Episode Details

Daniel fine-tuned Parakeet twice — once for Hebrew-English code-switching, once for niche technical vocabulary. Can he staple those fine-tunes together into one model that keeps both skills? This episode walks through the model merging family — weight averaging, task arithmetic, TIES-Merging, and LoRA merging — and then asks the harder question underneath: is independently building specialized fine-tunes and merging them later a real, evidence-supported path to better ASR? Whisper merges are showing twelve percent relative WER drops, and TIES with correction cut Fair-Speech WER from 7.38 to 5.13. But tokenizer mismatch and non-local merging may be the actual blockers. Key sources: • nvidia/parakeet-tdt-0.6b-v3 model card, Hugging Face, released 2025-08-14 — https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 • NeMo-Speech Fine-Tuning docs — https://docs.nvidia.com/nemo/speech/latest/asr/fine_tuning.html • arcee-ai/mergekit merge_methods docs — https://github.com/arcee-ai/mergekit/blob/main/docs/merge_methods.md Episode 6026 — myweirdprompts.com/n/6026 (permanent short link: myweirdprompts.com/757014)