How to Remove Timestamps from a Transcript or Subtitle File

Subtitle and transcript files are built around timing, which is exactly what you don’t want when you’re repurposing the content as an article, a blog post, a quote, or a script for translation. A raw SRT file dumped into a document looks like this:

12
00:04:21,500 --> 00:04:24,800
and that's really the core idea

Readable prose needs just the words: “and that’s really the core idea.”

What actually needs to be stripped

• Cue numbers. The sequential index (12, 13, …) that precedes each block in SRT.
• Timestamp lines. The 00:04:21,500 --> 00:04:24,800 line, in either SRT’s comma format or VTT’s period format.
• The WEBVTT header, if present.
• Formatting tags. Italic/bold markup (<i>, <b>), positioning cues, and speaker-label tags that some VTT files include.
• Blank-line spacing between cues. Usually you want this collapsed into normal paragraph or sentence spacing, not preserved as-is.

Why doing this by hand (or with find-and-replace) goes wrong

A simple regex to strip timestamp lines usually works fine for short files, but two things commonly break it: inconsistent line endings (files exported on different operating systems mix \n and \r\n), and cue numbers that look like regular text at a glance but trip up a naive pattern match. On a long file (a full episode transcript, a lecture, a multi-hour interview), a single missed line means re-scanning the whole thing by eye.

There’s also a subtler issue: when two cues are split mid-sentence (common when a sentence runs long and crosses a cue boundary), simply deleting the timing lines leaves you with a sentence broken across two lines with no punctuation connecting them. A good stripping tool rejoins these properly instead of leaving fragments.

Doing it properly

Our timestamp remover takes an SRT or VTT file and outputs clean paragraph text: cue numbers, timing, and tags gone, sentences rejoined where they were split across cues, all processed locally in your browser so the transcript never leaves your device. It’s the fastest path from “timed subtitle file” to “readable text” without manually scrubbing a few hundred timestamp lines.