MusicGen Maxmsp
Python/Maxmsp
2024
MusicGen Maxmsp is an innovative workflow that transforms simple, user-recorded melodies into unique, AI-enhanced musical sketches by blending personal creativity with intuitive style selections.
Problem Statement & Motivation
Many text-to-music generators rely solely on descriptive prompts, which can leave experienced musicians feeling disconnected from the creative process. Instead of starting from scratch, MusicGen Maxmsp empowers you to build on your own melody. This project was born out of the desire to give musicians greater control, blending their initial ideas with AI-driven enhancements that bring their unique vision to life.
How It Works / Workflow
Record your basic melody using Max/MSP’s virtual keyboard or by capturing audio directly. Your melody is saved as a file (e.g., melody.wav).
Choose adjectives (such as “dark” or “classical”) and set a genre and duration to define the style and length of your output.
Max/MSP automatically constructs a text prompt from your style selections and pairs it with your recorded melody.
Your input is sent via OSC (Open Sound Control) to a Python server, ensuring a clear separation of tasks between the user interface and the AI engine.
The Python OSC server receives your message, triggers the melody-conditioned MusicGen model, and generates a customized music clip that honors your original melody and style choices.
Once the audio is generated, a confirmation is sent back to Max/MSP, allowing you to immediately listen to your AI-enhanced creation.
Technical Details
Max/MSP MusicGen uses a melody-conditioned variant of the MusicGen model, originally designed for text-to-audio generation. By feeding in your melody along with a descriptive prompt, the model preserves your musical idea while adding stylistic nuances. The workflow splits responsibilities:
- Max/MSP manages user input and audio playback.
- Python with OSC handles the AI computation.
This separation ensures smooth integration and a reliable performance, giving you both control and innovation in one seamless package.
Results, Limitations, and Future Directions
Original Melody:
( it is a bit loud)
Outputs:
Brooding Post-punk
Hypnotic Hiphop
The current workflow effectively transforms simple, user-recorded melodies into unique, style-infused musical sketches. Demo clips showcase how your initial melodic ideas are expanded with carefully chosen adjectives and genres, highlighting the creative potential of blending human input with AI-driven enhancements.
However, there are a couple of areas for improvement. Processing times are longer than ideal because the system relies on a local API, which can slow down the overall experience. Additionally, while the melody-conditioned model does a decent job of incorporating your input, its audio quality sometimes falls short of expectations.
To overcome these challenges, future iterations will focus on optimizing processing speed and exploring the development of a dedicated audio generation model directly within Max/MSP. This approach aims to deliver a smoother, higher-quality creative process that better meets the needs of musicians seeking both control and innovation.