Comment by Franklinjobs617
19 hours ago
I’m currently building YTVidHub—a tool that focuses on solving a very specific, repetitive workflow pain for researchers and content analysts.
The Pain Point: If you are analyzing a large YouTube channel (e.g., for language study, competitive analysis, or data modeling), you often need the subtitle files for 50, 100, or more videos. The current process is agonizing: copy-paste URL, click, download, repeat dozens of times. It's a massive time sink.
My Solution: YTVidHub is designed around bulk processing. The core feature is a clean interface where you can paste dozens of YouTube URLs at once, and the system intelligently extracts all available subtitles (including auto-generated ones) and packages them into a single, organized ZIP file for one-click download.
Target Users: Academic researchers needing data sets, content creators doing competitive keyword analysis, and language learners building large vocabulary corpora.
The architecture challenge right now is optimizing the backend queuing system for high-volume, concurrent requests to ensure we can handle large batches quickly and reliably without hitting rate limits.
It's still pre-launch, but I'd love any feedback on this specific problem space. Is this a pain point you've encountered? What's your current workaround?
How coincidental - I needed exactly this just a couple days ago. I ended up vibecoding a script to feed an individual URL into yt-dlp then pipe the downloaded audio through Whisper - not quite the same thing as it's not downloading the _actual_ subtitles but rather generating its own transcription, but similar. I've only run it on a single video to test, but it seemed to work satisfactorily.
I haven't upgraded to bulk processing yet, but I imagine I'd look for some API to get "all URLs for a channel" and then process them in parallel.
That is some fantastic validation, thank you! It’s cool to hear you already vibecoded a solution for this.
You've basically hit on the two main challenges:
Transcription Quality vs. Official Subtitles: The Whisper approach is brilliant for videos without captions, but the downside is potential errors, especially with specialized terminology. YTVidHub's core differentiator is leveraging the official (manual or auto-generated) captions provided by YouTube. When accuracy is crucial (like for research), getting that clean, time-synced file is essential.
The Bulk Challenge (Channel/Playlist Harvesting): You're spot on. We were just discussing that getting a full list of URLs for a channel is the biggest hurdle against API limits.
You actually mentioned the perfect workaround! We tap into that exact yt-dlp capability—passing the channel or playlist link to internally get all the video IDs. That's the most reliable way to create a large batch request. We then take that list of IDs and feed them into our own optimized, parallel extraction system to pull the subtitles only.
It's tricky to keep that pipeline stable against YouTube’s front-end changes, but using that list/channel parsing capability is definitely the right architectural starting point for handling bulk requests gracefully.
Quick question for you: For your analysis, is the SRT timestamp structure important (e.g., for aligning data), or would a plain TXT file suffice? We're optimizing the output options now and your use case is highly relevant.
Good luck with your script development! Let me know if you run into any other interesting architectural issues.
I've built something similar before for my own use cases and one thing I'd push back on are official subtitles. Basically no video I care about has ever had "official" subtitles and the auto generated subtitles are significantly worse than what you get by piping content through an LLM. I used Gemini because it was the cheapest option and still did very well.
The biggest challenge with this approach is that you probably need to pass extra context to LLMs depending on the content. If you are researching a niche topic, there will be lots of mistakes if the audio isn't if high quality because that knowledge isn't in the LLM weights.
Another challenge is that I often wanted to extract content from live streams, but they are very long with lots of pauses, so I needed to do some cutting and processing on the audio clips.
In the app I built I would feed an RSS feed of video subscriptions in, and at the other end a fully built website with summaries, analysis, and transcriptions comes out that is automatically updated based on the youtube subscription rss feed.
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I did consider building a tool like this before I pivot to something else. I'm learning materials in Chinese Mandarin language from a YouTube playlist. NotebookLLM doesn't support Chinese language yet so you must make sure your app supports Chinese Mandarin so I can use it. :)
A way to find specific materials would be nice. Think of converting the whole playlist into something like RAG then you can search anything from this playlist.
Wow, thanks for this validation! Hearing from someone who almost built the solution themselves confirms we’re on the right track.
You hit the nail on the head regarding language support.
Mandarin/Multilingual Support: Absolutely, supporting a wide range of languages—especially Mandarin—is a top priority. Since we focus on extracting the official subtitles provided by YouTube, the language support is inherently tied to what the YouTube platform offers. We just need to ensure our system correctly parses and handles those specific Unicode character sets on the backend. We'll make sure CJK (Chinese, Japanese, Korean) languages are handled cleanly from Day 1.
The RAG/Semantic Search Idea: That is an excellent feature suggestion and exactly where I see the tool evolving! Instead of just giving the user a zip file of raw data, the true value is transforming that data into a searchable corpus. The idea of using RAG to search across an entire playlist/channel transcript is something we're actively exploring as a roadmap feature, turning the tool from a downloader into a Research Assistant.
Thanks for the use case and the specific requirements! It helps us prioritize the architecture.
> Since we focus on extracting the official subtitles provided by YouTube, the language support is inherently tied to what the YouTube platform offers.
You can use video understanding from Gemini LLM models to extract subtitles even the video doesn't have official subtitles. That's expensive for sure. But you should provide this option to willing users. I think.
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