Show HN: Bulk Creation of Transcripts from YouTube Playlists with Whisper • Hacker News Board

Re: Show HN: Bulk Creation of Transcripts from YouTube Playlists with Whisper

#41

Post by jdthedisciple » Tue, Nov 14, 2023, 12:54 PM UTC

evanreichard wrote: post #28

Demcox wrote: post #10

Hoping this can help me cut down the time I need to use on watching YT videos for uni. Outputting 20-30 mins into a .txt and feeding it to ChatGPT for summarizing. Thanks!

Coincidentally I threw something together this weekend that attempts to do just that. [0] It's really simple - just extracts subtitles and feeds it to ChatGPT to generate a markdown "article". [0] https://vreader.va.reichard.io/ [1] https://gitea.va.reichard.io/evan/VReader

This is interesting. I assume the samples on the website you linked are all precomputed outputs, right?

Re: Show HN: Bulk Creation of Transcripts from YouTube Playlists with Whisper

#42

Post by evanreichard » Tue, Nov 14, 2023, 2:52 PM UTC

jdthedisciple wrote: post #41

evanreichard wrote: post #28

Earlier quoted context omitted.

Coincidentally I threw something together this weekend that attempts to do just that. [0] It's really simple - just extracts subtitles and feeds it to ChatGPT to generate a markdown "article". [0] https://vreader.va.reichard.io/ [1] https://gitea.va.reichard.io/evan/VReader

This is interesting. I assume the samples on the website you linked are all precomputed outputs, right?

Well it's live, so you can throw a YouTube URL into it right now and get an auto generated article from GPT-3.5 in about 15 seconds. The items on the site right now are myself / others using it. Depends on the video length, but each generated article costs about $0.005 in API usage.

Edit: Ah just understood your question (coffee just kicked in). Yes, they're precomputed from when others used it. I save the generated articles in markdown format and sort by most recently generated.

Re: Show HN: Bulk Creation of Transcripts from YouTube Playlists with Whisper

#43

Post by ldenoue » Tue, Nov 14, 2023, 8:46 PM UTC

jdthedisciple wrote: post #20

Demcox wrote: post #10

Hoping this can help me cut down the time I need to use on watching YT videos for uni. Outputting 20-30 mins into a .txt and feeding it to ChatGPT for summarizing. Thanks!

Not sure why people are downvoting. I can confirm for some types of lectures this is a wholly legitimate approach.

If you want to try scribe I developed last week, it adds punctuations from the raw YouTube transcripts so you can read them more easily. It also adds chapters every 3 paragraphs to more easily skim the content. All runs in your browser using 2 models https://www.appblit.com/scribe

Re: Show HN: Bulk Creation of Transcripts from YouTube Playlists with Whisper

#44

Post by ldenoue » Tue, Nov 14, 2023, 8:58 PM UTC

eigenvalue wrote: post #30

jfoster wrote: post #29

Earlier quoted context omitted.

Right, but it opens up a small page element that isn't very aesthetically pleasing to read from, and feels fairly useless.

That’s true. But I coincidentally also made another mini project a few months ago for making those built-in transcripts much nicer to read from. You may find it useful: https://github.com/Dicklesworthstone/youtube_transcript_clea... Now that I think about it, this would work equally well for the transcripts generated by my new tool. I should just include that html file in my new repo as an added feature.

I also wrote a web app but it adds missing punctuations and chapters to further enhance readability. The AI models run locally in your browser, no ChatGPT used at all.

An iOS app is also coming soon so you’ll be able to listen and read while offline

https://www.appblit.com/scribe

Re: Show HN: Bulk Creation of Transcripts from YouTube Playlists with Whisper

#45

Post by pavel_lishin » Thu, Nov 16, 2023, 9:08 PM UTC

eigenvalue wrote: post #5

tehlike wrote: post #4

You might want to look into diarization also http://gladia.io/ seem to be doing it well. It makes a great difference to have transcripts with speaker annotation.

Thanks, I haven’t seen an easy and reliable way to do this using open source stuff yet. Theoretically just separating out speakers seems like it wouldn’t be that hard; just compute a bunch of FFTs to arrive at a sort of frequency-based “voice fingerprint” for each speaker and then use something like XGboost to match up the audio for each second to one of the speakers. The problem is then what do you with that informa…

> Turning those abstract speaker identifications into actual names would seem to require a fair bit of intelligence

As someone looking for this functionality, this is the easiest part for me to do manually. Just give me "Speaker A, Speaker B, Speaker C", and I can change their names. It's the breaking apart of audio into separate speakers that's difficult - I'm trying out a few different tools, and none of them do a great job so far.