Kaldi Speech Recognition Toolkit VS kaldi-gstreamer-server

- 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.

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.

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.

I'm not how sure it stacks with recent state of art, but Kaldi toolkit (https://github.com/kaldi-asr/kaldi) used to be popular for building all kinds of practical integrations and experiments for speech recognition.

Kaldi ASR is a well-known open source Speech Recognition platform. To use its Speaker Diarization library, you’ll need to either download their PLDA backend or pre-trained X-Vectors, or train your own models.

Vosk-api isn't an SST engine itself, it is built using the Kaldi speech recognition toolkit (https://github.com/kaldi-asr/kaldi) and nicely implements and packages an API for Kaldi chain/LF-MMI models.

https://kaldi-asr.org/ (best out of the box accuracy but it is a complicated toolkit and not beginner friendly)

Through my research, the most promising real-time transcription options appear to be Vosk or Kaldi Gstreamer. I’ve set them both up & they appear to work well for general transcription, but I’m not sure how to handle the user-specific custom vocabularies.

It is kind of difficult to find something like this free of charge (and open source) since the ASR service needs to be hosted somewhere. If you are really interested in the topic then you could take a lit into kaldi and its pretrained models (but kaldi is kind of difficult to learn so I don't really recommend it if you want something quick) and then you could also combine that with kaldi-gstreamer in order to set up a server which you can turn on and off whenever you like.