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

• URL: http://arxiv.org/abs/2506.14293v3
• Date: Wed, 25 Jun 2025 08:18:37 GMT
• Title: SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling
• Authors: Tawsif Ahmed, Andrej Radonjic, Gollam Rabby,
• Abstract summary: To the best of our knowledge, there are no open-source high-quality datasets representing popular and well-known songs for generative music modeling tasks.<n>Our dataset changes this narrative and provides a dataset that is constructed using actual popular music and world-renowned artists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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