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Amr-h/arabic-eou-marbert

sourceHugging Faceapache-2.0updated 10mo agoView on Hugging Face
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Arabic End-of-Utterance (EOU) Detection Model

This model detects whether a speaker has finished their turn in Arabic conversations, with emphasis on Saudi dialect. It's designed for real-time voice agent applications like LiveKit.

Model Description

  • —Base Model: UBC-NLP/MARBERT
  • —Language: Arabic (with focus on Saudi/Gulf dialect)
  • —Task: Binary classification (Complete vs Incomplete utterance)
  • —Use Case: Real-time turn detection for voice agents

Labels

LabelIDDescription
INCOMPLETE0Speaker has not finished their turn
COMPLETE1Speaker has finished their turn

Performance

Out-of-Distribution Test Results (200 samples)

MetricComplete (1)Incomplete (0)
Precision100.00%85.94%
Recall83.64%100.00%
F1-Score91.09%92.44%

Overall Weighted F1: 91.76%

Key Characteristics

  • —✅ Zero false interruptions - Model never incorrectly predicts "Complete" for incomplete utterances
  • —✅ Conservative behavior - Ideal for voice agents (better to wait than interrupt)
  • —✅ Fast inference - Suitable for real-time applications

Usage

Quick Start

python
from transformers import pipeline

# Load the model
eou_detector = pipeline(
    "text-classification",
    model="Amr-h/arabic-eou-marbert",
    device=0  # Use GPU, or -1 for CPU
)

# Detect end of utterance
text = "هل بلغوك انهم بيحتاجون ساعات اضافيه؟"
result = eou_detector(text)[0]

print(f"Label: {result['label']}")  # COMPLETE or INCOMPLETE
print(f"Confidence: {result['score']:.2%}")

With Model and Tokenizer

python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("Amr-h/arabic-eou-marbert")
tokenizer = AutoTokenizer.from_pretrained("Amr-h/arabic-eou-marbert")

# Inference
text = "انتظر خلني اشوف وين حطيت ال"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()

label = "COMPLETE" if prediction == 1 else "INCOMPLETE"
print(f"Prediction: {label}")

For LiveKit Integration

python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

class ArabicEOUDetector:
    def __init__(self, model_name="Amr-h/arabic-eou-marbert"):
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model.eval()
        
    def predict(self, text: str) -> tuple[bool, float]:
        """
        Returns (is_complete, confidence)
        """
        inputs = self.tokenizer(
            text, 
            return_tensors="pt", 
            truncation=True, 
            max_length=64
        )
        
        with torch.no_grad():
            outputs = self.model(**inputs)
            probs = torch.softmax(outputs.logits, dim=-1)
            prediction = torch.argmax(probs, dim=-1).item()
            confidence = probs[0][prediction].item()
        
        is_complete = prediction == 1
        return is_complete, confidence

Training Details

  • —Training Data: ~12,000 Saudi dialect Arabic utterances
  • —Validation Data: ~1,500 samples
  • —Test Data: ~1,500 samples
  • —Epochs: 3
  • —Learning Rate: 2e-5
  • —Batch Size: 32
  • —Max Length: 64 tokens

Intended Use

  • —Real-time voice agents and conversational AI
  • —Turn-taking detection in Arabic dialogue systems
  • —LiveKit agent integration
  • —Customer service voice bots

Limitations

  • —Optimized for Saudi/Gulf Arabic dialect
  • —May require fine-tuning for other Arabic dialects
  • —Designed for spoken/conversational text, not formal written Arabic

Citation

If you use this model, please cite:

bibtex
@misc{arabic-eou-marbert,
  author = {YOUR_NAME},
  title = {Arabic End-of-Utterance Detection Model},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Amr-h/arabic-eou-marbert}
}

License

Apache 2.0