Update README.md

README.md

@@ -1,199 +1,135 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- asr
+- arabic
+license: mit
+datasets:
+- rsalshalan/MGB2
+language:
+- ar
+base_model:
+- microsoft/speecht5_asr
+pipeline_tag: automatic-speech-recognition
 ---
 
-# Model Card for Model ID
+# Model Card for ArTST_v2
 
-<!-- Provide a quick summary of what the model is/does. -->
+# ArTST (ASR task)
 
+ArTST model finetuned for  automatic speech recognition (speech-to-text) on MGB2. 
 
 
-## Model Details
-
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- **Developed by:** Speech Lab, MBZUAI
+- **Model type:** SpeechT5
+- **Language:** Arabic
+- **Finetuned from:** (ArTST pretrained)[https://github.com/mbzuai-nlp/ArTST]
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
+```python
+import soundfile as sf
+from transformers import (
+    SpeechT5Config,
+    SpeechT5FeatureExtractor,
+    SpeechT5ForSpeechToText,
+    SpeechT5Processor,
+    SpeechT5Tokenizer,
+)
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+from custom_tokenizer import CustomTextTokenizer
 
-#### Speeds, Sizes, Times [optional]
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+tokenizer = SpeechT5Tokenizer.from_pretrained("mbzuai/artst_asr_v2")
+processor = SpeechT5Processor.from_pretrained("mbzuai/artst_asr_v2" , tokenizer=tokenizer)
+model = SpeechT5ForSpeechToText.from_pretrained("mbzuai/artst_asr_v2").to(device)
 
-[More Information Needed]
+audio, sr = sf.read("audio.wav")
 
-## Evaluation
+inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt")
+predicted_ids = model.generate(**inputs.to(device), max_length=250)
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+print(transcription[0])
+```
 
-### Testing Data, Factors & Metrics
+## Usage with Pipeline
 
-#### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+```python
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
-[More Information Needed]
+device = "cuda"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-#### Factors
+model_id = "MBZUAI/artst_asr_v2"
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+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,
+)
 
-[More Information Needed]
+audio , sr = sf.read("path/to/audio/file")
+if sr != 16000: 
+  audio = librosa.resample(np.array(example["audio"]['array']), orig_sr=sr, target_sr=16000)
+result = pipe(audio)
+print(result['text'])
+```
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
 
-### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+### Model Sources [optional]
+- **Repository:** [github](https://github.com/mbzuai-nlp/ArTST)
+- **Paper :** [Arxiv](https://arxiv.org/abs/2411.05872)
+<!-- - **Demo [optional]:** [More Information Needed] -->
 
-[More Information Needed]
 
 ## Citation [optional]
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@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",
+}
+```