For qualitative researchers, accurate transcription is foundational to analyzing conversations, interviews, and audio data. Without a reliable process, it’s easy to get bogged down in manual work, detracting from the deeper analysis that drives meaningful insights. Transcribing by hand—although thorough—can be time-consuming and exhausting, especially when handling large volumes of data.
Exploring Alternatives to Manual Transcription
My journey into using NLP for qualitative research was motivated by the need for a better transcription process. OpenAI’s release of the Whisper automatic speech recognition (ASR) tool for open access significantly changed how I approached transcription. Before Whisper, I was doing 100% of my transcriptions manually, listening and typing out everything as I went along. Since I had no budget for transcription services, I experimented with various free and paid solutions like Otter and Rev. While they provided some relief in small-scale projects, their limitations—such as word limit caps, inconsistent accuracy, and the cost per transcription—created friction, especially when working with large datasets.
Enter Whisper: A Game-Changer for Transcription
One of the graduate students my advisor was working with on a machine learning project introduced her to Whisper for batch transcribing large numbers of audio files. The student also shared the Jupyter Notebook code they had used, and I was thrilled to experiment with it. (Side note: I love Jupyter Notebooks, but that’s a discussion for another day!)
Running Whisper on some YouTube audio files was a revelation. Despite not having a fancy GUI, the process was remarkably smooth. Whisper’s high accuracy, even with complex jargon and varying audio quality, made it stand out from the other tools I had tried. Most importantly, Whisper supported batch processing, which saved me hours of tedious work that I had struggled with in other tools.
Overcoming Hardware Barriers
While Whisper’s capabilities are impressive, I quickly realized that unlocking its full potential required powerful hardware. Whisper is best run on a computer equipped with a graphics card (GPU) with CUDA cores, which can be costly—upwards of $1000 for the GPU alone, plus the cost of a high-end PC.
Fortunately, my passion for VR gaming worked in my favor. I already owned an Nvidia RTX 3090, purchased for my Skyrim VR addiction. For those without access to high-end hardware, there are still ways to leverage Whisper. Cloud services like Google Colab allow you to rent GPUs at a fraction of the cost, and for smaller projects, Whisper can run on less powerful systems. There’s also MacWhisper, a proprietary Apple software that lets you use Whisper on MacBooks, utilizing the built-in GPU and CPU infrastructure.
The Role of Human Insight in Automated Transcription
Despite Whisper’s strengths, I’m not advocating for a totally automated transcription process. Through my early work in manual transcription, I’ve come to appreciate how important it is to become intimately familiar with your data corpus. Automated transcription cuts into that phase of research, but it can still be part of a smoother overall process. With Whisper, you aren’t constantly stopping and starting to type. However, the process still requires the researcher to engage with the audio—listening, reading the transcript, and, if applicable, using the transcript as subtitles for video data.
With Whisper handling the bulk of the transcription, I’m now able to focus on what really matters: analyzing the content. The hours I’ve saved typing out interviews can be channeled into coding transcripts and conducting deeper qualitative analysis.
Expanding the Use of NLP in Research
Whisper has also made me think more deeply about what types of analysis I could apply to these transcriptions. It isn’t just about transcription—Whisper is part of a larger revolution in NLP tools that accelerate the qualitative research process. Paired with tools like NLTK, spaCy, and BERTopic, these advancements allow researchers to focus less on data preparation and more on generating meaningful insights. While the initial setup can feel daunting, investing time in learning tools like Whisper has been transformative for my research.
For anyone struggling with manual transcription or overwhelmed by large datasets, I encourage you to explore these new possibilities. The time saved and the insights gained make the effort more than worth it.
The Future of NLP in Qualitative Research
As NLP technology continues to evolve, tools like Whisper are just the beginning of how we’ll integrate machine learning into qualitative research. This shift opens up exciting opportunities to streamline data preparation, deepen analysis, and ultimately, uncover insights that might have been previously out of reach.
Have you tried using Whisper or any other NLP tools in your research? Share your experiences in the comments—I’d love to hear how you’re navigating the transcription process!
