Datasets:
license: apache-2.0
dataset_info:
features:
- name: song_id
dtype: string
- name: title
dtype: string
- name: artist_names
sequence: string
- name: artist_ids
sequence: string
- name: album_name
dtype: string
- name: album_id
dtype: string
- name: isExplicit
dtype: bool
- name: views
dtype: string
- name: duration
dtype: int64
splits:
- name: train
num_bytes: 2069255857
num_examples: 12320916
download_size: 750206954
dataset_size: 2069255857
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- music
pretty_name: LAION DISCO
size_categories:
- 10M<n<100M
The LAION-DISCO-12M dataset contains 12M links to music on YouTube, inspired by the methodology of DISCO-10M.
Starting from an initial seed list of artists, we can discover new artists by recursively exploring the artists listed in the "Fans might also like" section. We explore the related artists graph for as long as we are able to find new artists. For a given artist, we can extract their metadata, such as their name and number of subscribers, as well as a list of all of their songs and music videos. Importantly, each song or music video is associated with a YouTube URL (obtained from its ID). The collected metadata fields are: song_id, title, artist_names, artist_ids, album_name, album_id, isExplicit, views, duration.
The authors of DISCO-10M used a seed list of 18 artists, chosen to represent a variety of genres. However, we found that this is not sufficient for exploring the artist graph of YouTube Music. Starting from this seed list, we were able to discover only 90,007 artists and 5,399,389 songs.
We therefore compiled a larger seed list by considering the artists that appear on YouTube Music charts of top songs by country and genre playlists. This resulted in an initial list of 45,218 artists. The artist graph exploration starting from this seed list resulted in 250,516 artists and 12,648,485 songs.
This work was inspired by DISCO-10M, consider citing them if you use this dataset.