plaidml VS dragonfly

basically agree with all of this, however I do want to highlight that there is no 'ZKML protocol plan' - the panel here are all involved in quite different projects and interested in ZKML for a variety of reasons. As one of the authors of https://github.com/plaidml/plaidml I'm not expecting any kind of standard protocol to evolve for several years; the group behind the AMA though is optimistic about the potential of ZKML and this AMA is part of the start of developing useful protocols.

It understands my Swedish attempts at English really well with the medium.en model. (Although, it gives me a funny warning: `UserWarning: medium.en is an English-only model but receipted 'English'; using English instead.`. I guess it doesn't want to be told to use English when that's all it can do.)

However, it runs very slowly. It uses the CPU on my macbook, presumably because it hasn't got a NVidia card.

Googling about that I found [plaidML](https://github.com/plaidml/plaidml) which is a project promising to run ML on many different gpu architectures. Does anyone know whether it is possible to plug them together somehow? I am not an ML researcher, and don't quite understand anything about the technical details of the domain, but I can understand and write python code in domains that I do understand, so I could do some glue work if required.

Have you tried PlaidML https://github.com/plaidml/plaidml

This doesn't answer your question, but it would be cool if we had something based on MLIR for GPU compute. From what I've read, it closes the gap between NVIDIA and other GPU vendors a lot more than pure compute shaders. e.g. ONNX-MLIR, PlaidML, and IREE.

There is a library called PlaidML which is supposed to support Keras on a wide variety of GPUs, including the Iris. But it doesn't. I get the issue reported as Issue #168, which was first reported in 2018 and is still open. That's what I mean by not well supported.

Not really: https://github.com/plaidml/plaidml/commits/plaidml-v1

There are reports on github of plaidML conking out on older CPUs with a similar "illegal instruction err.

https://github.com/dictation-toolbox/dragonfly

https://github.com/dictation-toolbox/dragonfly

https://github.com/daanzu/kaldi-active-grammar

As someone who suffered some severe mobility impairment a few years ago and relied extensively on eye tracking for just over a year, https://precisiongazemouse.org/ (Windows) and https://talonvoice.com/ (multiplatform) are great. In my experience the hardware is already surprisingly good, in that you get accuracy to within an inch or half an inch depending on your training. Rather, it's all about the UX wrapped around it, as a few other comments have raised.

IMO Talon wins* for that by supporting voice recognition and mouth noises (think lip popping), which are less fatiguing than one-eye blinks for common actions like clicking. The creator is active here sometimes.

(* An alternative is to roll your own sort of thing with https://github.com/dictation-toolbox/dragonfly and other tools as I did, but it's a lot more effort)

I've experimented with whisper. I don't know of a way to do commands without parsing dictation. Bottom line, the model has to pass 30 seconds of audio to my knowledge. So say if you're utterance is 5 seconds, you'll need 25 seconds of silence.

Depending on the platform you're targeting.

https://github.com/dictation-toolbox/dragonfly

What you don't have years of experience typing one handed? Oh well you'll become an expert now. Ive seen this tool used to program python with dragon naturally speaking, maybe give it a go... https://github.com/dictation-toolbox/dragonfly

I have been coding entirely by voice for approximately 10 years now (by hand long before that). Most of that time I have been using the Dragonfly (https://github.com/dictation-toolbox/dragonfly) library to construct my own customized voice coding system. The library is highly flexible and open source, allowing you to easily customize everything to suit what you need to be productive. It is perhaps the power user analogue to Dragon Naturally Speaking. With it, you can certainly be highly productive coding by voice. In fact, I develop kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), a free and open source speech recognition backend usable by Dragonfly, itself entirely by voice. There's also a community of voice coders using Dragonfly and other tools that build on top of it, such as Caster (https://github.com/dictation-toolbox/Caster).