marbert-arabic-eou

MARBERT Arabic End-of-Utterance Detection

Fine-tuned MARBERT model for Arabic End-of-Utterance (EOU) detection in real-time voice agents.

Model Description

• Base Model: UBC-NLP/MARBERT (163M parameters)
• Task: Binary sequence classification (complete vs incomplete utterance)
• Language: Arabic (emphasis on Saudi/Gulf dialect)
• Training Data: 125K samples from SADA22 dataset
• Inference Speed: ~30ms average latency on CPU

Performance

Test Set: 31,289 samples (50% complete, 50% incomplete)

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("azeddinShr/marbert-arabic-eou")
tokenizer = AutoTokenizer.from_pretrained("azeddinShr/marbert-arabic-eou")

def predict_eou(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=-1)
        eou_prob = probs[0][1].item()
    return eou_prob

# Example
text = "شكرا جزيلا على المساعدة"
prob = predict_eou(text)
is_complete = prob > 0.5
print(f"EOU Probability: {prob:.3f} - {'Complete' if is_complete else 'Incomplete'}")

Training Details

• Epochs: 6
• Batch Size: 16 (train), 32 (eval)
• Learning Rate: 2e-5
• Optimizer: AdamW
• Max Length: 128 tokens
• Training Time: ~2 minutes (GPU)

Use Cases

• Real-time Arabic voice agents
• Turn-taking detection in conversations
• Streaming speech-to-text applications
• Voice assistant interrupt handling

Limitations

• Best performance on Saudi/Gulf Arabic dialects
• Requires Arabic text input (not audio)

Citation

@model{marbert-arabic-eou,
  author = {azeddinShr},
  title = {MARBERT Arabic End-of-Utterance Detection},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/azeddinShr/marbert-arabic-eou}
}

Dataset

Training dataset: azeddinShr/arabic-eou-sada22