SubExtract for Accessibility Teams

Auditing existing captions for accuracy before claiming compliance

1. 1Pull the full list of public videos on the channel with /channel-videos to build a video inventory — titles, URLs, publish dates.
2. 2For each video, extract the existing captions with /video-captions — whatever's already there, auto-generated or human-uploaded.
3. 3Spot-check a sample of the extracted transcripts against the actual audio, paying extra attention to accents, jargon, and music-heavy segments where auto-captions tend to slip.
4. 4Flag any video below your accuracy bar for manual correction before it's cited in a compliance report.
5. 5Log a pass/fail status per video so the audit has a paper trail.

Reusing free auto-generated captions instead of paying a captioning vendor

1. 1Check whether the video already has YouTube auto-generated or creator-uploaded captions — most public videos do.
2. 2Extract the existing transcript with /video-captions instead of sending the video out for a fresh, paid caption order.
3. 3Export as .srt (Pro) for direct use in your video player or LMS, or as plain text for a transcript page.
4. 4Proofread against the audio — auto-captions run roughly 90–95% accurate on clear audio and drop off with accents or technical terms.
5. 5Publish the corrected file in place of the vendor order you were about to commission.

Building a searchable transcript archive for a channel's full video library

1. 1Export every public video on the channel — title, URL, video ID, publish date — with /channel-videos.
2. 2Work through the list extracting captions for each video with /video-captions.
3. 3Save each transcript as a .txt file named by video ID so it maps straight back to the export.
4. 4Load the collected .txt files into a shared drive or notes tool your team can search across — there's no built-in transcript search inside SubExtract itself, so this step is what makes the archive searchable.
5. 5Use the archive to answer accessibility requests or internal "did we ever cover X" questions without re-watching hours of video.

A nonprofit auditing a 200-video YouTube archive before an ADA deadline

With a compliance review two weeks out, the team runs /channel-videos to get every public upload as one list, then works through it with /video-captions to pull the existing transcript for each. Spot-checking a sample against the audio turns up about a dozen older videos where the auto-captions garble the organization's own program names — those go to a human for correction; the rest are logged as passing and cited as-is in the review.

A university accessibility office spot-checking course-video captions

A department flags that some lecture recordings might have unreliable auto-captions. Rather than re-captioning the whole course from scratch, the accessibility office extracts the existing transcript for each lecture with /video-captions, reads a few minutes against the recording to check for dropped technical terms, and only sends the videos that actually fail the check out for professional correction — instead of every video in the course.