How to Search Inside Audio Files on Mac (Yes, It’s Possible)

Most Mac users assume that searching inside audio files is off the table. Spotlight certainly doesn’t support it, and Finder won’t help either. For years, audio content has lived in a kind of searchable void. If you didn’t remember the filename or manually transcribe the content, it was effectively invisible.

But in 2025, that’s no longer the case. Thanks to local AI and new macOS-native tools, you can finally search inside spoken content, podcasts, interviews, and recordings instantly and privately.

Let’s look at how.

Why Audio Search Is Hard

Audio search doesn’t work like searching a PDF or text document. Unlike visual or written media, audio lacks natural indexing. The words exist, but they’re hidden behind waveform data.

That means to make audio searchable, it needs to be transcribed first. In the past, this meant sending your files to cloud services for transcription. That raised obvious privacy issues, not to mention cost and speed limitations.

The breakthrough is on-device transcription powered by local AI.

Local AI Transcription on macOS

With the introduction of Apple’s MLX framework and chips like the M1 and M2, your Mac can now run sophisticated AI models locally. That includes whisper.cpp, an open-source transcription engine that converts audio into searchable text — all without an internet connection.

This means you can:

• Transcribe voice memos and Zoom recordings

• Search interview segments by topic or keyword

• Index entire podcast libraries

And do it without ever uploading anything to the cloud.

We covered more about MLX-native tools in The Best Local AI Apps for Mac That Run on MLX in 2025, where whisper-based transcription tools were standouts.

How Fenn Makes This Useful

If transcription is the first step, semantic and keyword search is the second. After all, converting audio to text is great but scrolling through hundreds of transcripts isn’t much better than scrubbing audio.

Fenn solves this with AI-powered search that understands meaning, not just exact keywords. Once your audio is transcribed, Fenn can:

• Let you search: “The part where she mentions funding round”

• Jump straight to the relevant timestamp

• Surface related clips and themes across multiple recordings

We explored this shift in search experience in Semantic vs Keyword Search on Mac: What’s the Difference and Why It Matters.

Use Cases That Actually Matter

This isn’t just for podcasters. Audio search on Mac is changing workflows for:

• Researchers: Quickly find insights across hours of recorded interviews

• Journalists: Pull quotes and verify statements without re-listening

• Students: Search lecture recordings by topic

• Creators: Tag, sort, and reuse voice recordings or soundbites

All of it happens privately on your Mac, often in seconds.

What You Need to Try It

To get started:

1. Use Fenn to transcript and index your audio files
   Fenn can ingest your audio files, convert them to transcript, index them, and enable semantic and keyword search.

2. Start finding the exact moment you need
   Add transcription to your import process. Once you do, audio files become as searchable as PDFs or images.

If you’re already using Fenn to search inside video or PDF files (as we outlined in Search Inside a PDF on Mac, Down to the Exact Word), this fits right into your toolkit.

The Future of Audio Search on Mac

We’re entering a world where every file format is searchable, not just in name, but in essence. With local AI, the invisible becomes visible. Audio is just the beginning.

Once you experience the ability to type “Where did he mention the design pivot?” and land directly on the sentence in your meeting recording, it’s hard to go back.

Your Mac can now hear you. Better yet, it remembers.