---
library_name: transformers
tags:
- asr
license: mit
language:
- ar
pipeline_tag: automatic-speech-recognition
---
# ArTST-v3 (ASR task)

ArTST model finetuned for  automatic speech recognition (speech-to-text) on MGB2 (best for Modern Standard Arabic) 


### Model Description

- **Developed by:** Speech Lab, MBZUAI
- **Model type:** SpeechT5
- **Language:** Arabic


## How to Get Started with the Model

```python
import soundfile as sf
from transformers import (
    SpeechT5Config,
    SpeechT5FeatureExtractor,
    SpeechT5ForSpeechToText,
    SpeechT5Processor,
    SpeechT5Tokenizer,
)


device = "cuda" if torch.cuda.is_available() else "CPU"

model_id="mbzuai/artst_asr_v3"

tokenizer = SpeechT5Tokenizer.from_pretrained(model_id)
processor = SpeechT5Processor.from_pretrained(model_id , tokenizer=tokenizer)
model = SpeechT5ForSpeechToText.from_pretrained(model_id).to(device)

audio, sr = sf.read("audio.wav")

inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt")
predicted_ids = model.generate(**inputs.to(device), max_length=150, num_beams=10)

transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])
```

or using pipeline

```python
import librosa
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor


model_id="mbzuai/artst_asr_v3"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)

wav, sr = librosa.load("audio.wav", sr=16000)
pipe(wav, generate_kwargs={'num_beams': 10, 'early_stopping': True})['text']
```





### Model Sources [optional]
- **Repository:** [github](https://github.com/mbzuai-nlp/ArTST)
- **Paper :** [ArXiv](https://arxiv.org/pdf/2411.05872)
<!-- - **Demo [optional]:** [More Information Needed] -->


## Citation 

**BibTeX:**
```
@misc{djanibekov2024dialectalcoveragegeneralizationarabic,
      title={Dialectal Coverage And Generalization in Arabic Speech Recognition}, 
      author={Amirbek Djanibekov and Hawau Olamide Toyin and Raghad Alshalan and Abdullah Alitr and Hanan Aldarmaki},
      year={2024},
      eprint={2411.05872},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.05872}, 
}

@inproceedings{toyin-etal-2023-artst,
    title = "{A}r{TST}: {A}rabic Text and Speech Transformer",
    author = "Toyin, Hawau  and
      Djanibekov, Amirbek  and
      Kulkarni, Ajinkya  and
      Aldarmaki, Hanan",
    booktitle = "Proceedings of ArabicNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.arabicnlp-1.5",
    doi = "10.18653/v1/2023.arabicnlp-1.5",
    pages = "41--51",
}
```