DiariZen

Type

software

Language

English

Authors

Han Jiangyu, DCGM (FIT)
Pálka Petr, Ing., FIT (FIT), DCGM (FIT)

Description

DiariZen is a cutting-edge speaker diarization toolkit developed by BUT Speech@FIT, combining end-to-end neural diarization (EEND) based on WavLM and Conformer with VBx clustering for accurate and scalable “who spoke when” analysis. Built on the Pyannote framework, it offers modularity, reproducibility, and seamless integration into speech processing pipelines. Structured pruning ensures efficiency without sacrificing performance.

Keywords

speech recognition, speaker, diarization, toolkit, clustering

URL

https://github.com/BUTSpeechFIT/DiariZen

License

In order to use the result by another entity, it is always necessary to acquire a license

License Fee

The licensor does not require a license fee for the result

Projects

Linguistics, Artificial Intelligence and Language and Speech Technologies: from Research to Applications, EU, MEZISEKTOROVÁ SPOLUPRÁCE, EH23_020/0008518, start: 2025-01-01, end: 2028-12-31, running

Research groups

Speech Data Mining Research Group BUT Speech@FIT (RG SPEECH)

Departments

Department of Computer Graphics and Multimedia (DCGM)
Speech Data Mining Research Group BUT Speech@FIT (RG SPEECH)