mrkn1

fwiw kimchi-derived probiotic bacterium (Leuconostoc mesenteroides CBA3656) was shown to bind nanoplastics and help mice excrete more of them. But it’s not yet proven that eating kimchi removes microplastics from humans

Has anyone tried the others listed? Any feedback?

This runs for free on CPU https://github.com/kouhxp/textsnap

The key moments feat is neat. Been working on a free opensource offline transcriber that runs fast on CPU and does diarization too

https://github.com/kouhxp/yapsnap

No rigorous eval, and I love Tesseract. Here's the example that motivated me to build textsnap (which is in the github's README), parsed with Tesseract:

https://imgur.com/a/i2eQra8

No reason other than their Q4 model working reasonably well and fast on my CPU laptop. Should work with any ONNX VLM model

109 languages, including other alphabets.

Just ran

  textsnap "https://i.ytimg.com/vi/LBNDfxjEYlA/maxresdefault.jpg"

  $('.count').each(function () {
  $('this').prop('Counter', 0).animate({
    Counter: $('this').text()
  }, {
      duration: 4000,
      easing: 'swing',
      step: 'function (now) {
          $('this").text(Math.ceil(now));
      }
    }); 
  });

thank you!

thank you! what is it about?

I have not. My experience has been a few seconds for an 1024x1024 with medium density of text, FWIW. Feel free to try it on a few test images, model is pretty small and fast, but yeah no formal evals on CPU.

It should support 109 languages. More info here: https://huggingface.co/PaddlePaddle/PaddleOCR-VL

No, simply, my laptop only has a CPU.

I haven't. But the evals of the underlying model are published here, including on Omnibench. https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5

https://github.com/kouhxp/fftext

For 100% local CPU fact checking, I made this: https://news.ycombinator.com/item?id=48301003

A lot of my queries are summarize/explain/fact check, and these are covered 100% on my CPU locally [0], reducing frontier model reliance

[0] https://news.ycombinator.com/item?id=48301003

just released new version that implements your ideas

new version released is up to 3x faster on CPU. Let me know!

thank you being thorough

clipboard: rn input is treated like any other source, so text gets written to ./textsnaps/clipboard_ocr.txt, and stdout just prints that path. Nothing goes back to the clipboard in this version (stay tuned)

portability: agreed, and it's a small change. textsnap already looks for the checksum manifest next to the script before falling back to the cache, so extending it should be easy. I make a note for next version.

Great question. I'm not familiar with docling-serv but pretty different beasts from what I gathered. Docling is a heavier pipeline (actually uses GPU).textsnap is the opposite: single-file CLI, small VLM running on plain CPU cores, one command, no server. Tradeoff is CPU decode is sequential so it's slower on dense pages, and it OCRs one image rather than doing full layout.

If docling-serve is already meeting your needs it's probably not an upgrade. But it installs in one command, so would love to hear how it stacks up on your images, if you end up trying it.

thanks! yapsnap is audio to text, and textsnap is image to text. Both have been daily use cases for me for a while. And yes, the feedback on yapsnap encouraged me to also release textsnap on github

Reading this means a lot, thank you! Even faster version in the coming days, stay tuned and PRs welcome!

Added Diarization / Speaker Separation that is fast and CPU only. Thank you all for the great feedback and support. PRs welcome!

  yapsnap "https://www.youtube.com/watch?v=NzKJ-xO-VhE" --diarize

  SPEAKER_00 [00:00]: Welcome to the show.
  SPEAKER_01 [00:03]: Glad to be here, thanks for having me.
  SPEAKER_00 [00:08]: Let's get started.

done! new version separates speakers on CPU fast

done! just pushed a new version with CPU diarization

Kroko's website says benchmarks aren't formalized yet. FWIW, this url says 5% WER for English [0]. though it doesn't specify the dataset, so not directly comparable to Parakeet's 6.32 on the Open ASR Leaderboard

Best way to judge is to try it on your own audio

[0] https://huggingface.co/hudaiapa88/sherpa-stt-onnx

in the roadmap!

will work on it, that would be neat. I love pyannote but not happening on CPU at reasonable speeds lol

thank you! good use case, what hetzner box specs have you chosen?