I really want to put DeepSpeech 2 with Baidu's model up against Mozilla's model and see which is better, seems like it could be quite interesting!
My impression is that a lot of the hardware costs from modern AI comes from training and updating the model.
So (speaking as a non-expert) it doesn't seem like there's any technological problem with running a model locally on a private network -- you just need access to the model and you need someone else to generate it. And that's exactly what Google isn't providing. It's like they found a way to turn modern AI into even more of a black box.
At the point where I'm OK consuming someone else's model and not being able to control how it gets updated, then I'm probably also OK with using a compressed, static model and needing to occasionally download and deploy new versions.
DeepSpeech offers trained models that are about 70% right, but none of them use the Common Voice corpus yet. I think that plus recent changes to the codebase should produce a much better transcription, but I don't have the GPU resources to go and train a model sadly, Mozilla will hopefully release another trained model soon though!
I am working on a basic web frontend and API for DeepSpeech: https://github.com/AccelerateNetworks/DeepSpeech_Frontend
Also, here is Common Voice: https://voice.mozilla.org/en
that's progress. how long did it take you to get these things running (including downloads and dependency installation)?
The quality is far below Google’s speech API as the model is somewhat out of date and more importantly the training data set is much smaller and less general.
The best pretrained speech to text model I’ve seen is from Baidu’s DeepSpeech 2 repository. They provide pretrained models for English and Chinese based on their internal data. The quality is astonishingly good!
Edit: both of these models can be comfortably run in real-time on a desktop. At Arm I recently worked on a project to run <5% word error rate models in real-time on a mobile phone.