I'm sure you guys know the situation... creating software for different platforms requires multiple computers. Not only do you need a Mac and PC, but you need multiple versions to test different operating systems and software updates. A few years back, I got tired of switching between multiple computers to get the job done, so I created triple boot PC/Hack/Linux machine. It works well. No complaints. Then I decided to upgrade my GPU to an RTX3090 for faster 3D rendering. It's quite a beast! The issue is MacOS does not work with Nvidia chips, so every time I wanted to boot up macos, I had to unplug my GPU. I also started to get annoyed by not being able to use multiple OS at one time. Powering down MacOS to use Windows can quickly get old.
Since I'm relying heavily on virtualization to run my mini datacenter, I took a shot at creating a virtualized workstation. The result?

One computer that simultaneously runs Monterey, BigSur, Win10, Win11, Linux desktop and Ubuntu server!
I'm still in the testing stages, but it's working wonderfully. I've successfully exported plugins from HISE on each platform.
The system is running on Proxmox. Not to be confused with things like VirtualBox or Parallels (which are type 2 hypervisors that run on top of an existing OS), Proxmox is a type 1 hypervisor. That means that your computer's actual hardware is divided up and given to each virtual machine.
Since the allocation of hardware is sectioned off and given to a Virtual Machine, that means you need a computer with a bit of horsepower to use multiple operating systems at the same time, but you may already have what you need. Or worst case, You only run 1 OS at a time with full resources.
By using some utilities like VirtualHere (share usb over ethernet) and Barriers (use one mouse/keyboard to control multiple computers) I'm able to use one set of hardware resources (usb sound card, dongles, etc) and share them with each computer. Each system sees the devices as if they are actually attached to the computer. No layer in between. The only downfall is since it's "directly attached", you can't share your sound card with multiple machines simultaneously.... I'm mean, that sounds like a nightmare anyway! hahahh
Right now, the Mac virtual machines feel like they have a little bit of lag, so I'm going to have to install a GPU for those machines to use.
If you guys are interested, I can post some details on building a machine like this.

It looks like you're trying to hook up a gain reduction meter to a dynamics module.
here ya go! in 13 easy steps
I'm using "right click" auto complete as much as possible.

#1 - Add Dynamics module

#2 - Create a reference to the module

#3 - Create a slider and add reference

#4 - create a timer

#5 - Create function for timer

#6 - create variable to hold a reference of gain reduction

#7 - convert reduction to dB

#8 - set value of slider to gain reduction

#9 - set slider mode to decibel

#10 - Start Timer

#11 - test that reduction meter is working

#12 - set slider image to your strip

#13 - test that strip is working

Merry merry everyone!

I would love to have SideChain inputs available in the bus routing.

Finally had a second to make a quick video. I apologize for the poor quality and zero planning... but you get the idea

I just ran into this when opening older projects on my new rig. Images and binary are way below 50mb.
Did a little searching and found that this may happen if Visual Studio doesn't use the 64 bit toolset. I think it's some kind of global change between VS2017 and 2019.
Manually setting the architecture in Environmental Variables fixed the problem for me.
https://phoenixnap.com/kb/windows-set-environment-variable

:person_cartwheeling_medium-light_skin_tone: :person_cartwheeling_light_skin_tone:

@Morphoice The basics are they are using ML models to analyze and replicate the behavior of analog equipment. They run various test signals through them and use the results to train a model that can predict how the device will respond to any input signal.
The Kemper Profiler uses a different approach. They capture the full response of an amp or effect at different settings. It creates a snapshot of the device and builds a profile. This is closer to convolution than ML.
The real problem you're going to run into with time-based dynamics (like a compressor with slow release) is the model will start to emphasize the non-linear behavior. The result of these ML compressors ends up sounding like saturators rather than compressors.
I'm on the same path as @griffinboy where I'm focusing on making models of specific components and inserting them between other analog models.
If you want to start tinkering with Neural models in HISE, here's a thread of my starting process:
https://forum.hise.audio/topic/8701/simple-ml-neural-network/108?_=1748359797127
@griffinboy also posted a thread about some details to the training scripts.
I haven't had time to fully dig in for the last few months, but plan to soon.

@parabuh said in Whitebox Packages - plugins changes into folders:

If i set my username it works but i think it will not work on different machine.

If you select "keep owner and group" when you add the file to Packages, it SHOULD use the owner/group of the logged in user who is installing. Try that.

@parabuh I've found that this is 100% a permissions issue.
In my setup, If I place a binary on my network share and create an installer via packages, the bundle will turn into a folder after install. If the binary is placed on my local machine and I create an installer, the result is a proper bundle.

@Christoph-Hart said in Bad CPU type in executable:

@d-healey file size is smaller but the real reason is I just forgot that people still use intel machines :)

Any chance you can swap it out with the UB? Virtualization of ARM processors is still in the experimental phase.

I'm seeing this in the latest version of HISE as well on multiple MacOS:

batchCompileOSX: line 7: /HISE/tools/Projucer/xcbeautify: Bad CPU type in executable

macOS 15.4.1 + Xcode 16.3
macOS 13.7.5 + Xcode 15.4

building from the autogenerated projucer file works as expected.

I think I've been here before:
https://forum.hise.audio/topic/8742/how-to-debug-server-isonline

The functions check google and amazon to see if they are reachable, but some macos systems have issues with http calls.
I've updated the source to point to my https domain. I reasoned it wasn't important if they are online but unable to reach my server.

@Christoph-Hart This new found magical power is intoxicating!

@Christoph-Hart I'm using markdown for my embedded documentation. Please don't kill it

@Christoph-Hart said in 8 Times more CPU consumption on Aida-X Neural Models:

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.

There is also widespread adoption for the NAM format. Darkglass just released a NAM compatible product:
https://www.darkglass.com/creation/anagram/
https://www.tone3000.com/blog/unveiling-anagram-darkglass-electronics

@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.