CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915468087918592 |
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| author | Deng, Ruifan Gong, Yitian Gao, Qinghui Jin, Luozhijie Cheng, Qinyuan Fei, Zhaoye Li, Shimin Qiu, Xipeng |
| author_facet | Deng, Ruifan Gong, Yitian Gao, Qinghui Jin, Luozhijie Cheng, Qinyuan Fei, Zhaoye Li, Shimin Qiu, Xipeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation Deng, Ruifan Gong, Yitian Gao, Qinghui Jin, Luozhijie Cheng, Qinyuan Fei, Zhaoye Li, Shimin Qiu, Xipeng Audio and Speech Processing Sound 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. |
| title | CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2508.20660 |