@griffinboy some guides on this kind of stuff would be amazing. I'd love to have a crack at some of this too. When you have the time it would be awesome.
Best posts made by ccbl
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RE: 3rd party Header Files in Hise?
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RE: Simple ML neural network
@griffinboy Here's the link to their discord channel for RTNeural
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RE: Simple ML neural network
@resonant that's awesome. Would love to pick their brains and see if we can get it up and running for the rest of us.
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RE: Simple ML neural network
@ccbl for instance, how I plan to use HISE is to create plugins where I use a NN to model various non-linear components such as transformers, tubes, fet preamps etc, and then use the regular DSP in between. I'm just a hobbiest who plans to release everything FOSS though, so I'll have to wait and see what you much more clever folks come up with.
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RE: Simple ML neural network
I realise we already hashed this discussion out, and people might be sick of it. But IMO the NAM trainer has a really intuitive GUI trainer which allows for different sized networks, at various sample rates. It also has a very defined output model format, which seems to be a sticking point with RTNeural.
Given the existence of the core C++ library https://github.com/sdatkinson/NeuralAmpModelerCore
Might it be easier to implement this instead, given many people want to use ML Networks for non-linnear processing for the most part?
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RE: Simple ML neural network
@Christoph-Hart so for now I decided to try and make a very simple audio processor using scriptnode. I followed the tutorial project adding the weights for the math.neural module to pickup, and it does see the object, however when I select it HISE crashes, complete CTD.
It's an LSTM network generated by a RTNeural based project (guitarML automatedguitarampmodelling pipline).
Not sure what the issue is.
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RE: Simple ML neural network
@Christoph-Hart said in Simple ML neural network:
@ccbl And can't you just convert the models to work in RTNeural? In the end it's just running maths and I'm not super interested in adding the same thing but in 5 variations.
I understand why you feel that way. There's no point chasing your tail every time a new NN comes on the scene. What I would say though is that since NAM has been released, I've seen at least 4 different companies bring out their own NN capture tech. And not a single one of those has taken off, all combined they have less captures publicly available than one comprehensive NAM pack on ToneHunt .
Multiple commercial companies have incorporated NAM including Melda, Amplifire, Two Notes, Audio Assualt, and Solemn Tones amongst others. AnalogueX now uses NAM to capture preamps and compressors. Past of Future also does this.
Essentially NAM has become the industry standard format for Analogue gear capture using neural networks at this point. So personally I think it would be worth implementing the NAM core tech as there's a far higher chance people are going to want to use a NAM model than any of the other tech.
Beggars can't be choosers though. I don't mean this to come across as a demand so much as trying to present a compelling case. I don't have the skills to implement this myself. I understand there's only some much time and will power a person has to dedicate to a project like this.
So I'm highly grateful for any way to incorporate NAM into the signal processing using HISE. Whether that involves conversion or native processing.
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RE: Simple ML neural network
@Christoph-Hart A few things I guess. The number of NAM captures currently dwarfs all the other that are supported by RTNeural (I have used guitar ML in the past for instance) and it's only growing (here for example https://tonehunt.org/). Not just 1000s of amp snap shots but people are really getting into studio gear captures too. There's a huge group of people for support in generating good captures and technical training support. And of course it is probably the best sounding in terms of accuracy right now.
I'm interested enough in using a neural net that I'm willing to use RTNeural, it's still a great system. NAM is becoming a defacto standard in a lot of NN capture spaces currently though. So for the future it seems like a good addition to the code base. And on a personal level I've already created over 1000 NAM models.
Maybe once I've learned more of the basics, if someone is willing to help me with it I would appreciate it.
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RE: Simple ML neural network
@Lindon I'm well aware of how NAM works. It is possible to make a parameterized model. But the thing is with the right DSP surrounding it you don't really need to and you drastically cut down the number of input output pairs you need to make. For instance, you can split the pre-amp and poweramp and just do digital EQ in between given that the EQ tends to be after the non-linear gain, and the EQ itself behaves linearly. But those eq inputs feed into the power amp model.
A lot of the time for what you want to do, a single snapshot is actually fine, just varying the input gain alters the amount of saturation, say in a tube mic pre or something like that. So given NAM is the best out there right now it would make it a really useful module I think in HISE.
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RE: Simple ML neural network
Sorry to hijack this thread potentially. I wonder if it would be worth implementing https://github.com/sdatkinson/NeuralAmpModelerCore
NAM has been gathering a lot of steam recently, the training process for new models is really easy to do and IMO it produces the best sounding models of gear to date, at least gear without time constants. It would be a great way to produce amp sims, or even just add a really accurate post sound processing option in synths and virtual instruments. Imagine an E-piano or organ etc with a great tube amp drive processor.
Latest posts made by ccbl
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RE: Simple ML neural network
@griffinboy Here's the link to their discord channel for RTNeural
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RE: Simple ML neural network
@Dan-Korneff What sample rate were you using? I'd love to train at 96khz or even 192khz for aliasing reduction reasons. My ultimate plan is to stack several smaller models of individual components together sandwiched between regular DSP so I think reducing aliasing should be important in this case.
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RE: Simple ML neural network
I asked Jatin to have a quick look through the thread to see if he could see any issues, he just had this to say.
"Hmmm, it seems to me that the model JSON file that is being loaded into the "Neural Node" is structured as a TensorFlow-style JSON file, but it's being loaded with the HISE's
loadPytorchModel()
method? I don't really know what the Neural Node does internally, so idk how much I can help beyond that." -
RE: Simple ML neural network
@resonant that's awesome. Would love to pick their brains and see if we can get it up and running for the rest of us.
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RE: Simple ML neural network
@Dan-Korneff In this instance the dataset refers to the input output audio pairs that NAM uses for it's training, not the resulting model. Basically they're saying they added info in their training script that can detect the NAM audio pairs and train and Aida-X model based on those.
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RE: 3rd party Header Files in Hise?
@griffinboy some guides on this kind of stuff would be amazing. I'd love to have a crack at some of this too. When you have the time it would be awesome.
-
RE: Simple ML neural network
@ccbl for instance, how I plan to use HISE is to create plugins where I use a NN to model various non-linear components such as transformers, tubes, fet preamps etc, and then use the regular DSP in between. I'm just a hobbiest who plans to release everything FOSS though, so I'll have to wait and see what you much more clever folks come up with.
-
RE: Simple ML neural network
I realise we already hashed this discussion out, and people might be sick of it. But IMO the NAM trainer has a really intuitive GUI trainer which allows for different sized networks, at various sample rates. It also has a very defined output model format, which seems to be a sticking point with RTNeural.
Given the existence of the core C++ library https://github.com/sdatkinson/NeuralAmpModelerCore
Might it be easier to implement this instead, given many people want to use ML Networks for non-linnear processing for the most part?
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RE: Simple ML neural network
Attached the model in case someone wants to look at it.