guitarml / neuralpi Goto Github PK

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

License: GNU General Public License v3.0

C++ 78.01% CMake 5.84% Shell 8.37% Inno Setup 6.43% Batchfile 1.35%

neuralpi's Introduction

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. The NeuralPi includes model selection, EQ, and gain/volume controls from a remote instance of the plugin over WiFi. 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

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.

WARNING: The audio output of the HiFiBerry DAC + ADC is at line level. Guitar amplifiers expect a low level electric guitar signal (instrument level). Use NeuralPi only where line level inputs are expected.

There are four main components to the guitar pedal:

This is the normal plugin (v1.3.0), available for Windows (Standalone, VST3) and Mac (Standalone, AU, VST3). After connencting the Raspberry Pi and remote computer to the same local WiFi network, enter the RaspberryPi's IP address (keep the default ports) to enable control over WiFi. The Win/Mac plugins are fully functional guitar plugins that allow you to try out GuitarML's most advanced amp/pedal models without building the Raspberry Pi pedal.

Note: The plugin must be restarted after using the Import Tone button for changes to take effect.

Installing the plugin

See the Release page for the cross-compiled Raspberry Pi / Elk Audio OS compatible VST3 plugin and Win/Mac installers.

After running the plugin or standalone for the first time, the two default models will be copied to the following locations. Any imported models will be copied here as well. Model files must be manually removed from these locations to perform model clean-up.

Mac/Linux: /home/<username>/Documents/GuitarML/NeuralPi/tones 
Windows: C:/Users/<username>/Documents/GuitarML/NeuralPi/tones
Elk Audio OS: /home/mind/Documents/GuitarML/NeuralPi/tones

Conditioned Models

Starting with version 1.3, NeuralPi can load tones conditioned on the Gain parameter. The three default tones included with NeuralPi are now conditioned models (TS9 pedal, Fender Blues Jr. amp, and Blackstar HT40 amp set to overdrive channel). The conditioned model uses a neural network for the full range of the Gain/Drive parameter, rather than just a snapshot model. When a conditioned model is loaded, the Gain knob will turn red.

Adding New Models

Once your NeuralPi is set up, you can add new models from a remote computer using the following steps:

From the remote computer, run the plugin and add new models using the "Import Tone" button. Optionally, you can manually add new json files to the Documents/GuitarML/Chameleon/tones directory. Note: The "tones" directory is created the first time you run NeuralPi.
Turn on your WiFi enabled NeuralPi (see Elk documentation for connecting the Raspberry Pi to a local WiFi network)
Download the update_models.bat(Windows) or update_models.sh(Mac/Linux) to your remote computer. These scripts are located in the "scripts/" directory of this repository. You must change the rpi_ip_address and host_model_path to the Raspberry Pi's IP address and path to your json tones (on remote computer). The json files will be first copied from the remote computer to the NeuralPi, and then back from the NeuralPi to the remote computer. This allows updating models from the NeuralPi when you connect a new remote computer.
From the remote computer connected to the same local WiFi network as NeuralPi, run the update_models.bat(Windows) or update_models.sh(Mac/Linux) from a cmd terminal.

Note: It is important that all models files have unique names with no spaces.
Note: Ensure from the terminal output that you were able to connect over WiFi, and that the model files were copied properly.
Restart both the NeuralPi and the remote instance of the NeuralPi plugin. From the remote NeuralPi GUI, enter the Raspberry Pi's IP address. As long as both devices are connected to the local WiFi network, you will be able select models from the NeuralPi plugin dropdown list to change models running on the Raspberry Pi.

IMPORTANT: The plugin uses a sort() function to order the models alphabetically. Due to differences in the behaviour of this function on Linux (Elk OS) vs. Win/Mac, you must start json filenames with a capital letter, otherwise the NeuralPi on Elk will sort models starting with a lowercase letter at the end of the list and the controller will be out of sync with the NeuralPi pedal.

MIDI control of NeuralPi parameters

The “config_neuralpi_MIDI.json” file contains MIDI mapping of NeuralPi parameters.

The names of parameters are: "Gain", "Master", "Bass", "Mid", "Treble", "Presence", "Delay", "Reverb", "Model", "Ir". In that json file, you can see that those parameters have been asigned to incoming MIDI CC# messages "1", "2", "3", "4, "5", "6", "7", "8", "9" and "10" respectively. But editing the file allows you to chose whatever CC# to whatever parameter, by just changing values in the “cc_number” and “parameter_name” commmands.

Sushi will listen to incoming MIDI CC# messages, will normalize (0, 127) MIDI values range to (0, 1) Sushi range, and will set that value to correspondent parameter. For instance, if your MIDI controller sends a CC2 message with value "127", Sushi will receive that message and set "Master" parameter (“Master” is assigned to “CC2”) to be "1" (MIDI “127” value normalized to “1”).

You´ll need to copy the config file to the Raspberry, for instance through ssh over Wifi (login as root):

scp -r config_neuralpi_MIDI.json root@:/home/mind/config_files/

For connecting a MIDI device:

1 - Plug your MIDI device into any Raspberry USB port.

2 - Login as “mind” user, “elk” password, and run Sushi with the MIDI config:

sushi -r --multicore-processing=2 –c ~/config_files/config_neuralpi_MIDI.json &

3 – To list MIDI devices connected to the Raspberry, run:

aconnect –l

4 – You can now connect your MIDI device to Sushi either by their listed ports, or by their names. Run:

aconnect "your-listed-device-name" "Sushi"

NOTE 1: Currentlly, "Model" and "Ir" parameters are a little tricky to control. NeuralPi asigns a value to each file saved in "tones" or "Ir" directory. It divides the (0, 1) range of values by the number of files available, so for instance, if you had just 2 tone files in the directory, one of them would respond to any value in the (0, 0.49) range, and the other would respond to any value in the (0.5, 1) range.

To Do

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.
GuitarML maintains a fork with a few extra helpful features, including a Colab training script. 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.

Note: The GuitarML fork of the Automated-GuitarAmpModelling code now contains helper scripts for training conditioned models, which are compatible with NeuralPi v1.3.

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

Build with Cmake

Dependencies

Ubuntu 22.04

The following packages will need to be installed on Ubuntu in order for build to succeed:

apt install cmake libx11-dev libxrandr-dev libxinerama-dev libxcursor-dev libxft2-dev

Build

# Clone the repository
$ git clone https://github.com/GuitarML/NeuralPi.git
$ cd NeuralPi

# initialize and set up submodules
$ git submodule update --init --recursive

# build with CMake
$ cmake -Bbuild
$ cmake --build build --config Release

The binaries will be located in NeuralPi/build/NeuralPi_artefacts/

Build with Projucer

Clone or download this repository.
Download and install JUCE This project uses the "Projucer" application from the JUCE website.
Download the RTNeural submodule (cd into the NeuralPi repo first):

git submodule update --remote --recursive
Download and extract: json Json for c++.
Open the NeuralPi.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

Build NeuralPi 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.

neuralpi's People

Contributors

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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.

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/

Cab emulation

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

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.

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.

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?

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.

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 😎

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?

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.

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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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

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!

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.

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

Windows V1.3.0 exe

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

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.

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