vrSTT models
Model weights for vrSTT — local speech-to-text for VR and streaming. Everything vrSTT runs is here so the app can fetch the one model a user chose, verify it, and never touch the network again.
This repository redistributes third-party models. It trains nothing and modifies nothing except file format. Licences differ per directory and are stated below; the upstream terms travel with the weights.
Layout
manifest.json # what the fetcher reads first
whisper/ggml-<name>.bin # whisper.cpp ggml format, one file each
translation/<name>/ # CTranslate2 format, a directory each
manifest.json carries a sha256 and a byte count for every file. The fetcher
verifies both before handing a path to whisper.cpp or CTranslate2 — a truncated
ggml file otherwise fails at load with an error about magic bytes, which sends
you looking at the wrong thing.
What is here
Speech recognition — whisper.cpp ggml conversions, byte-identical to ggerganov/whisper.cpp. Mirrored rather than linked so the bytes vrSTT downloads are pinned and hashed.
tiny, tiny.en, base, base.en, small, small.en, medium,
medium.en, large-v1, large-v2, large-v3, large-v3-turbo
vrSTT defaults to small.en for commits and base.en for live previews —
the split is deliberate, previews are allowed to be wrong and are replaced within
a second, so they should not spend the larger model.
Translation — NLLB-200
converted to CTranslate2 with ct2-transformers-converter --quantization float16, from facebook/nllb-200-*.
nllb-200-distilled-600M (default), nllb-200-distilled-1.3B, nllb-200-1.3B
float16 rather than int8 because CTranslate2 can cast down at load time but not up — one artifact serves both, and converting to int8 here would cap quality for everyone. 600M is the default because vrSTT's stated minimum-VRAM target is 8 GB, and a 1.3B model beside the ASR stages and a game does not fit comfortably there.
Licences — read this before redistributing further
| Directory | Licence | Commercial use |
|---|---|---|
whisper/ |
MIT (OpenAI Whisper; ggml conversions by Georgi Gerganov) | permitted |
translation/ |
CC-BY-NC-4.0 (Meta AI, NLLB-200) | NOT permitted |
NLLB-200 is non-commercial. Redistribution is allowed with attribution and the licence must travel with it — which is what this section is — but neither Meta's weights nor anything derived from them may be used commercially. If vrSTT ever becomes a paid product, the translation models have to be replaced rather than relicensed.
NLLB-200 also carries Meta's own caveats, which apply unchanged here: it is
research work, not certified for production translation, it was trained on
Wikimedia-domain text and does not target long-form or domain-specific input,
and translation quality varies widely by language pair. Mistranslations can
carry real consequences and vrSTT displays them live on a stream — the
include_original setting exists so the source text stays visible beside the
translation for exactly this reason, and it is on by default.
Citation
Whisper — Radford et al., Robust Speech Recognition via Large-Scale Weak Supervision, OpenAI, 2022.
NLLB-200 — NLLB Team et al., No Language Left Behind: Scaling Human-Centered Machine Translation, Meta AI, 2022.