🎯 Namo Turn Detector v1 - MultiLingual

🚀 Namo Turn Detection Model for Multiple Languages

🇸🇦 Arabic, 🇮🇳 Bengali, 🇨🇳 Chinese, 🇩🇰 Danish, 🇳🇱 Dutch, 🇩🇪 German, 🇬🇧🇺🇸 English, 🇫🇮 Finnish, 🇫🇷 French, 🇮🇳 Hindi, 🇮🇩 Indonesian, 🇮🇹 Italian, 🇯🇵 Japanese, 🇰🇷 Korean, 🇮🇳 Marathi, 🇳🇴 Norwegian, 🇵🇱 Polish, 🇵🇹 Portuguese, 🇷🇺 Russian, 🇪🇸 Spanish, 🇹🇷 Turkish, 🇺🇦 Ukrainian, and 🇻🇳 Vietnamese

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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 Multilingual model uses advanced natural language understanding to distinguish between:

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

Built on mmBERT 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 multilingual speech transcripts.
• Low Latency: Optimized with quantized ONNX for <29ms inference.
• Robust Performance: Average 90.25% accuracy on multilingual utterances.
• Easy Integration: Compatible with Python, ONNX Runtime, and VideoSDK Agents SDK.
• Enterprise Ready: Supports real-time conversational AI and voice assistants.

📊 Performance Metrics

📊 Evaluated on 25,000+ Multilingual 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-Multilingual"):
        """
        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 = 8192
        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 = [
        "They're often made with oil or sugar.",                         # Expected: End of Turn
        "I think the next logical step is to",                           # Expected: Not End of Turn
        "What are you doing tonight?",                                   # Expected: End of Turn
        "The Revenue Act of 1862 adopted rates that increased with",     # 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()

# Initialize Multilingual turn detector for VideoSDK Agents
turn_detector = NamoTurnDetectorV1()

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

📖 Citation

@model{namo_turn_detector_en_2025,
  title={Namo Turn Detector v1: Multilingual},
  author={VideoSDK Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/videosdk-live/Namo-Turn-Detector-v1-Multilingual},
  note={ONNX-optimized mmBERT for turn detection in 23 Languages}
}

📄 License

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

Made with ❤️ by the VideoSDK Team