---
license: cc0-1.0
task_categories:
- automatic-speech-recognition
language:
- en
- de
tags:
- imatrix
- calibration
- quantization
- gguf
- speech
pretty_name: CrispASR imatrix calibration set (Common Voice EN+DE)
---

# CrispASR imatrix calibration set — Common Voice EN + DE

A tiny, **CC0**, multilingual read-speech sample used to compute
**importance matrices (imatrix)** for GGUF quantisation of ASR models with
[CrispASR](https://github.com/CrispStrobe/CrispASR).

- `en/` — 24 English clips
- `de/` — 24 German clips

## Provenance

Clips are drawn from the **`dev` split** of
[Mozilla Common Voice 17.0](https://commonvoice.mozilla.org) (via the
`fsicoli/common_voice_17_0` mirror), which is released under
**[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)** (public
domain). Re-distributed here unchanged, same licence.

## Why this exists

`llama.cpp`-style imatrix quantisation improves low-bit quality by weighting
per-tensor quantisation error by the activation energy the model actually uses.
For **audio** models that means running *audio* through the model, not a text
corpus (which is what the common `calibration_datav3` text file does — it only
calibrates the text decoder). There is no off-the-shelf audio imatrix corpus,
so this is a clean-licence starting point.

**Language coverage matters.** In CrispASR's A/B harness
(`tools/imatrix_ab.py`), calibrating qwen3-asr-0.6b q4_k on this EN+DE set
improved prefill-logit cosine vs the f16 gold from **0.890 → 0.941 (+0.051)**,
every held-out clip up — whereas an **English-only** corpus *regressed* it.
Calibrate on the languages/domains you actually target, and scale this up
(more clips, more languages) for production.

## Use

```bash
export CRISPASR_IMATRIX_OUT=model.imatrix.gguf
for f in en/*.mp3 de/*.mp3; do
    crispasr -m model-f16.gguf -f "$f"   # merges into the imatrix each run
done
crispasr-quantize model-f16.gguf model-q4_k.gguf q4_k --imatrix model.imatrix.gguf
```

See [`docs/quantize.md`](https://github.com/CrispStrobe/CrispASR/blob/main/docs/quantize.md).