We use Docker so you can try easily our decoding demo
Run the demo using the two commands:
- download image
docker pull ufaldsg/pykaldi
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
- Note the demo downloads the pretrained models and test data which you may safe using
- download image
- Start exploring the demo source codes online_demo/pykaldi-online-latgen-recogniser.py and 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).
- Our priority is to deploy it on Ubuntu 14.04 and also keep Travis running on Ubuntu 12.04
- Read INSTALL.rst and INSTALL first!
- INSTALL.rst contains instructions specific for this fork. INSTALL stores general instructions for Kaldi.
- This Kaldi fork is released under the Apache license, Version 2.0, which is also used by Kaldi itself.
We also publicly released Czech and English data for
kaldi/egs/vystadial_{cz,en}
recipe under Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license:- Czech data https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0023-4670-6
- English data https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0023-4671-4
Note that the data are automatically downloaded in the recipe scripts.
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
andPyOnlineLatticeRecogniser
, which produce the exact same lattices using the same setup.
- pykaldi/src/onl-rec stores C++ code for
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
- This Kaldi fork is developed under Vystadial project.
- Based on the Svn trunk of Kaldi project which is mirrored to branch
svn-mirror
. - The svn trunk is mirrored via
git svn
. Checkout tutorials: Git svn, Svn branch in git