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.
Whisper is not an llm, just because it is trained on big datasets does not make it an LLM. Copyright infringement still happened during the training.
no, but claude is, and it’s the main contributor to the repo.
Which software are we talking about? Unfortunately no one links to it, so I will guess. Whisper: website | source is an Ai model, specifically powered by OpenAi.
Brother, I don’t think you know what a large language model is if you think it being trained on a human lifespan’s worth of audio transcripts isn’t enough.
Its job is NLP. It is an LLM, if not as general-use as something like ChatGPT.
LLM is a subset of NLP. All LLMs are NLP but not all NLP are LLMs. To be more precise, LLM is a method of achieving NLP while NLP is the goal. Whisper is just a specialised speech-to-text engine. It doesn’t have the text reasoning engine that is the core of LLMs. Yes, it has a dataset to inform it’s encoder and decoder, but that doesn’t make it an LLM. If you strip out the encoder, then you’re getting closer.