/music-genres-dataset

Dataset of 1494 genres, each containing 200 songs

Primary LanguagePythonThe UnlicenseUnlicense

Music genres dataset

Dataset

  • 1494 genres
  • each genre contains 200 songs
  • for each song, following attributes are provided:
    • artist
    • song name
    • position within the list of 200 songs
    • main genre
    • sub-genres (with popularity count, which could be interpreted as weight of the sub-genre)
    • tags (every label that is not some existing genre, usually emotions, "My top 10 favourite tracs" etc.; also with popularity count)

This dataset is basically list of genres and songs available at EveryNoise extended with data from Spotify and Last.FM.

[ DOWNLOAD DATASET AS ZIP ]

Scraping scripts

This repository contains scripts to scrape data from internet and then transform it to format that could be easily imported into database.

Scraping data from internet

  1. Install Scrapy: pip install scrapy

  2. Register at http://www.last.fm/api to obtain Last.FM API key, then save it as a file /data/last_fm_api.key

  3. Run scripts in this order:

    scrapy runspider genre_sprider.py -o data/genres.jl \
&& scrapy runspider playlist_spider.py -o data/spotify_playlists.jl \
&& scrapy runspider songs_spider.py -o data/songs.jl \
&& scrapy runspider tags_spider.py -o data/tags.jl

When process finishes (it could take several minutes or maybe hours), the following files should be present in /data folder:

  • genres.jl
  • songs.jl
  • spotify_playlists.jl
  • tags.jl

Data size: ~100 MB Scraping time: ~2,5 hours

Transforming data to CSV format

Run python scripts in folder /csv-scripts in arbitrary order, they should create output files with corresponding names in folder /data/csv.

Importing into database

CSV files generates in previous step should be easily importable into database (tested just on PostgreSQL). After import is finished, run SQL script /sql-scripts/tag.tag_is_genre.sql. to fill up tags.tag_is_genre column.