Filip Jurčíček

Key Action Notes Ctrl+o toggle panes on/off Ctrl+l redraw screen This is on  all terminals Ctrl+PgUp goto parent dir Ctrl+Enter copy selected filename to command line %f is equivalent Ctrl+x+p copy unselected panel's path to command line %D is equivalent Ctrl+x ! External panelize Display paths returned from external command Shift+mouse select text Insert toggle selection of highlighted file * toggle selection + add pattern to selection - remove pattern from selection F3 view F4 edit F5 copy F6 rename F7 mkdir F8 remove F9 menu F10 Exit

Well, I had again to do something ;-) The task is to generate/create/update a decoding graph for KALDI on the fly. In my case, I aim at changing a G (grammar) in the context of a dialogue system. One can generate a new HCLG but this would take a lot of time as this involves FST determinization, epsilon-removal, minimization, etc. Therefore, I tried to use on-the-fly composition of statically prepared HCL and G. At first, I struggled with it but later I made it work. See  https://github.com/jpuigcerver/kaldi-decoders/issues/1 Here is a short summary: At the end, I managed to get LabelLookAheadMatcher to work. It is mostly based on the code and examples in opendcd, e.g. https://github.com/opendcd/opendcd/blob/master/script/makegraphotf.sh . First, Here is how I build and prepare the HCL and G. Please not that OpenFST must be compiled with  --enable-lookahead-fsts , see http://www.openfst.org/twiki/bin/view/FST/ReadMe . #--------------- fstdeterminize ${lang}/...

I had to do one more thing: to edit a trained  kaldi  nnet3 chain model and add a softmax layer on top of the chain model. The reason for this is to get "probability" like output directly from the chain model First, let's look at the nnet structure: nnet3-am-info final.mdl input-dim: 20 ivector-dim: -1 num-pdfs: 6105 prior-dimension: 0 # Nnet info follows. left-context: 15 right-context: 15 num-parameters: 15499085 modulus: 1 input-node name=input dim=20 component-node name=L0_fixaffine component=L0_fixaffine input=Append(Offset(input, -1), input, Offset(input, 1)) input-dim=60 output-dim=60 component-node name=Tdnn_0_affine component=Tdnn_0_affine input=L0_fixaffine input-dim=60 output-dim=625 component-node name=Tdnn_0_relu component=Tdnn_0_relu input=Tdnn_0_affine input-dim=625 output-dim=625 component-node name=Tdnn_0_renorm component=Tdnn_0_renorm input=Tdnn_0_relu input-dim=625 output-dim=625 component-node name=Tdnn_1_affine component=Tdnn_1_affi...

I had a task to edit a trained kaldi  nnet3 chain model so that the output node is the output-xent instead the original output. First, let's look at the nnet structure: nnet3-am-info final.mdl input-dim: 20 ivector-dim: -1 num-pdfs: 6105 prior-dimension: 0 # Nnet info follows. left-context: 15 right-context: 15 num-parameters: 15499085 modulus: 1 input-node name=input dim=20 component-node name=L0_fixaffine component=L0_fixaffine input=Append(Offset(input, -1), input, Offset(input, 1)) input-dim=60 output-dim=60 component-node name=Tdnn_0_affine component=Tdnn_0_affine input=L0_fixaffine input-dim=60 output-dim=625 component-node name=Tdnn_0_relu component=Tdnn_0_relu input=Tdnn_0_affine input-dim=625 output-dim=625 component-node name=Tdnn_0_renorm component=Tdnn_0_renorm input=Tdnn_0_relu input-dim=625 output-dim=625 component-node name=Tdnn_1_affine component=Tdnn_1_affine input=Append(Offset(Tdnn_0_renorm, -1), Tdnn_0_renorm, Offset(Tdnn_0_renorm, 1)) in...

Filip Jurčíček: „Zajímá mě přirozenost komunikace pomocí hlasu“ : 'via Blog this'

An exact solution to the problem of online SVM learning has been found by Cauwenberghs and Poggio (2001). Their incremental algorithm (hereinafter referred to as a C&P algorithm) updates an optimal solution of an SVM training problem after one training example is added (or removed). 'via Blog this'

Very inspiring. How to maintain a successful open source project — Medium : 'via Blog this'

Backup your hard drive over the network with dd and ssh : Citation: ... wanted to copy everything off the disk and send it over the network. So we can do it with ssh. First zero out the non used space on the running disk to make compressing the image much eaiser. Using the command: dd if=/dev/zero of=0bits bs=20M; rm 0bits Then boot knoppix (or any other bootable linux distro like sysrescuecd) from the machine you want to image and give the command: dd if=/dev/sda | gzip -1 - | ssh user@hostname dd of=image.gz Assuming sda is your hard drive. This sends the local disks data to the remote machine. To restore the image boot knoppix on the machine to restore and pull the image that you created and dump it back with the command: ssh user@hostname dd if=image.gz | gunzip -1 - | dd of=/dev/sda

php strict warnings | Drupal Groups : Thanks, forcing 5.2 worked. Login  or  register  to post comments Disabling warnings from php strict in Drupal Posted by  DarrellDuane  on  September 26, 2012 at 3:21pm I'm well aware that hacking core is frowned upon, but sometimes ya gotta do what ya gotta do. Running drupal 6 with views 2.x using PHP 5.4 (Fedora 17 in this case) yields a number of strict warnings that are not easily turned off. PHP doesn't allow you to turn off these strict warnings in its php.ini file, they come through even if E_STRICT is disabled, see http://stackoverflow.com/questions/4692999/e-strict-messages-thrown-thou...  for more details. In order to fix this, I have edited includes/bootstrap.inc and put the following at the beginning of function drupal_set_message starting after line 991: // filter out strict warnings due to conflicts between views-2.x and PHP 5.4 if( !strncmp($message, 'strict warning:', 14) ) { return isset(...

You and Your Research : "r. Somehow they seem to work on slightly the wrong thing - not much, but enough that they miss fame." 'via Blog this'

Those who have a problem compiling old versions of Perl on Ubuntu, might want to read the next article. Ubuntu 11.04 and perlbrew installation problems · Issue #64 · gugod/App-perlbrew · GitHub : 'via Blog this'

Named locking in Python VM Farms: Cross Process Locking and Synchronization in Python : 'via Blog this'

This is very important for those using Julius ASR. This is a must. Julius • View topic - Using a SRILM N-gram on Julius : 'via Blog this'

Introduction to ASR and HTK in particular 'via Blog this'

USING HTK : UNDERSTANDING HTK ERROR MESSAGES 'via Blog this'

A nice tutorial about the HTK :  http://my.fit.edu/~vkepuska/HTK/icassp06_tutorial.pdf 'via Blog this'

One of the most interesting courses ever ;-) Watch the lectures 9 - 14! Information Theory, Pattern Recognition, and Neural Networks : 'via Blog this'

How do I sync multiple google calendars to my iPad.. It will only sync one. - Google Groups : Use this:  www.google.com/calendar/ iphoneselect 'via Blog this'

This is worth reading. McDonald, Ryan. 2007. Generalized Linear Classifiers in NLP . Course presented to the Swedish Graduate School in Language Technology.