How to Turn Videos Into Text You Can Feed to AI

TL;DR: When I have a pile of tutorial videos I'll never finish watching, I don't watch them — I convert them to text and let AI read them for me. I import the whole batch into GeekLink, run speech recognition on the ones where the lesson is spoken and OCR on the ones where the knowledge is on-screen text, export everything to plain TXT, and paste that into Claude or ChatGPT. Now instead of scrubbing through hours of video, I ask questions and get answers with the source material behind them. I learned SEO this way, and last week I used the OCR path to pull the captions out of a batch of French-language videos for a friend.

The problem with a video backlog

Everyone who learns online has the same graveyard: a folder of courses, a playlist of conference talks, a stack of screen-recorded walkthroughs someone sent you. All of it useful, none of it watched. Even at 2x speed, a dozen hour-long videos is a full working day you don't have — and video is a terrible format to search. When you half-remember that one tutorial mentioned a specific trick, you can't grep a video. You have to scrub.

The thing is, almost none of that content actually needs to be watched. What you want from a tutorial is the information, and the information is text — it's just trapped inside a video container. So I stopped treating these as videos to watch and started treating them as documents to extract.

And to set expectations up front, because it's the question everyone asks: you can't reliably just hand your videos to ChatGPT or Claude. They're getting better at short, clean clips — you might get a rough transcript out of one — but there's no dependable timing, quality drops off on longer or noisier video, none of it is built for a batch of full-length lessons, and speech alone never captures text that's only on-screen. So the real task is getting clean text out of the video first. Once you have that, the AI part is easy. This whole post is about doing that first step well, in batch.

The method, in four steps

The whole workflow is boring on purpose. There are only four steps:

1. Import the batch. Drop the whole folder of videos in at once — not one at a time.
2. Turn each video into text. Speech recognition for the ones where the lesson is spoken; OCR for the ones where the knowledge lives in on-screen text or burned-in captions. More on choosing between them below.
3. Export to plain TXT. Not SRT with timestamps — just the words, as a clean transcript you can read top to bottom.
4. Feed it to AI. Paste the text into Claude, ChatGPT, or a notebook-style tool, and now you have a knowledge base you can ask questions instead of a backlog you have to watch.

GeekLink does steps 1 through 3 — it's the extraction layer that turns the video pile into text. The AI does the reading and answering. The point of the workflow is that the boring, slow part (getting words out of video) is automated and batched, so the only thing left is the part you actually care about: asking questions.

Path A: spoken lessons → speech recognition

Most tutorials are someone talking. For those, speech recognition is the path: it listens to the audio and writes down what's said. GeekLink uses Whisper for this, runs it locally, and streams a transcript out per video. Point it at a batch and you come back to a stack of transcripts.

This is how I actually learned SEO. I had collected a mess of talks, webinars, and long YouTube explainers — the kind of thing where you know the good stuff is in there somewhere but you're not going to sit through fourteen hours of it. I ran the whole batch through speech recognition, exported the transcripts, and dumped them into an AI chat. From there I could ask real questions: "What do these sources say about internal linking?" "Where do two of these speakers disagree?" "Summarize everything on title tags into a checklist." I was learning from fourteen hours of video in an afternoon, and — this is the part that matters — the answers were grounded in the actual material I'd chosen, not the model's generic web knowledge.

That last point is why this beats just asking an AI about SEO directly. A general model gives you the average of the internet. Feeding it your curated sources gives you answers from the specific people you decided to trust.

Path B: on-screen text → OCR

Speech recognition only captures what's spoken. Plenty of videos carry their real substance on the screen instead: slides dense with text, code walkthroughs, product demos where names and numbers are shown but never read aloud, and foreign-language videos where the meaning is in burned-in subtitles. For those, transcribing the audio misses the point entirely — you need to read the picture. That's OCR.

Last week a friend who makes French-language perfume videos ran into exactly this. Her reference material was a batch of French videos where the details she needed — ingredient names, fragrance notes, brand text — were on-screen captions, not narration. Transcribing the audio would have thrown away the very thing she was after. So we used the OCR path: GeekLink scanned each video's frames, pulled the burned-in text out with its timing, and exported it. From there it was plain text she could translate and feed to AI like any other transcript. Same four-step workflow, different extraction method — because the knowledge was in the pixels, not the audio.

If you want the deep dive on the OCR side specifically, I wrote a separate guide on extracting hardcoded subtitles from video using OCR.

Which path for which video

The rule I use is simple: ask where the knowledge lives.

• In the narration — a lecture, a podcast-style explainer, a talking-head tutorial → speech recognition.
• On the screen — text-heavy slides, code, data, or a foreign-language video with burned-in subtitles → OCR.
• In both — a lot of real tutorials are both. You can run speech recognition for the spoken half and OCR for the on-screen half, then hand AI both transcripts for the same video.

You don't have to decide perfectly up front. When I'm unsure, I default to speech recognition, skim the transcript, and if it's clearly missing on-screen detail, I run OCR on that one too.

How I actually feed it to AI

A few things I've learned make the AI half work much better:

• Combine related videos into one text file. Instead of pasting ten separate transcripts, I concatenate a topic's worth of videos into a single document. The AI can then compare and synthesize across all of them at once, which is where the real value is — spotting where sources agree and disagree.
• Label each source. I put a line like === Source: [video title] === before each transcript. Then when the AI tells me something, I can ask "which source said that?" and actually trace it back.
• Ask for structure, not just summaries. The highest-leverage prompts aren't "summarize this." They're "turn this into a study outline," "make a quiz from this," "list every actionable step mentioned," or "what would an expert disagree with here?" You're using AI to reorganize the material for how you learn, not just to shorten it.
• Translate before or after, whichever's cleaner. For foreign-language sources, GeekLink can translate the extracted text as part of the same run, so what reaches the AI is already in the language you think in.

Why I do it locally and in batch

Two reasons this particular setup holds up once you do it more than once.

Batch, because the whole point is volume. Building a knowledge base from a single video isn't worth the trouble — you'd just watch it. The value shows up at ten, thirty, fifty videos, and doing that one file at a time defeats the purpose. Importing a whole folder and letting it grind through unattended is the difference between a workflow and a chore.

Local, because of what these videos usually are. Course material you paid for, internal training recordings, a friend's unpublished footage — this is exactly the content you don't want to upload to a random web converter. Running the extraction on your own machine means the video never leaves it; only the finished text goes wherever you choose to send it. It's also just faster for a big batch than uploading everything to a server and waiting.

None of this is a special mode or a hidden feature. It's the ordinary transcription and OCR that GeekLink already does — the reframe is just realizing that a "subtitle tool" is also the fastest way to turn a video backlog into something an AI can read. If you want to try the workflow, GeekLink is a free download on Mac.

FAQ

Disclosure: GeekLink is our own product. This post describes how I personally use it; your mileage will depend on the videos, the languages, and what you ask the AI afterward.