Abhijith has been writing for the Web since 2011 and has contributed to sites like Beebom and TechWiser. He is curious about making the best of tech accessible to everyone.
He started writing as a hobby after getting a computer at 16. Since then, he has found technical writing a space where he belongs and could make a difference. A curious and self-motivated person, Abhijith's writings focus on productivity, Android, and the Internet.
He holds a Master's degree in English and a PhD in Humanities. Enthusiastic about language, literature, and culture, he works as an Assistant Professor of English.
If not writing or reading, you can find Abhijith playing Chess or CODM.
Audio transcriptions once meant uploading your files to an audio platform or paying for expensive software. Either way, you had to rely on a third-party tool to get the job done. If you had sensitive content, it became even more problematic, as you had to know where to put your trust; expensive software was typically the best option.
But now, fully offline audio transcription is readily available to almost anyone through AI-powered transcription models. These days, I can transcribe hours of audio for free, with no data leaving my device -- and the results are better than ever.
What I used for my AI-powered audio transcription project
It's Whisper, minus the upload button
As you may have guessed, I needed a few things to transcribe audio offline. The first is a transcription model that analyzes the audio and converts it into text. These are also called automatic speech recognition (ASR) systems in the world of AI.
Various AI platforms have launched ASR systems, and Whisper from OpenAI is among the most popular, along with others like Parakeet from Nvidia. The best part here is that OpenAI Whisper, which packs the Whisper Large V3 Turbo model, is completely free to use when you self-host it.
The second part of the stack I needed was a self-hosting mechanism. You can set up features like this using Terminal and Python, but they are, in addition to being non-GUI, pretty hectic when you want to transcribe hours of data. There are also tools like Wispr Flow, which allow real-time dictation using AI-powered transcription models, but they're not great for prerecorded content. Given these situations, I chose MacWhisper, a freemium tool for transcribing audio and video locally on a Mac.
In hindsight, these two picks were pretty great what I had ahead.
Getting MacWhisper running took ten minutes, not a weekend
No terminal gymnastics, no Python environment battles
The benefit of using a free transcription model with MacWhisper is the ease of setup and use. I downloaded the setup file from the official website, and the installation took only a few seconds. The installation file isn't heavy, but model downloads may take a few minutes depending on your internet connection. Once the setup was complete, MacWhisper asked me to download local transcription models.
Some advanced models are available only with the paid version of MacWhisper, though, so you may want to ensure that. But keep in mind that all Whisper models are free to use.
Once these downloads are done, you can start using MacWhisper for a variety of transcription and dictation needs. Some features are exclusive to the paid version, once again. Even then, using MacWhisper to get the basic transcription done is easy with the free version. You only need the advanced version of MacWhisper for features like automatic speaker recognition, cleanup, and speaker-wise transcription.
Overall, getting started with MacWhisper was easy. After that, I started assigning it a variety of transcription tasks, starting with meetings and quick notes, but larger assignments were on the way.
MacWhisper
OS macOS
Individual pricing Free, Pro version available
Developer(s) Good Snooze
MacWhisper is a macOS app that lets you transcribe audio and video files and offers other features like dictation
See at MacWhisper
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I threw my messiest audio at it on purpose
Interviews, voice notes, and one very loud coffee shop recording
Close
When I say the free model nailed the transcription job almost every time, a couple of factors are at play.
For one, results varied depending on the specific variant of the Whisper transcription model and the language of the recording. Most of my recordings were in English, but there were a few outliers. It meant I had to switch between multiple models during the process.
For instance, Whisper's Large v3 Turbo model was effective when I wanted to balance efficiency and speed. I threw a 46-minute meeting recording with multiple speakers and topics at it, and the entire transcription was completed in under three minutes. Given that I was using an M2 MacBook Air with just 8GB of RAM, this speed was impressive. Thanks to MacWhisper, I got more than a typical transcript. There were even options to export the transcribed data into different formats.
Over time, I have used this Whisper + MacWhisper combination to transcribe a variety of content, including occasional voice notes and detailed interviews. Regardless of what I threw at it, the response seemed pretty accurate. Whenever I need advanced features, I can always switch between models as I wish. All these are on top of the relief that I don't have to upload anything to any third-party server. The audio content, transcription model, and the output stay on my device.
I've also used this system to generate subtitles for video recordings. Even when the audio output was a little messy, the Whisper models pulled through, and the manual editing options are pretty useful for subtitles. Similarly, when I come across a podcast with great content, I might use the transcription feature rather than uploading the entire thing to NotebookLM.
Where it actually stumbled
Like any other AI-based tool, transcription models aren't perfect, and Whisper models were no exception. Though it handled hours of recorded data with ease, some sections of the content caused delays or required additional editing. This discrepancy is more about the type of data these models were trained on. In particular, these issues concerned names, jargon, and speakers with heavy accents.
Nevertheless, these limitations are not deal-breakers given the convenience and privacy they offer. The fact that you don't have to pay for these always-improving models is the cherry on the cake.
