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.

介绍了一种高保真度的神经网络通用音频压缩算法，它结合了高保真度音频生成的进展以及图像领域的更好的向量量化技术，并使用改进的对抗和重构损失将高维自然信号压缩成较低维度的离散令牌，该算法可以通过单一的通用模型压缩所有领域（语音、音乐等）的信号。