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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+translation/nllb-200-1.3B/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+translation/nllb-200-distilled-1.3B/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+translation/nllb-200-distilled-600M/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,88 @@
+---
+license: other
+license_name: mixed-see-below
+tags:
+  - automatic-speech-recognition
+  - translation
+  - whisper.cpp
+  - ctranslate2
+  - vrstt
+---
+
+# vrSTT models
+
+Model weights for [vrSTT](https://github.com/kittynXR/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](https://github.com/ggerganov/whisper.cpp)
+ggml conversions, byte-identical to
+[ggerganov/whisper.cpp](https://huggingface.co/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](https://ai.meta.com/research/no-language-left-behind/)
+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.

manifest.json

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+  "revision": "main",
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translation/nllb-200-1.3B/config.json

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translation/nllb-200-1.3B/special_tokens_map.json

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+}

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translation/nllb-200-distilled-1.3B/special_tokens_map.json

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