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Audio codec models are widely used in audio communication as a crucialtechnique for compressing audio into discrete representations. Nowadays, audiocodec models are increasingly utilized in generation fields as intermediaterepresentations. For instance, AudioLM is an audio generation model that usesthe discrete representation of SoundStream as a training target, while VALL-Eemploys the Encodec model as an intermediate feature to aid TTS tasks. Despitetheir usefulness, two challenges persist: (1) training these audio codec modelscan be difficult due to the lack of publicly available training processes andthe need for large-scale data and GPUs; (2) achieving good reconstructionperformance requires many codebooks, which increases the burden on generationmodels. 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 AudioCodec model, HiFi-Codec, which only requires 4 codebooks. We train all themodels using publicly available TTS data such as LibriTTS, VCTK, AISHELL, andmore, with a total duration of over 1000 hours, using 8 GPUs. Our experimentalresults show that HiFi-Codec outperforms Encodec in terms of reconstructionperformance despite requiring only 4 codebooks. To facilitate research in audiocodec and generation, we introduce AcademiCodec, the first open-source audiocodec 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}

HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec