#4460 ESP32 Python Synth Driver for Korg Protein
#4460 ESP32 Python Synth Driver for Korg Protein
#4460 Can an ESP32 run a python to calculate and "push" synth patches and sequences to a hardware synth using MIDI?
Yes, absolutely. You can run Python on an ESP32 using either MicroPython or CircuitPython to calculate math, generate sequences, parse/create patch data, and "push" them to a hardware synthesizer via MIDI.
The ESP32 is a fantastic candidate for this because it has hardware UARTs (serial ports) that can easily match the unusual MIDI baud rate, and it packs plenty of RAM and processing power to handle algorithmic music generation.
Here is a breakdown of how it works, what the code looks like, and what you need hardware-wise.
1. The Core Approaches (How it Handles MIDI)
Depending on your hardware synth, you can push MIDI in a few different ways from Python:
Traditional 5-Pin DIN or TRS MIDI: This uses the ESP32’s built-in Hardware UART. It is incredibly stable and standard.
USB MIDI: If you use an ESP32-S3 or ESP32-S2 (which have native USB capabilities) running CircuitPython, the board can act as a USB MIDI device or host directly.
BLE MIDI (Bluetooth): The ESP32 can send MIDI wirelessly over Bluetooth Low Energy, which many modern synths (or Bluetooth MIDI adapters) accept.
2. Pushing Patch Data vs. Sequences
The Python code will handle sequences and patches quite differently:
A. Sequencing (Notes, CC, and Clock)
To push sequences, your Python code calculates note pitches, velocities, and steps, then streams them out as standard 3-byte MIDI channel messages (e.g., Note On, Note Off, Control Change). You can use Python's time.sleep_ms() or native hardware timers to keep the clock steady.
B. Patch Pushing (System Exclusive / SySex)
Hardware synths store and change entire patches using SySex (System Exclusive) messages. These are variable-length packets of raw bytes starting with 0xF0 and ending with 0xF7.
You can write Python algorithms to dynamically calculate filter settings, envelope times, or oscillator shapes.
Python packages these calculated values into a byte array (conforming to your specific synth's MIDI implementation chart).
The ESP32 dumps the raw array to the MIDI port, instantly changing the synth's patch.
3. The Code Structure (MicroPython Example)
Because Python treats serial ports as basic stream objects, sending MIDI data over a physical MIDI jack requires no massive, bloated libraries. You just configure the hardware serial port to the strict MIDI speed of 31,250 baud.
Here is a simple example of how MicroPython handles calculating a sequence and sending a raw SySex patch dump:
from machine import UART
import time
# Initialize UART2 on the ESP32.
# MIDI standard speed is strictly 31250 baud.
# You can map this to almost any free GPIO pins (e.g., TX=17, RX=16)
uart = UART(2, baudrate=31250, tx=17, rx=16)
def send_midi(msg):
"""Sends a list/bytes of MIDI data."""
uart.write(bytes(msg))
def push_calculated_patch(cutoff, resonance):
"""
Example algorithm to 'calculate' and push a custom SySex patch.
(Actual byte structures depend entirely on your specific synthesizer)
"""
# Clamp values to safe 7-bit MIDI range (0-127)
sys_cutoff = max(0, min(127, int(cutoff)))
sys_res = max(0, min(127, int(resonance)))
# SySex format: [Start, Manufacturer ID, Model ID, Data..., End]
sysex_packet = [0xF0, 0x41, 0x10, sys_cutoff, sys_res, 0xF7]
send_midi(sysex_packet)
def play_sequence():
"""Generates a simple generative arithmetic sequence."""
base_note = 60 # Middle C
for i in range(16):
# Calculate a simple generative melody
note = base_note + ((i * 5) % 12)
# Note On: [0x90 + Channel(0), Note Number, Velocity]
send_midi([0x90, note, 100])
time.sleep_ms(200)
# Note Off: [0x80 + Channel(0), Note Number, 0]
send_midi([0x80, note, 0])
time.sleep_ms(50)
# --- Main Execution ---
# 1. Tweak the synth parameters via Python calculations
push_calculated_patch(cutoff=110, resonance=64)
time.sleep_ms(100)
# 2. Fire the sequence
print("Playing calculated sequence...")
play_sequence()
4. The Hardware You Need
Microcontrollers operate at 3.3V, whereas classic MIDI instruments run on a current loop that historically expected 5V. To safely connect your ESP32's TX pin to a hardware synth's 5-Pin DIN or TRS MIDI In jack, you need a basic optocoupler circuit or a simple schematic adjustment.
According to the official MIDI 1.0 Electrical Specification (3.3V update), you can wire a MIDI OUT port directly to your ESP32 using just two resistors:
Connect the ESP32 3.3V pin through a 33 Omega resistor to Pin 4 of the DIN jack.
Connect the ESP32 TX pin through a 10 Omega resistor to Pin 5 of the DIN jack.
Connect Pin 2 of the DIN jack to GND (shield).
(Alternatively, you can just buy a cheap, pre-made "TRS/DIN MIDI Breakout Board" for microcontrollers for a few dollars to handle the pins safely.)
💡 A Quick Tip on Firmware Selection
If you want to build this, CircuitPython (by Adafruit) has a phenomenal, pre-built library called adafruit_midi that abstracts everything into clean objects like MIDI.send(NoteOn(60, 120)). If you choose standard MicroPython, you will usually write raw bytes directly to the UART stream as shown in the example code above, which gives you maximum control over heavy SySex patch dumps.
What hardware synth are you planning to control with this setup? I can help you look at how it structures its SySex or CC data.
Could I make this work with my Behringer MonoPoly?
Yes, you can absolutely use an ESP32 to sequence and control your Behringer MonoPoly over MIDI—but with a major structural catch unique to this specific synth's architecture.
Here is the exact reality of how the MonoPoly behaves under MIDI control, and how you can tailor your Python script to make it work.
The Catch: Real-Time vs. Global Parameters
Like the classic 1981 Korg original it’s based on, the Behringer MonoPoly is a true, discrete analog instrument. The 62 knobs and switches on the front panel are connected directly to analog control voltages (CV), components, and traces. They are not digital encoders. Because of this, the MonoPoly has no digital memory for saving patches on the machine, and its analog components cannot listen to real-time MIDI CC messages.
You cannot send a MIDI message to sweep the physical filter Cutoff knob or change a VCO's waveform mid-sequence via Python.
There are no MIDI CC numbers mapped to the synthesizer's knobs.
What You Can Push via Python
The digital brain inside the Behringer MonoPoly handles the keyboard tracking, portamento, MIDI routing, and arpeggiator clock sync. Using the ESP32, you can push two things:
Generative Sequences & Pitch Data: Standard Note On, Note Off, Pitch Bend, and Mod Wheel messages work perfectly.
Global System Configurations via SySex: You can use Python to instantly reprogram the underlying global digital utilities of the synth.
1. Pushing Global Configurations (The SySex Blueprint)
Behringer exposes the MonoPoly’s internal configuration menu via specific System Exclusive (SySex) commands. Your ESP32 can calculate these bytes and push them down the wire to dynamically change how the synth behaves.
According to Behringer’s official MIDI implementation for the MonoPoly, the SySex header structure looks like this:
F0 00 20 32 00 01 12 [Device ID] [Command] [Value] F7
Useful Python SySex Commands for the MonoPoly:
Change Sync Clock Source (Command 1B):
Dynamically switch whether the MonoPoly's arpeggiator or effects sync to internal tempo or an external MIDI clock generated by your ESP32.
Internal Clock: [0xF0, 0x00, 0x20, 0x32, 0x00, 0x01, 0x12, 0x00, 0x1B, 0x00, 0xF7]
MIDI Clock: [0xF0, 0x00, 0x20, 0x32, 0x00, 0x01, 0x12, 0x00, 0x1B, 0x01, 0xF7]
Alter Mod Wheel Output (Command 21):
Change what happens when the ESP32 sends a standard Mod Wheel CC message.
2. MicroPython Implementation for the MonoPoly
Since you cannot automate patch parameters directly over MIDI, the best strategy for a "Single-Person Think Tank" style algorithmic music approach is to generate complex sequences, note patterns, and clock ticks via Python, while manually shaping the raw analog texture on the physical MonoPoly panel.
Here is how you can set up MicroPython on the ESP32 to change the MonoPoly's clock source via SySex, and then stream a math-generated algorithmic sequence:
from machine import UART
import time
# Initialize hardware UART2 on the ESP32 (31250 baud for standard MIDI)
# Connect ESP32 TX (Pin 17) to MonoPoly MIDI IN (using the 2-resistor circuit)
midi_port = UART(2, baudrate=31250, tx=17, rx=16)
def send_midi(byte_list):
midi_port.write(bytes(byte_list))
def set_monopoly_clock_source(source="internal"):
"""
Pushes a SySex packet to alter the MonoPoly's internal digital brain.
source: "internal" (0x00) or "midi" (0x01)
"""
val = 0x01 if source == "midi" else 0x00
# Behringer MonoPoly Specific SySex Header
# F0 (Start), 00 20 32 (Behringer ID), 00 01 12 (MonoPoly Model ID)
sysex_packet = [0xF0, 0x00, 0x20, 0x32, 0x00, 0x01, 0x12, 0x00, 0x1B, val, 0xF7]
send_midi(sysex_packet)
print(f"MonoPoly clock source set to: {source}")
def play_algorithmic_sequence(steps=32):
"""
Generates a changing arithmetic pattern to take advantage
of the MonoPoly's 4-VCO Key Assign Modes (Poly/Unison/Share).
"""
base_note = 48 # C2 (Great for rich analog bass)
print("Streaming sequence to MonoPoly...")
for step in range(steps):
# Generates a pseudo-random but repeating mathematical pattern
note_offset = (step * 7) % 24
current_note = base_note + note_offset
# 1. Note On (Channel 1 -> 0x90, Note, Velocity -> 100)
send_midi([0x90, current_note, 100])
time.sleep_ms(150)
# 2. Note Off (Channel 1 -> 0x80, Note, Velocity -> 0)
send_midi([0x80, current_note, 0])
time.sleep_ms(50)
# --- Execution ---
# Configure the synth's internal system settings first
set_monopoly_clock_source("internal")
time.sleep_ms(200)
# Fire the algorithmic note stream
play_algorithmic_sequence(steps=64)
How about a Waldorf Protein?
If you are thinking of Waldorf’s compact wavetable synthesizer, the Protein, the short answer is yes—and it will work significantly better for deep automation than the MonoPoly does. Because the Protein is built on a modern, highly digital architecture (inheriting its DNA from Waldorf's legendary Microwave 1 and flagship Iridium), its entire sound engine is exposed to MIDI.
While the MonoPoly completely locks you out of real-time parameter control, the Waldorf Protein blows the doors wide open. Here is exactly how an ESP32 workflow shifts when you pivot to this machine.
1. Real-Time Patch Sculpting via MIDI CC
Unlike the analog MonoPoly, nearly every single one of the 21 knobs, dual oscillators, digital Curtis-modeled filters, envelopes, and matrix modulators on the Protein has an assigned MIDI Control Change (CC) number.
This means your ESP32 can run Python math loops to do things that are physically impossible for a human hand, such as:
Generative Wavetable Sweeps: Smoothly sweeping the wavetable position index of Oscillator 1 and 2 independently using LFO equations calculated in Python.
Dynamic Modulations: Modulating the filter cutoff, digital drive types (tube, diode, "crunch"), or noise burst length on a per-step basis inside your sequence.
Because it handles standard CC messages, the code is incredibly clean. You don't need to construct massive, complex SySex arrays; you just send simple 3-byte packets: [0xB0, CC_Number, Value].
2. Pushing Massive Patches (SySex)
If you want to treat the ESP32 as an external patch generator or a randomizer, the Protein fully supports deep System Exclusive dumps. Because the Protein features 4-part multitimbral layering, you can use the ESP32 to calculate entirely different synth parameters for Layer A, B, C, and D, and push them down the wire.
You can even tell the Protein to cycle through these layers in a "round-robin" arrangement via Python, or split them across 4 different MIDI channels to play an entire algorithmic multi-part track from a single microcontroller.
3. MicroPython Code: Generative Wavetable Sweep
Here is an example of how your MicroPython script changes for the Protein. Instead of just sending static notes, the ESP32 uses a mathematical sine wave loop to calculate and push a dynamic filter and wavetable sweep while playing a sequence:
from machine import UART
import math
import time
# Initialize hardware UART2 on the ESP32 (31250 baud)
# Note: The Protein uses a 3.5mm TRS MIDI jack, so you'll run your
# ESP32 TX line directly to a 3.5mm Type-A TRS MIDI breakout cable.
midi_port = UART(2, baudrate=31250, tx=17, rx=16)
def send_midi(byte_list):
midi_port.write(bytes(byte_list))
def send_cc(control_num, value, channel=1):
"""Sends a standard MIDI Control Change message."""
status_byte = 0xB0 + (channel - 1)
# Clamp value to 7-bit MIDI range
val = max(0, min(127, int(value)))
send_midi([status_byte, control_num, val])
def play_evolving_sequence():
base_note = 48 # C2
# Let's assume standard Waldorf CC mappings for this example:
CC_CUTOFF = 74
CC_WAVETABLE_POS = 79 # Standard Waldorf target for wave tracking
print("Streaming generative sequence & real-time CC to Protein...")
for step in range(128):
# 1. Use math functions to calculate evolving textures
# Calculate a smooth sine wave cycle for the filter (scaled to 0-127)
cutoff_val = 64 + int(50 * math.sin(step * 0.1))
# Calculate a gradual linear climb/sawtooth for the wavetable index
wave_pos_val = (step * 4) % 127
# 2. "Push" the new patch parameters to the engine instantly
send_cc(CC_CUTOFF, cutoff_val)
send_cc(CC_WAVETABLE_POS, wave_pos_val)
# 3. Calculate and play the notes
note_offset = (step * 5) % 12
current_note = base_note + note_offset
send_midi([0x90, current_note, 90]) # Note On
time.sleep_ms(100)
send_midi([0x80, current_note, 0]) # Note Off
time.sleep_ms(50)
# Execute the sequence
play_evolving_sequence()
4. Physical Connection Advantage
The hardware setup for this is actually easier than it is for older instruments. The Protein utilizes space-saving 3.5mm TRS MIDI mini-jacks instead of bulky 5-pin DIN ports, and it ships with the necessary adapters.
Furthermore, because the Protein is highly digital and operates internally on low-voltage logic processing, it handles the ESP32’s 3.3V logic levels flawlessly without requiring complex optocoupler isolation circuits on the output stage. You can just run the TX line through a protective resistor straight into the TRS adapter tip, power the Protein via USB-C, and watch the wavetables dance.
Can an ESP32 with WifI access an online LLM?
Yes, absolutely. Since the ESP32 has a built-in $2.4\text{GHz}$ Wi-Fi stack, it can easily act as an internet-connected client, make HTTPS requests to any cloud-based LLM provider (like OpenAI, Anthropic, Groq, or OpenRouter), and process the text it receives.
However, when programming an ESP32 in MicroPython or CircuitPython, you cannot use standard desktop libraries like pip install openai or pip install langchain. Those libraries are too large and resource-heavy for a microcontroller's limited RAM.
Instead, you write raw, optimized HTTP requests using standard JSON payloads.
The Architectural Constraints
Before looking at the code, keep three specific constraints in mind:
The HTTPS Memory Overhead: Setting up a TLS/SSL secure connection over Wi-Fi takes a significant bite out of the ESP32’s available SRAM. If you use a basic ESP32, keep your code light. If you use an ESP32-S3 with external PSRAM, you'll have plenty of headroom.
The JSON Buffer Problem: Standard LLM API responses wrap the actual answer inside a mountain of metadata (tokens used, model details, headers). If you request a long response, the incoming JSON string can easily cause an Out-Of-Memory (OOM) crash.
The Token Target Rule: Always instruct the LLM's system prompt to keep its output short, structured, and concise (e.g., "Respond only in a valid raw JSON format containing 16 MIDI bytes").
MicroPython Code Example
This script connects the ESP32 to Wi-Fi, hits the OpenAI API (or any OpenAI-compatible endpoint like Groq, Together AI, or a local server running Ollama on your local network), extracts the message text, and prints it out.
import network
import urequests as requests
import ujson as json
import time
# --- Configuration ---
WIFI_SSID = "Your_WiFi_Name"
WIFI_PASS = "Your_WiFi_Password"
API_URL = "https://api.openai.com/v1/chat/completions"
API_KEY = "your-actual-api-key-here"
def connect_wifi():
wlan = network.WLAN(network.STA_IF)
wlan.active(True)
if not wlan.isconnected():
print("Connecting to Wi-Fi...")
wlan.connect(WIFI_SSID, WIFI_PASS)
# Wait for connection with a 10-second timeout
timeout = 10
while not wlan.isconnected() and timeout > 0:
time.sleep(1)
timeout -= 1
if wlan.isconnected():
print("Connected! IP Info:", wlan.ifconfig())
else:
print("Wi-Fi Connection Failed.")
def query_llm(prompt_text):
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}
# Construct an optimized, low-token payload
payload = {
"model": "gpt-4o-mini", # Use small, fast models
"messages": [
{
"role": "system",
"content": "You are an algorithmic music assistant. Keep answers ultra-short."
},
{
"role": "user",
"content": prompt_text
}
],
"max_tokens": 50, # Guard against massive string payloads blowing up RAM
"temperature": 0.7
}
print("Sending request to LLM...")
try:
# Perform the POST request
response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
if response.status_code == 200:
response_json = response.json()
# Parse down through the standard OpenAI response tree
reply = response_json['choices'][0]['message']['content']
return reply.strip()
else:
print(f"Error Code: {response.status_code}")
print("Response:", response.text)
return None
except Exception as e:
print("Network or parsing error occurred:", e)
return None
finally:
# ALWAYS close your sockets on a microcontroller to free memory sockets
try:
response.close()
except:
pass
# --- Execution ---
connect_wifi()
# Test prompt
prompt = "Generate a chord progression in Roman numerals for a dark ambient synth track."
ai_response = query_llm(prompt)
print("\n--- LLM Response ---")
print(ai_response)
💡 Pro-Tip: The Proxy Strategy
If you are building an advanced system where the LLM is supposed to dynamically generate complex MIDI sequences or patch parameters for instruments like your Waldorf Protein, the Direct Approach shown above can get messy due to micro-parsing strings into raw bytes on-chip.
An elegant alternative configuration used by many makers is a Local Proxy Architecture:
ESP32 (Wi-Fi) ──[Lightweight REST/JSON]──> Your Local PC / Server (Python/Flask)
│
(Heavy SDK Calls)
▼
Cloud LLM API / Ollama
Why? Your desktop PC runs a tiny Python/Flask or FastAPI script. It takes a simple phrase from the ESP32, calls the heavy LLM API, strips away all the API garbage, translates the text response into a raw array of integers/bytes, and hands the ESP32 only the clean data arrays it needs to stream immediately to the hardware MIDI ports. This bypasses the ESP32's memory constraints entirely.
From <https://gemini.google.com/app/> Google Gemini (3.5 Flash)
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