j_s_gan / README.md
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metadata
language:
  - en
license: mit
datasets:
  - aligned_bach_chorales
tags:
  - gan
  - music-generation
  - pytorch
model-index:
  - name: Experimental GAN for Bach-like Textures
    results:
      - task:
          name: Music Generation
          type: music-generation
        dataset:
          name: Aligned Bach Chorales Dataset
          type: aligned_bach_chorales
        metrics:
          - name: Quality of Generated Music
            type: subjective
            value: Experimental

Experimental GAN for Bach-like Textures

Description

This repository contains an experimental Generative Adversarial Network (GAN) model designed to generate Bach-like textures. The model is based on the Aligned Bach Chorales Dataset, which can be found here.

License

This project is licensed under the MIT License.

Model Overview

The GAN consists of two parts: a generator and a discriminator. Both models were trained on the Aligned Bach Chorales Dataset, which represents Bach chorales in a binary matrix format.

Installation and Requirements

To use this model, you need to have PyTorch version 2.1.0+cu121 installed. The dataset loader, model structure, and training scripts are all included in the torch_binary2_v1 notebook.

Usage

  1. Clone the Repository:
git clone https://huggingface.co/Egorp/j_s_gan
  1. Load the Models:
  • Use torch_binary2_v1 notebook to load the provided state dictionaries for both the generator and the discriminator.
  1. Convert Binary Matrices to MIDI:
  • Utilize the binary_to_midi notebook included in this repository to convert the binary matrices generated by the GAN into MIDI format.

Dataset

The dataset used for training this model can be found in the folder named "np_convert". This folder contains binary representations of Bach chorales.

Contributing

Contributions to this project are welcome. Please feel free to submit issues and pull requests.

Contact

For any queries or discussions regarding this project, please open an issue in this repository.

Acknowledgements

Special thanks to the creators and maintainers of the Aligned Bach Chorales Dataset for providing the data used to train this model.