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---
license: apache-2.0
language:
- ar
- en
pipeline_tag: automatic-speech-recognition
tags:
- audio
- speech-recognition
- transcription
- arabic
library_name: transformers
---
# 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](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).
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<table>
 <tbody>
 <tr>
 <th>Name</th>
 <td><strong>cohere-transcribe-arabic-07-2026</strong></td>
 </tr>
 <tr>
 <th>Architecture</th>
 <td>conformer-based encoder-decoder</td>
 </tr>
 <tr>
 <th>Input</th>
 <td>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.</td>
 </tr>
 <tr>
 <th>Output</th>
 <td>transcribed text</td>
 </tr>
 <tr>
 <th>Model</th>
 <td>a large Conformer encoder extracts acoustic representations, followed by a lightweight Transformer decoder for token generation</td>
 </tr>
 <tr>
 <th>Training objective</th>
 <td>supervised cross-entropy on output tokens</td>
 </tr>
 <tr>
 <th>Languages</th>
 <td>
 <ul>
 <li>Arabic</li>
 <li>English</li>
 </ul>
 </td>
 </tr>
 <tr>
 <th>License</th>
 <td>Apache 2.0</td>
 </tr>
 </tbody>
</table>
✨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)
```
<details>
<summary><b>Long-form transcription</b></summary>
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)
```
</details>
<!-- <details>
<summary><b>Punctuation control</b></summary>
Pass `punctuation=False` to obtain lower-cased output without punctuation marks.
```python
inputs_pnc = processor(audio, sampling_rate=16000, return_tensors="pt", language="ar", punctuation=True)
inputs_nopnc = processor(audio, sampling_rate=16000, return_tensors="pt", language="ar", punctuation=False)
```
By default, punctuation is enabled.
</details> -->
<details>
<summary><b>English transcription</b></summary>
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)
```
</details>
### vLLM Integration
For production serving we recommend running via vLLM following the instructions below.
<details>
<summary><b>Run cohere-transcribe-arabic-07-2026 via vLLM</b></summary>
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"
```
</details>
## Results
<details>
<summary><b>Open Universal Arabic ASR Leaderboard (as of 07.07.2026)</b></summary>
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<div style="overflow-x: auto;">
<table class="simple text-web3-14 font-body">
 <thead>
 <tr>
 <th>Model</th>
 <th class="num">Average<span class="metric-hint">WER · CER</span></th>
 <th class="num">SADA<span class="metric-hint">WER · CER</span></th>
 <th class="num">Common Voice<span class="metric-hint">WER · CER</span></th>
 <th class="num">MASC clean<span class="metric-hint">WER · CER</span></th>
 <th class="num">MASC noisy<span class="metric-hint">WER · CER</span></th>
 <th class="num">MGB-2<span class="metric-hint">WER · CER</span></th>
 <th class="num">Casablanca<span class="metric-hint">WER · CER</span></th>
 </tr>
 </thead>
 <tbody>
 <tr class="highlight-row">
 <th><strong style="white-space:nowrap">Cohere Transcribe Arabic 07-2026</strong></th>
 <td class="num highlight-cell"><span class="metric"><strong>25.87</strong></span><span class="metric-cer"><strong>11.80</strong></span></td>
 <td class="num"><span class="metric"><strong>37.47</strong></span><span class="metric-cer"><strong>23.53</strong></span></td>
 <td class="num"><span class="metric"><strong>5.82</strong></span><span class="metric-cer"><strong>1.62</strong></span></td>
 <td class="num"><span class="metric">19.60</span><span class="metric-cer">6.45</span></td>
 <td class="num"><span class="metric">27.07</span><span class="metric-cer">10.13</span></td>
 <td class="num"><span class="metric">15.54</span><span class="metric-cer">8.40</span></td>
 <td class="num"><span class="metric"><strong>49.71</strong></span><span class="metric-cer"><strong>20.66</strong></span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">OmniASR LLM 7B</th>
 <td class="num"><span class="metric">28.32</span><span class="metric-cer">12.52</span></td>
 <td class="num"><span class="metric">41.61</span><span class="metric-cer">24.95</span></td>
 <td class="num"><span class="metric">8.75</span><span class="metric-cer">2.71</span></td>
 <td class="num"><span class="metric">19.69</span><span class="metric-cer">5.76</span></td>
 <td class="num"><span class="metric">29.29</span><span class="metric-cer">10.66</span></td>
 <td class="num"><span class="metric">14.13</span><span class="metric-cer">7.10</span></td>
 <td class="num"><span class="metric">56.46</span><span class="metric-cer">23.96</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">OmniASR LLM 3B</th>
 <td class="num"><span class="metric">29.96</span><span class="metric-cer">13.77</span></td>
 <td class="num"><span class="metric">46.18</span><span class="metric-cer">27.27</span></td>
 <td class="num"><span class="metric">9.15</span><span class="metric-cer">2.80</span></td>
 <td class="num"><span class="metric">19.90</span><span class="metric-cer">6.13</span></td>
 <td class="num"><span class="metric">30.03</span><span class="metric-cer">11.27</span></td>
 <td class="num"><span class="metric">14.22</span><span class="metric-cer">7.06</span></td>
 <td class="num"><span class="metric">60.27</span><span class="metric-cer">28.06</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">OmniASR LLM 1B</th>
 <td class="num"><span class="metric">29.96</span><span class="metric-cer">13.40</span></td>
 <td class="num"><span class="metric">43.84</span><span class="metric-cer">24.54</span></td>
 <td class="num"><span class="metric">9.55</span><span class="metric-cer">2.97</span></td>
 <td class="num"><span class="metric">20.03</span><span class="metric-cer">6.14</span></td>
 <td class="num"><span class="metric">30.26</span><span class="metric-cer">11.18</span></td>
 <td class="num"><span class="metric">15.34</span><span class="metric-cer">7.56</span></td>
 <td class="num"><span class="metric">60.68</span><span class="metric-cer">28.02</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Cohere Transcribe 03-2026</th>
 <td class="num"><span class="metric">30.67</span><span class="metric-cer">16.37</span></td>
 <td class="num"><span class="metric">60.11</span><span class="metric-cer">45.44</span></td>
 <td class="num"><span class="metric">8.17</span><span class="metric-cer">2.49</span></td>
 <td class="num"><span class="metric"><strong>8.66</strong></span><span class="metric-cer"><strong>2.97</strong></span></td>
 <td class="num"><span class="metric"><strong>19.01</strong></span><span class="metric-cer"><strong>7.71</strong></span></td>
 <td class="num"><span class="metric">25.33</span><span class="metric-cer">9.28</span></td>
 <td class="num"><span class="metric">62.71</span><span class="metric-cer">30.31</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Qwen3-Omni 30B</th>
 <td class="num"><span class="metric">30.71</span><span class="metric-cer">13.67</span></td>
 <td class="num"><span class="metric">44.82</span><span class="metric-cer">26.11</span></td>
 <td class="num"><span class="metric">11.46</span><span class="metric-cer">4.28</span></td>
 <td class="num"><span class="metric">21.47</span><span class="metric-cer">5.59</span></td>
 <td class="num"><span class="metric">30.85</span><span class="metric-cer">11.28</span></td>
 <td class="num"><span class="metric"><strong>13.09</strong></span><span class="metric-cer"><strong>6.20</strong></span></td>
 <td class="num"><span class="metric">62.55</span><span class="metric-cer">28.53</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">NVIDIA Conformer-CTC (LM)</th>
 <td class="num"><span class="metric">32.91</span><span class="metric-cer">13.84</span></td>
 <td class="num"><span class="metric">44.52</span><span class="metric-cer">23.76</span></td>
 <td class="num"><span class="metric">8.80</span><span class="metric-cer">2.77</span></td>
 <td class="num"><span class="metric">23.74</span><span class="metric-cer">5.63</span></td>
 <td class="num"><span class="metric">34.29</span><span class="metric-cer">11.07</span></td>
 <td class="num"><span class="metric">17.20</span><span class="metric-cer">6.87</span></td>
 <td class="num"><span class="metric">68.90</span><span class="metric-cer">32.97</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">OmniASR LLM 300M</th>
 <td class="num"><span class="metric">32.96</span><span class="metric-cer">14.84</span></td>
 <td class="num"><span class="metric">51.38</span><span class="metric-cer">29.10</span></td>
 <td class="num"><span class="metric">12.03</span><span class="metric-cer">4.04</span></td>
 <td class="num"><span class="metric">20.66</span><span class="metric-cer">6.22</span></td>
 <td class="num"><span class="metric">32.45</span><span class="metric-cer">12.23</span></td>
 <td class="num"><span class="metric">16.58</span><span class="metric-cer">7.86</span></td>
 <td class="num"><span class="metric">64.64</span><span class="metric-cer">29.61</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Gemma 4 E4B</th>
 <td class="num"><span class="metric">32.98</span><span class="metric-cer">13.71</span></td>
 <td class="num"><span class="metric">43.40</span><span class="metric-cer">20.96</span></td>
 <td class="num"><span class="metric">19.65</span><span class="metric-cer">7.48</span></td>
 <td class="num"><span class="metric">24.86</span><span class="metric-cer">7.76</span></td>
 <td class="num"><span class="metric">33.59</span><span class="metric-cer">12.25</span></td>
 <td class="num"><span class="metric">17.72</span><span class="metric-cer">8.67</span></td>
 <td class="num"><span class="metric">58.63</span><span class="metric-cer">25.11</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Qwen3-ASR 1.7B</th>
 <td class="num"><span class="metric">33.36</span><span class="metric-cer">12.33</span></td>
 <td class="num"><span class="metric">45.53</span><span class="metric-cer">19.90</span></td>
 <td class="num"><span class="metric">16.90</span><span class="metric-cer">5.06</span></td>
 <td class="num"><span class="metric">24.37</span><span class="metric-cer">5.72</span></td>
 <td class="num"><span class="metric">34.29</span><span class="metric-cer">10.84</span></td>
 <td class="num"><span class="metric">16.57</span><span class="metric-cer">6.25</span></td>
 <td class="num"><span class="metric">64.47</span><span class="metric-cer">26.23</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Voxtral-Small 24B</th>
 <td class="num"><span class="metric">34.47</span><span class="metric-cer">15.29</span></td>
 <td class="num"><span class="metric">50.82</span><span class="metric-cer">28.85</span></td>
 <td class="num"><span class="metric">15.25</span><span class="metric-cer">5.54</span></td>
 <td class="num"><span class="metric">23.96</span><span class="metric-cer">7.06</span></td>
 <td class="num"><span class="metric">34.43</span><span class="metric-cer">12.22</span></td>
 <td class="num"><span class="metric">16.03</span><span class="metric-cer">7.41</span></td>
 <td class="num"><span class="metric">66.30</span><span class="metric-cer">30.64</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">NVIDIA Conformer-CTC (greedy)</th>
 <td class="num"><span class="metric">34.74</span><span class="metric-cer">13.37</span></td>
 <td class="num"><span class="metric">47.26</span><span class="metric-cer">22.54</span></td>
 <td class="num"><span class="metric">10.60</span><span class="metric-cer">3.05</span></td>
 <td class="num"><span class="metric">24.12</span><span class="metric-cer">5.63</span></td>
 <td class="num"><span class="metric">35.64</span><span class="metric-cer">11.02</span></td>
 <td class="num"><span class="metric">19.69</span><span class="metric-cer">7.46</span></td>
 <td class="num"><span class="metric">71.13</span><span class="metric-cer">30.50</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Gemma 4 E2B</th>
 <td class="num"><span class="metric">35.87</span><span class="metric-cer">15.34</span></td>
 <td class="num"><span class="metric">46.23</span><span class="metric-cer">23.47</span></td>
 <td class="num"><span class="metric">23.76</span><span class="metric-cer">9.13</span></td>
 <td class="num"><span class="metric">27.47</span><span class="metric-cer">8.99</span></td>
 <td class="num"><span class="metric">36.15</span><span class="metric-cer">13.93</span></td>
 <td class="num"><span class="metric">20.72</span><span class="metric-cer">10.15</span></td>
 <td class="num"><span class="metric">60.87</span><span class="metric-cer">26.35</span></td>
 </tr>
 <tr>
 <th style="font-weight:normal;">Whisper Large v3</th>
 <td class="num"><span class="metric">36.86</span><span class="metric-cer">17.21</span></td>
 <td class="num"><span class="metric">55.96</span><span class="metric-cer">34.62</span></td>
 <td class="num"><span class="metric">17.83</span><span class="metric-cer">5.74</span></td>
 <td class="num"><span class="metric">24.66</span><span class="metric-cer">7.24</span></td>
 <td class="num"><span class="metric">34.63</span><span class="metric-cer">12.89</span></td>
 <td class="num"><span class="metric">16.26</span><span class="metric-cer">7.74</span></td>
 <td class="num"><span class="metric">71.81</span><span class="metric-cer">35.04</span></td>
 </tr>
 </tbody>
</table>
</div>
Link to the live leaderboard: [Open Universal Arabic ASR Leaderboard](https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard).
</details>
## 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 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 [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{cassini_cohere_transcribe_ar_2026,
 author = { Shaun Cassini and Sebastian Vincent and Xiaolu Lu and Julian Mack and Dhruti Josh and Pierre Richemond },
 title = { cohere-transcribe-07-2026 },
 year = 2026,
 url = { https://huggingface.co/CohereLabs/cohere-transcribe-arabic-07-2026 },
 publisher = { Hugging Face }
}
``` -->

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