标题： 音频编解码器基准：音频编解码器评估的全面基准 显示英文标题

标题： AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation

摘要： 多模态大语言模型（MLLMs）已被广泛应用于语音和音乐。 这种趋势导致了对大型模型（LMs）音频分词的关注。 与仅包含语义的文本标记不同，音频标记必须同时捕捉全局语义内容并保留精细的声学细节。 此外，它们为语音和音乐提供了一种离散的方法，可以有效地集成到MLLMs中。 然而，现有的研究在语义标记和声学标记的定义上是不合适的。 此外，对不同编解码器的评估通常集中在特定领域或任务上，例如重建或自动语音识别（ASR）任务，这阻碍了公平和全面的比较。 为了解决这些问题，本文提供了适合的语义和声学标记的定义，并引入了一个系统评估框架。 该框架允许对编解码器的能力进行全面评估，该评估跨越四个维度：音频重建指标、代码本索引（ID）稳定性、仅解码器变换器困惑度以及在下游探测任务上的表现。 我们的结果表明了所提供适合定义的正确性，以及重建指标、代码本ID稳定性、下游探测任务和困惑度之间的相关性。 显示英文摘要

摘要： Multimodal Large Language Models (MLLMs) have been widely applied in speech and music. This tendency has led to a focus on audio tokenization for Large Models (LMs). Unlike semantic-only text tokens, audio tokens must both capture global semantic content and preserve fine-grained acoustic details. Moreover, they provide a discrete method for speech and music that can be effectively integrated into MLLMs. However, existing research is unsuitable in the definitions of semantic tokens and acoustic tokens. In addition, the evaluation of different codecs typically concentrates on specific domains or tasks, such as reconstruction or Automatic Speech Recognition (ASR) task, which prevents fair and comprehensive comparisons. To address these problems, this paper provides suitable definitions for semantic and acoustic tokens and introduces a systematic evaluation framework. This framework allows for a comprehensive assessment of codecs' capabilities which evaluate across four dimensions: audio reconstruction metric, codebook index (ID) stability, decoder-only transformer perplexity, and performance on downstream probe tasks. Our results show the correctness of the provided suitable definitions and the correlation among reconstruction metrics, codebook ID stability, downstream probe tasks and perplexity.