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arXiv · 2509.21968

AUV: Teaching Audio Universal Vector Quantization with Single Nested Codebook

Yushen Chen·Kai Hu·Long Zhou·Shulin Feng·Xusheng Yang·Hangting Chen·Xie Chen
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Abstract

We propose AUV, a unified neural audio codec with a single codebook, which enables a favourable reconstruction of speech and further extends to general audio, including vocal, music, and sound. AUV is capable of tackling any 16 kHz mixed-domain audio segment at bit rates around 700 bps. To accomplish this, we guide the matryoshka codebook with nested domain-specific partitions, assigned with corresponding teacher models to perform distillation, all in a single-stage training. A conformer-style encoder-decoder architecture with STFT features as audio representation is employed, yielding better audio quality. Comprehensive evaluations demonstrate that AUV exhibits comparable audio reconstruction ability to state-of-the-art domain-specific single-layer quantizer codecs, showcasing the potential of audio universal vector quantization with a single codebook. The pre-trained model and demo samples are available at https://swivid.github.io/AUV/.

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Yushen Chen, Kai Hu, Long Zhou, Shulin Feng, Xusheng Yang, Hangting Chen, Xie Chen. 2025-09-26. AUV: Teaching Audio Universal Vector Quantization with Single Nested Codebook. https://arxiv.org/abs/2509.21968

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