [noise]."}], "id": "7851281f-e187-4c24-9b58-4f3a5cba3dce"}

Send audio using chunked transfer encoding at an audio byte rate; you can see from the worker logs that decoding starts already when the first chunks have been received:

curl -v -T test/data/english_test.raw -H "Content-Type: audio/x-raw-int; rate=16000" --header "Transfer-Encoding: chunked" --limit-rate 32000  "http://localhost:8888/client/dynamic/recognize"

Output (like before):

{"status": 0, "hypotheses": [{"utterance": "one two or three you fall five six seven eight. yeah."}], "id": "4e4594ee-bdb2-401f-8114-41a541d89eb8"}

Websocket-based client-server protocol

Opening a session

To open a session, connect to the specified server websocket address (e.g. ws://localhost:8888/client/ws/speech). The server assumes by default that incoming audio is sent using 16 kHz, mono, 16bit little-endian format. This can be overriden using the 'content-type' request parameter. The content type has to be specified using GStreamer 1.0 caps format, e.g. to send 44100 Hz mono 16-bit data, use: "audio/x-raw, layout=(string)interleaved, rate=(int)44100, format=(string)S16LE, channels=(int)1". This needs to be url-encoded of course, so the actual request is something like:


Audio can also be encoded using any codec recognized by GStreamer (assuming the needed packages are installed on the server). GStreamer should recognize the container and codec automatically from the stream, you don't have to specify the content type. E.g., to send audio encoded using the Speex codec in an Ogg container, use the following URL to open the session (server should automatically recognize the codec):


Sending audio

Speech should be sent to the server in raw blocks of data, using the encoding specified when session was opened. It is recommended that a new block is sent at least 4 times per second (less frequent blocks would increase the recognition lag). Blocks do not have to be of equal size.

After the last block of speech data, a special 3-byte ANSI-encoded string "EOS" ("end-of-stream") needs to be sent to the server. This tells the server that no more speech is coming and the recognition can be finalized.

After sending "EOS", client has to keep the websocket open to receive recognition results from the server. Server closes the connection itself when all recognition results have been sent to the client. No more audio can be sent via the same websocket after an "EOS" has been sent. In order to process a new audio stream, a new websocket connection has to be created by the client.

Reading results

Server sends recognition results and other information to the client using the JSON format. The response can contain the following fields:

  • status -- response status (integer), see codes below
  • message -- (optional) status message
  • result -- (optional) recognition result, containing the following fields:
    • hypotheses - recognized words, a list with each item containing the following:
      • transcript -- recognized words
      • confidence -- (optional) confidence of the hypothesis (float, 0..1)
    • final -- true when the hypothesis is final, i.e., doesn't change any more

The following status codes are currently in use:

  • 0 -- Success. Usually used when recognition results are sent
  • 2 -- Aborted. Recognition was aborted for some reason.
  • 1 -- No speech. Sent when the incoming audio contains a large portion of silence or non-speech.
  • 9 -- Not available. Used when all recognizer processes are currently in use and recognition cannot be performed.

Websocket is always closed by the server after sending a non-zero status update.

Examples of server responses:

{"status": 9}
{"status": 0, "result": {"hypotheses": [{"transcript": "see on"}], "final": false}}
{"status": 0, "result": {"hypotheses": [{"transcript": "see on teine lause."}], "final": true}}

Server segments incoming audio on the fly. For each segment, many non-final hypotheses, followed by one final hypothesis are sent. Non-final hypotheses are used to present partial recognition hypotheses to the client. A sequence of non-final hypotheses is always followed by a final hypothesis for that segment. After sending a final hypothesis for a segment, server starts decoding the next segment, or closes the connection, if all audio sent by the client has been processed.

Client is reponsible for presenting the results to the user in a way suitable for the application.

Client software

Javascript client is available here:


If you use this software for research, you can cite the paper where this software is described (available here:

  author={Tanel Alum\"{a}e},
  title="Full-duplex Speech-to-text System for {Estonian}",
  booktitle="Baltic HLT 2014",
  address="Kaunas, Lithuania"

Of course, you should also acknowledge Kaldi, which does all the hard work.


Real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framwork.







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