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

Overview

NeuralPi

NeuralPi is a guitar pedal using neural networks to emulate real amps and pedals on a Raspberry Pi 4. The NeuralPi software is a VST3 plugin built with JUCE, which can be run as a normal audio plugin or cross-compiled to run on the Raspberry Pi 4 with Elk Audio OS. NeuralPi is intended as a bare-bones plugin to build on. The pedal runs high quality amp/pedal models on an economical DIY setup, costing around $120 for hardware to build yourself.
Check out a video demo on YouTube
Check out the step by step build guide published on Towards Data Science

app

NeuralPi can sound like an amplifier or distortion/overdrive pedal using the power of neural networks. Models trained from recordings of real amps and pedals can be loaded into the plugin for endless possiblities on your guitar. Create your own models or use custom tones from GuitarML.

There are four main components to the guitar pedal:

  1. Raspberry Pi 4b
  2. HiFiBerry DAC + ADC
  3. Elk Audio OS
  4. NeuralPi VST3 plugin

app
This is the normal VST3 plugin, which can be compiled for Windows/Mac/Linux using JUCE. It includes a model import feature, dropdown menu, and gain/level controls. The plugin compiled for Elk Audio OS is headless, meaning there is no GUI.

Installing the plugin

For the cross-compiled Raspberry Pi / Elk Audio OS compatible VST3 plugin, download here.

WARNING: The audio output of the NeuralPi is at line level. Guitar amplifiers expect a low level electric guitar signal (instrument level). When using the NeuralPi with a guitar amp, start with the master volume at 0 and SLOWLY increase from there.

Changing Models

The NeuralPi running on Elk will load one model per instance of the plugin. By default this is the "bj_model_best.json" file. Model select controls will be added in the future, but for now you must overwrite this file to load different models. On Elk Audio OS, the models will be installed here after running the plugin for the first time:

/home/mind/.config/JUCE/NeuralPi/tones

Backup the Blues Junior amp model with this:

cp bj_model_best.json bj_backup.json

And overwrite the original model to try a new one:

cp ts9_model_best.json bj_model_best.json

If you need to revert back to the original state, remove the /tones folder and restart the plugin. The "bj_model_best.json" is a Fender Blues Jr. amplifier at full gain, and the "ts9_model_best.json" is the Ibanez TS9 overdrive pedal at full drive.

To Do

Currently, the NeuralPi plugin running on Elk OS has no user controls. It runs a single model that can be swapped out before running the plugin. The next step is to add user controls via OSC messages, so that a remote instance of the plugin can control the NeuralPi over Wifi. These controls will include Gain/Volume, EQ, and model selection.

Elk Audio OS also supports physical controls through Sensei. Gain/Volume and EQ knobs can be added, as well as a LCD screen for selecting different models. One could build an actual guitar pedal with NeuralPi and any number of other digital effects and controls.

While running PyTorch locally on the Raspberry Pi might be a stretch, it is fully capable of recording high quality audio with the HiFiBerry hat. Implement a capture feature by automating the recording of input/output samples, pushing to remote computer for training, then updating the Pi with the newly trained model.

Info

The neural network is a re-creation of the LSTM inference model from Real-Time Guitar Amplifier Emulation with Deep Learning

The Automated-GuitarAmpModelling project was used to train the .json models.
IMPORTANT: When training models for NeuralPi, ensure that a LSTM size of 20 is used. NeuralPi is optimized to run models of this size, and other sizes are not currently compatible.

The plugin uses RTNeural, which is a highly optimized neural net inference engine intended for audio applications.

The HiFiBerry DAC+ADC card used for this project provides 192kHz/24bit Analog-to-Digital and Digital to Analog, which is industry standard for high quality audio devices. The plugin processes at 44.1kHz (specified in config file) for the neural net DSP.

Build Instructions

To build the plugin for use on the Raspberry Pi with Elk Audio OS, see the official Elk Audio Documentation

To build for Windows/Mac/Linux:

  1. Clone or download this repository.

  2. Download and install JUCE This project uses the "Projucer" application from the JUCE website.

  3. Download the RTNeural submodule (cd into the NeuralPi repo first):

    git submodule update --remote --recursive

  4. Download and extract: json Json for c++.

  5. Open the Chameleon.jucer file and in the appropriate Exporter Header Search Path field, enter the appropriate include paths. For example:

  <full-path-to>/json-develop/include
  <full-path-to>/NeuralPi/modules/RTNeural
  <full-path-to>/NeuralPi/modules/RTNeural/modules/xsimd/include
  1. Build Chameleon from the Juce Projucer application for the intended build target.

Note: Make sure to build in Release mode unless actually debugging. Debug mode will not keep up with real time playing.

Comments
  • Looking for high quality time-based effects to add

    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.

    opened by GuitarML 12
  • Syncing models between Raspberry Pi and external device

    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
    opened by GuitarML 10
  • LV2 builds?

    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.

    opened by dromer 8
  • Run individual NeuralPi plugins on each input/output channel

    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?
    opened by digi-chris 4
  • Use model from SmartAmpPro

    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 😎

    opened by R0Wi 3
  • file extension error in Mac update script

    file extension error in Mac update script

    Line 32 should be:

    scp $host_ir_path/*.wav [email protected]$rpi_ip_address:$rpi_ir_path/

    Line 36 should be:

    scp [email protected]$rpi_ip_address:$rpi_model_path/*.json $host_model_path/

    opened by billheese 2
  • Update FileChooser to use latest JUCE standards

    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.

    opened by GuitarML 1
  • Convert json to the correct format

    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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      "blocks.4.conv.bias": [
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      "blocks.4.film.adaptor.weight": [
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    }
    
    opened by creativeguitar 1
  • MLAMSK Sr. Design Team's Work

    MLAMSK Sr. Design Team's Work

    Hello!

    The following pull request includes all of our work on NeuralPi. There weren't too many changes, but here are they are:

    • Implemented C++ Template to generate model loading code for whatever model type is used and removed duplicate load_json function. Hopefully this will make future explorations in Parameterized models easier to implement.
    • Added support for a two parameter model which keeps inline with the current implementation and takes the "Level" knob and uses it for the second parameter. The GUI is updated with a Red knob like before.

    Alright, I think that is about it. As always, if you want anything changed or if something went in a direction you don't feel is right, please let us know and we will make the changes and update the pull request.

    Thanks a lot! MLAMSK Team

    opened by mtseng15 1
  • MIDI control of NeuralPi parameters

    MIDI control of NeuralPi parameters

    Added a new config file, "config_neuralpi_MIDI.json", which now includes MIDI CC# mappings of NeuralPi parameters.

    Also, REAMDE file has been modified in order to include the explanation of MIDI mapping.

    opened by Sedaxel 1
  • Questing regarding bitdepth/sample rate

    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.

    opened by mabushey 3
  • Segmentation Fault in mod-host. Not listed in guitarix. Ubuntu 22.04

    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.

    opened by rominator1983 7
  • Feature Request (VST version): volume normalisation for IR files

    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.

    opened by IsaakCode 0
  • Garbled output when run standalone

    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.

    opened by davex25 2
  • JUCE built-in submodule version doesn't allow headless build, Elk OS SDK does but it's a fork from a different version

    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

    opened by MaxPayne86 2
Releases(v1.3.0)
  • v1.3.0(Oct 31, 2021)

    NeuralPi 1.3.0 Release

    New Features:

    -Added ability to load conditioned models in addition to snapshot models. These models are assumed to be conditioned on the Gain/Drive parameter. The Gain knob turns red when a conditioned model is loaded when using the Windows/Mac plugin.

    -Swapped the default snapshot models of the TS9 and Blues Jr. with conditioned models. Added the HT40 amp model as a third default.

    Available formats (from installer): -Windows 7 and up, 32-bit or 64-bit (Standalone, VST3) -Mac 10.12 and up (Standalone, AU, VST3) -Elk Audio OS (VST3)

    -Added Linux deb package 2/2/2022 (Standalone, VST3, LV2)

    Note: The NeuralPi-Elk-VST3.zip contains the cross compiled VST3, model update scripts, and example elk configuration files.

    Source code(tar.gz)
    Source code(zip)
    Install-NeuralPi-v1.3-Mac.dmg(9.25 MB)
    NeuralPi-Linux-x64-1.3.deb(2.30 MB)
    NeuralPi-v1.3-Elk-VST3.zip(4.63 MB)
    NeuralPi-Win-1.3.0.exe(6.65 MB)
  • v1.2.0(Aug 14, 2021)

    NeuralPi 1.2.0 Release

    New Features:

    • Added IR loading
    • Added Delay and Reverb effects
    • Updated scripts to add new IR's to the Raspberry Pi in addition to models

    Available formats (from installer): -Windows 7 and up, 32-bit or 64-bit (Standalone, VST3) -Mac 10.12 and up (Standalone, AU, VST3) -Elk Audio OS (VST3)

    Note: The NeuralPi-Elk-VST3.zip contains the cross compiled VST3, model update scripts, and example elk configuration files.

    Release Update (10/11/2021): Fixed update_models.sh script, added Elk MIDI config file

    Source code(tar.gz)
    Source code(zip)
    Install-NeuralPi-v1.2-Mac.dmg(9.06 MB)
    NeuralPi-v1.2-Elk-VST3.zip(4.60 MB)
    NeuralPi-Win-1.2.0.exe(6.58 MB)
  • v1.1.0(Jun 21, 2021)

    NeuralPi 1.1.0 Release

    New Features:

    • Updated Elk OS VST3 to support controls over WiFi
    • Model Selection, EQ, Gain/Volume controls over WiFi, with update GUI
    • Installers for Mac/Windows
    • Cmake integration
    • update model scripts to add new models to the Raspberry Pi

    Available formats (from installer): -Windows 7 and up, 32-bit or 64-bit (Standalone, VST3) -Mac 10.11 and up (Standalone, AU, VST3) -Elk Audio OS (VST3)

    Note: The NeuralPi-Elk-VST3.zip contains the cross compiled VST3, model update scripts, and example elk configuration files.

    Update (7/14/21) - NeuralPi_3Dprinted_case.zip has been added, containing 3 .stl files to create a 3-d printed case for NeuralPi.

    Source code(tar.gz)
    Source code(zip)
    Install-NeuralPi-Mac.dmg(6.56 MB)
    Install-NeuralPi-Win-1.1.0.exe(6.41 MB)
    NeuralPi-Elk-VST3.zip(4.56 MB)
    NeuralPi_3Dprinted_case.zip(515.86 KB)
  • v1.0(May 22, 2021)

    This zip file contains the cross compiled vst3 for Raspberry Pi 4 with Elk Audio OS. It also contains two configuration files for running the plugin on the Raspberry Pi, one with only NeuralPi, and one with NeuralPi + Delay/Reverb.

    The NeuralPi.vst3 must be copied to the Raspberry Pi running Elk Audio OS. The typical install folder is "/home/mind/plugins"

    The config files must be copied to the Raspberry Pi running Elk Audio OS. The typical install folder is "/home/mind/config_files"

    Source code(tar.gz)
    Source code(zip)
    NeuralPi-elk.zip(3.55 MB)
Owner
Keith Bloemer
Anyone can get great guitar tone using modern computing technology. GuitarML is a community for advancing machine learning for use in electric guitar plugins.
Keith Bloemer
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