TLDR, this is about an effort to collect a new large dataset for speech-to-text and an exploration of model design choices to enable programmers without huge co

TLDR, this is about an effort to collect a new large dataset for speech-to-text and an exploration of model design choices to enable programmers without huge computational resources to work in the space (it mostly does not touch on fine-tuning so 'ImageNet moment' is a bit overclaiming, but then again it does say 'Towards'). I found the "Why not share this in an academic paper" part interesting (and I expect it to be interesting to HN). As more practitioners enter the field, a lot of empirical knowledge will be gained but will be tricky to share since they will likely not know the convention of academic writing, Latex, etc. (to be clear, I think when done well papers are a good format to share information). Perhaps stuff like this and distill.pub will become more common? Seems like a good development IMO (as is researchers writing blog posts in addition to papers).