AI Transcription 2.16 Mac

AI Transcription is a Mac app that turns audio and video files into text entirely on your own machine, with no internet connection needed.

Pull searchable quotes and timestamped subtitles from interview recordings

• Interview recordings you need to search or quote from later.
• Turning meeting audio into timestamped subtitles via SRT or WEBVTT export.
• Podcasters who want a plain-text draft of an episode before editing.
• Journalists working with WAV or MP3 files from a field recorder.
• Quick CSV exports when you want to hand a spreadsheet of spoken lines to a colleague.

OpenAI Whisper runs locally, so heavy accents and older Intel Macs slow it down

• The engine is OpenAI Whisper, which is solid on clear speech but can stumble with heavy accents or overlapping voices.
• It runs locally, so a large file on an older Intel Mac will take noticeably longer than on Apple Silicon.
• Output is as good as the source audio — background hum or a distant mic will show in the transcript.
• You need macOS 14.6 or later.

First days with AI Transcription

Drop a short, clean recording in first. A two-minute voice memo from your phone, spoken clearly in a quiet room, gives you a baseline for speed and accuracy before you throw an hour-long panel discussion at it.

Check the export formats early, even if you do not need them yet. Opening the same transcript as plain text, CSV, SRT and WEBVTT shows you which one fits your usual workflow. SRT timestamps, for instance, look different in a video editor than they do in a text editor, and knowing that on day one saves rework later.

If you plan to use the app regularly, decide on a split method and stick with it. The sentence split reads more naturally in a document; the duration split, set to something like 30 seconds, is easier to sync with video. Switching back and forth between projects creates extra cleanup work.

Give Apple Speech Recognition a try on one file, especially for English dictation with a single speaker. It is faster than Whisper on the same hardware, and for clean audio the difference in accuracy can be negligible. You switch engines in the app preferences, and the choice sticks until you change it again.

Workflow: from field recording to searchable interview notes

This is a common task for a reporter who records interviews on a portable recorder, producing WAV files, and needs to pull quotes without listening to the whole tape twice.

Start by dragging the WAV onto the AI Transcription window. The app detects the format and queues the file. While it processes, open a notes app or a blank document alongside — the transcript will land in the same workspace.

Once the transcript appears, switch to the sentence split view. Read through once without stopping to correct anything. Highlight phrases that sound like usable quotes and paste them into your notes document with a rough timestamp from the SRT export, which you can generate afterwards in seconds.

Export an SRT file and drop it into a media player that supports sidecar subtitles. Scrubbing through the audio with the transcript overlaid lets you verify a quote's exact wording before publication. Keep the plain-text export as a searchable record; months later, a quick Spotlight search on the transcript folder finds that half-remembered line faster than digging through audio files.

Your audio never leaves the Mac—no account, no upload, no internet required

Does the audio stay on my Mac? Yes. The entire transcription pipeline runs locally. Nothing is uploaded, and the app does not require an account or an internet connection to function. If your Mac is offline, the app works the same as it does online.

Which engine should I use, Whisper or Apple Speech Recognition? Whisper generally handles varied vocabulary and moderate background noise better, but it is slower. Apple Speech Recognition is faster and well tuned to clear single-speaker recordings in the languages macOS supports at the system level. If you transcribe mostly English voice memos, try Apple's engine first and switch to Whisper when accuracy drops.

Will it handle a two-hour file without crashing? Long files work, but processing time grows with length. On an Apple Silicon Mac, a one-hour WAV might finish in a few minutes; an older Intel machine will take longer. The app does not impose an artificial length cap, so the practical limit is patience and disk space for the export files.

What if the recording has two people talking? The transcript will contain the words both speakers said, but the app does not perform speaker diarization — it will not label who said what. Overlapping speech tends to produce garbled lines. For interviews, best results come from recordings where one person speaks at a time and the mic is positioned accordingly.

Scenario: a podcaster preparing show notes

A podcast host records a 45-minute conversation with a guest as an M4A file, using a desktop mic in a treated room. The recording is clean, with little background noise and no crosstalk. The host needs a rough transcript not for publication, but to write show notes and timestamps for the episode description.

After dragging the M4A into the app, the host selects Whisper and waits roughly four minutes for the processing to complete. The transcript appears, split by sentence. The host reads through, copying interesting lines and noting the approximate minute markers from the sentence index. Rather than exporting SRT immediately, the host exports a plain-text file and highlights key passages in a separate text editor, adding manual timestamps by cross-referencing the audio in a player.

The plain-text file becomes the basis for the episode description: the host rewrites the highlighted lines into a narrative summary, pulls two verbatim quotes for social media, and notes a timestamp for the moment the guest mentioned an upcoming project. The whole process, from dropping the file to having a completed set of show notes, takes under 20 minutes. The M4A never left the Mac, and the transcript file sits in the episode folder for future reference.

• macOS 14.6 or later
• Apple Silicon or Intel 64-bit