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I work on OpenSubs, a free, open source (AGPL-3.0) subtitle tool that runs entirely in the browser tab.

You drop in a video file, and Whisper transcribes it on your own machine, using transformers.js with WebGPU where available and WebAssembly otherwise. The model (40–250 MB) downloads once and is cached. There is no upload endpoint in the product, so the video has nowhere to go.

After that you can:

  • fix lines by typing over them (click a timestamp to jump to that moment)
  • translate into 20 languages with Chrome's built-in on-device translator
  • pick one of 12 caption styles, including word-by-word highlighting
  • export SRT / VTT / ASS, or burn the subtitles into an MP4 (libass compiled to WebAssembly, encoded with WebCodecs)

A few things I learned building it:

  • Whisper hallucinates on silence and music ("Thanks for watching!", or the Japanese equivalent). A Silero VAD pass runs before Whisper, and a cleanup step drops the known stock phrases.
  • Singing doesn't count as speech for the VAD, so a music video gets a "no speech found" warning. You can still force it.

Honest limits: it only takes video files, not audio-only files. Cue timings can't be edited yet. Builds are release candidates. Everything that runs locally is free with no account; the only paid part is optional cloud translation on our backend (US$5 for 1000 credits), and you can bring your own Claude / OpenAI / DeepL key instead.

Site: https://opensubs.app/ Code: https://github.com/open-subs/opensubs

Feedback welcome, especially on languages where the transcription goes wrong.

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[-] VonReposti@feddit.dk 1 points 13 hours ago

BERT is a special case. The academic consensus is rightly considering it an LLM since it operates on text but the industry doesn't consider it an LLM since LLM in its colloquial meaning has drifted to mean a model generating text (which BERT doesn't). Whisper is not an LLM since it's working on audio, so it is an Automatic Speech Recognition model (ASR).

Text in = LLM in academic circles (BERT and GPT)

Text in, text out = LLM in all circles (GPT only)

Audio in, text out ≠ LLM (Whisper)

Now, we can argue whether we want to accept the colloquial meaning of LLM, but the fact is that Whisper is not an LLM. And neither ASR nor BERT causes even a fraction of the damage GPT is doing, but that's another discussion.

this post was submitted on 21 Sep 2026
160 points (91.7% liked)

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