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.

翻译：语言模型已被成功应用于自然信号建模，如图像、语音和音乐。这类模型的核心组件是高质量神经压缩模型，能将高维自然信号压缩为低维离散化令牌。为此，我们提出一种高保真通用神经音频压缩算法，可将44.1KHz音频压缩至仅8kbps带宽的令牌，实现约90倍压缩率。该成果融合了高保真音频生成领域的进展与图像域改进型向量量化技术，并采用增强型对抗损失与重构损失。通过单一通用模型即可压缩所有音频域（语音、环境音、音乐等），可广泛适用于各类音频生成建模任务。与同类音频压缩算法相比，我们的方法展现出显著性能优势。我们对每个设计选择进行了详尽消融实验，并开源了代码与预训练模型权重。期望本工作能为下一代高保真音频建模奠定基础。