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Towards audio language modeling -- an overview

Haibin Wu
Xuanjun Chen
Yi-Cheng Lin
Kai-Wei Chang
Ho-Lam Chung
Alexander H. Liu
Hung-yi Lee
Abstract

Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes, which can be employed to develop audio language models (LMs). Numerous high-performance neural audio codecs and codec-based LMs have been developed. The paper aims to provide a thorough and systematic overview of the neural audio codec models and codec-based LMs.

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