Lecture transcripts: archive a university channel

University channels are archives that nobody can search. MIT OpenCourseWare has thousands of lectures; a departmental seminar channel accumulates a decade of talks. As video, that material is close to unusable — you cannot find the lecture where a method was introduced without knowing which lecture it was. As text it becomes a corpus you can search, cite, and study at scale. This guide covers channel-wide extraction, organising the result so it stays usable, accessibility uses, and building a research corpus that survives peer scrutiny.

What channel-wide lecture extraction produces

One transcript per video, plus enough metadata to organise, filter and cite them. A channel run returns the same record for every video, whether or not a transcript came back.

isAutoGenerated is the field that decides how much you can trust the text. Universities that caption for accessibility compliance upload human-corrected tracks, and those are close to verbatim. Everything else is machine output with a predictable error profile.

Extract a whole channel

The steps are short; the decisions before them are what matter.

1. Get the channel identifier. A URL, an @handle, or a UC... channel ID all work. youtube.com/@MITOCW and youtube.com/channel/UC... are equivalent inputs.
2. Set a video cap. This is the single most important setting on a large channel — it bounds runtime and cost. Channels are processed newest first, so a cap of 200 gives you the 200 most recent uploads.
3. Set the caption language to the language the lectures are delivered in.
4. Decide about timestamps before you run, not after. For a research corpus and for accessibility work, turn them on. Retro-fitting means running the whole channel again.
5. Run it once, save the raw output untouched, and treat that file as your archive of record.

A channel is not a course. A university channel mixes lecture series, admissions videos, open-day recordings and one-off seminars. Extraction gives you everything; the organising is yours to do, and it is the step people skip.

Two things a channel run does not include. Shorts sit on a separate feed and are not returned with long-form uploads — no loss for a lecture archive. And members-only or unlisted videos are not publicly listed, so they are not there.

If the material you want is one course rather than a whole department, use that course’s playlist instead. It is smaller, ordered correctly, and already curated.

Organise the output so it stays usable

Write one file per video, named by video ID, with the metadata in a header. This is the single decision that determines whether the archive is still usable in a year.

mkdir -p lectures
jq -c 'select(.success)' channel.jsonl | while read -r row; do
  id=$(printf '%s' "$row" | jq -r .videoId)
  printf '%s' "$row" | jq -r '
    "---",
    "title: \(.title)",
    "video: \(.videoUrl)",
    "channel: \(.channel)",
    "duration_s: \(.durationSeconds)",
    "auto_captions: \(.isAutoGenerated)",
    "---",
    "",
    .fullText' > "lectures/${id}.md"
done

Video ID as filename, always. Lecture titles contain colons, slashes and inconsistent numbering (Lecture 3, Lec 03, 3. Eigenvalues), and any scheme built on them breaks the first time a title is edited. The ID never changes and it reconstructs the URL.

Grouping a flat directory into series is usually a matter of the course code in the title:

# Group by a leading course code such as 18.06 or CS229
jq -r 'select(.success) |
  [ (.title | capture("(?<code>^[A-Z0-9]+\\.?[0-9]*)").code // "misc"),
    .videoId, .title ] | @tsv' channel.jsonl | sort

Whatever pattern that channel uses, work it out once, write it down beside the archive, and record the extraction date. A corpus without a collection date cannot be cited and cannot be reproduced.

Search a lecture archive

Once it is a directory of text files, ordinary tools are enough. There is no need for a database until you have tens of thousands of documents.

# Which lectures mention a topic, ranked by how often
rg -c -i "eigenvalue" lectures/ | sort -t: -k2 -rn | head

# Show the surrounding sentence, not the bare hit
rg -i -o ".{120}singular value decomposition.{120}" lectures/

# Recover the lecture title for a hit
rg -l -i "bayes" lectures/ | xargs -I{} sh -c 'head -2 {} | tail -1'

For repeated use, put the transcripts into a full-text index — SQLite’s FTS5 handles a few thousand lectures in a single file with no server:

CREATE VIRTUAL TABLE lectures USING fts5(video_id, title, body);
-- then
SELECT video_id, title, snippet(lectures, 2, '[', ']', '…', 12)
FROM lectures WHERE lectures MATCH 'markov NEAR/8 chain' LIMIT 20;

Proximity search is what makes an archive of lectures searchable rather than merely greppable. Two terms in the same lecture means very little across a 90-minute transcript; two terms within eight words of each other is a passage about the relationship between them.

Accessibility uses

A published transcript is a real accessibility gain and it is worth being precise about who benefits and how, because it is not the same group as captions serve.

Three points of accuracy. First, automatic captions are not an accessibility measure on their own — the error rate on technical vocabulary is exactly where comprehension breaks, and guidance from accessibility bodies has been consistent about this for years. An extracted automatic transcript is a starting draft for a human to correct, not a finished accommodation.

Second, transcripts serve a different need to captions and do not replace them. Captions are synchronous with the video; a transcript is a document. Provide both.

Third, if you are correcting transcripts for a department, prioritise by use: the introductory lecture that 400 students watch is worth more correction effort than a seminar that fifteen people watched. Sort by view count, which comes back with every row, and start at the top.

Build a research corpus

Lecture transcripts are usable research data — for discourse analysis, for studying how a field’s terminology shifts, for pedagogy research on explanation strategies. What makes them usable is documentation, not volume.

Record these five things alongside the corpus, because a reviewer will ask for all of them:

1. Extraction date and method. “Captions retrieved from publicly published YouTube caption tracks, 2 September 2026.” Undated corpora are not reproducible.
2. Selection criteria. Which channels, which date range, what cap. State the cap explicitly — “the 200 most recent uploads” is a sampling decision and it biases towards recent material.
3. Caption provenance per document. Keep isAutoGenerated. Machine and human captions have different error characteristics, and mixing them silently undermines any lexical claim.
4. Non-response. Which videos returned nothing and why, by error code. A corpus that reports only its successes conceals the shape of its sample.
5. Known transformations. If you stripped filler words, normalised casing or removed segments, say which and in what order.

The main methodological trap is treating machine transcription as verbatim speech data. It is not. Disfluencies are removed, false starts are cleaned up, punctuation is inferred, and technical terms are frequently wrong. That rules out several kinds of analysis outright:

For sampling, weight by what you are claiming. If your claim is about a field, a single channel is a case study, not a sample of the field — say so. If your claim is about one department’s teaching, a channel-wide run is a census of what that department published, which is a genuinely strong basis.

Word counts are useful for reporting scale: roughly 9,000 words per hour of lecture. A 200-lecture channel of hour-long lectures is around 1.8 million words.

Limitations

• Lectures without captions return nothing. No tool can retrieve a transcript that was never published, and older archive uploads are the worst affected.
• Private, unlisted and members-only videos are not accessible. If it does not appear in a logged-out browser, extraction cannot see it.
• Board work and slides are lost. Mathematics, chemistry and any lecture built on visual derivation loses most of its content in text. “As you can see here” marks a hole in the record.
• Automatic captions are wrong exactly where it matters — technical terms, names, notation and numbers. Verify anything you quote or count.
• No speaker separation. Student questions, seminar discussion and co-taught lectures come back as one undivided stream.
• A channel run is newest-first and capped. Any cap is a sampling decision that biases towards recent uploads. State it.
• Shorts are a separate feed and are not included in a channel run.
• Channels change. Videos are removed, made private and re-uploaded. Two runs a year apart will not match, which is why the extraction date belongs in the corpus documentation.
• Machine transcripts are not verbatim speech data. Disfluency, prosody and timing analysis are not possible on them.

Do I need a YouTube API key to get lecture transcripts?

No. The official Data API cannot return auto-generated captions, which is what most lecture uploads have. Caption data is retrieved from the publicly served tracks instead — no key, no OAuth, no quota.

Can I extract every lecture on a channel at once?

Yes, that is what channel-wide extraction does: every long-form public video, capped by whatever limit you set. On a large university channel, set the cap deliberately — it bounds both runtime and cost, and it determines your sample.

How accurate are automatic lecture captions?

General spoken English comes back close to verbatim. Technical vocabulary, surnames, notation read aloud and numbers are where errors concentrate, and those are the words a lecture turns on. Use the text to find a passage, then confirm the wording against the video before quoting it.

Can I use lecture transcripts in a published paper?

You can cite and quote them as you would any published source: attribute the speaker and institution, link to the video, give a timestamp. What you cannot generally do is redistribute full transcripts as a public dataset — the lectures are copyrighted works. Publishing derived measures, or a list of video IDs and your extraction method so others can reproduce it, avoids the problem entirely and is better practice anyway.

Is a transcript enough to meet accessibility requirements?

Not on its own, and not in automatic form. Accessibility guidance treats uncorrected machine captions as insufficient, because the error rate falls on the technical vocabulary that carries the meaning. An extracted transcript is a strong starting draft for human correction, and it is a genuine addition alongside captions — but it does not replace either.

What if the lectures I want are one course rather than a channel?

Use that course’s playlist. It is smaller, it comes back in lecture order, and it has already been curated by whoever built it. Channel-wide extraction is for archiving a department or a whole catalogue, where no single playlist covers what you need.

How do I handle a channel with thousands of videos?

Cap it and go in passes. Take the most recent few hundred, look at what came back, work out the title pattern the channel uses, and only then decide whether you need the rest. Most people discover after the first pass that they wanted two lecture series rather than the entire catalogue.