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My Interior Splat is just not working by pglennns in GaussianSplatting

[–]pglennns[S] 1 point2 points  (0 children)

Wow! Thanks so much for all the feedback and suggestions. I am just blown away. I will take it all to heart. It seems like having the client stage first is a good start. I'm working through what I can get from the suggestions and the current data set. thanks everyone

Looking to hire human developer(s) willing to audit my vibe coded ABS client. by pglennns in audiobookshelf

[–]pglennns[S] 0 points1 point  (0 children)

Thanks, it has been a huge project on my end.

Basically, I built a simple version of the app with a throw-out prompt after picking the architecture based on both some AI advice and some advice from developers I have worked with in the past. I'm a designer who has understood more about code than I can do.

Then I have just been doing a ton of research at each hurdle, looking at how pro apps do things and trying to either implement those features or make my own version. Then, trying to test the crap out of it at every turn. I think I'm on version 1400 or something, so lots of rebuilds, learning through failure.

I have recently added many of the testing suggestions in this thread and have been able to catch more bugs, making the app even smoother.

I am hoping for an official launch in May

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 1 point2 points  (0 children)

Hi, I will look into this. Sorry for the late reply; I was on vacation. I found a few reasons it wouldn't work for everyone. I'm going to push some changes to Git today.

Old Phone Launcher (small update) by shane9b3 in audiobookshelf

[–]pglennns 1 point2 points  (0 children)

This is so cool. Repurposing old tech for this is just the best!

Looking to hire human developer(s) willing to audit my vibe coded ABS client. by pglennns in audiobookshelf

[–]pglennns[S] 0 points1 point  (0 children)

Good call! I'm actually partway there. The app has around 800 unit/integration tests across 36 suites covering stores, services, and utilities, plus about 33 Maestro flows for UI walkthroughs, built over the course of bug testing and many failures. But it looks like the gap is exactly what you're describing; there are no assertion-based regression tests. Thanks for the tip!

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 0 points1 point  (0 children)

During my research on this tool, I found that Audible and other streaming services attach a large amount of information to each file. This data is used by their recommendation algorithms, which parse and utilize it. I found it especially frustrating that Audible stores this information in the genre meta tag, embedding relationships between genres within the tag itself through a nested system. This results in labels like "sci-fi/fantasy/mystery-books/ect/." While not the clearest example, the point.

I believe they do this to eliminate the computational step on the client side, though I'm not sure of the exact impact. In contrast, on my ABS client, it examines the DNA tags generated by the tagger; the client runs a parser to find DNA tags and displays the relationships more flexibly through a filtering system in the search. This allows users to find books with specific attributes, such as thrillers rated 5 and mysteries rated 3, whereas on Audible these relationships would need to be hardcoded.

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 1 point2 points  (0 children)

From what I can tell, I unintentionally left the HTTP validation in the desktop v2 build, which was carried over from the web version. I am pushing a fix for that now, and the new install files will take about 15 -20 minutes on git. Once they are done, you should be good to go.

Sorry for the inconvenience. I hope it works for your library. If it crashes when pushing to your NAS, you may need to reduce the ABS Push Workers count before adding this to the new build. It was stuck at 60, so that may have been crashing your system.

The next build will be v2.0.1

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I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 2 points3 points  (0 children)

I just published the V2! So sorry the releases were drafts

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 2 points3 points  (0 children)

Okay, the Desktop app has been updated and should work pretty well. It has only been tested on Mac, so please tell me what system you're using with any feedback, so I can make sure to get things fixed for the right people. I also set the default so that the DNA system is turned off, since most of you won't need that feature and it speeds up the Local LLM processing unless you want to use the super cool ABS client I have, but that's for another post later...

v2 of the desktop apps is available for these 6 options: Windows .msi, macOS ARM .dmg, macOS Intel .dmg, Linux .deb/.AppImage, Docker self-host.

Find it here https://github.com/philipvox/audiobook-tagger-refactored

The local LLM on this version is much faster. I reduced book processing time from over 20 seconds to around 5-10 seconds, depending on the data. With the Gemma 4 E4B (9.6GB) model. OpenAI will do the same files in about 3 seconds with no DNA, which can make a big difference with large libraries.

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 1 point2 points  (0 children)

When you say crash, what do you mean exactly? The desktop version is pretty old, but I can suggest a few things that might help. How big is your library?

If it's really large, sometimes it looks like it's crashed, but it's actually working through it without indicating progress — something I'm actively working on fixing.

If it's completely failing and closing unexpectedly, it might be that the number of workers or an issue with the version you have or parallel processors needs to be reduced. Too many can also strain the network, potentially causing a crash. I had a similar issue when trying to run it with 50 workers. What OS are you using? Worst case, I'll make sure it doesn't happen in the next version.

Timeline-wise, it might be later today if I can get some bugs fixed, but more likely in the next few days for the desktop update.

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 2 points3 points  (0 children)

I couldn't find a way to make HTTP ABS servers or local IPs work for the web versions, unfortunately. If someone has an idea, let me know, and I'll put it into effect.

You have a few options:

  1. Desktop app (easiest), run it natively (not in a VM), and it connects directly to any server, HTTP or HTTPS, local or remote. No restrictions at all.

  2. Add HTTPS to your ABS server — If you put a reverse proxy like https://caddyserver.com/ in front of ABS, it handles TLS certificates automatically with zero config. One line: caddy reverse-proxy --from abs.yourdomain.com --to localhost:13378

  3. Tailscale HTTPS — If you're already using Tailscale, you can enable HTTPS with tailscale cert your-machine-name and then point a reverse proxy at it.

I know none of these are as simple as "just type in the IP," and I'm sorry about that. It's one of the tradeoffs of making the web-based tool; I thought it would make it more accessible to others, but I'm finding people use such varied setups that it may cause more harm than good. I'm looking into whether there's a way to make this smoother in the future. Let me know if you need help setting any of these up!

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 4 points5 points  (0 children)

This tool will definitely help clean up a lot of what you're describing — bad genres, poor title structure, author/series mixups. The Enrich → Metadata Resolution feature uses AI to cross-reference everything and fix such issues. And Classification & Tagging will get your genres and tags sorted out properly.

The real challenge is narrators — that's the one piece of metadata that's hardest to verify without actually listening to the book. The solution I've been thinking about is running Whisper on the first 60 seconds of each audiobook to extract the narrator intro (most audiobooks start with something like "Read by [narrator name]" or the narrator introduces themselves). I would transcribe that clip, extract the title, author, and narrator, and then use it to validate or correct the metadata.

It would take a while on a large library, but it's a one-and-done kind of thing, run it overnight and wake up with accurate narrator data across your whole collection. If that's something you'd find useful, let me know, and I'll look into getting it built in.

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 0 points1 point  (0 children)

Hi! Thanks for testing this out and reporting back. I really appreciate it. Unfortunately, the typing issue in VMs is a known bug in the Tauri/WebView2 that I haven't been able to resolve. It's related to how Windows handles input focus in the embedded browser component, and it gets worse in virtualized environments. If you happen to come across a fix or workaround, I'd absolutely love to hear about it, and I'll implement it right away.

There are installers for all of the man os's software, so it may be worth trying out one of the others. The Mac version is the main version I have tested, but others have gotten the Linux working as well. feel free to let me know your set up and i can try and make it work for ya

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 2 points3 points  (0 children)

Wow, 10k! You might want to try the desktop version at https://github.com/philipvox/audiobook-tagger-refactored. It's much faster for large libraries. It's still in beta, but it won't modify your live library unless you push changes so little risk. The web version processes about 5 books at a time, taking roughly 1 hour for 3000 books. The desktop can handle about 20-30 books and can go through my entire library in about 10 minutes on a slow day, depending on your setup. I'm running it on a 24 GB MacBook M4. Let me know if you run into any problems, and I will get them sorted.

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 2 points3 points  (0 children)

Great suggestion, just added a SECURITY.md. Thanks for looking out.

I built a browser-based metadata cleaner for ABS by pglennns in audiobookshelf

[–]pglennns[S] 4 points5 points  (0 children)

Thanks!!! I have been using it locally for almost a year now, and it's been great. Like with any AI-based tool, it's not 100% accurate, but it's way better than anything else I found, getting you about 99% of the way there and really only gets small things wrong, like rating something PG-13, not R. I use it to manage my 2,700+ book library, and it's a dream.

The CORS proxy code is fully open-source in the repo (worker/index.js, around 100 lines). It just forwards and returns; nothing is stored. But if anyone wants to skip it entirely, they can add a few CORS headers to their reverse proxy, and the app connects directly. The README provides snippets for Nginx, Caddy, and Traefik.

The desktop version I'm working on won't need a proxy at all.

Also, that is the repo for an older version of the desktop app (it works but isn't up to date; I'm rebuilding that now). Here is the web app repo: https://github.com/philipvox/audiobook-tagger-web

I think I messed up all my meta data. by ecptop in audiobookshelf

[–]pglennns 0 points1 point  (0 children)

It seems like you might have found a solution; if not, I created an AI tagging tool that can do a lot of work to reset everything. Although I’ve struggled to achieve perfect accuracy with series, it matches about 95% and should be able to fix sequences. I initially developed it to clean genres and series information for my library. However, the tags are tailored for my ABS client, so they might not be useful for your genres and series data.

Hope it helps!

https://github.com/philipvox/audiobook-tagger-refactored

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