Kaldi Speech Recognition Toolkit VS bert-for-inference

Yeah, whisper is the closest thing we have, but even it requires more processing power than is present in most of these edge devices in order to feel smooth. I've started a voice interface project on a Raspberry Pi 4, and it takes about 3 seconds to produce a result. That's impressive, but not fast enough for Alexa.

From what I gather a Pi 5 can do it in 1.5 seconds, which is closer, so I suspect it's only a matter of time before we do have fully local STT running directly on speakers.

> Probably anathema to the space, but if the devices leaned into the ~five tasks people use them for (timers, weather, todo list?) could probably tighten up the AI models to be more accurate and/or resource efficient.

Yes, this is the approach taken by a lot of streaming STT systems, like Kaldi [0]. Rather than use a fully capable model, you train a specialized one that knows what kinds of things people are likely to say to it.

[0] http://kaldi-asr.org/

You can study CTC in isolation, ignoring all the HMM background. That is how CTC was also originally introduced, by mostly ignoring any of the existing HMM literature. So e.g. look at the original CTC paper. But I think the distill.pub article (https://distill.pub/2017/ctc/) is also good.

For studying HMMs, any speech recognition lecture should cover that. We teach that at RWTH Aachen University but I don't think there are public recordings. But probably you should find some other lectures online somewhere.

You also find a lot of tutorials for Kaldi: https://kaldi-asr.org/

Maybe check this book: https://www.microsoft.com/en-us/research/publication/automat...

The relation of CTC and HMM becomes intuitively clear once you get the concept of HMMs. Often in terms of speech recognition, it is all formulated as finite state automata (FSA) (or finite state transducer (FST), or weighted FST (WFST)), and the CTC FST just looks a bit different (simpler) than the traditional HMMs, but in all cases, you can think about having states with possible transitions.

This is all mostly about the modeling. The training is more different. For CTC, you often calculate the log prob of the full sequence over all possible alignments directly, while for HMMs, people often use a fixed alignment, and calculate framewise cross entropy.

I did some research on the relation of CTC training and HMM training: https://www-i6.informatik.rwth-aachen.de/publications/downlo...

- https://github.com/kaldi-asr/kaldi

Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.

Frameworks as well as toolkits like Kaldi were at first promoted by the research study area, yet nowadays used by both scientists and also market experts, reduced the access obstacle in the advancement of automatic speech recognition systems. Nonetheless, cutting edge methods need big speech data readies to achieve a usable system.

Hello. I'm trying to package the openfst library (here)[https://github.com/void-linux/void-packages/pull/39015] but a developer says 32-bit must be cross compiled from 64-bit. I see xbps-src has a nocross option, but I don't see a way to only cross compile. What do you think I should do? I have currently limited the archs to 64-bit ones. Here's my issue with the developer's response: https://github.com/kaldi-asr/kaldi/issues/4808 Thank you.

If you interested in unix-like software design and not yet familiar with kaldi toolkit, you definitely need to check it https://kaldi-asr.org

It extended Unix design with archives, control lists and matrices and enabled really flexible unix-like processing. For example, recognition of a dataset looks like this:

extract-wav scp:list.scp ark:- | compute-mfcc-feats ark:- ark:- | lattice-decoder-faster final.mdl HCLG.fst ark:- ark:- | lattice-rescore ark:- ark:'|gzip -c > lat.gzip'

Another example is gstreamer command line.

No, speaker diarization is not part of Whisper. There are open source projects - such as Kaldi [1], but it's hard to get them running if you are not an area expert.

[1] https://kaldi-asr.org/

State-of-the-art ASR, like what you get on smartphones, has unfortunately high resource requirements. Some recent smartphone models are able to run ASR on-device, but more typically, ASR is done by sending audio to a web service. Check out the (currently experimental) Web SpeechRecognition API in a Chrome browser. Here is a demo of the API in action. For something open source, check out Kaldi ASR.

Super vague answer:

Put the questions in some semantic embedding space. Now you’ll have a vector representing each question. Then for each question, you can sort all the questions by how far the Euclidean distance is between their vectors. Or use some clustering algorithm like k means to find clusters.

By Googling I found this to tutorial to put sentences in an embedding space: https://github.com/BramVanroy/bert-for-inference/blob/master...

I did not read this and am not endorsing it, but it looks like it’s doing the right thing.