A pipeline to build a dataset from your own music library and use it to fill the missing genres
Read the article on Medium
Required install:
eyed3
sox --with-lame
tensorflow
tflearn
- Create folder Data/Raw/
- Place your labeled .mp3 files in Data/Raw/
To create the song slices (might be long):
python main.py slice
To train the classifier (long too):
python main.py train
To test the classifier (fast):
python main.py test
- Most editable parameters are in the config.py file, the model can be changed in the model.py file.
- I haven't implemented the pipeline to label new songs with the model, but that can be easily done with the provided functions, and eyed3 for the mp3 manipulation. Here's the full pipeline you would need to use.
