Tag
parakeet
7 posts · all tags
24 Cheshvan 5787
Improving a personal ASR model by using it every day
How to improve a personal speech-to-text model by using it daily: an agent reviews your transcript history, you confirm the errors, and small fine-tunes follow. A playbook from Parakeet and Hebrish, with a PDF and a Claude Code skill.
23 Cheshvan 5787
Fritter: a Parakeet fine-tune for English tech dictation
Fritter is my Parakeet TDT 0.6B v2 fine-tune for English dictation without Hebrew: tech terms, numbers and brand names. Tech WER falls from 7.53% to 4.01% on my voice. Browser demo and GGUF builds.
23 Cheshvan 5787
Proofed: could a speech model proofread its own transcript?
An idea in development: fine-tune Parakeet on cleanup alone, so dictation comes out without fillers, with digits and paragraphs, in one pass and with no LLM afterwards. Research done, data being recorded.
10 Cheshvan 5787
Jachnun and Sabich: the first stable Hebrish models, in depth
A deep dive into the first stable Hebrish models, Jachnun v1.0 and Sabich v1.0: the data, the training recipe, the results against stock Parakeet and Whisper, the formats, and the full white paper.
9 Cheshvan 5787
Boxty v1.0: English dictation without the Hebrew
Boxty is the no-Hebrew sibling of my Jachnun code-switching model: Parakeet fine-tuned on my voice for plain English and tech dictation. Tech WER drops from 2.51% (stock) to 1.35%, and general English holds.
8 Cheshvan 5787
Voice typing that understands Hebrish
Speech-to-text mangles the Hebrew words English speakers in Israel use every day. Hebrish fixes that: free on-device apps for Android, Linux and Windows, the Jachnun and Sabich models, results, and how you can help.
4 Cheshvan 5787
Can speech-to-text spell ‘makolet’? I tested seven models on Hebrish
English speakers in Israel talk Hebrish, and dictation software mangles the Hebrew words. I built an open benchmark to measure it: my fine-tune gets two-thirds of them right, off-the-shelf models a third to under half.