OpenAI Charges by the Minute, So Make the Minutes Shorter

> I wonder if there's a way to automatically detect how "fast" a person talks in an audio file

Transcribe it locally using whisper and output tokens/sec?

Even a last-decade transcription model could be used to detect a rough number of syllables per unit time, and the accuracy of that model could be used to guide speed-up and dead-time detection before sending to a more expensive model. As with all things, it's a question of whether the cost savings justify the engineering work.

> I wonder if there's a way to automatically detect how "fast" a person talks in an audio file.

Stupid heuristic: take a segment of video, transcribe text, count number of words per utterance duration. If you need speaker diarization, handle speaker utterance durations independently. You can further slice, such as syllable count, etc.

It's a shame platforms don't generally support speeds greater than 2x. One of my "superpowers" or a curse is that I cannot stand normal speaking pace. When I watch lectures, I always go for maximum speed and that still is too slow for me. I wish platforms have included 4x but done properly (with minimal artefacts).

In the meantime I realized that the apad part is nonsensical - it pads the end of the stream, not at each silence-removed cut. I wanted to get angry at o3 for proposing this, but then I had a look at the silenceremove= documentation myself: https://ffmpeg.org/ffmpeg-filters.html#silenceremove

Good god. You couldn't make that any more convoluted and hard-to-grasp if you wanted to. You gotta love ffmpeg!

I now think this might be a good solution:

    ffmpeg -i video-audio.m4a \
           -af "silenceremove=start_periods=1:stop_periods=-1:stop_duration=0.15:stop_threshold=-40dB:detection=rms" \
           -c:a aac -b:a 128k output.m4a -y

> I, like many hackers, hated school because they just threw one-size-fits-all knowledge at you

"This specific knowledge format doesnt work for me, so I'm asking OpenAI to convert this knowledge into a format that is easier for me to digest" is exactly what this is about.

I'm not quite sure what you're upset about? Unless you're referring to "one size fits all knowledge" as simplified topics, so you can tackle things at a surface level? I love having surface level knowledge about a LOT of things. I certainly don't have time to have go deep on every topic out there. But if this is a topic I find I am interested in, the full talk is still available.

Breadth and depth are both important, and well summarized talks are important for breadth, but not helpful at all for depth, and that's ok.

University didn't agree with me mostly because I can't pay attention to the average lecturer. Getting bored in between words or while waiting for them to write means I absorbed very little and had to teach myself nearly everything.

Audiobooks before speed tools were the worst (are they trying to speak extra slow?) But when I can speed things up comprehension is just fine.

If you're not listening to summaries of different audiobooks at 2x speed in each ear you're not contentmaxing.

> We're willingly heading for such stupidity, I don't get it. I don't get how we can all be so blind at what this is going to create.

Your doomerism and superiority doesn't follow from your initial "I like many hackers don't like one size fits all".

This is literally offering you MANY sizes and you have the freedom to choose. Somehow you're pretending pushed down uniformity.

Consume it however you want and come up with actual criticisms next time?

This is similar to strategies in “how to read a book” (Adler).

By understanding the outline and themes of a book (or lecture, I suppose), it makes it easier to piece together thoughts as you delve deeper into the full content.

I've already done that [1]. A fraction of the price, 24-hour limit per file, and speedup tricks like the OP's are welcome. :)

[1] https://speechischeap.com

Sure, but now the world is a better place because he shared something useful!

Pre-processing of the audio still a valid biz, multiple types of pre-processing might be valid

Or openai will do it themselves for transcription tasks

> Doesn't YouTube do this for you automatically these days within a day or so?

Oh yeah, we do a check first and use youtube-transcript-api if there's an automatic one available:

https://github.com/jdepoix/youtube-transcript-api

The tool usually detects them within like ~5 mins of being uploaded though, so usually none are available yet. Then it'll send the summaries to our internal Slack channel for our editors, in case there's anything interesting to 'follow up on' from the meeting.

Probably would be a good idea to add a delay to it and wait for the automatic ones though :)

> I wonder if this "speed up the audio" trick would save you even more.

At this point you'll need to at least check how much running ffmpeg costs. Probably less than $0.01 per hour of audio (approximate savings) but still.

> Doesn't YouTube do this for you automatically these days within a day or so?

Last time I checked, I think the Google auto-captions were noticeably worse quality than whisper, but maybe that has changed.

I don't. I have a MacBook Pro from 2019 with an Intel chip and 16 GB of memory. Pretty sure when I tried the large whisper model it took like 30 minutes to an hour to do something that took hardly any time via Groq. It's been a while though so maybe my times are off.

I like that your post deliberately gets to the point first and then (optionally) expands later, I think it's a good and generally underutilized format. I often advise people to structure their emails in the same way, e.g. first just cutting to the chase with the specific ask, then giving more context optionally below.

It's not my intention to bloat information or delivery but I also don't super know how to follow this format especially in this kind of talk. Because it's not so much about relaying specific information (like your final script here), but more as a collection of prompts back to the audience as things to think about.

My companion tweet to this video on X had a brief TLDR/Summary included where I tried, but I didn't super think it was very reflective of the talk, it was more about topics covered.

Anyway, I am overall a big fan of doing more compute at the "creation time" to compress other people's time during "consumption time" and I think it's the respectful and kind thing to do.