From voice to ink (VINK): Development and assessment of an automated, free-of-charge transcription tool

Abstract

AbstractVerbatim transcription of qualitative data is a cornerstone of analytic quality and rigor, yet the time and energy required for such transcription can drain resources, delay analysis and hinder the timely dissemination of qualitative insights. In recent years, software programs have presented a promising mechanism to accelerate transcription, but the broad application of such programs has been constrained due to expensive licensing or “per-minute” fees, data protection concerns, and limited availability of such programs in many languages. In this article, we outline our process of developing and adapting a free, open-source, speech-to-text algorithm (Whisper by OpenAI) into a usable and accessible tool for qualitative transcription. Our program, which we have dubbed “Vink” for voice to ink, is available under a permissive open-source license (and thus free of cost). We assessed Vink’s reliability in transcribing authentic interview audio data in 14 languages, and identified high accuracy and limited correction times in most languages. A majority (9 out of 12) of reviewers evaluated the software performance positively, and all reviewers whose transcript had a word-error-rate below 20% (n=9) indicated that they were likely or very likely to use the tool in their future research. Our usability assessment indicates that Vink is easy-to-use, and we are continuing further refinements based on reviewer feedback to increase user-friendliness. With Vink, we hope to contribute to facilitating rigorous qualitative research processes globally by reducing time and costs associated with transcription, and expanding the availability of this transcription software into several global languages. With Vink running on the researcher’s computers, data privacy issues arising within many other solutions do not apply.Summary boxWhat is already known on this topic:Transcription is a key element to ensure quality and rigor of qualitative data for analysis. Current practices, however, often entail high costs, variable quality, data privacy concerns, stress for human transcribers, or long delays of analysis.What this study adds:We present the development and assessment of a transcription tool (Vink) for qualitative research drawing upon an open-source automatic speech recognition system developed by OpenAI and trained on multilingual audio data (Whisper). Initial validation in real-life data from 14 languages shows high accuracy in several languages, and an easy-to-use interface.How this study might affect research, practice or policy:Vink overcomes limitations of transcription by providing a ready to use, open source and free-of-cost tool, with minimal data privacy concerns, as no data is uploaded to the web during transcription.