README.md

---
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](https://github.com/measure-map/aligned_bach_chorales).

## 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
```
2. **Load the Models**: 
- Use torch_binary2_v1 notebook to load the provided state dictionaries for both the generator and the discriminator.
3. **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.