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+# CrisperWhisper 2.0 in Russian, measured
+
+Date: 2026-08-09. Vikunja V-665.
+Corpus: `bond005/sberdevices_golos_10h_crowd`, test split, first 200 clips.
+Harness: `~/Programs/cw2-eval` on workpc, not in this repo.
+Runner: `./.venv/bin/python run_asr.py <arm>...` then `score.py`.
+
+The model card benchmarks disfluency F1 in German and English. It never names
+Russian and publishes no per-language WER. So the measurement came before the
+wiring.
+
+## The corpus
+
+200 clips, 13.7 minutes, 1001 reference words. Median clip 3.91s, range 1.04s
+to 13.5s. Golos crowd is short crowd-sourced Russian spoken close to the
+microphone, which is the nearest public thing to someone talking to Maven. The
+alternatives are read speech, which flatters every model equally.
+
+Two rows carry a null transcription and are skipped.
+
+Scoring normalizes both sides: lowercase, `ё` to `е`, punctuation stripped, and
+digits expanded to Russian words through num2words. Without that last step a
+model is penalized for writing `60000` where the reference says
+`шестьдесят тысяч`. Thousands separators are joined before expansion, or
+`60 000` expands to `шестьдесят ноль`.
+
+## Headline
+
+| arm | WER | CER | exact | empty | RTF |
+|---|---|---|---|---|---|
+| cw2-turbo-intended | **10.4%** | 3.4% | 65.5% | 0 | 0.065 |
+| cw2-turbo-verbatim | 10.8% | **3.1%** | **66.5%** | 0 | 0.065 |
+| whisper-turbo | 11.8% | 4.1% | 64.0% | 0 | 0.031 |
+| cw2-large-intended | 12.3% | 3.8% | 63.5% | 0 | 0.107 |
+| whisper-small | 27.5% | 9.8% | 35.0% | 0 | 0.026 |
+
+`whisper-small` is the floor, because `ggml-small.bin` is what mavsttd loads on
+homesrv today. CW2 turbo beats it by 17 points of WER and takes exact matches
+from 35.0% to 65.5%.
+
+Two results are worth naming beyond the winner. CW2 turbo beats its own base
+model, whisper-large-v3-turbo, by 1.4 points. And it beats CW2 large by 1.9
+points, which inverts what the card implies by calling turbo a degraded draft.
+No arm returned an empty transcript.
+
+## Intended and verbatim are closer than the mode names suggest
+
+The two modes disagree on 70 of the 200 clips before normalization and on 29
+after it. So the raw difference is mostly casing and punctuation, which
+normalization removes and which Maven does not read either.
+
+Verbatim scores worse on WER and better on CER and exact matches. The reason is
+script, not disfluency:
+
+```text
+ref: футбольный матч челси брайтон
+int: Футбольный матч Chelsea-Брайтон.
+ver: Футбольный матч Челси Брайтон.
+```
+
+Intended writes foreign entity names in Latin script and verbatim
+transliterates them. Golos references are Cyrillic throughout, so verbatim
+collects the exact matches. That is a property of this corpus rather than a
+quality difference.
+
+**This corpus cannot settle the mode choice.** Golos crowd is clean short
+commands with almost no disfluency. The two modes have nothing to disagree
+about here. They separate on spontaneous speech with fillers, restarts and
+repairs, which is what the owner speaks. Intended stays the choice for the
+reason it was always the choice. Maven wants what was meant, not every stumble
+on the way there.
+
+The Latin-script habit is the one finding here that touches routing. The
+routing heads were trained on Cyrillic utterances, so an entity name arriving
+in Latin script is out of distribution for them. Nothing measures that yet.
+
+## The runtime is workpc, because whisper.cpp cannot load CW2
+
+`num_languages()` in `deps/whisper.cpp/src/whisper.cpp` derives the language
+count from the vocabulary size:
+
+```cpp
+return n_vocab - 51765 - (is_multilingual() ? 1 : 0);
+```
+
+CW2 carries 31 extra tokens, so `n_vocab` is 51897 and this yields 131
+languages. The derived `dt` offset becomes 33 and shifts seven special token
+ids, including `token_beg` and `token_transcribe`. The architecture is
+otherwise byte-identical to whisper-large-v3-turbo, and the new tokens sit
+above every whisper special id.
+
+So loading CW2 in whisper.cpp is a patch to a vendored dependency, not a port.
+It was not taken, because STT is moving to workpc anyway under V-486. CW2 turbo
+becomes the preferred remote and `ggml-small.bin` on homesrv stays the floor,
+which is the shape `modelSeam` already uses for routing and replies. The 27.5%
+floor is what a turn falls back to when the workstation is down, and this table
+is what that costs.
+
+## License
+
+Standard CW2 weights carry `nyra-health-non-commercial-research`. The Pro
+variants are commercial-license only. Maven is personal and self-hosted, so the
+standard weights are usable and the Pro ones are not free to take.
+
+## What is not measured
+
+Disfluent spontaneous speech, which is the whole reason to prefer Intended.
+Long-form audio beyond 13.5s. Far-field or noisy microphones. English, which
+Maven also speaks. The ONNX turbo export, which was never run, since the
+transformers path already meets the latency budget at RTF 0.065.