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song-genre-predictor

Classifying songs using Convolutional Neural Networks trained on Mel spectrograms

Important: Files whose names are bolded should be of particular interest to reviewers. To use this repository, see the Using the Repository section first before returning here.

Datasets

File Name Description
dataset.csv Original dataset downloaded from Kaggle
data_100_genre.csv Cleaned dataset with preview URLs added to 100 tracks for each of the 12 prevalent genres
data_200_genre.csv Cleaned dataset with preview URLs added to 200 tracks for each of the 12 prevalent genres

Note: All entries from the original dataset is contained in the "cleaned" ones. However, they will be ignored when the scripts for downloading the audio files and generating Mel spectrograms are run.

Data Processing Scripts

Note: The following scripts can be run in the order they are listed to utimately generate Mel spectrograms as inputs for the neural networks. If using the provided cleaned datasets, mutate.py and clean.py can be ignored.

File Name Description
mutate.py Adds preview_url column to the original dataset
clean.py Produces a cleaned dataset with preview URLs added to the first TRACKS_PER_GENRE (constant defined in the script) tracks of each of the listed genres. The generated dataset will be saved under the name dataset_clean_<current UNIX timestamp>.csv.
convert_wav.py Downloads the preview tracks as MP3 files and convert them into WAV files. The files are stored in the directories mp3_files and wav_with_labels respectively.
generate_spectrograms.py Generates Mel spectrograms from the WAV files and stores them in a directory named spectrogram.
util_funcs.py Contains function to convert file names to path-friendly versions.

Jupyter Notebooks for Models

Note: The following notebooks can be run independently from each other. They also contain detailed descriptions provided as comments.

File Name Description
perform_cnn.ipynb Convolutional Neural Network trained on the generated Mel spectrograms. Evaluation on test data and example predictions are included.
transfer.ipynb Transferred EfficientNet model trained on the generated Mel spectrograms. Evaluation on test data and example predictions are included.
forest.ipynb Random forest model trained on high-level audio features.
xgboost.ipynb XGBoost model trained on high-level audio features.
demo.ipynb Used for the presentation demo. Should be disregarded.

Using the Repository

Managing dependencies

Run pip install -r requirements.txt. You may need to use pip3 depending on your Python version.

Defining environment variables for fetching data

If you plan to create your own version of a cleaned dataset or run clean.py, you will need to:

  1. Create a Spotify Developer account
  2. Get access to your client ID and client secret
  3. Create a .env in the root directory of this project
  4. Add the following lines to your .env file: 
    SPOTIFY_CLIENT_ID=[YOUR CLIENT ID]
SPOTIFY_CLIENT_SECRET=[YOUR CLIENT SECRET]