> It achieves a 7.5% word error rate on the LibriSpeech test clean benchmark Anyone have a comparison for how good/bad that is compared to other solutions, and what it means for practical usage, if that can be guessed at from a single number?
For real world applications, this is absolutely crucial, users want latency numbers on the order of milliseconds, not seconds. This is why, if you run a standard test set like LibriSpeech on, say, a commercial offering from Google, it will perform considerably worse than state of the art according to Google papers.
This repository [0] has a benchmark of some commercial offerings. Our model beats all of those on Librispeech clean and other (except for Speechmatics on Librispeech clean), as well as on Common Voice. But note that the Common Voice corpus used in that benchmark is very old.
In sum, I would compare this against solutions that go for the same space: fast, client-side ASR, rather than state of the art.
[0] https://github.com/Franck-Dernoncourt/ASR_benchmark#benchmar...