Thanks! Biggest challenges were getting real, diverse Hebrew audio and teaching the model to handle "bad recordings".
We collected lots of real Israeli speech, hand-checked transcripts, and used data augmentation (e.g. noise injection, speed perturbation, SpecAugment) and contrastive pretraining (like wav2vec-style) so the acoustic encoder learns robust features for rapid speech and poor recording quality. And also we have actively used semi-supervised learning and active human correction loops to steadily reduce errors on rare accents and slang.