| Open-weight models | Closed models | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Jais-ASR | Audar-ASR-V1-Turbo | cohere-transcribe-arabic-07-2026 | omniASR-LLM-7B | Qwen3-Omni-30B-A3B | Qwen3-ASR-1.7B | Voxtral-Small-24B | Melia 1 Speechmatics | Scribe v2 ElevenLabs | |
| Word error rate (WER, %) ↓ | |||||||||
Average Macro-average, six test sets | 22.85 | 23.17 | 25.87 | 28.32 | 30.71 | 33.36 | 34.47 | 23.51 | 24.54 |
SADA Saudi dialectal broadcast | 30.85 | 28.92 | 37.47 | 41.61 | 44.82 | 45.53 | 50.82 | 34.74 | 33.96 |
Common Voice 18 MSA read speech | 8.56 | 8.09 | 5.82 | 8.75 | 11.46 | 16.90 | 15.25 | 5.19 | 8.71 |
MASC clean MSA lectures, clean | 16.12 | 16.73 | 19.60 | 19.69 | 21.47 | 24.37 | 23.96 | 16.23 | 16.91 |
MASC noisy MSA lectures, noisy | 25.44 | 27.19 | 27.07 | 29.29 | 30.85 | 34.29 | 34.43 | 25.89 | 27.15 |
MGB-2 MSA broadcast news | 9.59 | 11.08 | 15.54 | 14.13 | 13.09 | 16.57 | 16.03 | 12.61 | 10.30 |
Casablanca 8 dialects, conversational | 46.56 | 47.02 | 49.71 | 56.46 | 62.55 | 64.47 | 66.30 | 46.41 | 50.20 |
| Character error rate (CER, %) ↓ | |||||||||
Average Macro-average, six test sets | 8.37 | 9.23 | 11.80 | 12.52 | 13.67 | 12.33 | 15.29 | 10.62 | 9.96 |
SADA Saudi dialectal broadcast | 14.33 | 13.40 | 23.53 | 24.95 | 26.11 | 19.90 | 28.85 | 21.21 | 16.48 |
Common Voice 18 MSA read speech | 2.70 | 2.46 | 1.62 | 2.71 | 4.28 | 5.06 | 5.54 | 1.62 | 3.00 |
MASC clean MSA lectures, clean | 3.76 | 5.32 | 6.45 | 5.76 | 5.59 | 5.72 | 7.06 | 5.11 | 4.97 |
MASC noisy MSA lectures, noisy | 7.51 | 9.78 | 10.13 | 10.66 | 11.28 | 10.84 | 12.22 | 9.47 | 9.36 |
MGB-2 MSA broadcast news | 3.92 | 5.64 | 8.40 | 7.10 | 6.20 | 6.25 | 7.41 | 5.88 | 4.78 |
Casablanca 8 dialects, conversational | 18.02 | 18.75 | 20.66 | 23.96 | 28.53 | 26.23 | 30.64 | 20.41 | 21.18 |
Error rates in %; lower is better. Average is the macro-average over the six test sets. Jais-ASR was measured with the leaderboard's evaluation script. Scores for the other open-weight models are taken from the leaderboard; Speechmatics Melia 1 and ElevenLabs Scribe v2 were measured by IFM.
## Capabilities Jais-ASR brings Arabic speech transcription, code-switching, diacritization, and language identification into one model, with controls to tailor the output to your application. - **Transcribe multilingual speech as spoken.** Enable `code_switch=True` to keep English, French, Spanish, or Italian spans in their original script alongside Arabic, useful for interviews and everyday conversations. - **Add diacritics for clearer Arabic pronunciation.** Enable `diacritics=True` to add vowel marks to Arabic transcripts, making them easier to read and supporting language learning. Diacritics and code-switching can be enabled together. - **Detect the language and dialect as you transcribe.** Each transcript comes with its recognized language or dialect label, so you can organize recordings and route text to downstream tools without a separate identification model. - **Handle non-speech audio explicitly.** When audio contains no speech (e.g., music or noise), the model returns `[IFM-NoTranscript]` with the flag `refused=True` instead of hallucinated content, giving applications a clear signal to handle the case separately. - **Process multiple recordings at once.** Batch transcription supports files with different durations and sample rates. ### Language and Dialect Settings Automatic language and dialect detection is enabled by default. If the language of the audio is known in advance, set it with one of the codes below: `ar` allows Modern Standard Arabic and all 19 supported dialects, `msa` selects Modern Standard Arabic, and a dialect code such as `EG` selects that dialect. | Language | Setting | |---|---| | Arabic, including all supported dialects | `ar` | | Modern Standard Arabic | `msa` | | English | `en` | | French | `fr` | | Spanish | `es` | | Italian | `it` | The 19 Arabic dialect codes are: Algerian (`DZ`) · Bahraini (`BH`) · Egyptian (`EG`) · Emirati (`AE`) · Iraqi (`IQ`) · Jordanian (`JO`) · Kuwaiti (`KW`) · Lebanese (`LB`) · Libyan (`LY`) · Mauritanian (`MR`) · Moroccan (`MA`) · Omani (`OM`) · Palestinian (`PS`) · Qatari (`QA`) · Saudi (`SA`) · Sudanese (`SD`) · Syrian (`SY`) · Tunisian (`TN`) · Yemeni (`YE`) ## Quickstart Install PyTorch for your hardware, then install the inference dependencies: ```bash pip install 'transformers==5.16.1' accelerate soundfile soxr ``` The examples below use a GPU with bfloat16 support. The bf16 weights are 11.4 GB; plan on roughly 12 GB of GPU memory. ### Transformers Pipeline ```python import torch from transformers import pipeline asr = pipeline( "automatic-speech-recognition", model="IFM/Jais-ASR", trust_remote_code=True, device_map="auto", dtype=torch.bfloat16, ) result = asr("speech.wav", max_new_tokens=256) print(result["text"]) ``` The pipeline accepts an audio file path or a waveform dictionary such as `{"array": waveform, "sampling_rate": 16000}`. For batches and structured metadata, use the model and processor directly as shown below. ### Batch Transcription This example transcribes two files together, including clips with different durations or sample rates. ```python import soundfile as sf import torch from transformers import AutoModelForCausalLM, AutoProcessor model_id = "IFM/Jais-ASR" processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) processor.tokenizer.padding_side = "left" model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, device_map="auto", dtype=torch.bfloat16, ).eval() paths = ["speech_1.wav", "speech_2.wav"] audio = [sf.read(path, dtype="float32") for path in paths] waveforms = [waveform for waveform, _ in audio] sample_rates = [sample_rate for _, sample_rate in audio] prompt = processor.tokenizer.apply_chat_template( [{"role": "user", "content": [{"type": "audio"}]}], tokenize=False, add_generation_prompt=True, # diacritics=True, # Add Arabic vowel marks. # code_switch=True, # Keep other languages in their original script. ) inputs = processor( text=[prompt] * len(waveforms), audio=waveforms, sampling_rate=sample_rates, padding=True, return_tensors="pt", ).to(model.device) with torch.inference_mode(): output = model.generate( **inputs, do_sample=False, max_new_tokens=256, # language="EG", # Set the target language to override automatic detection. ) transcripts = processor.post_process_transcription( output, input_lengths=inputs["input_ids"].shape[1], ) for path, result in zip(paths, transcripts): print(path, { "text": result.text, "language": result.language, "refused": result.refused, }) ``` `language` contains a language or dialect label, such as `msa`, `EG`, or `en`. When the model emits `[IFM-NoTranscript]`, postprocessing sets `refused=True` and returns an empty transcript. Uncomment either prompt option above to request diacritics or code-switching; both can be enabled together. > [!TIP] > Pass each recording's original sample rate; the processor resamples it to 16 kHz automatically. Increase `max_new_tokens` for longer recordings: a result with `truncated=True` means generation ended before the transcript was complete. ### Serving with vLLM Jais-ASR ships with a vLLM plugin in [`vllm_plugin/`](https://huggingface.co/IFM/Jais-ASR/tree/main/vllm_plugin). Install it in its own environment, then start the server: ```bash pip install 'vllm==0.23.0' 'transformers==5.5.4' accelerate soundfile soxr hf download IFM/Jais-ASR --include "vllm_plugin/*" --local-dir Jais-ASR pip install ./Jais-ASR/vllm_plugin vllm serve IFM/Jais-ASR --trust-remote-code --reasoning-parser jais_asr_tag --max-model-len 2048 ``` Transcribe a file through the OpenAI-compatible endpoint: ```bash curl http://localhost:8000/v1/audio/transcriptions \ -F file=@clip.wav \ -F model=IFM/Jais-ASR ``` ## Citation ```bibtex @misc{jaisasr2026, title = {Introducing Jais-ASR: Arabic Speech Recognition for Every Dialect}, author = {{IFM Team}}, year = {2026}, url = {https://huggingface.co/IFM/Jais-ASR}, } ```