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arXiv:2005.00341eess.AS

Jukebox: A Generative Model for Music

Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever

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Abstract

We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at https://jukebox.openai.com, along with model weights and code at https://github.com/openai/jukebox

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DOI
Subjects
eess.ASAudio and Speech Processing cs.LGMachine Learning cs.SDSound stat.MLMachine Learning (Statistics)

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    • I don't know about linear models, but this kind of hierarchical modelling is quite a common idea in speech research. For example, OpenAI's Jukebox (2020) [1], which uses a proto-neural audio codec, has three levels of encoding that get coarser and coarser. They use a language…

      vvolhejnView on HNParent context

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