cohere-transcribe-arabic-07-2026 — GGUF
GGUF weights for CohereLabs/cohere-transcribe-arabic-07-2026 — Cohere Labs' 2B-parameter Arabic ASR model (a FastConformer encoder + Transformer decoder), released July 2026 (~11% WER on FLEURS Arabic per the source card).
These GGUFs run on CPU/Metal/CUDA/Vulkan via CrispASR — a C++ runtime for the Cohere Conformer-encoder / Transformer-decoder architecture.
License: Apache 2.0 (inherited from source model). See the original model card for full terms.
Files
| File | Size | Type |
|---|---|---|
cohere-transcribe-arabic-f16.gguf | 4.1 GB | F16 (reference precision) |
cohere-transcribe-arabic-q8_0.gguf | 2.4 GB | Q8_0 |
cohere-transcribe-arabic-q4_k.gguf | 1.5 GB | Q4_K |
cohere-transcribe-arabic-q4_k-imatrix.gguf | 1.5 GB | Q4_K + importance matrix (Arabic-calibrated) |
cohere-transcribe-arabic-ref.gguf | small | per-stage reference activations for crispasr-diff |
All quants keep LayerNorm weights / biases at F32. The -imatrix build is
calibrated on CC0 Common Voice Arabic (fsicoli/common_voice_17_0, ar/dev) and
recovers quality at the 4-bit budget; prefer it over plain q4_k.
On an M1 (Metal), end-to-end encode+decode on an 11 s clip is on par with the
transcribe.cpp reference runtime (~1.5 s, load excluded).
Quick start
# Build CrispASR (see the repo for full instructions)
git clone --recursive https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -G Ninja -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j
# Shorthand: auto-downloads the recommended imatrix GGUF and defaults
# the language to Arabic — no -m / --hf-repo / -l needed.
build/bin/crispasr --backend cohere-ar audio.wav
cohere-ar is a CLI alias for the cohere backend: it routes to the same
runtime, resolves cohere-transcribe-arabic-q4_k-imatrix.gguf via -m auto,
and sets -l ar unless you pass an explicit -l (which always wins — useful
if you want to run this model with the LID pre-step or force another
language for testing). It's equivalent to:
build/bin/crispasr --backend cohere \
--hf-repo cstr/cohere-transcribe-arabic-07-2026-GGUF:cohere-transcribe-arabic-q4_k-imatrix.gguf \
audio.wav -l ar
Or point -m at a locally downloaded GGUF:
build/bin/crispasr --backend cohere -m cohere-transcribe-arabic-q4_k.gguf audio.wav -l ar
Supported languages: en and ar only
This finetune's config.json lists exactly two: en, ar. That matters more
than it sounds, because nothing else can tell you. The finetune keeps the
base tokenizer, so all 183 ISO-639-1 <|xx|> tokens are present in the vocab —
<|de|>, <|ru|>, <|ja|> all decode without error. And Cohere Transcribe
answers a wrong language fluently rather than failing. On one 8 s Arabic
clip, -l ru added a hallucinated leading word, -l ja swapped the quotation
marks for brackets, and -l de changed the diacritics — all plausible, none
flagged.
So CrispASR reads the whitelist from the GGUF and substitutes loudly:
cohere: language 'de' is not supported by this model — using 'en' instead. Supported: en, ar
The GGUFs here carry cohere_transcribe.supported_languages. For any Cohere
GGUF converted before that key existed, declare it at runtime instead:
CRISPASR_COHERE_LANGS=en,ar build/bin/crispasr --backend cohere -m old.gguf audio.wav -l auto
With the list present and -l auto, CrispASR identifies the language by
probing this model itself — one short decode per candidate, no whisper-tiny
download — and can therefore only ever return en or ar:
cohere[lid]: en len=64 agree=1.00 div=0.79 score=158 :: The city is located in the city of Jerry, a large city of Je
cohere[lid]: ar len=82 agree=1.00 div=1.00 score=328 :: العاصفة شبه الاستوائية "جيري" تغا
crispasr: LID -> language = 'ar' (cohere-probe, p=0.675)
Architecture
| Component | Details |
|---|---|
| Encoder | 48-layer FastConformer, d=1280, heads=8, head_dim=160, ffn=5120, conv_kernel=9 |
| Decoder | 8-layer causal Transformer, d=1024, heads=8, head_dim=128, ffn=4096 (ReLU), max_ctx=1024 |
| Vocab | 16,384 SentencePiece tokens |
| Audio | 16 kHz mono, 128 mel bins, n_fft=512, hop=160, win=400, 8× time subsampling |
| Parameters | ~2B |
Prompt tokens (decoder): the runtime prepends decoder_start_token_id (▁,
13764) to the control-token prompt, matching the reference decoder_input_ids.
Mel frame count is floor(n/hop)+1 (NeMo FilterbankFeatures), giving T_enc = calc_length(T_mel); both are required for the cross-attention context to line up.
Validation (crispasr-diff)
cohere-transcribe-arabic-ref.gguf holds per-stage reference activations dumped
from the transformers model by tools/dump_reference.py. Reproduce the
per-layer parity check with:
build/bin/crispasr-diff cohere cohere-transcribe-arabic-f16.gguf \
cohere-transcribe-arabic-ref.gguf audio.wav
Related
- Source model: CohereLabs/cohere-transcribe-arabic-07-2026
- English sibling: cstr/cohere-transcribe-03-2026-GGUF — Cohere Transcribe 2B (lowest English WER)
- C++ runtime: CrispStrobe/CrispASR
Provenance and EU AI Act Art. 53 note
- Upstream model: CohereLabs/cohere-transcribe-arabic-07-2026 — published by
CohereLabs. - 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). 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.