Local Llm

I like the idea of running my own AI. No bill, no data leaving my house. So when I needed to clean up a long transcript, I tried the local way first. The result changed my mind about when “free and local” is worth it. The Need: Speech-to-Text Gets Names Wrong Whisper turns a Korean sermon into text very well, but it makes a certain kind of mistake. It hears Bible words as ordinary words. A town name becomes a nonsense word. A book of the Bible becomes a different book. And one example I like: the Korean word for Psalms, sipyeon (시편), sounds almost like “ten pyeon” (10편). Whisper wrote “10” where the speaker said “Psalms”. ...

A transcript without speaker names is hard to use. If an audio file has two people talking, I need to know which words came from which person. My pipeline can label speakers automatically. The question was whether I could trust the labels. I did not want to trust them just because the output looked clean. So I made an answer key by hand, and I let the computer grade itself against it. ...