README.md

---
datasets:
- IbrahimSalah/The_Arabic_News_speech_Corpus_Dataset
language:
- ar
tags:
- Arabic
- MSA
- Speech
- Syllables
- Wav2vec
- ASR
---
 # Arabic syllables recognition with tashkeel
 **paper DOI** : https://doi.org/10.60161/2521-001-001-006 \
This is fine tuned wav2vec2 model to recognize arabic syllables from speech.        
The model was trained on Modern standard arabic dataset .\
5-gram language model is available with the model.

To try it out :

```
!pip install datasets transformers
!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode
```

```
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from transformers import Wav2Vec2ProcessorWithLM
processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Syllables_final_Large')
model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Syllables_final_Large")
```
```
import pandas as pd
dftest = pd.DataFrame(columns=['audio'])
import datasets
from datasets import Dataset
path ='/content/908-33.wav'
dftest['audio']=[path]  ## audio path
dataset = Dataset.from_pandas(dftest)
```
```
import torch
import torchaudio
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["audio"])
    print(sampling_rate)
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
    batch["audio"] = resampler(speech_array).squeeze().numpy()
    return batch
```
```
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(inputs.input_values).logits
    print(logits.numpy().shape)

transcription = processor.batch_decode(logits.numpy()).text
print("Prediction:",transcription[0])
```


# You can then convert the syllables to full word using our fine tuned mT5 model[IbrahimSalah/Arabic_Syllables_to_text_Converter_Using_MT5]

## Citation

**BibTeX:**

```bibtex
@article{2024SyllableBasedAS,
  title={Syllable-Based Arabic Speech Recognition Using Wav2Vec},
  author={إبراهيم عبدالعال and مصطفى الشافعي and محمد عبدالواحد},
  journal={مجلة اللغات الحاسوبية والمعالجة الآلية للغة العربية},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:269151543}
}