SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling

We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song. To the best of our knowledge, there are no open-source high-quality dataset representing popular and well-known songs for generative music modeling tasks such as text-music, music-captioning, singing-voice synthesis, melody reconstruction and cross-model retrieval. Past contributions focused on isolated and constrained factors whose core perspective was to create synthetic or re-recorded music corpus (e.g. GTSinger, M4Singer) and arbitrarily large-scale audio datasets (e.g. DISCO-10M and LAIONDISCO-12M) had been another focus for the community. Unfortunately, adoption of these datasets has been below substantial in the generative music community as these datasets fail to reflect real-world music and its flavour. Our dataset changes this narrative and provides a dataset that is constructed using actual popular music and world-renowned artists.

@article{ahmed2025_2506.14293,
  title={ SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling },
  author={ Tawsif Ahmed and Andrej Radonjic and Gollam Rabby },
  journal={arXiv preprint arXiv:2506.14293},
  year={ 2025 }
}