I was trying to summarize a 40-minute talk with OpenAI’s transcription API, but it was too long. So I sped it up with ffmpeg to fit within the 25-minute cap. It worked quite well (Up to 3x speeds) and was cheaper and faster, so I wrote about it.

Felt like a fun trick worth sharing. There’s a full script and cost breakdown.

We discovered this last month.

There is also prob a way to send a smaller sampler of audio at diff speeds and compare them to get a speed optimization with no quality loss unique for each clip.

would this also work for my video consisting of 10,000 PDFs?

https://news.ycombinator.com/item?id=44125598

Longer*

There was a similar trick which worked with Gemini versions prior to Gemini 2.0: they charged a flat rate of 258 tokens for an image, and it turns out you could fit more than 258 tokens of text in an image of text and use that for a discount!

A point on skimming vs taking the time to read something properly.

I read a transcript + summary of that exact talk. I thought it was fine, but uninteresting, I moved on.

Later I saw it had been put on youtube and I was on the train, so I watched the whole thing at normal speed. I had a huge number of different ideas, thoughts and decisions, sparked by watching the whole thing.

This happens to me in other areas too. Watching a conference talk in person is far more useful to me than watching it online with other distractions. Watching it online is more useful again than reading a summary.

Going for a walk to think about something deeply beats a 10 minute session to "solve" the problem and forget it.

Slower is usually better for thinking.

it's still decoding every frame and matching phonemes either way, but speeding it up reduces how many seconds they bill you for. so you may hack their billing logic more than the model itself.

also means the longer you talk, the more you pay even if the actual info density is the same. so if your voice has longer pauses or you speak slow, you maybe subsidizing inefficiency.

makes me think maybe the next big compression is in delivery cadence. just auto-optimize voice tone and pacing before sending it to LLM. feed it synthetic fast speech with no emotion, just high density words. you lose human warmth but gain 40% cost savings

> Is It Accurate?

> I don’t know—I didn’t watch it, lol. That was the whole point. And if that answer makes you uncomfortable, buckle-up for this future we're hurtling toward. Boy, howdy.

This is a great bit of work, and the author accurately summarizes my discomfort

Heads up, the token cost breakdown tables look white on white to me. I'm in dark mode on iOS using Brave.

With transcribing a talk by Andrej, you already picked the most challenging case possible, speed-wise. His natural talking speed is already >=1.5x that of a normal human. One of the people you absolutely have to set your YouTube speed back down to 1x when listening to follow what's going on.

In the idea of making more of an OpenAI minute, don't send it any silence.

E.g.

    ffmpeg -i video-audio.m4a \
      -af "silenceremove=start_periods=1:start_duration=0:start_threshold=-50dB:\
                         stop_periods=-1:stop_duration=0.02:stop_threshold=-50dB,\
                         apad=pad_dur=0.02" \
      -c:a aac -b:a 128k output_minpause.m4a -y

will cut the talk down from 39m31s to 31m34s, by replacing any silence (with a -50dB threshold) longer than 20ms by a 20ms pause. And to keep with the spirit of your post, I measured only that the input file got shorter, I didn't look at all at the quality of the transcription by feeding it the shorter version.

    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 use the youtube trick, will share it here, but upload to youtube and use their built in transcription service to translate to text for you, and than use gemini pro 2.5 to rebuild the transcript.

ffmpeg \ -f lavfi \ -i color=c=black:s=1920x1080:r=5 \ -i file_you_want_transcripted.wav \ -c:v libx264 \ -preset medium \ -tune stillimage \ -crf 28 \ -c:a aac \ -b:a 192k \ -pix_fmt yuv420p \ -shortest \ file_you_upload_to_youtube_for_free_transcripts.mp4

This works VERY well for my needs.

This is really interesting, although the cheapest route is still to use an alternative audio-compatible LLM (Gemini 2.0 Flash Lite, Phi 4 Multimodal) or an alternative host for Whisper (Deepinfra, Fal).

When extracting transcripts from YouTube videos, can anyone give advice on the best (cost effective, quick, accurate) way to do this?

I'm confused because I read in various places that the YouTube API doesn't provide access to transcripts ... so how do all these YouTube transcript extractor services do it?

I want to build my own YouTube summarizer app. Any advice and info on this topic greatly appreciated!

I have a way that is (all but) free -- just watch the video if you care about it, or decide not to if you don't, and move on with your life.

Why would you give up your privacy by sending what interests you to OpenAI when whisper doesn't need that much computer in the first place?

With faster-whisper (int8, batch=8) you can transcripe 13 minutes of audio in 51 seconds on CPU.

Appreciated the concise summary + code snippet upfront, followed by more detail and background for those interested. More articles should be written this way!

For anybody trying to do this in bulk, instead of using OpenAI's whisper via their API, you can also use Groq [0] which is much cheaper:

[0] https://groq.com/pricing/

Groq is ~$0.02/hr with distil-large-v3, or ~$0.04/hr with whisper-large-v3-turbo. I believe OpenAI comes out to like ~$0.36/hr.

We do this internally with our tool that automatically transcribes local government council meetings right when they get uploaded to YouTube. It uses Groq by default, but I also added support for Replicate and Deepgram as backups because sometimes Groq errors out.

Love this idea but the accuracy section is lacking. Couldnt you do a simple diff of the outputs and see how many differences there are? .5% or 5%?

The whisper model weights are free. You could save even more by just using them locally.

This seems like a good place for me to complain about the fact that the automatically generated subtitle files Youtube creates are horribly malformed. Every sentence is repeated twice. In many subtitle files, the subtitle timestamp ranges overlap one another while also repeating every sentence twice in two different ranges. It's absolutely bizarre and has been like this for years or possibly forever. Here's an example - I apologize that it's not in English. I don't know if this issue affects English. https://pastebin.com/raw/LTBps80F

Solution: charge by number of characters generated.

I built a Chrome extension with one feature that transcribes audio to text in the browser using huggingface/transformers.js running the OpenAI Whisper model with WebGPU. It works perfect! Here is a list of examples of all the things you can do in the browser with webgpu for free. [0]

The last thing in the world I want to do is listen or watch presidential social media posts, but, on the hand, sometimes enormously stupid things are said which move the SP500 up or down $60 in a session. So this feature queries for new posts every minute, does ORC image to text and transcribe video audio to text locally, sends the post with text for analysis, all in the background inside a Chrome extension before notify me of anything economically significant.

[0] https://github.com/huggingface/transformers.js/tree/main/exa...

[1] https://github.com/adam-s/doomberg-terminal

Omg long post. TLDR from an LLM for anyone interested

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.

;)