guitarml / neuralpi

Raspberry Pi guitar pedal using neural networks to emulate real amps and effects.

Hi there 👋

Things I'm doing

• Creating guitar plugins that use machine learning to emulate real amps and pedals.
• Creating amp and pedal captures to use in my plugins: ToneLibrary
• Building DIY effects hardware
• Making tutorials and demos on YouTube
• Writing about the tech used in my software on Medium
• Working with local Universities: UAH Research Project
• Making music! 🎸

Get Involved

• Create an issue or discussion on any of my public repositories to jump in!
• Email for questions or to contribute tone models: [email protected]
• Support on Patreon or donate via Paypal

For product downloads and more information, see my website.

neuralpi's Issues

Failed to load tracks from Json config file

Not sure what I'm doing wrong. I followed the guide and have everything where it should be, but when I run sushi -r --multicore-processing=2 -c ~/config_files/config_neuralpi_fx.json I get the error Failed to load tracks from Json config file.

Garbled output when run standalone

Not using rpi, just standalone on a desktop.

Steps:

built tag v1.3.0 (64e554) successfully with cmake
run jack via cadence, 44.1khz, buffer size 256 samples, 2 periods/buffer (tried varying these settings also)
guitar plugged into USB audio device
connect standalone to system ins and outs using carla

Sounds like maybe periodic buffer underruns, where period gets longer with a larger buffer size.

Convert json to the correct format

Hi,
is it possible to use or modify any of your scripts to format my json file into the correct format?

reverb_full.json

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}

Performance on Pi3?

Hi! just reviewing your documentation. Looks like there was some overhead left over on the 4. any insights on performance on a pi 3 b+? Thanks!

Feature Request (VST version): volume normalisation for IR files

Really love the tones I get out of NeuralPi. The Blues Jr tone capture with Fender 68 Vibrolux IRs sound fantastic!

I had an issue when browsing through some imported IR cabinet files, that they had very different volume levels. So with some IRs the volume would drop significantly and there is no way to sufficiently compensate for that inside the plugin (even turning up "level" fully up doesn't compensate sufficiently for the volume drop). Could you implement a volume normalisation for the loaded IR? I think a small checkbox would suffice.

JUCE built-in submodule version doesn't allow headless build, Elk OS SDK does but it's a fork from a different version

Current JUCE module is https://github.com/lv2-porting-project/JUCE.git which is version 6.0.8 from a particular JUCE fork

Elk OS SDK which is currently signaled as the only headless build is using https://github.com/elk-audio/JUCE/commits/mind/headless_plugin_client_next which is a fork of 5.4.5 and is fixing headless builds for VST3 only. Search for JUCE_AUDIOPROCESSOR_NO_GUI=1. Afaik these patches have never been merged in JUCE repo.

Back on built-in JUCE,
JUCE_AUDIOPROCESSOR_NO_GUI=1

is not producing any tangible effect and builds fails when including juce_gui_basics.h since

X11/Xlib.h: No such file or directory

the removal of this dependency is a key for embedding this plugin into already existing platforms like Mod Duo or any whatsoever headless platform

file extension error in Mac update script

Line 32 should be:

scp $host_ir_path/*.wav root@$rpi_ip_address:$rpi_ir_path/

Line 36 should be:

scp root@$rpi_ip_address:$rpi_model_path/*.json $host_model_path/

Syncing models between Raspberry Pi and external device

Implement a method, preferably in c++ using no additional dependencies, to sync json model files between the Raspberry Pi and another device running the NeuralPi GUI. (see "osc-user-controls" feature branch). Currently, additional models must be uploaded to the Pi and imported on the other device separately. Implement a feature where the user can upload a model to the remote device using the NeuralPi GUI "Import Tone" button, and also send the model to the Rpi for NeuralPi to receive. It would be beneficial to check and send new models from the Pi to the other device, so that new devices can access the models stored on the Pi. This transfer would be done over a local WiFi network, after specifying the Pi's IP and port number on the GUI.

Acceptance Criteria:

• Implement a model sync feature on the "osc-user-controls" feature branch
• Ability to transfer additional models from a device running NeuralPi GUI to the Pi, and reverse over a local WiFi network
• Model selection in the drop-down menu must be properly synced with the models on the Pi, either by a known ordering (alphabetical) or model ID.
• Using the c++ Standard Library (TCP/IP file transfer) would be nice, but an external script called from the plugin works too

Segmentation Fault in mod-host. Not listed in guitarix. Ubuntu 22.04

Hi,

I am usingUbuntu 22.04 and the latest deb-file from the releases on this github-project.
I checked, that the plugin is installed in my users .lv2 directory. I also tried to move the plugin to /usr/lib/lv2

I get a segmentation fault in mod-host when I drag the plugin into my pedalboard in the constructor pane.
Guitarix does not even list me the plugin. I did not check the guitarix logs yet. I suppose that the underlying problem is the same.

Is this plugin fit to be used in those applications or would I have to use it only in standalone form?
Anybody experiencing the same issues? Or maybe already fixed somewhere?

I will maybe try to build it on my own and pin the code line where the segfault happens. But could be a waste of time dependending on the answers to above questions.

Looking for high quality time-based effects to add

I'm opening this issue as a place to recommend open source time based effects to add to NeuralPi. This includes effects like Reverb, Delay, Chorus, Flange. Ideally these would be high quality effects written in c++ without too many user controls, I'd like to keep it simple. As long as permissions allow, it could be modified to only include the most relevant controls.

The models sound good by themselves, but these effects would add a whole new dimension to NeuralPi's capability. I also know that JUCE has some DSP capability built in, including IR, but have not tested these features yet.

Run individual NeuralPi plugins on each input/output channel

I have a use-case where I'm currently taking my guitar signal, splitting it to run through two different effects boards and then out to two separate amplifiers.

What I'd like to be able to do is use NeuralPi in place of my amps when playing at home, recording, or even potentially in a live scenario to a PA.

Since the NeuralPi + HifiBerry DAC/ADC has two input channels and two output channels, would it be possible to run two versions of NeuralPi in mono, and then set each NeuralPi to a different amplifier model?

I've been trying to get to grips with the Sushi configuration files just to see if I could at least run NeuralPi in mono on one channel only as a first step, but I'm not getting very far. Here's my config file at the moment:

{
    "host_config" : {
        "samplerate" : 44100
    },
    "tracks" : [
        {
            "name" : "channel1",
            "mode" : "mono",
            "inputs" : [
                {
                    "engine_bus" : 0,
                    "track_bus" : 0,
                    "engine_channel" : 0,
                    "track_channel" : 0
                }
            ],
            "outputs" : [
                {
                    "engine_bus" : 0,
                    "track_bus" : 0,
                    "engine_channel" : 0,
                    "track_channel" : 0
                }
            ],
            "plugins" : [
                {
                    "uid"  : "NeuralPi",
                    "path" : "/home/mind/plugins/NeuralPi.vst3",
                    "name" : "NeuralPi",
                    "type" : "vst3x"
                }
            ]
        },
        {
            "name" : "channel2",
            "mode" : "mono",
            "inputs" : [
                {
                    "engine_bus" : 0,
                    "track_bus" : 0,
                    "engine_channel" : 1,
                    "track_channel" : 1
                }
            ],
            "outputs" : [
                {
                    "engine_bus" : 0,
                    "track_bus" : 0,
                    "engine_channel" : 1,
                    "track_channel" : 1
                }
            ],
            "plugins" : [
                {
                    "uid"  : "NeuralPi",
                    "path" : "/home/mind/plugins/NeuralPi.vst3",
                    "name" : "NeuralPi",
                    "type" : "vst3x"
                }
            ]
        }
    ],
    "midi" : {
    }
}

This results in Sushi responding with:

Failed to load tracks from Json config file

I don't see anything in the documentation for Sushi that describes how to run one plugin on a specific channel.

So I guess my first question is,

• How do I run NeuralPi twice, one input and output from/to channel 0, and then again for channel 1?

.. and then secondly,

• I assume if I can get this far, my next problem is, how can I control both NeuralPi plugins? Assuming they'll both be listening on the same port, is something going to go wrong?

Use model from SmartAmpPro

Hi and first thank's for this really interesting project! I was wondering which model formats are currently supported by NeuralPI. If i got things right currently the black box modeling of an amp or distorsion pedal has to be done by https://github.com/Alec-Wright/Automated-GuitarAmpModelling. For me the workflow behind https://github.com/GuitarML/SmartAmpPro looks a lot easier so i asked myself if it's possible to train a model on my desktop machine using SmartAmpPro and then transferring it to my PI using the provided scripts in this repo?

Thank's for your feedback 😎

Cab emulation

hi,
consider using JUCE Convolution class for adding impulse response for cabinet emulation
https://docs.juce.com/master/classdsp_1_1Convolution.html

sample rate converter

At the moment the processing is fixed on 44.1, but many systems run on 48k (or even higher). see #2 (comment)

It would be great if we are able to run at other samplerates, does this require training with other datasets?

LV2 builds?

As you may know jatinchowdhury18 uses a JUCE fork to enable LV2 builds for their plugins.

Will you consider doing the same? this would make your plugins available to a wider audience.

Update FileChooser to use latest JUCE standards

Currently, the FileChooser related code will not compile with later than JUCE 6.0.8. Update to latest JUCE 6.1.5 standards by using methods discussed on this JUCE thread.

Questing regarding bitdepth/sample rate

The plugin processes at 44.1kHz (specified in config file) for the neural net DSP.

What would be involved to make it work at 24/96? I'm OK throwing a lot more CPU than a PI at it.

Windows V1.3.0 exe

Hi,
La version ne s'installe pas avec windows 7 32bits ???
cf64 not expand....