High-Fidelity Audio Compression with Improved RVQGAN
Authors: name: Rithesh Kumar, name: Prem Seetharaman, name: Alejandro Luebs, name: Ishaan Kumar, name: Kundan Kumar

Language models have been successfully used to model natural signals, such as
images, speech, and music. A key component of these models is a high quality
neural compression model that can compress high-dimensional natural signals
into lower dimensional discrete tokens. To that end, we introduce a
high-fidelity universal neural audio compression algorithm that achieves ~90x
compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve
this by combining advances in high-fidelity audio generation with better vector
quantization techniques from the image domain, along with improved adversarial
and reconstruction losses. We compress all domains (speech, environment, music,
etc.) with a single universal model, making it widely applicable to generative
modeling of all audio. We compare with competing audio compression algorithms,
and find our method outperforms them significantly. We provide thorough
ablations for every design choice, as well as open-source code and trained
model weights. We hope our work can lay the foundation for the next generation
of high-fidelity audio modeling.