Differences

This shows you the differences between two versions of the page.

--- dcc:itsol:whisper [2025/09/10 13:25] – Small text edits alba
+++ dcc:itsol:whisper [2025/12/17 08:20] (current) – added beta release warning giulio
@@ Line 4: / Line 4: @@
 ===== Attention! =====
-**The use of Whisper is at the user's own responsibility. The setup illustrated in this guide creates an instance of Whisper that is run locally and that does not send data outside of the local environment. Please make sure to handle your data correctly. If you have any doubts about how to do so, please start by following the steps on this page of our wiki: [[dcc:itsol:whisper:datamanage]].**
+**The use of Whisper is at the user's own responsibility. The setup illustrated in this guide makes use of an instance of Whisper that is run locally and that does not send data outside of the local environment. Please make sure to handle your data correctly. If you have any doubts about how to do so, please start by following the steps on this page of our wiki: [[dcc:itsol:whisper:datamanage]].**
 **If you have any questions on Whisper that this guide does not answer, please feel free to send us a message at [[dcc@rug.nl|dcc@rug.nl]].**
+**News Item (16-12-2025):** We have released a beta version of the Whisper interface with the option to add diarization (speaker recognition) to the transcription/translation job. This version of the interface is not final yet, so please keep in mind that not everything might work the way you want it to. We will complete this new interface in January, but in the meantime, feel free to test it out using the default parameters.
 ===== Introduction =====
-This guide takes you through the steps to set up a personal system of speech-to-text transcription on the University of Groningen infrastructure (for UG staff and students) on the basis of the [[https://openai.com/research/whisper|OpenAI Whisper automatic speech recognition (ASR) model]] running on the [[https://iris.service.rug.nl/tas/public/ssp/content/detail/service?unid=0d51dd1aa44f4cdcb4949f1702d1829f|Hábrók High Performance Computing]] (HPC) cluster.
+This guide takes you through the steps to set up a series of folders and a script to run speech-to-text transcription on the University of Groningen infrastructure (for UG staff and students) based on the [[https://openai.com/research/whisper|OpenAI Whisper automatic speech recognition (ASR) model]] running on the [[https://iris.service.rug.nl/tas/public/ssp/content/detail/service?unid=0d51dd1aa44f4cdcb4949f1702d1829f|Hábrók High Performance Computing]] (HPC) cluster.
-The process of transcribing spoken audio to text is usually a very time consuming manual process. The UG offers a licensed version of [[https://www.audiotranskription.de/en/f4transkript/|F4 Transkript]] on the University Workplace as an aid for manual transcription, but doesn't offer automatic speech recognition software.
+The process of transcribing spoken audio to text is usually a very time-consuming manual process. The UG offers a licensed version of [[https://www.audiotranskription.de/en/f4transkript/|F4 Transkript]] on the University Workplace as an aid for manual transcription, but doesn't offer automatic speech recognition software.
 This guide is offered by the DCC to help researchers process their research data as efficiently as possible, while optimizing data protection (keeping their audio files on UG storage instead of sending them to cloud services). For technical aspects, the service is supported by the Data Science and HPC team of the CIT. If you wish to read more on the detailed functionalities of Whisper, please refer to the [[https://github.com/openai/whisper|manual in their Git repository]].