This is a small project done by Ashkan Arabi, Princess Thomas, and Emi Reuth as our final project for AI4ALL.
This was mainly a learning-project, so there is definitely room for improvement.
This project used Conda as its main package manager. All the necessary (and unnecessary :p) packages can be found in the requirements.txt file ^^^.
If you wish to predict the genre of a .wav track that you have lying around, you can simply go to the Interface folder and run the Flask server (app.py). Then, simply open http://127.0.0.1:5000/ in your preferred browser and follow the instructions. If you are getting a FileNotFoundError when running the server, try changing your directory to the "interface" folder.
For training script to work, you can download the dataset and put it in a folder called "archive" in the root directory. Link can be found below. Then, you can open the "FINAL SCRIPT.py" file found in the folder "Jupyter".
The dataset can be downloaded from from this link: https://www.kaggle.com/datasets/asisheriberto/music-classification-wav
Something doesn't work? Please reach out to Ashkan at ashkan.arabim@gmail.com . The project was tested on his system, so most problems are his fault!