HossamEL-Dein/arabic-eou-model

Arabic End-of-Utterance Detection Model

Model Description

This model detects End-of-Utterance (EOU) in Arabic conversations, specifically optimized for Saudi dialects. It predicts the probability that a speaker has finished their conversational turn based on text transcription.

Use Case: Real-time conversational AI agents (voice assistants, chatbots, customer service)

Performance

Metric	Score
Test Accuracy	99.6%
Precision	100%
Recall	99.45%
F1 Score	99.73%
AUC-ROC	99.96%
Inference Time	~15-20ms

Training Data

• Total samples: 5,000
• SADA22 (Real Saudi audio): 104 samples (2.1%)
• Synthetic (Saudi patterns): 4,896 samples (97.9%)
• Splits: 80% train / 10% validation / 10% test

Quick Start

Installation

pip install transformers torch

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
model = AutoModelForSequenceClassification.from_pretrained("HossamEL-Dein/arabic-eou-model")
tokenizer = AutoTokenizer.from_pretrained("HossamEL-Dein/arabic-eou-model")
model.eval()

# Predict EOU
text = "مرحبا كيف حالك اليوم"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    eou_probability = probs[0][1].item()

print(f"EOU Probability: {eou_probability:.2%}")
# Output: EOU Probability: 98.56%

Integration with LiveKit

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

class EOUDetector:
    def __init__(self, threshold=0.7):
        self.model = AutoModelForSequenceClassification.from_pretrained("HossamEL-Dein/arabic-eou-model")
        self.tokenizer = AutoTokenizer.from_pretrained("HossamEL-Dein/arabic-eou-model")
        self.model.eval()
        self.threshold = threshold
    
    def check_eou(self, transcript_text):
        inputs = self.tokenizer(transcript_text, return_tensors="pt")
        with torch.no_grad():
            outputs = self.model(**inputs)
            probs = torch.softmax(outputs.logits, dim=-1)
            eou_prob = probs[0][1].item()
        
        return {
            'probability': eou_prob,
            'is_eou': eou_prob > self.threshold
        }

# Use in LiveKit agent
detector = EOUDetector()
result = detector.check_eou("مرحبا كيف حالك")
if result['is_eou']:
    print("User finished speaking!")

Model Architecture

• Base Model: aubmindlab/bert-base-arabertv02
• Task: Binary sequence classification
• Input: Arabic text (up to 128 tokens)
• Output: 2-class probability distribution [Non-EOU, EOU]
• Parameters: 136M

Training Details

• Framework: PyTorch + Transformers
• Epochs: 3
• Batch Size: 16
• Learning Rate: 2e-5
• Optimizer: AdamW
• Training Time: ~3 hours on T4 GPU

Intended Use

Primary Use Cases

• ✅ Real-time voice assistants
• ✅ Arabic conversational AI
• ✅ Turn-taking detection in dialogues
• ✅ LiveKit agent integration

Limitations

• Trained primarily on Saudi dialect patterns
• Requires text input (not raw audio)
• Best for conversational context (5-10 seconds)
• May need threshold tuning for specific use cases

Dataset

Training dataset available at: HossamEL-Dein/arabic-eou-dataset

Citation

@misc{arabic-eou-2024,
  author = {HossamEL-Dein},
  title = {Arabic End-of-Utterance Detection Model},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/HossamEL-Dein/arabic-eou-model}
}

License

Apache 2.0

Contact

For questions or issues, please open an issue on the model repository.