首个面向生成音乐的高质量流行歌曲数据集，含900万条真实热门曲目。
SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling
- 基于真实流行音乐和知名艺人构建，非合成或重录数据
- 涵盖900万条曲目，支持文本到音乐、歌声合成等任务
- 适合研究真实世界音乐生成与跨模态检索的研究者
我们提出 Sleeping-DISCO 9M，一个大规模预训练音乐数据集，专为生成式音乐建模设计。据我们所知，目前尚无开源的高质量数据集能代表流行且知名的歌曲，适用于文本-音乐生成、音乐图文描述、歌声合成、旋律重建及跨模型检索等任务。以往工作多聚焦于孤立受限因素，如构建合成或重录音乐语料（如 GTSinger、M4Singer），或创建超大规模音频数据集（如 DISCO-10M、LAIONDISCO-12M）。然而，这些数据集在生成音乐社区中的实际应用有限，因其未能反映真实音乐及其风格特征。Sleeping-DISCO 9M 改变了这一现状，通过真实流行的音乐和世界级艺术家作品构建，具备更贴近现实世界的音乐表现力。
原文摘要 · Abstract (English)
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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