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arXiv · 2411.19842

Scaling Transformers for Low-Bitrate High-Quality Speech Coding

Julian D Parker·Anton Smirnov·Jordi Pons·CJ Carr·Zack Zukowski·Zach Evans·Xubo Liu
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Abstract

The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Traditionally such tokenization models have concentrated on low parameter-count architectures using only components with strong inductive biases. In this work we show that by scaling a transformer architecture with large parameter count to this problem, and applying a flexible Finite Scalar Quantization (FSQ) based bottleneck, it is possible to reach state-of-the-art speech quality at extremely low bit-rates of $400$ or $700$ bits-per-second. The trained models strongly out-perform existing baselines in both objective and subjective tests.

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BibTeXRIS

Julian D Parker, Anton Smirnov, Jordi Pons, CJ Carr, Zack Zukowski, Zach Evans, Xubo Liu. 2024-11-29. Scaling Transformers for Low-Bitrate High-Quality Speech Coding. https://arxiv.org/abs/2411.19842

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