I was wondering how that compared to Vosk, a lightweight pip-installable speech-to-text library. So I ran Vosk on some of the videos to compare. The model I use

I was wondering how that compared to Vosk, a lightweight pip-installable speech-to-text library. So I ran Vosk on some of the videos to compare. The model I used is 40MB and transcribed the 32min keynote in 2m12s on 1 CPU core, using 400MB of memory. The result is not that far. There are some errors of course, notably on acronyms and technical terms ("IBM", "GNU", "GitHub"), but a lot of the mismatches are in areas where both models got things wrong. Neither of the models got "FOSDEM" right, though Whisper is consistent at using the word "fosstem", whatever that is?