JNapoli/autoencoda

Deep learning approach for audio featurization and popularity prediction

Hit Predict

Hit Predict is a Python library for predicting the success of your music.

Overview

This repo contains scripts for building a data ingestion, pre-processing, and modeling pipeline to build a model that predicts the probability of an audio sample appearing on the Billboard Hot 100 chart, a ranking of the top 100 songs of the week.

Here, audio samples are featurized using information from their Mel spectrograms (example below), which contain information about the time-evolution of the different frequencies present in the music.

Installation

The following steps have been verified to be reproducible on MacOS. The code requires python version 3.6.8. It is recommended to first create and activate a python environment using Miniconda:

conda create -n TESTING python=3.6.8
source activate TESTING

This package can then be downloaded and run as follows:

1. Clone this repo using:

git clone https://github.com/JNapoli/autoencoda.git
cd autoencoda/

2. Create a virtual environment (ensure that Venv is available):

python3 -m venv my-env
source my-env/bin/activate

3. Install required packages via pip3:

pip3 install -r requirements.txt

Usage

Minimal use case

The most common use case will be to make a prediction on a new mp3 file. This may be done by providing the mp3 file's full path to the prediction script:

python3 predict.py --path_track /FULL/PATH/TO/AUDIO/FILE/track.mp3

Full pipeline

For each script in the autoencoda subdirectory, arguments and their descriptions can be viewed as follows:

python3 SCRIPT-NAME.py --help

The full data ingestion and modeling pipeline can be executed as follows:

1. Run billboard_query.py, which uses the billboard.py package to scrape songs from the Billboard Hot-100 chart. This generates a set of tracks that appeared on the chart from a user-specified date to the present.

2. Run ingest.py to get mp3 samples for each track in the set using the Spotipy package and to featurize them using librosa. A Spotify Client ID and secret key will be required to make requests via the Spotify API, both of which can be passed as arguments to the script.

3. Run preprocess.py to pre-process the spectrograms in order to use them as features for model training.

4. Train models to the data. Several custom models have been implemented using Keras. For more information about the script arguments, please use:

python3 models.py --help

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT