Lora

OpenAI’s Whisper is one of the best open-source speech models around. Out of the box, whisper-large-v3-turbo hits about 8% word error rate (WER) on general English tests like LibriSpeech. But point it at radiology reports, esports commentary, court audio, or factory SOPs and that number can spike to 30-50%. The model just hasn’t seen enough of those niche terms in training.

You can fix this. Fine-tuning Whisper on a small set of domain audio, as little as one to three hours, with LoRA adapters cuts domain-term WER by 30-60%. The full training run fits on a single consumer GPU with 12-16 GB of VRAM. It takes a couple of hours and yields an adapter file under 100 MB. Below is the full path from data prep to deployment.

The best way to fine-tune Stable Diffusion XL 2.0 is with Low-Rank Adaptation (LoRA) : a small adapter that injects your style or subject without touching the base weights. Instead of retraining the full model, LoRA trains a tiny side network next to the frozen base. The result is a 50 to 300 MB file you can load, swap, and stack at inference, trained on a 12 GB GPU in an afternoon.