Audio codec models are widely used in audio communication as a crucial technique for compressing audio into discrete representations. Nowadays, audio codec models are increasingly utilized in generation fields as intermediate representations. For instance, AudioLM is an audio generation model that uses the discrete representation of SoundStream as a training target, while VALL-E employs the Encodec model as an intermediate feature to aid TTS tasks. Despite their usefulness, two challenges persist: (1) training these audio codec models can be difficult due to the lack of publicly available training processes and the need for large-scale data and GPUs; (2) achieving good reconstruction performance requires many codebooks, which increases the burden on generation models. In this study, we propose a group-residual vector quantization (GRVQ) technique and use it to develop a novel \textbf{Hi}gh \textbf{Fi}delity Audio Codec model, HiFi-Codec, which only requires 4 codebooks. We train all the models using publicly available TTS data such as LibriTTS, VCTK, AISHELL, and more, with a total duration of over 1000 hours, using 8 GPUs. Our experimental results show that HiFi-Codec outperforms Encodec in terms of reconstruction performance despite requiring only 4 codebooks. To facilitate research in audio codec and generation, we introduce AcademiCodec, the first open-source audio codec toolkit that offers training codes and pre-trained models for Encodec, SoundStream, and HiFi-Codec. Code and pre-trained model can be found on: \href{https://github.com/yangdongchao/AcademiCodec}{https://github.com/yangdongchao/AcademiCodec}

翻译：音频编解码模型作为将音频压缩为离散表示的关键技术，被广泛用于音频通信领域。当前，这类模型越来越多地作为中间表示应用于生成领域——例如AudioLM使用SoundStream的离散表示作为训练目标进行音频生成，VALL-E则采用Encodec模型作为中间特征辅助语音合成任务。尽管应用广泛，但仍有两大挑战亟待解决：(1) 因缺乏公开训练流程且需大规模数据与GPU支持，音频编解码模型的训练存在困难；(2) 为获得良好重建性能需使用大量码本，这增加了生成模型的负担。本研究提出分组残差向量量化（GRVQ）技术，并基于该技术开发了新型高保真音频编解码器HiFi-Codec，仅需4个码本即可实现高效编码。我们使用LibriTTS、VCTK、AISHELL等公开语音合成数据集（总时长超1000小时）在8块GPU上完成模型训练。实验结果表明，HiFi-Codec在仅需4个码本的情况下，重建性能已超越Encodec。为促进音频编解码与生成领域的研究，我们推出了AcademiCodec——首个开源音频编解码工具包，提供Encodec、SoundStream及HiFi-Codec的训练代码与预训练模型。代码与预训练模型可见：\href{https://github.com/yangdongchao/AcademiCodec}{https://github.com/yangdongchao/AcademiCodec}