https://github.com/jakespracher/ml-synth-preset-generator

Machine Learning with Synthesizers
https://github.com/jakespracher/ml-synth-preset-generator

Last synced: 16 days ago
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Machine Learning with Synthesizers

• Host: GitHub
• URL: https://github.com/jakespracher/ml-synth-preset-generator
• Owner: jakespracher
• License: gpl-3.0
• Created: 2021-03-02T21:18:51.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-05-06T15:19:28.000Z (over 1 year ago)
• Last Synced: 2024-08-01T00:44:02.507Z (3 months ago)
• Language: HTML
• Size: 2.89 MB
• Stars: 34
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Generating Musical Synthesizer Patches with Machine Learning

This repo accompanies this [blog post](https://jakespracher.medium.com/generating-musical-synthesizer-patches-with-machine-learning-c52f66dfe751). The premise is to generate high-quality presets for the Ableton Analog synthesizer in a particular style automatically using generative machine learning models.

## Setup

This repo currently requires Python 3.7.9 until Tensorflow can be migrated to version 2+.

Feel free to acquire this interpreter via your preferred method. One way to do so would be

1. [Install pyenv](https://github.com/pyenv/pyenv#installation)

2. `pyenv install 3.7.9`

3. `pyenv global 3.7.9`

4. `python -m venv venv`

5. `source venv/bin/activate`

6. `Pip install -r requirements.txt`

## Running Training

To run training you can use the `run_gan.py` script like this:

`python run_gan.py --config-file data/configs/config_240_cgan.json`

It also supports various CLI options including hyperparameter search. Run `python run_gan.py --help`

By default, this will output models, plots, and presets to data/generated in the project directory.

## Generating Presets Using the Model

To generate presets from a model, use the `generate_from_gan.py` script. Example usage:

`python generate_from_gan.py --config-file data/configs/config_240_cgan.json --model-path data/configs/config_240_cgan_generator_model_e1000.h5 --dest-path . --n-samples 10`

To evaluate presets, you will need [Ableton](ableton.com). You can get a 90 day free trial [here](https://www.ableton.com/en/trial/)

## Running Tests

Test coverage is currently very limited. To run the tests use `python -m pytest tests/`

## Architecture

This project uses the Keras framework (built in to Tensorflow) to construct and train the neural network.

The general flow is as follows:

1. Read training data directly from the repo at `data/analog_library` (theres not much data available sadly)

2. Parse presets into vectors (logic in `presetml/parsing/ableton_analog.py`)

3. Run training (logic in `generation/gan.py`)