Episode Details

Daniel fine-tuned a speech recognition model on two hours of his own voice and hit 60% accuracy on code-switched Hebrew, up from under 20% across every leading model he tested. The catch: Parakeet V2 is English-only, and Hebrew isn't in its 25-language sibling either. This episode digs into what a fine-tune actually captures — new tokens versus new accents, why control sentences matter, and whether a 30% English ratio is the right number. Plus: why fine-tuning helps where a model is ignorant and hurts where it's merely imprecise, and the hallucination risk that comes with over-controlling. Key sources: • NVIDIA Parakeet TDT 0.6B V2 model card, release 2025-05-01 — https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2 • Daniel's Whisper fine-tune benchmark — https://github.com/danielrosehill/Whisper-Fine-Tune-Accuracy-Eval • Choosing Epochs for ASR Fine-Tuning, 2025-11-25 — https://gist.github.com/danielrosehill/0fd5579dd554596b9c4a73fedf917697 Episode 6022 — myweirdprompts.com/n/6022 (permanent short link: myweirdprompts.com/443386)