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library_name: transformers
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#
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:**
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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tags:
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- audio
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- automatic-speech-recognition
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license: mit
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language:
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- ar
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# ArTST (ASR task)
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ArTST model finetuned for automatic speech recognition (speech-to-text) on MGB2.
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** Speech Lab, MBZUAI
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- **Model type:** SpeechT5
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- **Language:** Arabic
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- **Finetuned from:** (ArTST pretrained)[https://github.com/mbzuai-nlp/ArTST]
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## How to Get Started with the Model
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```python
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import soundfile as sf
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from transformers import (
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SpeechT5Config,
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SpeechT5FeatureExtractor,
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SpeechT5ForSpeechToText,
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SpeechT5Processor,
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SpeechT5Tokenizer,
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)
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from custom_tokenizer import CustomTextTokenizer
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device = "cuda" if torch.cuda.is_available() else "cpu"
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tokenizer = SpeechT5Tokenizer.from_pretrained("mbzuai/artst_asr")
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processor = SpeechT5Processor.from_pretrained("mbzuai/artst_asr" , tokenizer=tokenizer)
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model = SpeechT5ForSpeechToText.from_pretrained("mbzuai/artst_asr").to(device)
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audio, sr = sf.read("audio.wav")
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inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt")
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predicted_ids = model.generate(**inputs.to(device), max_length=150)
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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print(transcription[0])
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```
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### Model Sources [optional]
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- **Repository:** [github](https://github.com/mbzuai-nlp/ArTST)
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- **Paper :** [ArabicNLP2023](https://aclanthology.org/2023.arabicnlp-1.5/)
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<!-- - **Demo [optional]:** [More Information Needed] -->
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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```
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@inproceedings{toyin-etal-2023-artst,
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title = "{A}r{TST}: {A}rabic Text and Speech Transformer",
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author = "Toyin, Hawau and
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Djanibekov, Amirbek and
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Kulkarni, Ajinkya and
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Aldarmaki, Hanan",
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booktitle = "Proceedings of ArabicNLP 2023",
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month = dec,
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year = "2023",
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address = "Singapore (Hybrid)",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2023.arabicnlp-1.5",
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doi = "10.18653/v1/2023.arabicnlp-1.5",
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pages = "41--51",
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}
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```
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<!-- **APA:**
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[More Information Needed]
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<!-- [More Information Needed]
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## More Information [optional]
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## Model Card Contact
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[More Information Needed] -->
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