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Fork of Kaldi for developing custom recognisers for Alex spoken dialogue system framework

News & info

  • We use Docker <https://registry.hub.docker.com/search?q=ufaldsg/pykaldi>_ so you can try easily our decoding demo

    • Run the demo using the two commands:

      1. download image docker pull ufaldsg/pykaldi
      2. run the demo docker run ufaldsg/pykaldi /bin/bash -c "cd online_demo; make gmm-latgen-faster; make online-recogniser; make pyonline-recogniser"

        • Note the demo downloads the pretrained models and test data which you may safe using docker commit functionality
    • Start exploring the demo source codes online_demo/pykaldi-online-latgen-recogniser.py <https://github.com/UFAL-DSG/pykaldi/blob/master/online_demo/pykaldi-online-latgen-recogniser.py> and onl-rec/onl-rec-latgen-recogniser-demo.cc <https://github.com/UFAL-DSG/pykaldi/blob/master/onl-rec/onl-rec-latgen-recogniser-demo.cc>

    • Please note, that you need to change the source code of Pykaldi in the docker image to effect the demo behaviour when using docker.
  • The Python wrapper of C++ OnlineLatticeRecogniser implements MFCC, LDA+MLLT, bMMI acoustic models since it was the best speaker independent setup.

  • UPDATE: Since 11/18/2014 the Pykaldi fork uses the Kaldi official code (src/online2) which has very similar as our previous implementation (and was finished roughly 8 month after our implementations).

Install

.. image:: https://travis-ci.org/UFAL-DSG/pykaldi.svg?branch=master
:target: https://travis-ci.org/UFAL-DSG/pykaldi

  • Our priority is to deploy it on Ubuntu 14.04 and also keep Travis running on Ubuntu 12.04
  • Read INSTALL.rst <./INSTALL.rst> and INSTALL <./INSTALL> first!
  • INSTALL.rst <./INSTALL.rst> contains instructions specific for this fork.
    INSTALL <./INSTALL>
    stores general instructions for Kaldi.

LICENSE

History

The fork presented three new Kaldi features in thesis of Ondrej Platek (see commit 8e534b16bb8a350):

  • Training scripts which can be used with standard Kaldi tools or with the new OnlineLatticeRecogniser.
    The scripts for Czech and English support acoustic models obtained using MFCC, LDA+MLLT/delta+delta-delta feature transformations and acoustic models trained generatively or by MPE or bMMI training.

The new functionality was separated to different directories:

  • pykaldi/src/onl-rec stores C++ code for OnlineLatticeRecogniser.
  • pykaldi/pykaldi stores Python wrapper PyOnlineLatticeRecogniser.
  • kaldi/egs/vystadial_{cz,en}/s5 stores training scripts. [merged to oficial Kaldi repo]
  • kaldi/online_demo shows Kaldi standard decoder, OnlineLatticeRecogniser and PyOnlineLatticeRecogniser, which produce the exact same lattices using the same setup.

The OnlineLatticeRecogniser is used in Alex dialogue system (https://github.com/UFAL-DSG/alex).

In March 2014, the PyOnlineLatticeRecogniser recogniser was evaluated on Alex domain.
See graphs evaluating OnlineLatticeRecogniser performance at http://nbviewer.ipython.org/github/oplatek/pykaldi-eval/blob/master/Pykaldi-evaluation.ipynb.

An example posterior word lattice output for one Czech utterance can be seen at http://oplatek.blogspot.it/2014/02/ipython-demo-pykaldi-decoders-on-short.html

Other info

  • This Kaldi fork is developed under Vystadial project <https://sites.google.com/site/filipjurcicek/projects/vystadial>_.
  • Based on the Svn trunk of Kaldi project <svn://svn.code.sf.net/p/kaldi/code/trunk>_ which is mirrored to branch svn-mirror.
  • The svn trunk is mirrored via git svn.
    Checkout tutorials: Git svn <http://viget.com/extend/effectively-using-git-with-subversion>,
    Svn branch in git <http://ivanz.com/2009/01/15/selective-import-of-svn-branches-into-a-gitgit-svn-repository>
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