---

license: apache-2.0

language:

• en
• multilingual

tags:

• speech
• asr
• gguf
• ggml
• omniasr

pipeline_tag: automatic-speech-recognition

base_model: aadel4/omniASR-CTC-1B-v2

---

OmniASR CTC-1B-v2 — GGUF

GGUF conversion of aadel4/omniASR-CTC-1B-v2 for use with CrispASR.

OmniASR is Meta's multilingual ASR model family supporting 1600+ languages. Apache-2.0 license.

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

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.