Cohere Transcribe Arabic 07 2026 GGUF

License: Apache 2.0 (inherited from source model). See the original model card for full terms.

Files

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

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.