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，被广泛应用于音频通信领域。当前，此类模型正越来越多地作为中间表示用于生成任务：例如，AudioLM 采用 SoundStream 的离散表示作为训练目标，而 VALL-E 则使用 Encodec 模型作为辅助文本转语音任务的中间特征。尽管这些模型已展现效用，但仍面临两大挑战：(1) 训练过程缺乏公开实现，且依赖大规模数据与GPU资源，导致模型训练困难；(2) 为获得良好重建性能需使用大量码本，这会增加生成模型的负担。本研究提出一种分组残差向量量化(GRVQ)技术，并基于该技术开发了新型高保真音频编解码模型——HiFi-Codec，该模型仅需4个码本。我们利用LibriTTS、VCTK、AISHELL等公开可用的文本转语音数据集（总时长超1000小时），在8块GPU上完成模型训练。实验结果表明，尽管HiFi-Codec仅需4个码本，其重建性能仍优于Encodec。为推进音频编解码与生成领域的研究，我们推出了首个开源音频编解码工具包AcademiCodec，提供Encodec、SoundStream及HiFi-Codec的训练代码与预训练模型。代码及预训练模型详见：\href{https://github.com/yangdongchao/AcademiCodec}{https://github.com/yangdongchao/AcademiCodec}