FidelityFX-FSR2 VS kaldi-active-grammar

- https://github.com/GPUOpen-Effects/FidelityFX-FSR2/releases/download/v2.2.1/FidelityFX-FSR2-v2.2.1-prebuilt-sample.zip

AMD's scaler is open-source. Here's the github for FSR2.

Sure, go ahead: here is the link. Go contribute and make it better, as it's an OPEN SOURCE project.

DLSS is a proprietary black box that developers have no control over (bad). FSR is open source, you can check the code, fix the code, modify the code, and port it to new devices (good). It also means there is a community of developers who are trying new things and submitting code for inclusion (there are currently 148 forks on the github).

literally not true. FSR has been constantly updated in the background and 2.2.1 was released 3 weeks ago.

If I get the FSR API files (ffx_fsr2_api_dx12_x64.lib, ffx_fsr2_api_vk_x64.lib and ffx_fsr2_api_x64.lib) from github what directory structure do they need to go into? Looking at working DLSS and XeSS as a template...

From the official docs:

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

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

With an open system/engine, you can train your own personal speech model. For kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), you can do so without all that much difficulty, although the process/documentation could certainly use improvement.

I bootstrapped my personal speech model by retaining the commands from me using WSR. My voice is quite abnormal, and it took only 10 hours of speech data to train a model orders of magnitude more accurate than any generic model I've ever used. And of course, I retain much of my usage now with Kaldi, so my model improves more and more over time. A virtuous flywheel!

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).

- Demo: https://www.youtube.com/watch?v=Qk1mGbIJx3s / Software: https://github.com/daanzu/kaldi-active-grammar

Far field audio is usually harder for any speech system to get correct, so having a good quality mic and using it nearby will _usually_ help with the transcription quality. As a long time Linux user, I would love to see it get some more powerful voice tools - really hope that this opens up over the next few years. Feel free to drop me an email (on my profile) happy to help with setup on any of the above.

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