Captioning all my YouTube videos with AI (13 min. read)

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Every month or two, I get an email asking whether I could enable captions on my YouTube videos. I also get asked on Twitter, on Reddit, and even on the orange site. Unfortunately, every time I’m forced to give the same answer: I already have auto-captioning enabled on my videos, but for some reason YouTube sometimes simply does not generate captions. The most common case appears to be because the video is too long (somewhere around 2h), but I’ve seen it happen for shorter videos as well.

Each time I give that reply, it makes me sad. It means that someone who expressed an interest in learning from my videos was (at least in part) prevented from doing so, and that sucks. So, with the last email I got, I decided to finally do something about it.

Ages ago, a co-worker of mine suggested I might be able to use AI to generate captions for my videos. There are a bunch of such services around these days, but the one he linked me to was Gladia. So when I finally decided to generate captions for all my videos, that’s where I started. The API is pretty straightforward: you send them a video or audio file (or even a YouTube URL), and they return a list of captions, each with an associated start time and end time1. That list can then pretty easily be turned into SRT or VTT caption files (Gladia also supports producing them directly, though I didn’t use that feature). Seemed easy enough!

Unfortunately, it turns out that Gladia (and many other similar platforms) have a max limit on the length of the file they are able to caption. For Gladia, it’s currently 135 minutes (though they recommend you split your audio files into ~60 minutes chunks). Now, if you’ve watched my videos, you know that most of them are longer than that, so some smartness was needed (more on that in a second).

I also faced another issue: my video backlog is somewhere around 250 hours of video. At the time of writing, Gladia charges €0.000193 per second of audio (which seems to be roughly where the industry has landed), which works out to €174. That’s not nothing, especially with a bit of trial and error needed to get the aforementioned splitting right. Luckily, when I reached out to them pointing out that I wanted to caption a bunch of programming teaching resources, and was willing to share my experience and code afterwards, they graciously agreed to cover the cost of the bulk encoding. Yay!

With that out of the way, let’s get to the how. You can also just look at the code directly if you want!

Generating captions for long videos

My videos vary a fair bit in length. The shortest are 60-90m (so within the Gladia length limit), while the longest one is 7h20m. Some may call that too long, but that’s outside the scope of this. This raises the question: how do you generate captions for a 7 hour long video in bursts of approximately 60 minutes? The naive approach is to just split the video in 60 minute chunks, caption each one independently, and then join them together, but this presents a few problems:

  1. You may cut the video mid-sentence, leading to a broken caption.
  2. You may cut the video during a short silence where the next caption should follow on from a sentence just before the silence. Splitting here would lead to odd-looking captions where the next caption appears to start a new sentence.
  3. Depending on how you cut, you may end up with slightly-offset captions in later segments if the cutting isn’t using precise time codes.
  4. You may end up with “weird ends” where the last segment is only a few seconds long, possibly without any captions. This isn’t inherently a problem, though it does mean that progress can appear kind of random.

Instead, you have to be slightly smarter about how you cut. Here’s what I landed on.

First, get the audio file for the video locally somehow. If it’s a YouTube video, you can use a tool like yt-dlp or yt-download to grab it:

$ yt-dlp -x -f 'bestaudio' ""

In my case, I have the files for all my videos locally, so I just used those.

Next, take the length of the video and divide it by 60 minutes. Round that number up to the nearest integer value. Then divide the length of the video by that value (call that value seg). That’s how long we’ll make each segment.

Then, extract the first segment (of length seg) with2

$ ffmpeg -i "$audiofile" -vn -c:a libopus -b:a 192k -f ogg -t "$seg"

It’s tempting to use -acodec copy here, but don’t — it leads to inaccurate cutting. We need to mux to get exactly-accurate cuts of the audio. So, we export to Opus audio in an Ogg container — it is modern, compact, and has good encoders. FLAC would be nice, but hits the 500MB file size limit too often. I decided against AAC since some AAC encoders are really bad.

Later segments can be extracted with:

$ ffmpeg -ss "$start" -i "$audiofile" -vn -c:a libopus -b:a 192k -f ogg -t "$seg"

Note that the last segment has to be extracted without the -t flag to make up for any rounding errors!

Sometimes, the audio stream in a video has a delay relative to the video stream. This might be to correct audio/video sync, or to make an intro sound line up with the visuals. This makes things weird because the caption timestamps are relative to the video. You can check whether this is the case for a given video file with this command:

$ ffprobe -i "$videofile -show_entries stream=start_time \
    -select_streams a -hide_banner -of default=noprint_wrappers=1:nokey=1

If this prints anything but 0, you’ll have to adjust the ffmpeg invocation for the first segment to include -af adelay=$offset_in_ms.

Note you have to have both the audio and video to run this command, so remove -x -f 'bestaudio' if you’re grabbing a YouTube video that might have such a delay.

But, how do you set $start for each segment?

First, ship the extracted audio segment to the Gladia API. Then, in the captions you get back3, walk backwards from the last caption, and look for the largest inter-caption gap in, say, the last 30 seconds of the segment. The intuition here is that the longest gap is the one least likely to be in the middle of a sentence. You can also improve this heuristic to look at what the caption immediately before the gap ends with. For example, if it ends with “,” or “…”, maybe skip that gap as the next caption is probably related and shouldn’t be split apart.

Once you’ve found that gap, set $start to be the time half-way through that gap. Discard all captions that follow $start from the current segment, then repeat the whole process for the next segment. Keep in mind that for all captions you get back from the API need to have $start added to their time codes!

Once you have all the captions from all the segments, all that remains is to write them out into the SVT format (one of the caption file formats that YouTube supports). The format is:

$caption.time_begin --> $caption.time_end

where $number starts at 1 for the first caption and increases by one for each subsequent caption, and the timestamps are printed like this:

fn seconds_to_timestamp(fracs: f64) -> String {
    let mut is = fracs as i64;
    assert!(is >= 0);
    let h = is / 3600;
    is -= h * 3600;
    let m = is / 60;
    is -= m * 60;
    let s = is;
    let frac = fracs.fract();
    let frac = format!("{:.3}", frac);
    let frac = if let Some(frac) = frac.strip_prefix("0.") {
    } else if frac == "1.000" {
        // 0.9995 would be truncated to 1.000 at {:.3}
    } else if frac == "0" {
        // integral number of seconds
    } else {
        unreachable!("bad fractional second: {} -> {frac}", fracs.fract())

Note that the SVT format is pretty strict. You must have an empty line between each caption, you must give them consecutive sequence numbers starting at 1, you must use -->, and you must format the sequence numbers with exactly three fractional digits.

I’ve coded up this whole process in this project on GitHub: I’ve also filed a feature request for Gladia to support something like this natively.

Mapping video files back to YouTube

If all you wanted to do was play someone else’s YouTube file with captions, then you’re basically done. Just pass the SRT file to your video player along with the YouTube URL (if it supports it) or the video file you downloaded, and you’re good to go.

If, like me, you want to update the YouTube video’s captions, you next need to figure out which YouTube video each caption belongs to. If you downloaded the audio from YouTube originally, or have a neatly organized video backup archive, then this is trivial. In my case, my local video archive only has video category and the recording time of the video, so there’s no real connection to the originating YouTube video. So, I had to also find a way to map the videos back to their respective YouTube upload.

To do this, I wrote a program that first queries the YouTube API for all my videos and extracts their id, title, publication date, and duration. Then, it walks all the video files in a given directory and determines their timestamp (from the file name) and duration (using symphonia). Finally, for each local file, it checks if any of the YouTube videos have a duration that differs in at most single-digit seconds, and has a publication date that differs by at most a day. If any such video is found, it associates the two, and outputs the YouTube id and title in the name of the caption file for that local file.

Uploading the captions

Armed with the caption files and the mapping back to YouTube videos, I really wanted to automate the process of uploading the captions as well. It’s not too important for captioning new videos, but when doing the backlog of almost 80 videos, that’s a lot of clicking through the YouTube studio API. Now, there is an API for uploading captions, but unfortunately there are two complications:

  1. It accesses private data, which requires OAuth 2.0 authentication. A simple API key won’t do it. It’s totally possible to implement OAuth 2.0 authentication from a command-line tool, it’s just annoying.
  2. YouTube’s upload API uses a particular kind of request encoding (chunked transfer encoding) that isn’t supported by the Rust HTTP library I’m using at the moment.

So I instead opted to do this part in Python (for now) based on the code in the “Try it” box on the caption API page (and the instructions for running it). This required getting a set of OAuth credentials from Google Cloud Console (not too bad since I already had an “application” there for my API key), adding myself as a “Test user” under “OAuth consent screen”, and tweaking the code a bit. The end result is this Python script, which you should easily be able to fit a slightly-different use-case.

It’s worth noting that the YouTube API has a pretty strict free quota, and that uploading captions consumes a fair bit of that quota (450 out of 10k daily limit). This means that in practice you can only upload about 20 captions a day through the API before YouTube will cut you off for the day. And getting that limit bumped is annoying.

End result

All my videos, including the super long ones, will soon have English captions (once the YouTube API allows), and I no longer need to apologize for YouTube auto-captioning’s shortcomings 🎉

  1. Gladia sometimes returns captions that are overly long. I haven’t found it to be an outright problem (like YouTube rejecting the captions), it’s just a bit awkward to read when it happens. It’d be nice if there was a way to limit the max caption length, so I’ve filed a feature request

  2. It’s a little unfortunate that the audio has to be split on the client side, especially given that the Gladia API supports providing YouTube URLs directly. It’d be so convenient if one could instead tell Gladia specifically which part of the posted URL’s audio to caption. So, I’ve filed a feature request

  3. The Gladia API returns a fairly big JSON payload because it also includes word-level timestamps. I didn’t need those here, but there isn’t currently a way to omit them.