BACKGROUND
Transcribing qualitative interviews or videos is time-consuming, but accurate transcriptions are essential for qualitative research.
With
Whisper , researchers can automate transcription processes, saving valuable time and effort. Whisper provides a reliable first draft of transcriptions, which can be refined and edited, allowing you to streamline your workflow and focus more on analyzing data rather than on manual transcription tasks. Whisper is an open-source automatic speech recognition (ASR) system developed by OpenAI.
WORKSHOP GOAL
In this workshop, participants will learn how to automate the transcription of audio files, saving time and improving efficiency in qualitative research. They will gain hands-on experience with Whisper and acquire skills to handle large volumes of qualitative data.
After the workshop, participants will be able to generate reliable first drafts of transcriptions, and streamline their workflow by integrating Whisper with Python for large-scale transcription tasks.
WORKSHOP CONTENT
Part 1: Introduction
- Comprehensive introduction to automated transcription, highlighting its benefits while also offering a critique of such automation in qualitative research.
- Data protection considerations when dealing with qualitative audio data and the use of AI-based tools.
- Evaluating the limitations of these technologies.
Part 2: Hands-On Session: Automated Transcriptions with Whisper
- Participants will be introduced to NoScribe , an open-source tool with an intuitive graphical interface for transcription tasks.
- Lastly, participants will explore Python programming techniques to efficiently transcribe large volumes of audio files.
TARGET AUDIENCE & PRIOR KNOWLEDGE
This workshop is designed for anyone who needs to transcribe audio files and wants to automate the process. It is primarily aimed at researchers working with qualitative data.
Participants must have basic Python coding skills, or they are required to attend our workshop “
Quickstart Python for Qualitative Data” workshop the day before as a preparation.
TECHNICAL REQUIREMENTS
ABOUT THE TRAINER
Annika Nolte and Nele Fuchs are data scientists for training and consulting at the DSC.
Annika holds a master’s degree in Environmental Sciences from the Technical University of Braunschweig (2019). She has over five years of experience in scientific programming, specializing in data management and processing, hydroinformatics, geospatial analysis, and AI for environmental modeling. In training and consulting, Annika draws on her research background and broad interdisciplinary expertise in Earth system sciences.
Nele Fuchs studied Philosophy, Material Culture: Textile (CvO University of Oldenburg), and Transcultural Studies (University of Bremen). As a data scientist in the humanities, she supports researchers in the areas of digital humanities, data science methods for qualitative research and FAIR-compliant qualitative data management, using her expertise in handling sensitive qualitative data.