MusicLM: Generating Music From Text

التفاصيل البيبلوغرافية
العنوان: MusicLM: Generating Music From Text
المؤلفون: Agostinelli, Andrea, Denk, Timo I., Borsos, Zalán, Engel, Jesse, Verzetti, Mauro, Caillon, Antoine, Huang, Qingqing, Jansen, Aren, Roberts, Adam, Tagliasacchi, Marco, Sharifi, Matt, Zeghidour, Neil, Frank, Christian
سنة النشر: 2023
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
الوصف: We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consistent over several minutes. Our experiments show that MusicLM outperforms previous systems both in audio quality and adherence to the text description. Moreover, we demonstrate that MusicLM can be conditioned on both text and a melody in that it can transform whistled and hummed melodies according to the style described in a text caption. To support future research, we publicly release MusicCaps, a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts.
Comment: Supplementary material at https://google-research.github.io/seanet/musiclm/examples and https://kaggle.com/datasets/googleai/musiccaps
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2301.11325
رقم الأكسشن: edsarx.2301.11325
قاعدة البيانات: arXiv