GAAAN

GAAAN is a Generative Album Art Adversarial Network that uses the DCGAN architecture to generate unique music album-art images. This repository contains folders for acquiring the data, training the GAN, and deploying the GAN with a Flask webserver.

Future Iterations

The plan is to experiment more with training and different architectures to produce more detailed results as well as re=implementing the user interface of the web service.

IMAGES

• Webservice Landing Page

• Training Output

• Output Examples

GETTING STARTED

Running The Web Service

• Download or clone the repository and change your working directory to the repository.
• Type these commands in to your terminal or command prompt:
• cd WebService
• pip install requirements
• python3 main.py

Making Your Own Training Dataset

• You sign up for a spotify developers credential here: https://developer.spotify.com/console/
• Use the albumArtDownloader.py to scrape Spotify and save a csv file with the artists info and weblinks to their album art covers
  • THIS DOES NOT ACTUALLY DOWNLOAD THE IMAGES.
• Use DataSetAnalysis.ipynb to load the masterAlbumList.csv and check for duplicates, make smaller subsets.
• THEN download the images to a directory
  • you will need to specify the directory name in the Album_Art_GAN.ipynb notebook so the training can find the images.

Doing Your Own Training

• You can train locally on your machine using Jupyter Notebooks or upload the repository to your Google Drive and train using Google Colab Pro.
• In the "data_and_training" directory, open Album Art Gan.ipynb
• If you are training locally, skip the first two cells of the Album Art Gan notebook.

BUILT WITH

• Tensorflow - ML model creation
• Keras - Model creation
• Google Colab - Free Cloud Training
• Flask - Web Server
• Pillow - Library for image handling
• Spotipy - Python library for connecting to Spotify API

REFERENCES

• Unsupervised representation learning with deep convolutional generative adversarial networks
• Generating modern arts using generative adversarial network gan on spell
• Generate Anime Style Face Using DCGAN and Explore Its Latent Feature Representation

Contributors

Michael McDermott, Joshua Matthews, Patrick Caldwell

LICENSE

This project is licensed under the MIT License - see the LICENSE.md file for details