English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 thelatest neural speech codec models, such as SoundStream and Encodec. Thanks tothe unified design with FunASR, FunCodec can be easily integrated intodownstream tasks, such as speech recognition. Along with FunCodec, pre-trainedmodels are also provided, which can be used for academic or generalizedpurposes. Based on the toolkit, we further propose the frequency-domain codecmodels, FreqCodec, which can achieve comparable speech quality with much lowercomputation and parameter complexity. Experimental results show that, under thesame compression ratio, FunCodec can achieve better reconstruction qualitycompared with other toolkits and released models. We also demonstrate that thepre-trained models are suitable for downstream tasks, including automaticspeech recognition and personalized text-to-speech synthesis. This toolkit ispublicly available at https://github.com/alibaba-damo-academy/FunCodec.

FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec