  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 https://github.com/alibaba-damo-academy/FunCodec.
