Audio Conditioning for Music Generation via Discrete Bottleneck Features

Simon Rouard, Yossi Adi, Jade Copet, Axel Roebel, Alexandre Défossez

Audio Conditioning for Music Generation via Discrete Bottleneck Features: 7 upvotes on Hugging Face Daily Papers, #11 of 19 papers on 2024-07-18. Day-by-day upvote history.

While most music generation models use textual or parametric conditioning (e.g. tempo, harmony, musical genre), we propose to condition a language model based music generation system with audio input. Our exploration involves two distinct strategies. The first strategy, termed textual inversion, leverages a pre-trained text-to-music model to map audio input to corresponding "pseudowords" in the textual embedding space. For the second model we train a music language model from scratch jointly with a text conditioner and a quantized audio feature extractor. At inference time, we can mix textual and audio conditioning and balance them thanks to a novel double classifier free guidance method. We conduct automatic and human studies that validates our approach. We will release the code and we provide music samples on https://musicgenstyle.github.io in order to show the quality of our model.

Paper page on Hugging Face · arXiv

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