[skim_milk]

I use a custom babbage model fined tuned using the original whisper transcripts as the prompt and the fixed transcript as the result. It does a very good job at correcting common jargon and names, correcting something like 1/3 to 1/2 of all total errors. Also is very good at correcting transcripts which whisper fails to put punctuation in for whatever reason by adding punctuation and capitalization where appropriate. I used this to fix 1000+ transcripts of lectures totaling around 700 hours of speech on the cheap.

Personally wouldn't recommend my approach however unless you are okay with doing some hardcore text manipulation and fuzzy math. It fails to produce text that matches up with the prompt 10% of the time and lots of other caveats with what to do with text that doesn't fit into the prompt.