CohereLabs / cohere-transcribe-arabic-07-2026

Cohere Transcribe Arabic

Cohere Transcribe Arabic is an open source release of a 2B parameter dedicated audio-in, text-out automatic speech recognition (ASR) model. The optimized for Arabic and English, with a focus on Arabic dialect performance and Arabic-English code-switching. Based on the Cohere Transcribe architecture.

Developed by: Cohere and Cohere Labs . Point of Contact: Cohere Labs .

✨Try the Cohere Transcribe Arabic demo ✨

Usage

Cohere Transcribe Arabic is supported natively in transformers . This is the recommended way to use the model for offline inference. For online inference, see the vLLM integration example below.

pip install transformers>=5.4.0 torch huggingface_hub soundfile librosa sentencepiece protobuf accelerate

Quick Start 🤗

Transcribe any audio file in a few lines:

from transformers import AutoProcessor, CohereAsrForConditionalGeneration
from transformers.audio_utils import load_audio
from huggingface_hub import hf_hub_download

processor = AutoProcessor.from_pretrained("CohereLabs/cohere-transcribe-arabic-07-2026")
model = CohereAsrForConditionalGeneration.from_pretrained("CohereLabs/cohere-transcribe-arabic-07-2026", device_map="auto")

# Example: transcribe Arabic audio
audio_file = "your_audio.wav"
audio = load_audio(audio_file, sampling_rate=16000)

inputs = processor(audio, sampling_rate=16000, return_tensors="pt", language="ar")
inputs.to(model.device, dtype=model.dtype)

outputs = model.generate(**inputs, max_new_tokens=256)
text = processor.decode(outputs, skip_special_tokens=True)
print(text)

from transformers import AutoProcessor, CohereAsrForConditionalGeneration
import time

processor = AutoProcessor.from_pretrained("CohereLabs/cohere-transcribe-arabic-07-2026")
model = CohereAsrForConditionalGeneration.from_pretrained("CohereLabs/cohere-transcribe-arabic-07-2026", device_map="auto")

audio = load_audio("your_long_audio.wav", sampling_rate=16000)
sr = 16000
duration_s = len(audio) / sr
print(f"Audio duration: {duration_s / 60:.1f} minutes")

inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt", language="ar")
audio_chunk_index = inputs.get("audio_chunk_index")
inputs.to(model.device, dtype=model.dtype)

start = time.time()
outputs = model.generate(**inputs, max_new_tokens=256)
text = processor.decode(outputs, skip_special_tokens=True, audio_chunk_index=audio_chunk_index, language="ar")[0]
elapsed = time.time() - start
rtfx = duration_s / elapsed
print(f"Transcribed in {elapsed:.1f}s — RTFx: {rtfx:.1f}")
print(text)

inputs = processor(audio, sampling_rate=16000, return_tensors="pt", language="en")
inputs.to(model.device, dtype=model.dtype)

outputs = model.generate(**inputs, max_new_tokens=256)
text = processor.decode(outputs, skip_special_tokens=True)
print(text)

vLLM Integration

For production serving we recommend running via vLLM following the instructions below.

uv venv --python 3.12 --seed
source .venv/bin/activate

uv pip install -U vllm==0.19.0 --torch-backend=auto
uv pip install vllm[audio]
uv pip install librosa

vllm serve CohereLabs/cohere-transcribe-arabic-07-2026 --trust-remote-code

curl -v -X POST http://localhost:8000/v1/audio/transcriptions \
 -H "Authorization: Bearer $VLLM_API_KEY" \
-F "file=@$(realpath ${AUDIO_PATH})" \
-F "model=CohereLabs/cohere-transcribe-arabic-07-2026"

Results

Resources

For more details and results:

• Technical blog post contains WERs and other quality metrics.
• Announcement blog post for more information about the model.
• The Open Universal Arabic ASR Leaderboard .

Strengths and Limitations

Strengths

Cohere Transcribe Arabic demonstrates strong transcription accuracy for Arabic and English. As a dedicated speech recognition model, it benefits from efficient inference via the Conformer encoder-decoder architecture.

Limitations

• Single language. The model performs best when remaining in-distribution of a single, pre-specified language. It does not feature explicit, automatic language detection and exhibits inconsistent performance on code-switched audio.

• Timestamps/Speaker diarization. The model does not feature either of these.

• Silence. Like most AED speech models, Cohere Transcibe Arabic is eager to transcribe, even non-speech sounds. The model benefits from prepending a noise gate or VAD (voice activity detection) model in order to prevent low-volume, floor noise from turning into hallucinations.

Model Card Contact

For errors or additional questions about details in this model card, contact [email protected] or raise an issue.

Terms of Use: We hope that the release of this model will make community-based research efforts into Arabic speech more accessible. This model is governed by an Apache 2.0 license.