But that's likely trained exclusively on CV audio and using a language model derived from the CV training data so that's also not a fair comparison unless the other engines were trained the same way.
Comparing systems trained on different datasets (with different language models) like this is like comparing apples to oranges. Mixing wildly different CPU and memory requirements into the benchmarks just makes it worse.
It should be fairly straightforward to do an unbiased comparison by training and evaluating with the standard Librispeech split and language model. It might be interesting to see how accuracy improves as the models scale up until they match the resource requirements of the other engines.
That said, the speed and memory usage are impressive and I like the focus on very low resource environments. Seems like it has a lot of potential even if it may not be SOTA.