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BUILD A 120 DOLLARS RASPBERRY PI PEDAL THAT CLONES ANY GUITAR AMP WITH NEURAL NETWORKS

Train a neural network on your own tube amp, then run the model on a Raspberry Pi at playable latency for about 120 dollars.

by Keith Bloemer

FULL CAD BOM FIRMWARE DOCS

AudioAI

difficulty
●●●●○
time
a weekend-plus
license
GPL-3.0
repo
repo ACTIVE1,199 stars

WHAT YOU’LL NEED

  • Electronic parts — full list with part numbers in the repo BOM
  • Dev board / microcontroller — runs the project firmware

Partner

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Videos
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COMPAREE VERDICT

This is a neural amp modeler that runs on a Raspberry Pi 4 with a HiFiBerry audio HAT, totaling about 120 dollars in hardware. The concept is sound: train a neural network on audio samples of a real tube amp, then run the trained model locally with low enough latency to play live. The repository includes everything you need to flash Elk Audio OS onto an SD card, compile the code, and load models. The community shares many amp and pedal models, so you do not have to train your own to start. The catch is that the documentation assumes comfort with command-line Linux, SSH, and compiling C++ code — there is no GUI installer. The other catch is that there is no case design or wiring guide, so you are building the enclosure from scratch or running it as a bench prototype. If a solder pad lifts on the HiFiBerry or the Pi locks up mid-build, you are debugging it yourself. The payoff is a working neural modeler for the price of a mid-range commercial pedal, and the ability to capture the exact sound of your own amp. If you have built Pi audio projects before and can tolerate an afternoon of Linux troubleshooting, this is a weekend-plus well spent. If this is your first embedded audio project, expect that timeline to double.

NOT IN THE REPO

  • —GPL-3.0 licensed, no commercial restrictions.
  • —README lists hardware requirements and approximate $120 cost to build.
  • —Code is present for both the Pi pedal and VST3 plugin builds.
  • —Setup instructions are thorough but assume you can flash an SD card and SSH into a Pi.
  • —No enclosure design or wiring diagram — you are expected to figure out the physical build.
  • —Community shares trained models; training your own requires audio capture of an actual amp and familiarity with Python.

Can I build this?

Printnothing required — optional: enclosure if you design one yourself
BuyRaspberry Pi 4 (4GB or 8GB), HiFiBerry DAC+ ADC or DAC+ ADC Pro, MicroSD card (16GB+), power supply, audio jacks and cables, optional footswitch
Toolssoldering iron for headers if not pre-soldered, computer to flash SD card, SSH client, optional 3D printer or enclosure fabrication tools
Skillscomfortable with command-line Linux, SSH, compiling code from source, basic soldering; training your own models requires Python and audio capture setup
Timea weekend to get it running headless; another half-day minimum if you want a case and footswitch wiring
Cost$$, dominated by the Raspberry Pi 4 and HiFiBerry board; the README estimate of $120 is realistic for the core hardware
Safety5V DC only, no mains voltage. Wear hearing protection when testing with amplified guitar signals. No other hazards beyond ordinary electronics care.

Build at your own risk. Projects involve tools, electronics and sometimes mains voltage — follow the creator’s safety notes.

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Videos

NeuralPi : RaspberryPi Guitar Pedal using Neural Networks

No official video walkthrough in repository or payload.

Gallery

https://img.youtube.com/vi/_3zFD6h6Wrc/maxresdefault.jpg

Start here

Navigation into the creator’s own docs — we don’t rewrite the guide, we route you to the source.

  1. 1.Read the hardware requirements in the README and order the Raspberry Pi 4 and HiFiBerry board. (The README lists compatible HiFiBerry models; confirm your choice before ordering.)
  2. 2.Flash Elk Audio OS onto a MicroSD card following the setup instructions. (Elk Audio OS is a real-time Linux distribution required for low-latency audio.)
  3. 3.SSH into the Pi and compile the NeuralPi code per the build instructions. (Assumes familiarity with SSH and command-line builds.)
  4. 4.Load a pre-trained model from the community collection or train your own. (Training your own requires Python and audio capture of the amp you want to model.)

Resources

Documentation, files and community threads for this build — we link straight to the original sources and never rehost the creator’s files.

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KNOWN ISSUES

  • The README says '$120' but that is US pricing and assumes you already own a soldering iron, SD card reader, and audio cables — factor in another $20-40 for those if you are starting from zero.
  • Elk Audio OS is not Raspbian; if you have never used it, expect an hour just getting SSH working and understanding the filesystem layout.
  • There is no wiring diagram or enclosure design — you are expected to either run it on a bench or design your own case and footswitch wiring from scratch.
  • Latency is 'low enough to play live' but what that means depends on your tolerance and the model complexity — test with a simple model first before assuming your 8-parameter amp model will run at the same speed.
  • If the HiFiBerry is not seated perfectly or a solder joint is cold, you will get crackling or no audio and the error messages will not tell you why.
  • Training a model requires recording clean and distorted audio from your actual amp, which means either a loadbox or a very patient neighbour.

Can I use a Raspberry Pi 3 instead of a Pi 4?

The README specifies Pi 4 for the processing power needed to run models at low latency. A Pi 3 may work with simpler models but is not officially supported.

Do I need to train my own models or can I use pre-trained ones?

The community shares many pre-trained models for popular amps and pedals. You can start with those and only train your own if you want to capture a specific piece of gear.

What is the latency in practice?

The repository claims low enough latency for live playing, but the exact figure depends on the model complexity and buffer settings. Expect single-digit milliseconds with optimised settings.

Can I run this as a plugin instead of building the hardware?

Yes, the same code compiles as a VST3 plugin for use in a DAW. The repository includes build instructions for both the Pi and plugin targets.

Community builds

No community builds yet — be the first, we feature the best ones.

Discussion1

FROM THE COMPAREE TEAM

1,199 stars and a thriving model-sharing community — the barrier is not the concept, it is the Linux and the lack of an enclosure design. If you have built this or tried it, what was the one thing that took longer than the README suggested?

CompareeTEAM22d ago

Practical notes from our verification: the repository is actively maintained under the GuitarML organization, the $120 cost estimate holds if you already own basic tools and cables, and the biggest time sink is not the software build — it is either designing an enclosure from scratch or accepting that you are playing through a bare Raspberry Pi on your pedalboard. The Reddit thread confirms that people do get it working, but also that latency tuning is trial-and-error and not every HiFiBerry board behaves identically. If you want a packaged neural modeler, buy one; if you want to understand how they work and are comfortable with embedded Linux, this is the weekend that teaches you.

Keith Bloemer

Keith Bloemer is the developer behind GuitarML, a collection of open-source neural network tools for guitar tone modeling. NeuralPi applies the same neural modeling techniques used in the GuitarML VST plugins to a standalone Raspberry Pi pedal, making the technology accessible outside the DAW.

GitHub

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  • Information is provided without warranty and may become outdated as projects evolve. Prices are indicative bands only — always check the creator’s parts list for current costs.
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