Recommended CTC model. Q4_K uses a mixed-quantization recipe (first 4 of 48 encoder layers kept at F16) that recovers nearly all of Q8_0's quality at ~65% of the size. Plain uniform Q4_K is preserved as *_old.gguf for reference.

Files

File	Size	Notes
`omniasr-ctc-1b-v2-q4_k.gguf`	658 MB	Recommended. Mixed Q4_K (first 4 encoder layers F16, rest Q4_K). 5% WER on JFK (one-word artefact `americans`→`americas`, model-internal).
`omniasr-ctc-1b-v2-q8_0.gguf`	1007 MB	Byte-perfect against the FP32 transformers reference. Use this if 5% WER on edge cases is unacceptable.
`omniasr-ctc-1b-v2.gguf`	1.8 GB	F16 source — feed to `crispasr-quantize` for custom recipes.
`omniasr-ctc-1b-v2-q4_k_old.gguf`	551 MB	Legacy. Uniform Q4_K. Drops characters under CTC argmax pressure (~22.7% WER on JFK). Kept for reproducibility / size-vs-quality study; do not use for production.

Why mixed Q4_K?

CTC argmax decoding is structurally sensitive to weight drift — small perturbations to encoder weights flip frame-level argmax decisions toward the blank token, which manifests as missing characters in the output (e.g. "americans" → "amercans" → "americas"). Per-layer activation analysis (via OMNIASR_DUMP_DIR=...) shows quantization noise enters at every encoder layer's matmul and compounds through the residual stream, peaking at layers 36-47 even though the cause is upstream.

Counter-intuitively, keeping the late layers at F16 made things worse — F16 math preserves accumulated upstream noise more faithfully than Q4_K math does (Q4_K rounding occasionally lands back near the right bin). The fix is to keep the first 4 layers at F16, stopping noise from entering the residual stream. This is automatic in crispasr-quantize for any GGUF whose general.architecture is omniasr-ctc. Override via env vars:

CRISPASR_OMNIASR_KEEP_F16_HEAD=N   # default 4; 0 = uniform Q4_K
CRISPASR_OMNIASR_KEEP_F16_TAIL=N   # default 0; >0 is counter-productive
CRISPASR_OMNIASR_QUANT_ALL=1       # full quant, smaller, ~22% WER

Full diagnosis in LEARNINGS.md under "Q4_K is too lossy as the default for CTC-decoded ASR" and its follow-up "mixed Q4_K head-skip recovers nearly all Q8_0 quality".

Quick Start

bash

git clone https://github.com/CrispStrobe/CrispASR && cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j$(nproc)

./build/bin/crispasr --backend omniasr -m auto --auto-download -f audio.wav

Conversion

Converted using CrispASR's converter scripts with fixed positional conv weight normalization (per-kernel-position norm, not per-output-channel).

Provenance and EU AI Act Art. 53 note

Upstream model: aadel4/omniASR-CTC-1B-v2 — published by aadel4.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

cstr/omniASR-CTC-1B-v2-GGUF

OmniASR CTC-1B-v2 — GGUF

Files

Why mixed Q4_K?

Quick Start

Conversion

Provenance and EU AI Act Art. 53 note

cstr/
omniASR-CTC-1B-v2-GGUF