Filip Jurčíček

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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}/L_disambig.fst

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)) input-d

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'