标题： CodecBench：一个用于声学和语义评估的全面基准 显示英文标题

标题： CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation

摘要： 随着多模态大语言模型（LLMs）的兴起，音频编解码器在将音频编码为离散标记中发挥着越来越重要的作用，使音频能够整合到基于文本的LLMs中。 当前的音频编解码器捕捉两种类型的信息：声学和语义。 由于音频编解码器被应用于语音语言模型的多种场景，它需要建模越来越复杂的信息并适应不同的上下文，例如多说话者场景、背景噪声或更丰富的副语言信息。 然而，现有的编解码器自身的评估受到简单指标和场景的限制，现有的音频编解码器基准测试并未针对复杂的应用场景进行设计，这限制了在复杂数据集上对声学和语义能力的评估性能。 我们引入了 CodecBench，一个全面的评估数据集，从声学和语义的角度在四个数据领域评估音频编解码器性能。 通过这个基准测试，我们的目标是识别当前的局限性，突出未来的研究方向，并促进音频编解码器的发展。 代码可在 https://github.com/RayYuki/CodecBench 获取。 显示英文摘要

摘要： With the rise of multimodal large language models (LLMs), audio codec plays an increasingly vital role in encoding audio into discrete tokens, enabling integration of audio into text-based LLMs. Current audio codec captures two types of information: acoustic and semantic. As audio codec is applied to diverse scenarios in speech language model , it needs to model increasingly complex information and adapt to varied contexts, such as scenarios with multiple speakers, background noise, or richer paralinguistic information. However, existing codec's own evaluation has been limited by simplistic metrics and scenarios, and existing benchmarks for audio codec are not designed for complex application scenarios, which limits the assessment performance on complex datasets for acoustic and semantic capabilities. We introduce CodecBench, a comprehensive evaluation dataset to assess audio codec performance from both acoustic and semantic perspectives across four data domains. Through this benchmark, we aim to identify current limitations, highlight future research directions, and foster advances in the development of audio codec. The codes are available at https://github.com/RayYuki/CodecBench.