Fine-tuned Gaddy EMG-to-text checkpoint / recipe for the 33.95% WER result?
#1
by erdenebayrd - opened
Hi Matteo, thank you for releasing TinyMyo and the silent_speech code.
I'm a student at MIT (Melbourne Institute of Technology in Australia) reproducing your EMG-to-text (recognition) results on the Gaddy
dataset as a baseline for some research. I've reproduced the from-scratch 4-layer setting
(~40.8% test WER, beam 1500) and I'm now fine-tuning from
pretraining/TinyMyo/TinyMyo.safetensors (8 layers) to reach the paper's ~33.95%.
Two questions, either would help a lot:
- Could you share the fine-tuned recognition checkpoint on Gaddy (the .pt that
start_training_from expects, i.e. the ~33.95% WER model)? I'd use it directly as a baseline. - If not, could you confirm the fine-tuning recipe: num_epochs / early-stopping patience,
LR schedule and peak LR, whether freeze_blocks was used, and the SizeAwareSampler token budget?
I've verified the backbone's transformer blocks load into the fork's EMGTransformer.
Thanks very much!
Erdenebayar Dovchindorj (MIT)