--- library_name: transformers tags: - audio - automatic-speech-recognition license: mit language: - ar --- # ArTST-V2 (ASR task) ArTST model finetuned for automatic speech recognition (speech-to-text) on QASR to improve dialectal generalization. ### Model Description - **Developed by:** Speech Lab, MBZUAI - **Model type:** SpeechT5 - **Language:** Arabic - **Finetuned from:** [ArTST-v2 pretrained](https://github.com/mbzuai-nlp/ArTST) ## How to Get Started with the Model ```python import soundfile as sf from transformers import ( SpeechT5Config, SpeechT5FeatureExtractor, SpeechT5ForSpeechToText, SpeechT5Processor, SpeechT5Tokenizer, ) from custom_tokenizer import CustomTextTokenizer device = "cuda" if torch.cuda.is_available() else "CPU" model_id = "mbzuai/artst-v2-asr" 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) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) print(transcription[0]) ``` ### Model Sources [optional] - **Repository:** [github](https://github.com/mbzuai-nlp/ArTST) - **Paper :** [pre-print](/) ## Citation [optional] **BibTeX:** ``` @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", } ```