Turning a YouTube video into text used to mean re-typing the whole thing or fighting with a screen recorder. This tool removes all of that: paste any watch?v= or youtu.be link (or just the video ID) and you get the complete transcript with per-line timestamps, ready to copy or download.
What you get
- Full transcript — every captioned line, in order, no truncation.
- Timestamps on every line — jump straight to any moment in the video.
- Four export formats — plain text (TXT), subtitles (SRT), web subtitles (VTT), or raw JSON with start/duration for each segment.
- Works without captions — if a video has no caption track, the audio is transcribed automatically with AI speech recognition.
- Any language — grab the original captions or translate into 100+ languages.
How it works
- Copy the URL of any public YouTube video.
- Paste it into the tool and press Extract. The caption track is pulled directly from YouTube — no account, no Data API key, no watermark.
- Read it on screen, copy it to your clipboard, or download it as TXT, SRT, or VTT.
When a video has no captions
Many videos — lectures, vlogs, older uploads — have no caption track at all. Most “YouTube to text” tools simply fail on those. This one does not: it downloads the audio and runs AI speech recognition on it, then hands you the same timestamped transcript. You get a video-to-text result for essentially the entire YouTube catalog, not just the captioned subset.
Video to text vs. screen recording + OCR
| yt-transcript | Screen recording + OCR | |
|---|---|---|
| Time per 10-min video | ~1 second | 10+ minutes |
| Accuracy | From the source caption data | OCR guesses per frame |
| Timestamps | Per line, exact | Frame-based, rough |
| Software to install | None | Recorder + OCR tool |
What do people use video-to-text for?
- Research & citations — find and quote exact lines from talks, interviews, and lectures.
- Content repurposing — turn one video into blog posts, social threads, newsletters, or show notes.
- Accessibility — give people who prefer reading a text version of the content.
- LLM pipelines — feed accurate transcripts into GPT, Claude, or local models instead of raw audio.