DiariZen
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0201226" target="_blank" >RIV/00216305:26230/26:0201226 - isvavai.cz</a>
Result on the web
<a href="https://github.com/BUTSpeechFIT/DiariZen" target="_blank" >https://github.com/BUTSpeechFIT/DiariZen</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
DiariZen
Original language 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.
Czech name
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Czech description
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Classification
Type
R - Software
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH23_020%2F0008518" target="_blank" >EH23_020/0008518: Linguistics, Artificial Intelligence and Language and Speech Technologies: from Research to Applications</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Internal product ID
DiariZen
Technical parameters
DiariZen is a speaker diarization toolkit driven by AudioZen and Pyannote 3.1. Languages: Jupyter Notebook 54.8%, Python 44.7%, Shell 0.5% The code in GitHub repository is licensed under the MIT license. The pre-trained model weights are released under the CC BY-NC 4.0 license. https://github.com/BUTSpeechFIT/DiariZen https://huggingface.co/BUT-FIT/diarizen-wavlm-large-s80-md
Economical parameters
DiariZen powers DiCoW, a companion tool that guides Whisper-based ASR for speaker-attributed transcription. The combined system achieved promising results—winning the Jury Prize at CHiME-8 and placing 2nd in the MLC-SLM Challenge. DiariZen has also been successfully adopted by multiple top-performing teams in the MISP 2025 Challenge, underscoring its robustness, generalization, and real-world impact.
Owner IČO
00216305
Owner name
Vysoké učení technické v Brně