Done as a semestral Project for MI-VMW.

This packages has the following requirements:

• Python 2.7
• node.js
• Essentia

If you have these requirements, run install.sh.

To build your dataset, run ./bin/extract_features.py <data_dir> <output_file>. The data dir should contain subdirs with the name of the genre and each subdir should contain audio files. The script will process them and output a CSV.

To train the model, execute train_model.py <dataset> <model_loc>. It will load the previously extracted data set and train the model, saving it to the model_loc.

To start the server, you can either execute directly python server.py <path_to_model> to run a single-process Flask server, or execute gunicorn -w n server:app to execute it with n workers. The server will be available on localhost:5000 if run in the former mode, or localhost:8000 if run in the latter.

After you succesfully execute the server, simply open the above-mentioned location in your browser. There you can upload your desired audio files and have them classified.