Simple ML neural network

iamlamprey

@Christoph-Hart yeah fair enough, i'll keep noodling on this additive synth but i'll definitely be trying the neural & loris pairing at some point

iamlamprey

@Christoph-Hart do you happen to have the training/dataloader for the sine model handy? the repo only has tanh

i'm currently migrating the example over to pure torch because torchstudio keeps uninstalling my local packages and in general is kinda gross

iamlamprey

@d-healey is there a Rhapsody update coming soon? I just tried loading the example as a rhapsody library and (duh) it broke everything, i assume the next version of Rhapsody will be built with the latest version of HISE?

d.healey

@iamlamprey said in Simple ML neural network:

is there a Rhapsody update coming soon?

Probably next month.

@iamlamprey said in Simple ML neural network:

i assume the next version of Rhapsody will be built with the latest version of HISE?

Yup

iamlamprey

@d-healey i am excited to ignite everyone's CPUs

d.healey

@iamlamprey I thought the ML stuff is only in scriptnode?

iamlamprey

@d-healey uncompiled networks work in Rhapsody, the snex and expr stuff doesn't though

d.healey

@iamlamprey Aha ok that's good to know

iamlamprey

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iamlamprey

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Lindon

@Christoph-Hart said in Simple ML neural network:

@iamlamprey nope, I‘ve suspended my journey into ML until I have a real use case for it :)

If anyone is using this stuff I‘m happy to implement new features or fix issues, but now that the „hello world“ is implemented I expect it to grow with actual projects and their requirement.

Well speak of the devil and he will appear.... I now have a customer who would like to make Amp sims using Neural Net ML....

So I've spent some time with the research papers, the youTube Videos and the blogs. It would seem I might need a statetful LSTM model - but I could easily be wrong.

My understanding of how I(we) might approach this is very very poor, but I think there any number of ways to get to the json code that would be needed to "make it work", but wow do I have a bunch of questions.... here's some of them:

is this even possible in HISE? (I mean the playback not the modelling or learning)
Can I "just" use (say) this stuff : https://www.youtube.com/watch?v=xkrqF0D8pfQn and take the .json output and load it into RTNeural and expect it to "sort of" work?
If not what's the best way to get from a bunch of guitar recordings to a set of .json files that I can then load into math.neural?
I assume all these Learned models are in fact static snapshots of dynamic processes, so every time the user changes the distortion control on the plugin I will need to load another model - if so how practical is that?

iamlamprey

@Lindon said in Simple ML neural network:

So I've spent some time with the research papers, the youTube Videos and the blogs. It would seem I might need a statetful LSTM model - but I could easily be wrong.

Yeh any recurrent network is fine for a guitar amp, Jatin prefers GRU's but all of the guitarML stuff is LSTM if i remember correctly, i think GRUs are a bit easier on the CPU

My understanding of how I(we) might approach this is very very poor, but I think there any number of ways to get to the json code that would be needed to "make it work", but wow do I have a bunch of questions.... here's some of them:

is this even possible in HISE? (I mean the playback not the modelling or learning)

if the entire RTNeural framework is implemented, recurrent models should be supported

Can I "just" use (say) this stuff : https://www.youtube.com/watch?v=xkrqF0D8pfQn and take the .json output and load it into RTNeural and expect it to "sort of" work?

if he's just using regular old pytorch and building simple models, you should be able to load the state_dict and run the export script, then bring the JSON into HISE

If not what's the best way to get from a bunch of guitar recordings to a set of .json files that I can then load into math.neural?

this is where training a model comes in, you'd need to learn some basic Python and familiarize yourself with packages like Torch or Tensorflow, as well as some audio-handling ones like Librosa

the short version is:

load and preprocess/augment the audio data
convert it into a dataset that can be read by a model
create the model & train it on the data
export the model's state_dict using the RTNeural export script
load it into a Math.Neural node

I assume all these Learned models are in fact static snapshots of dynamic processes, so every time the user changes the distortion control on the plugin I will need to load another model - if so how practical is that?

recurrent models aren't really "static", they can only estimate a single function at a given time, but that function can change depending on the "state" of the network, it basically has a short memory (literally in the name of LSTMs) so the function it's estimating is dependant on that memory

that being said, for a guitar amp the typical process is to train several models on various settings of the amp, then crossfade between them

Lindon

@iamlamprey said in Simple ML neural network:

Ok so first: thank you - this is very helpful, but of course it just leads to more questions - and Im slightly afraid this might turn into some sort of ML primer at your time expense, so - I will try and keep to a minimum...

@Lindon said in Simple ML neural network:

So I've spent some time with the research papers, the youTube Videos and the blogs. It would seem I might need a statetful LSTM model - but I could easily be wrong.

Yeh any recurrent network is fine for a guitar amp, Jatin prefers GRU's but all of the guitarML stuff is LSTM if i remember correctly, i think GRUs are a bit easier on the CPU

Ok so fine GRU or plain old LSTM - is the "kind of" model yes?

My understanding of how I(we) might approach this is very very poor, but I think there any number of ways to get to the json code that would be needed to "make it work", but wow do I have a bunch of questions.... here's some of them:

is this even possible in HISE? (I mean the playback not the modelling or learning)

if the entire RTNeural framework is implemented, recurrent models should be supported

here's one for @Christoph-Hart then: is GRU or LTSM implemented in the HIse implementation currently?

Can I "just" use (say) this stuff : https://www.youtube.com/watch?v=xkrqF0D8pfQn and take the .json output and load it into RTNeural and expect it to "sort of" work?

if he's just using regular old pytorch and building simple models, you should be able to load the state_dict and run the export script, then bring the JSON into HISE

Well of course I have no idea what he's doing - but interestingly what do you mean by "simple model"?

If not what's the best way to get from a bunch of guitar recordings to a set of .json files that I can then load into math.neural?

this is where training a model comes in, you'd need to learn some basic Python and familiarize yourself with packages like Torch or Tensorflow, as well as some audio-handling ones like Librosa

OK well unlike Christoph I once upon a time did a fair bit of Python work _ I really like it as a language...

the short version is:

load and preprocess/augment the audio data

So here I think (correct me if Im wrong) this means ; playing about 5 mins of guitar thru the amp - and capturing the output as well as capturing the DI-ed guitar signal - then making sure these are edited nicely to phase align with each other. Is that about it?

convert it into a dataset that can be read by a model

making it a pickle data set? Or perhaps NumPy

create the model & train it on the data

ha here we get to the bit Im most fuzzy on...-- create the model? So is this just set up a big set of neural net nodes which I can build with something like TensorFlow and Keras yes? no? maybe?

As an aside there seems to be a lot of parms I can set up for this - I guess more research..

export the model's state_dict using the RTNeural export script

I get a .json file yes?

load it into a Math.Neural node

Which I load into here...

I assume all these Learned models are in fact static snapshots of dynamic processes, so every time the user changes the distortion control on the plugin I will need to load another model - if so how practical is that?

recurrent models aren't really "static", they can only estimate a single function at a given time, but that function can change depending on the "state" of the network, it basically has a short memory (literally in the name of LSTMs) so the function it's estimating is dependant on that memory

that being said, for a guitar amp the typical process is to train several models on various settings of the amp, then crossfade between them

Ok so I need to load up these .json "models" dynamically as the user fiddles with the UI controls...

d.healey

@Lindon said in Simple ML neural network:

playing about 5 mins of guitar thru the amp - and capturing the output as well as capturing the DI-ed guitar signal

Is the NN result noticeably better than using an IR derived from the same recordings?

iamlamprey

@Lindon said in Simple ML neural network:

Ok so fine GRU or plain old LSTM - is the "kind of" model yes?

for a bit more nuance than a fully connected network for something like analog modelling, yep.

Well of course I have no idea what he's doing - but interestingly what do you mean by "simple model"?

by "simple model" i mean one that only has to estimate a function. other types like generative models (VAEs, GANs), or ones that make use of embedding layers might be outside the scope of the current implementation

So here I think (correct me if Im wrong) this means ; playing about 5 mins of guitar thru the amp - and capturing the output as well as capturing the DI-ed guitar signal - then making sure these are edited nicely to phase align with each other. Is that about it?

pretty much, you'd then cut those 5 minutes up into small pieces so you dont have 5 x 60 x 44100 samples being fed into the model at once

making it a pickle data set? Or perhaps NumPy

torch & tensorflow both have built-in tools for converting raw data into a readable format for the model, including batching tools which speed up training on a GPU

ha here we get to the bit Im most fuzzy on...-- create the model? So is this just set up a big set of neural net nodes which I can build with something like TensorFlow and Keras yes? no? maybe?

yep, you basically tell keras/tensorflow/torch which layers, activations (non-linear functions) and input/output shapes you want to ensure it can spit out the data you're expecting it to

sidenote: tensor shape errors are one of the worst parts of machine learning, prepare to spend a decent amount of time looking at these

I get a .json file yes?

yep, the export script in the repo spits out a json file to load into HISE

Which I load into here...

sorry, this was my mistake. you don't directly load the JSON into the Neural Node, you load it with

const nn = Engine.createNeuralNetwork("networkname");

nn.loadPytorchModel(myJSONobject);

then when you add a Math.neural node, "nn" should appear in the dropdown

Ok so I need to load up these .json "models" dynamically as the user fiddles with the UI controls...

i don't think hot-swapping the actual networks on the fly would be good, rather use multiple lightweight networks and fade between them with a Gain node or something

hope this helps, it's a big subject

Lindon

@d-healey said in Simple ML neural network:

@Lindon said in Simple ML neural network:

playing about 5 mins of guitar thru the amp - and capturing the output as well as capturing the DI-ed guitar signal

Is the NN result noticeably better than using an IR derived from the same recordings?

very good question..... anyone got an opinion? Im guessing the NN acts "more dynamically" compared to the IR....

iamlamprey

@Lindon id say they can be about as accurate as a decent whitebox model, 8.5-9.0/10

d.healey

@iamlamprey In that case I'd go with the IR which takes a few seconds to make - I have a script that can process 1000s of files to create loads of IRs in one go.

@iamlamprey said in Simple ML neural network:

rather use multiple lightweight networks and fade between them with a Gain node or something

Can do the same with multiple IRs

Lindon

@iamlamprey said in Simple ML neural network:

@Lindon id say they can be about as accurate as a decent whitebox model, 8.5-9.0/10

so to get this clear in my head - the ML version is no better at the dynamic processing of guitar input that a set of IRs is?

..and by "dynamic" I mean different volume levels of input as the instrument is played...quietly (for those AOR ballads) or loudly...(thrashing it)

iamlamprey

@Lindon it really depends on the implementation of the model and the data, having a robust model trained on a lot of varying data (different amp settings, different players etc) will sound great, and probably beat IRs and blackbox models

that being said, a dedicated whitebox model recreating each component of the signal path is still probably the best method for physical accuracy, i don't think a test comparing a proper neural net vs a proper physical model has ever been done, for obvious reasons ($$$)

there's still a lot of progress to be made with neural audio, so who knows how things will look in a year or two

Simple ML neural network

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