videosdk-live_Namo-Turn-Detector-v1-Korean

---

language: ko license: apache-2.0 library_name: onnxruntime pipeline_tag: voice-activity-detection tags:

• turn-detection
• end-of-utterance
• distilbert
• onnx
• quantized
• conversational-ai
• voice-assistant
• real-time base_model: distilbert-base-multilingual-cased datasets:
• videosdk-live/Namo-Turn-Detector-v1-Train model-index:
• name: Namo Turn Detector v1 - Korean results:
  • task: type: text-classification name: Turn Detection dataset: name: Namo Turn Detector v1 Test - Korean type: videosdk-live/Namo-Turn-Detector-v1-Test split: train metrics:
    • type: accuracy value: 0.973034 name: Accuracy
    • type: f1 value: 0.973214 name: F1 Score
    • type: precision value: 0.964602 name: Precision
    • type: recall value: 0.981982 name: Recall

---

🎯 Namo Turn Detector v1 - Korean

🚀 Namo Turn Detection Model for Korean

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📋 Overview

The Namo Turn Detector is a specialized AI model designed to solve one of the most challenging problems in conversational AI: knowing when a user has finished speaking.

This Korean-specialist model uses advanced natural language understanding to distinguish between:

• ✅ Complete utterances (user is done speaking)
• 🔄 Incomplete utterances (user will continue speaking)

Built on DistilBERT architecture and optimized with quantized ONNX format, it delivers enterprise-grade performance with minimal latency.

🔑 Key Features

• Turn Detection Specialist: Detects end-of-turn vs. continuation in Korean speech transcripts.
• Low Latency: Optimized with quantized ONNX for <14ms inference.
• Robust Performance: 97.3% accuracy on diverse Korean utterances.
• Easy Integration: Compatible with Python, ONNX Runtime, and VideoSDK Agents SDK.
• Enterprise Ready: Supports real-time conversational AI and voice assistants.

📊 Performance Metrics

Metric	Score
🎯 Accuracy	97.30%
📈 F1-Score	97.32%
🎪 Precision	96.46%
🎭 Recall	98.19%
⚡ Latency	<14ms
💾 Model Size	~135MB

📊 Evaluated on 800+ Korean utterances from diverse conversational contexts

⚡️ Speed Analysis

🔧 Train & Test Scripts

🛠️ Installation

To use this model, you will need to install the following libraries.

pip install onnxruntime transformers huggingface_hub

🚀 Quick Start

You can run inference directly from Hugging Face repository.

import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download

class TurnDetector:
    def __init__(self, repo_id="videosdk-live/Namo-Turn-Detector-v1-Korean"):
        """
        Initializes the detector by downloading the model and tokenizer
        from the Hugging Face Hub.
        """
        print(f"Loading model from repo: {repo_id}")
        
        # Download the model and tokenizer from the Hub
        # Authentication is handled automatically if you are logged in
        model_path = hf_hub_download(repo_id=repo_id, filename="model_quant.onnx")
        self.tokenizer = AutoTokenizer.from_pretrained(repo_id)
        
        # Set up the ONNX Runtime inference session
        self.session = ort.InferenceSession(model_path)
        self.max_length = 512
        print("✅ Model and tokenizer loaded successfully.")

    def predict(self, text: str) -> tuple:
        """
        Predicts if a given text utterance is the end of a turn.
        Returns (predicted_label, confidence) where:
        - predicted_label: 0 for "Not End of Turn", 1 for "End of Turn"
        - confidence: confidence score between 0 and 1
        """
        # Tokenize the input text
        inputs = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_tensors="np"
        )
        
        # Prepare the feed dictionary for the ONNX model
        feed_dict = {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"]
        }
        
        # Run inference
        outputs = self.session.run(None, feed_dict)
        logits = outputs[0]

        probabilities = self._softmax(logits[0])
        predicted_label = np.argmax(probabilities)
        confidence = float(np.max(probabilities))

        return predicted_label, confidence

    def _softmax(self, x, axis=None):
        if axis is None:
            axis = -1
        exp_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
        return exp_x / np.sum(exp_x, axis=axis, keepdims=True)

# --- Example Usage ---
if __name__ == "__main__":
    detector = TurnDetector()
    
    sentences = [
        "교남동은 종로구 내에서 상대적으로 보수세가 강한 지역으로 분류된다.",      # Expected: End of Turn
        "1937년 중화민국과 소련이 중소불가침조약을 체결하다 그래서", # Expected: Not End of Turn
    ]
    
    for sentence in sentences:
        predicted_label, confidence = detector.predict(sentence)
        result = "End of Turn" if predicted_label == 1 else "Not End of Turn"
        print(f"'{sentence}' -> {result} (confidence: {confidence:.3f})")
        print("-" * 50)

🤖 VideoSDK Agents Integration

Integrate this turn detector directly with VideoSDK Agents for production-ready conversational AI applications.

from videosdk_agents import NamoTurnDetectorV1, pre_download_namo_turn_v1_model

#download model
pre_download_namo_turn_v1_model(language="ko")

# Initialize Korean turn detector for VideoSDK Agents
turn_detector = NamoTurnDetectorV1(language="ko")

📚 Complete Integration Guide - Learn how to use NamoTurnDetectorV1 with VideoSDK Agents

📖 Citation

@model{namo_turn_detector_ko_2025,
  title={Namo Turn Detector v1: Korean},
  author={VideoSDK Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/videosdk-live/Namo-Turn-Detector-v1-Korean},
  note={ONNX-optimized DistilBERT for turn detection in Korean}
}

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Made with ❤️ by the VideoSDK Team