31–40 of 234 posts
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=…
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.
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.
You could have kept quiet and started a cheaper than openai transcription business :)
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=…
> 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. I wonder if there's a way to automatically detect how "fast" a person talks in an audio file. I know it's subjective and different people talk at different paces in an audio, but it'd be cool to kinda know when OP's trick fails…
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.
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!
https://github.com/jdepoix/youtube-transcript-api
For our internal tool that transcribes local city council meetings on YouTube (often 1-3 hours long), we found that these automatic ones were never available though.
(Our tool usually 'processes' the videos within ~5-30 mins of being uploaded, so that's also why none are probably available 'officially' yet.)
So we use yt-dlp to download the highest quality audio and then process them with whisper via Groq, which is way cheaper (~$0.02-0.04/hr with Groq compared to $0.36/hr via OpenAI's API.) Sometimes groq errors out so there's built-in support for Replicate and Deepgram as well.
We run yt-dlp on our remote Linode server and I have a Python script I created that will automatically login to YouTube with a "clean" account and extract the proper cookies.txt file, and we also generate a 'po token' using another tool:
https://github.com/iv-org/youtube-trusted-session-generator
Both cookies.txt and the "po token" get passed to yt-dlp when running on the Linode server and I haven't had to re-generate anything in over a month. Runs smoothly every day.
(Note that I don't use cookies/po_token when running locally at home, it usually works fine there.)
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%?
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.
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 uplo…
> We do this internally with our tool that automatically transcribes local government council meetings right when they get uploaded to YouTube
Doesn't YouTube do this for you automatically these days within a day or so?
Earlier quoted context omitted.
> 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. I wonder if there's a way to automatically detect how "fast" a person talks in an audio file. I know it's subjective and different people talk at different paces in an audio, but it'd be cool to kinda know when OP's trick fails…
> 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.
Apparently human language conveys information at around 39 bits/s. You could use a similar technique as that paper to determine the information rate of a speaker and then correct it to 39 bits/s by changing the speed of the video.