Posted by georgemandis 6/25/2025
Here(https://github.com/openai/whisper/blob/main/whisper/model.py...) is the relevant code in the whisper repo. You'd just need to change the for loop to an enumerate and subsample the context along its length at the point you want. I believe it would be:
for i, block in enumerate(self.blocks): x = block(x) if i==4: x = x[,,::2]
And if someone had this idea and pitched it to Claude (the model this project was vibe coded with) it would be like "what a great idea!"
Are people just staring it for meme value or something? Is this a scam?
I don't think a simple diff is the way to go, at least for what I'm interested in. What I care about more is the overall accuracy of the summary—not the word-for-word transcription.
The test I want to setup is using LLMs to evaluate the summarized output and see if the primary themes/topics persist. That's more interesting and useful to me for this exercise.
I'm actually curious, if I run transcriptions back-to-back-to-back on the exact same audio, how much variance should I expect?
Maybe I'll try three approaches:
- A straight diff comparison (I know a lot of people are calling for this, but I really think this is less useful than it sounds)
- A "variance within the modal" test running it multiple times against the same audio, tracking how much it varies between runs
- An LLM analysis assessing if the primary points from a talk were captured and summarized at 1x, 2x, 3x, 4x runs (I think this is far more useful and interesting)
I'm implementing a similar workflow for VideoToBe.com
My Current Pipeline:
Media Extraction - yt-dlp for reliable video/audio downloads Local Transcription - OpenAI Whisper running on my own hardware (no API costs) Storage & UI - Transcripts stored in S3 with a custom web interface for viewing
Y Combinator playlist https://videotobe.com/play/playlist/ycombinator
and Andrej's talk is https://videotobe.com/play/youtube/LCEmiRjPEtQ
After reading your blog post, I will be testing effect on speeding audio for locally-hosted Whisper models. Running Whisper locally eliminates the ongoing cost concerns since my infrastructure is already a sunk cost. Speeding audio could be an interesting performance enhancement to explore!
Speed your audio up 2–3× with ffmpeg before sending it to OpenAI’s gpt-4o-transcribe: the shorter file uses fewer input-tokens, cuts costs by roughly a third, and processes faster with little quality loss (4× is too fast). A sample yt-dlp → ffmpeg → curl script shows the workflow.
;)
(Thanks for your good sense of humor)
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.
LLMs as the operating system, the way you interface with vibe-coding (smaller chunks) and the idea that maybe we haven't found the "GUI for AI" yet are all things I've pondered and discussed with people. You articulated them well.
I think some formats, like a talk, don't lend themselves easily to meaningful summaries. It's about giving the audience things to think about, to your point. It's the sum of storytelling that's more than the whole and why we still do it.
My post is, at the end of the day, really more about a neat trick to optimize transcriptions. This particular video might be a great example of why you may not always want to do that :)
Anyway, thanks for the time and thanks for the talk!
I frequently do the same, and eventually someone sent me this HBR article summarizing the concept nicely as "bottom line up front". It's a good primer for those interested.
https://hbr.org/2016/11/how-to-write-email-with-military-pre...
I have been thinking for a while how do you make good use of the short space in those places.
LLM did well here.
Then there is no cost at all to run any length of audio. (since cost seems to be the primary factor of this article)
On my m1 mac laptop it takes me about 30 seconds to run it on a 3-minute audio file. I'm guessing for a 40 minute talk it takes about 5-10 minutes to run.