--- license: apache-2.0 language: - ar - en pipeline_tag: automatic-speech-recognition tags: - audio - speech-recognition - transcription - arabic - asr - arabic-asr - arabic-dialect - arabic-speech-recognition library_name: transformers base_model: - CohereLabs/cohere-transcribe-03-2026 --- # Cohere Transcribe Arabic **Cohere Transcribe Arabic is an open source 2B-parameter Arabic automatic speech recognition model for speech-to-text transcription.** It is optimized for Arabic, Arabic Dialects, English, and Arabic-English code-switched speech. Use it for Arabic ASR, Arabic audio transcription, dialectal Arabic speech recognition, and English speech-to-text. The model uses a Conformer encoder-decoder architecture and is supported natively in Transformers. Based on the [Cohere Transcribe](https://huggingface.co/CohereLabs/cohere-transcribe-03-2026) architecture. Developed by: [Cohere](https://cohere.com) and [Cohere Labs](https://cohere.com/research). Point of Contact: [Cohere Labs](https://cohere.com/research).
Name cohere-transcribe-arabic-07-2026
Architecture conformer-based encoder-decoder
Input audio waveform → log-Mel spectrogram. Audio is automatically resampled to 16kHz if necessary during preprocessing. Similarly, multi-channel (stereo) inputs are averaged to produce a single channel signal.
Output transcribed text
Model a large Conformer encoder extracts acoustic representations, followed by a lightweight Transformer decoder for token generation
Training objective supervised cross-entropy on output tokens
Languages
  • Arabic
  • English
License Apache 2.0
✨Try the Cohere Transcribe Arabic [demo](https://huggingface.co/spaces/CohereLabs/cohere-transcribe-arabic-07-2026)✨ ## 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. ```bash pip install transformers>=5.4.0 torch huggingface_hub soundfile librosa sentencepiece protobuf accelerate ``` ### Quick Start 🤗 Transcribe any audio file in a few lines: ```python 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) ```
Long-form transcription For audio longer than the feature extractor's `max_audio_clip_s`, the feature extractor automatically splits the waveform into chunks. The processor reassembles the per-chunk transcriptions using the returned `audio_chunk_index`. ```python 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) ```
English transcription The model also supports English. Specify `language="en"`: ```python 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.
Run cohere-transcribe-arabic-07-2026 via vLLM First install vLLM (refer to [vLLM installation instructions](https://docs.vllm.ai/en/latest/getting_started/installation/)): ```bash 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 ``` Start vLLM server ```bash vllm serve CohereLabs/cohere-transcribe-arabic-07-2026 --trust-remote-code ``` Send request ```bash 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
Open Universal Arabic ASR Leaderboard (as of 07.07.2026)
Model AverageWER · CER SADAWER · CER Common VoiceWER · CER MASC cleanWER · CER MASC noisyWER · CER MGB-2WER · CER CasablancaWER · CER
Cohere Transcribe Arabic 07-2026 25.8711.80 37.4723.53 5.821.62 19.606.45 27.0710.13 15.548.40 49.7120.66
OmniASR LLM 7B 28.3212.52 41.6124.95 8.752.71 19.695.76 29.2910.66 14.137.10 56.4623.96
OmniASR LLM 3B 29.9613.77 46.1827.27 9.152.80 19.906.13 30.0311.27 14.227.06 60.2728.06
OmniASR LLM 1B 29.9613.40 43.8424.54 9.552.97 20.036.14 30.2611.18 15.347.56 60.6828.02
Cohere Transcribe 03-2026 30.6716.37 60.1145.44 8.172.49 8.662.97 19.017.71 25.339.28 62.7130.31
Qwen3-Omni 30B 30.7113.67 44.8226.11 11.464.28 21.475.59 30.8511.28 13.096.20 62.5528.53
NVIDIA Conformer-CTC (LM) 32.9113.84 44.5223.76 8.802.77 23.745.63 34.2911.07 17.206.87 68.9032.97
OmniASR LLM 300M 32.9614.84 51.3829.10 12.034.04 20.666.22 32.4512.23 16.587.86 64.6429.61
Gemma 4 E4B 32.9813.71 43.4020.96 19.657.48 24.867.76 33.5912.25 17.728.67 58.6325.11
Qwen3-ASR 1.7B 33.3612.33 45.5319.90 16.905.06 24.375.72 34.2910.84 16.576.25 64.4726.23
Voxtral-Small 24B 34.4715.29 50.8228.85 15.255.54 23.967.06 34.4312.22 16.037.41 66.3030.64
NVIDIA Conformer-CTC (greedy) 34.7413.37 47.2622.54 10.603.05 24.125.63 35.6411.02 19.697.46 71.1330.50
Gemma 4 E2B 35.8715.34 46.2323.47 23.769.13 27.478.99 36.1513.93 20.7210.15 60.8726.35
Whisper Large v3 36.8617.21 55.9634.62 17.835.74 24.667.24 34.6312.89 16.267.74 71.8135.04
Link to the live leaderboard: [Open Universal Arabic ASR Leaderboard](https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard).
## Resources For more details and results: * [Technical blog post](https://huggingface.co/blog/CohereLabs/cohere-transcribe-arabic-07-2026-release) contains WERs and other quality metrics. * [Announcement blog post](https://cohere.com/blog/transcribe-arabic) for more information about the model. * The [Open Universal Arabic ASR Leaderboard](https://huggingface.co/spaces/elmresearchcenter/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 Transcribe 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 [labs@cohere.com](mailto:labs@cohere.com) 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. ### Citation To cite this model please use the following bibtex: ```bibtex @misc{shaun_cassini_2026, author = { Shaun Cassini and Sebastian Vincent and Xiaolu Lu and Julian Mack and Dhruti Joshi and Pierre Richemond }, title = { cohere-transcribe-arabic-07-2026 (Revision 0a8193c) }, year = 2026, url = { https://huggingface.co/CohereLabs/cohere-transcribe-arabic-07-2026 }, doi = { 10.57967/hf/9549 }, publisher = { Hugging Face } } ```