I don't think our assumptions are so far appart. The methods you mentioned made it from research to the open source community fairly quickly. In fact, most companies rely on this kind of open research to develop their models. In a lot of use cases, it has become more about finding the right data that improving the model code. (I like Andrew Ng thoughts on this: https://datacentricai.org/) At the same time, there are still a lot of unsolved engineering challenges with the code when it comes to productionalizing models, especially for real time speech transcription.

And we agree with your prediction. That's why we started Dioptra: to come up with a systematic way to curate high quality data so you can annotate just the data that matters.