I run whisper large-v3 on an m2 max 96gb and even with just inference the memory gets tight on longer audio, can only imagine what fine-tuning looks like. Does the 64gb vs 96gb make a meaningful difference for gemma 4 fine-tuning or does it just push the oom wall back a bit? Been wanting to try local fine-tuning on apple silicon but the tooling gap has kept me on inference only so far.

Hey I was literally just working on this today (I was racing ahead on an audio FT myself but OP beat me by a few hours). For audio inference definitely try running your input through VAD first to drop junk data and if necessary, as one of several preprocessing steps before sending the audio to the large model. You can check out how I did it here: https://github.com/accretional/vad/blob/main/pkg/vad/vad.go

I was using https://huggingface.co/onnx-community/pyannote-segmentation-... because with ONNX, I could run it on Intel servers with vectorized instructions, locally on my Mac, AND in-browser with transformers.js

VAD is absurdly time-effective (I think like O(10s) to segment 1hr of audio or something) and reduces the false positive rate/cost of transcription and multimodal inference since you can just pass small bits of segmented audio into another model specializing in that, then encode it as text before passing it to the expensive model.

Memory usage increases quadratically with sequence length. Therefore, using shorter sequences during fine-tuning can prevent memory explosions. On my 64GB RAM machine, I'm limited to input sequences of about 2,000 tokens, considering my average output for the fine-tuning task is around 1,000 tokens (~3k tokens total).

re: Whisper v3 -- how is this possible? Whisper has a 30s context window. You have to chunk it.