https://github.com/alphacep/vosk-api
Still, I've found that the Big players have much better recognition models, and the post-processing that I assume they do (grammatical, maybe syntactical inferences that improve the end result) are probably much more powerful too.
https://trello.com/c/lEZG0VYS/259-%E2%9C%A8-100-implement-ne... and https://trello.com/c/nxEstkvo/260-%E2%9C%A8-100-implement-ne...
My napkin calculation is that you need about $100k for each attempt at training a Conformer-Transducer. There's a pre-trained NVIDIA model but it appears to have a bad choice of hyperparameters and performance is much worse than what one would expect based on research literature and I believe you're not allowed to execute it on non-NVIDIA hardware.
A skilled team will maybe need 5-10 attempts for discovering a good set of hyperparameters. So the price to create the AI model will likely be around $1 mio. But if you have such large expenses, you have to plan things as a business venture. And that means an open source release is highly unlikely.
(unless, of course, someone like stability.ai is happy to bankroll 200 A100 GPUs for a few months each per target language. In that case, please contact me)
You can contribute to improving it here, too: https://commonvoice.mozilla.org/en
"You are in control of your data. Leon lives on your server"
Speech-to-Text: Google Cloud, IBM Watson, Coqui STT, Alibaba Cloud (coming soon), Microsoft Azure (coming soon)
So the AI assistant lives on my server, but if I want to have good quality speech recognition, everything I say is sent through a US cloud service. The only offline option, Coqui has a 7.5% word error rate [1] on LibriSpeech test clean, which is worse than Mozilla Deepspeech 2 from 2016 [2]. State of the art would be around 1.4% [3], meaning 81% less errors than Coqui.
[1] https://coqui.ai/blog/stt/deepspeech-0-6-speech-to-text-engi... [2] https://paperswithcode.com/paper/deep-speech-2-end-to-end-sp... [3] https://paperswithcode.com/paper/pushing-the-limits-of-semi-...