---
license: apache-2.0
language:
- ar
- arz
- ary
datasets:
- DynamicSuperb/DialectIdentification_ADI17
- rsalshalan/QASR
base_model:
- facebook/hubert-base-ls960
pipeline_tag: audio-classification
library_name: transformers
---

# 🗣️ HuBERT Arabic Spoken Dialect Classifier

## Model Overview

This model is a fine-tuned version of [`facebook/hubert-base-ls960`](https://huggingface.co/facebook/hubert-base0) for **Arabic spoken dialect classification**. It identifies Modern Standard Arabic (MSA) and 17 regional Arabic dialects from raw audio.

This model is intended for use in tasks such as dialect identification, linguistic research, and dialect-aware speech processing systems.

---

## 🧠 Model Details

- **Base Model**: `facebook/hubert-base-ls960`
- **Task**: Audio Classification (Dialect Identification)
- **Languages**: Arabic (MSA + 17 dialects)
- **Datasets**:
  - **QASR** corpus (for MSA)
  - **ADI17** dataset (for 17 Arabic dialects)

---

## 📊 Labels (`id2label`)

The model predicts one of the following 18 classes:

```json
{
    "0": "MSA",   // Modern Standard Arabic
    "1": "IRA",   // Iraqi Arabic
    "2": "EGY",   // Egyptian Arabic
    "3": "MAU",   // Mauritanian Arabic
    "4": "KSA",   // Saudi Arabic
    "5": "UAE",   // Emirati Arabic
    "6": "SYR",   // Syrian Arabic
    "7": "PAL",   // Palestinian Arabic
    "8": "LEB",   // Lebanese Arabic
    "9": "LIB",   // Libyan Arabic
    "10": "KUW",  // Kuwaiti Arabic
    "11": "ALG",  // Algerian Arabic
    "12": "OMA",  // Omani Arabic
    "13": "QAT",  // Qatari Arabic
    "14": "YEM",  // Yemeni Arabic
    "15": "SUD",  // Sudanese Arabic
    "16": "MOR",  // Moroccan Arabic
    "17": "JOR",  // Jordanian Arabic
}
```

---

## 🚀 Usage

```python
from transformers import Wav2Vec2FeatureExtractor, HubertForSequenceClassification
import torch
import torchaudio

# Load feature extractor and model
processor = Wav2Vec2FeatureExtractor.from_pretrained("IbrahimAmin/hubert-arabic-spoken-dialect-classifier")
model = HubertForSequenceClassification.from_pretrained("IbrahimAmin/hubert-arabic-spoken-dialect-classifier")

# Load audio (must be mono, 16kHz)
waveform, sample_rate = torchaudio.load("your_audio.wav")

# Convert to mono if not already
if waveform.shape[0] > 1:
    waveform = torch.mean(waveform, dim=0, keepdim=True)

# Resample if needed to 16 kHz
if sample_rate != 16000:
    resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
    waveform = resampler(waveform)

inputs = processor(waveform.squeeze(), sampling_rate=16_000, return_tensors="pt")

# Run inference
with torch.inference_mode():
    logits = model(**inputs).logits

# Get predicted label
predicted_label = torch.argmax(logits, dim=-1).item()
print(f"Predicted Dialect: {model.config.id2label[predicted_label]}")
```

---

## 🏋️ Training Datasets

This model was trained using:

- **QASR** corpus to represent Modern Standard Arabic (MSA).
- **ADI17** dataset, which includes 17 varieties of spoken Arabic dialects across different countries and regions.

---

## 📝 Citation

If you use this model in your research or application, please cite:

```bibtex
@misc{amin2025hubertarabicdialect,
  title={HuBERT Arabic Spoken Dialect Classifier},
  author={Ibrahim Amin},
  year={2025},
  publisher = {Hugging Face},
  howpublished={\url{https://huggingface.co/IbrahimAmin/hubert-arabic-spoken-dialect-classifier}},
}
```