This paper presents FunCodec, a fundamental neural speech codec toolkit,
which is an extension of the open-source speech processing toolkit FunASR.
FunCodec provides reproducible training recipes and inference scripts for the
latest neural speech codec models, such as SoundStream and Encodec. Thanks to
the unified design with FunASR, FunCodec can be easily integrated into
downstream tasks, such as speech recognition. Along with FunCodec, pre-trained
models are also provided, which can be used for academic or generalized
purposes. Based on the toolkit, we further propose the frequency-domain codec
models, FreqCodec, which can achieve comparable speech quality with much lower
computation and parameter complexity. Experimental results show that, under the
same compression ratio, FunCodec can achieve better reconstruction quality
compared with other toolkits and released models. We also demonstrate that the
pre-trained models are suitable for downstream tasks, including automatic
speech recognition and personalized text-to-speech synthesis. This toolkit is
publicly available at this https URL

FunCodec 是一个基于开源语音处理工具 FunASR 的基础神经语音编解码器工具包，提供训练可重现、推理脚本用于最新的神经语音编解码器模型，如 SoundStream 和 Encodec，并且可以轻松集成到下游任务中，如语音识别。其中，还提供预训练模型，可用于学术或一般用途。此工具包还进一步提出了频域编解码器模型 FreqCodec，与其他工具包和发布模型相比，在相同的压缩比下，FunCodec 可以实现更好的重构质量，并且证明预训练模型适用于包括自动语音识别和个性化文本转语音合成在内的下游任务。