66-Minute Video to Searchable Transcript in 5 Minutes

Long videos are becoming a primary vehicle for knowledge. Open courses, interviews and livestream replays routinely run over an hour, and the automatic captions platforms provide are text chopped up by the second, with no punctuation: readable, but not searchable. Turning a 66-minute video into a transcript that is segmented, timestamped and hit by keyword search usually means listening through and typing every line by hand, which typically takes TBD. This time, Talos produced a searchable 121-section, 14,000-character transcript in about 5 minutes.

There are three awkward things about this kind of task. First, the source material is dirty: the page is an 87-lecture collection with a total runtime of 41 hours 23 minutes, and it is easy to pull in content from other lectures by mistake; the captions are machine-recognized, with no punctuation and no paragraph breaks, and they are full of verbal tics and misheard words. Second, the timestamps cannot be estimated, because readers need them to go back and check the original video. Third, the content cannot be altered: charge names, statute numbers and the aliases used in case examples must be preserved exactly.

General-purpose speech transcription tools output one undivided block of text with timestamps cut by the second, so the reader still has to clean it up. The goal here was a transcript that can be searched directly and checked section by section against the original video.

Step 1: Write the delivery spec into the prompt

Every requirement for this task lived in a single prompt, with no follow-up questions. The full prompt:

I want to turn a long video into a searchable transcript.
Video link: https://www.bilibili.com/video/BV1M7411R7xW/
This is Lecture 1 of Luo Xiang's criminal law course for Houda Law Exam, 1 hour 6 minutes long. The page is an 87-lecture collection, so process only the first lecture currently playing and do not cross into any other lecture.
Please:
1. Open the video and extract the spoken content in full as text.
2. Organize it into a transcript: segment it by meaning, put a timestamp in front of each segment in mm:ss, switching to hh:mm:ss past the one-hour mark.
3. Save it as an .md file in this session's directory, named 罗翔讲刑法第一讲_文字稿.
Requirements:
- Timestamps must correspond to actual positions in the video; do not estimate.
- Verbal tics and repeated phrases may be removed; the lecture content and case examples must not be changed.
- Keep charge names, statutes and the aliases used in case examples exactly as the instructor said them.
- When done, tell me the total character count and where the file is.

The prompt has four key elements:

A clear scope: it states that the page is an 87-lecture collection and that only the first lecture currently playing should be processed, closing off the risk of pulling in other lectures up front. A specified timestamp format: it gives both mm:ss and hh:mm:ss and defines the point at which to switch. A fidelity boundary: verbal tics may be removed; lecture content, case examples, charge names, statutes and aliases may not be changed. Delivery and reporting requirements: it names the file, the location, and asks for the character count and file location in the report back.

Step 2: Talos executes automatically

After receiving the prompt, Talos completed six things in sequence:

1. Open the video page: locate the first lecture currently playing without triggering any other lecture in the collection
2. Read the caption track: wait for the pre-roll ad to finish before reading, an actual wait of 76 seconds
3. Segment by meaning: use the point where each segment begins as its timestamp, all taken from the caption track
4. Trim by rule: remove verbal tics and repeated sentences while keeping the line of argument, case details, charge names and statute numbers as they are
5. Fix recognition errors by reading through: restore misheard technical terms from context, and list doubtful spots separately in a proofreading note at the end
6. Write the file and count: output Markdown and count sections and characters

The whole run took about 5 minutes.

Step 3: Structure of the output transcript

The finished transcript has three parts and 121 body sections in total.

Three key figures stay in the body: the full transcript is 14,026 characters (including punctuation, excluding whitespace), of which 12,561 are Chinese characters; timestamps cover 0:10 to 1:06:35, matching the length of Lecture 1; the list of doubtful items at the end has 7 entries.

Talos produced a 121-section, 14,000-character transcript in about 5 minutes, with timestamps covering the full 66-minute video and no spillover into other lectures in the collection. The finished text can be searched by keyword and checked against the original video via its timestamps.

Teams working in content, research and training run into this same task over and over; only the frequency differs.

One-off production: the wait between seeing a long video and having a searchable transcript shrinks to the time it takes to drink a glass of water, so a recording can become quotable text the same day as the meeting.

Repeated production: save the prompt, change two lines and swap in another video. Course series and interview series can be produced in batches to the same spec, with a naturally consistent format.

Information quality: timestamps come from the caption track itself rather than being back-filled after transcription, so citations line up with the original video; doubtful recognition spots are listed separately, so you know before quoting where you need to listen again.

Format compatibility: Markdown is natively searchable and can be converted straight to PDF or Word for publication without re-layout.

1. Define the scope first: when the source page is a collection, a list or split into parts, state in the prompt that only the current item should be processed. It is far less work than deleting the extras afterward.
2. Give the timestamp format and its switch point: write down where mm:ss changes to hh:mm:ss, and the finished transcript will not vary from one segment to the next.
3. Say what may be deleted and what may not be changed: verbal tics and repeated sentences can go; charge names, statutes and aliases cannot move. The more concrete this boundary, the less review time the result needs.
4. Require doubtful spots to be surfaced: machine captions will always mishear something. Having them listed separately is more trustworthy than quietly fixing them in the body text.

Q: Are the timestamps estimated?

A: No. They are all taken from the video's caption track itself; each segment's timestamp is the actual point in the video where that segment begins, so you can check any section against the original by timestamp.

Q: The page is a collection of dozens of lectures. Could other lectures have slipped in?

A: No. The prompt limited processing to the first lecture currently playing, and the finished timestamps cover 0:10 to 1:06:35, the same length as this lecture; content from the rest of the collection did not enter the transcript.

Q: Can the edited transcript be published as is?

A: Verbal tics and repeated sentences have been removed according to the stated rules, while lecture content, case examples, charge names, statutes and aliases are unchanged. The proofreading note at the end lists 7 spots where the machine captions looked suspicious, and it is worth listening through them once against that list before publishing.

Q: Can this method be reused for other videos?

A: Yes. Swap the video link and change one sentence about runtime and scope, and the same prompt handles podcasts, recorded lectures, interviews and internal meeting recordings.