SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling
This dataset provides a resource for generative music tasks like text-music generation, potentially benefiting researchers and developers in AI music, though it is incremental as it builds on prior large-scale audio datasets.
The authors tackled the lack of open-source, high-quality datasets for generative music modeling by introducing Sleeping-DISCO 9M, a large-scale pre-training dataset built from actual popular music and world-renowned artists, addressing the gap in real-world music representation.
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.