Show HN: Teach a speech model (en) a name by saying it once, on your Mac

A developer released a LoRA adapter for Qwen3-ASR 1.7B that transcribes custom names after hearing a single spoken recording of each, packaged in a free local Mac app called Detail Keeper. On the project's open benchmark of audiobooks with 1,100 name mentions, the adapter spelled taught names correctly 89.6% of the time versus 88.2% for Eleven Labs Scribe with keyterms, a difference the author called not measurable, though it overwrites more untaught names and alters slightly more ordinary words. Detail Keeper requires an Apple M-series Mac on OS 26 or later, downloads about 3.6GB of models, works only in English, and the adapter is published on Hugging Face at huggingface.co/virgil-coach/own-names-qwen3-asr.

I wanted to see if a speech model could spell a name better if it heard it, not just read it. Most STT tools handle custom words as text keyterm list, fixing transcript after with llm, etc . So I trained an adapter to try it, specifically a LoRA on Qwen3-ASR 1.7b see benchmark . This LoRA transcribes words only after it hears a recording of each of your custom words. To make this model easier to use in day-to-day life, we also built Detail Keeper. It's a Mac app where you can put this adapter to use by specifying difficult-to-transcribe-correctly words and then having them picked up correctly in your recordings. We've found this helpful in two specific cases: 1. Words STT has never heard: invented characters and places, codenames, unusual surnames. 2. Your spelling of a word it does know: Kathryn and not Catherine, Jon and not John. I wasn't able to achieve both good recognition and recognizing many words 1k+ with the model alone. Detail Keeper first narrows each passage to the likeliest taught names by sound. Then the adapter writes the passage, and the app checks it against a plain transcript. On our open benchmark audiobooks, 1,100 name mentions it spells taught names as well as Eleven Labs Scribe with keyterms: 89.6% vs 88.2%, no measurable difference. The only downside is that it overwrites a few more untaught names and changes slightly more ordinary words. Everything, including where we lose, is at detailkeeper.com/benchmark. Note: It's free, local and nothing leaves your Mac, but it does require an Apple M series+ mac and OS 26+ currently. Also worth noting that it downloads about 3.6GB of models and only works in English right now. If you're interested, you can also find the adapter itself on HF: huggingface.co/virgil-coach/own-names-qwen3-asr Comments URL: https://news.ycombinator.com/item?id=50383785 https://news.ycombinator.com/item?id=50383785 Points: 2 Comments: 0