I replied, "never stop stopping!" and we had a stalemate, lol

This blog post here summed it up the best. These are tools, so let them be tools.

https://geohot.github.io/blog/jekyll/update/2026/07/11/ai-20...

Only way I have found that's relatively easy to inject technical vocab is to use whisper, but limited, I think to about 220 or so tokens. Whisper has sort of like a priming prompt where one can put in a bunch of technical words and it will try to recognize those. But again, that's limited to small number tokens. And that limits one use a relatively slow, by today's standards, whisper.cpp.

I benchmarked it across a bunch of different hardware that I have available, and Whisper gives decent performance as far as speed goes only on a pretty top-end GPU, such as a 5090 or 4070, like for example on Strix Halo, it's still relatively slow for longer transcriptions because I prefer just a stream of consciousness ramblings for minutes and then that being transcribed and cleaned up versus short sentences. So in that scenario something like 5090 really is good because the cleanup prompt runs fast using usually Qwen 3.6 MOE model. Whisper on 4070 itself is about 0.7 seconds for two or three minute transcription. So the total wait time for a three-minute transcription is roughly a second, or a little bit more than a second, so totally acceptable. But it does take decent hardware, and it grows to be double that on if running totally local. Well, in my case, it's all local, but it's my own hardware all over the place, but truly running on laptop, it's much faster using Parakeet, but then the cleanup is the bottleneck.

Anyway, it's just my experience messing around with this for the last year. I did start using AquaVoice, but their speed was exceptional, and tech vocab was exceptional, but they would have some annoying delays occasionally, and I didn't like paying the money and sending sensitive topics and screenshots into the cloud, and I had hardware, so my local solution is basically almost as good as commercial one. But I think they train their own model. So what I'm doing is I collect all the samples of my transcriptions, and I am slowly building my own data set that hopefully at some point when I get energy I will find some way to fine tune something.