Filip Jurčíček

Jan Švec and I wrote Gmail Backup back in 2008 — a one-click tool that pulled your Gmail down over IMAP into a local folder, with incremental backups so you only fetched what was new. It ran on Windows and Linux, the code went out under GPL v3, and for a few years it was the answer people gave when someone asked how to get their mail off Google's servers. It is old software and the world has moved on, but the site is still up: www.gmail-backup.com , with the downloads, the documentation, the FAQ and the forum threads intact. I recently moved the whole thing to static hosting and put it back on HTTPS, which is the only reason it still loads at all. Worth remembering the premise, which has not aged: your mail is only yours if you have a copy of it.

A note on what I do when I am not writing about KALDI graphs: I take on voice AI work through Voicebre Systems , a small consultancy based in Prague. Three things, mostly: custom speech recognition — including voice commands and languages that the off-the-shelf stacks handle badly; natural language understanding for conversational systems; and end-to-end voice agents, from VUI design through to integration with whatever is already running in the business. The interesting problems are usually not the model. They are the data you are allowed to use, the latency budget, and what happens on the turn where the user says something nobody anticipated. If that is the shape of your problem, get in touch .

Most dictation today is a round trip to someone else's GPU. That is fine until your voice is the part the model was never trained on — an accent, a speech difference, a bad microphone in a noisy room — and you have no way to make it adapt. That is the problem Vokarys goes after. It is a desktop app that turns speech into text with recognition running locally on your computer, with punctuation and capitalisation added as you talk. It learns from your own voice over time, and recordings stay on the machine unless you explicitly choose to contribute them. There is a voice chat mode wired to Gemini as well, and a browser version if you would rather not install anything. If you have given up on dictation because the generic model never quite got you, it is worth a look.

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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}/...

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...

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