Something to think about with this is that I, and many others, use what could seem like a burner email to sign up for everything as a security measure against data leaks.
Proton mail generates an alias so no two of my accounts online share the same email address, I still receive the emails though.
Very cool, I'll be going through this for sure.
Honestly just knobman for filmstrips and photoshop. I know it's not the most efficient, but I'm comfortable with it and I'm happy enough with the results.
I also just want to say @Christoph-Hart in the face of the really shitty attitude that person above showed. I love HISE so much. I never thought I'd be able to create a simple plugin that just changes the gain let alone the stuff I can do with HISE as someone with almost zero coding experience. It's such an awesome thing you've created.
@Dan-Korneff said in 8 Times more CPU consumption on Aida-X Neural Models:
@Christoph-Hart said in 8 Times more CPU consumption on Aida-X Neural Models:
@Dan-Korneff alright then it stays, the people have spoken :)
Just out of curiosity: who is using this in a real project and does it work there? Like except for the NAM loader?
To be honest: I‘m a bit „disincentivized“ in fixing the NAM stuff because I don‘t see a use case except for loading in existing models from that one website with potential infringement of other peoples work, but maybe my understanding of the possible use cases here is wrong.
Adding NAM support is super helpful for some people as the training scripts are solid and actively maintained. I'll be using RT-Neural on this end for the parameterized support. Still making a few tweaks to the training script I started with, but I’m planning to build around it soon.
I would love to see an example of a parameterised model up and running. The way I plan on profiling won't require parameters for the most part, but it would be useful every now and then.
@Christoph-Hart said in 8 Times more CPU consumption on Aida-X Neural Models:
@Dan-Korneff alright then it stays, the people have spoken :)
Just out of curiosity: who is using this in a real project and does it work there? Like except for the NAM loader?
To be honest: I‘m a bit „disincentivized“ in fixing the NAM stuff because I don‘t see a use case except for loading in existing models from that one website with potential infringement of other peoples work, but maybe my understanding of the possible use cases here is wrong.
@Christoph-Hart I'm only interesting in using models of things I built and trained myself. I plan on using this as part of a hybrid approach where I model certain physical circuits with NAM and then use DSP for other aspects. Not just grabbing stuff of ToneZone and putting a plugin wrapper around a single profile.
As @Dan-Korneff said, NAM has a much more solid and predictable training pipeline, not to mention an easy to use, free, fast, cloud based platform for the training. That's what makes it desirable. There really are a lot of options to make really cool stuff.
Hey folks! I've been out of the loop for a while. Wondering if there has been any progress with the NAM integration?
I actually got a functional plugin working with LSTM but the training procedure is very chaotic so I haven't moved ahead much with it. I will probably provide some updates on that in a different thread though for more discussion around the particulars because I think there are other efficiencies I think might need to be ironed out.
@scottmire Hi there. I think I cheated basically. I used a safe bypass module matrix that switches the network based on which buttons are pressed at the time. I'm not sure if this has performance implications, it certainly seems like the more networks are in the code base the worse the plugin runs. When I get more time I think a new thread is in order where we can all discuss optimisations and solutions.
@JulesV GVST GTUNE is still incredible. I'm assuming OP is asking though because they want to build a tuner into part of a larger plugin, not because they think there's a gap in the market for a standalone.
TBH if we come up with a good solution, I'd love to use that code too.
@Christoph-Hart One thing, in the Neural Example in the docs it says this "It requires HISE to be built with the RTNeural framework to enable real time inferencing..."
I don't remember doing this explicitly when I built HISE, I just built it the standard way and it all works. I'm assuming this is just no longer a requirement? Otherwise could it explain the performance penalty?
@Lindon said in Plugin doesn't respect UI Zoom Factor onInit:
@ccbl said in Plugin doesn't respect UI Zoom Factor onInit:
@oskarsh how would that look in context?
inline function onFiftyControl(component, value) { Settings.setZoomLevel(0.5); }; Content.getComponent("Fifty").setControlCallback(onFiftyControl)Do you define the value as 1.0?
I really don't know coding very well.
so like this:
inline function onFiftyControl(component, value) { if(value) Settings.setZoomLevel(0.5); }; Content.getComponent("Fifty").setControlCallback(onFiftyControl)
After flushing my %appdata% config this worked! Thanks!
@Christoph-Hart cool. Well like I said, if you want any kind of models for testing, let me know. I can do either NAM-wavenet or LSTM, of any size.
Something else coming back to the performance difference. I vaguely remember saying that there were optimisations pre-compiled when it came to inferencing certain architecture sizes. Given that Aida for example has a pretty specific pipeline that people use on collab, maybe they optimised that specific number of Layers and Hidden Size?
@orange OK sweet, I'll try some tweaks and report back.
@oskarsh how would that look in context?
inline function onFiftyControl(component, value)
{
Settings.setZoomLevel(0.5);
};
Content.getComponent("Fifty").setControlCallback(onFiftyControl)
Do you define the value as 1.0?
I really don't know coding very well.
@orange Hmm I haven't where do you set flags? In the project settings?
@ccbl Is it possible the overhead is from defining the model in the script instead of loading it from a file?
@d-healey yeah it didn't make a difference. Do you think it could be the buttons being in a radio group that is messing things up? Or do you think that it's just the script doesn't read the state of the buttons until one of them is pressed for the first time?
Still a little puzzled why Settings.setZoomLevel(0.5); outside of the button callbacks doesn't correctly set the zoom level on init though.
@d-healey I'll give it a try. I used
Settings.setZoomLevel(0.5)
because the compiler said that
Engine.setZoomLevel
was depreciated.