urdu-turn-detection-distilbert-quantized

🏷️ Urdu Turn Detection Model — INT8 Quantized Variant (DistilBERT)

<div align="center"> <a href="https://huggingface.co/PuristanLabs1/urdu-turn-detection-distilbert-quantized"> <img src="https://img.shields.io/badge/HF-Model-blue?logo=huggingface" /> </a> <a href="https://huggingface.co/datasets/PuristanLabs1/Urdu-Turn-Detection-10k"> <img src="https://img.shields.io/badge/Dataset-Urdu_Turn_Detection_10k-green" /> </a> <a href="https://www.apache.org/licenses/LICENSE-2.0"> <img src="https://img.shields.io/badge/License-Apache_2.0-yellow" /> </a> </div>

This repository contains the quantized INT8 version of the PuristanLabs1/urdu-turn-detection-distilbert Detection model.
It classifies Urdu sentences into:

• Complete → Speaker has finished speaking
• Incomplete → Speaker intends to continue

🔍 Why Turn Detection Is Not the Same as VAD

Voice Activity Detection (VAD) detects whether speech is present in the audio — it only answers:
➡️ “Is the speaker making sound or not?”

It cannot determine whether a sentence is logically complete, because it relies solely on acoustic features like energy, pitch, and silence gaps.

Turn Detection, on the other hand, is a linguistic task:
➡️ “Is this utterance syntactically and semantically complete?”

Turn detection requires understanding:

• Grammar
• Sentence structure
• Pausing patterns
• Incomplete clauses
• Pragmatic cues in text

Thus:

• VAD → audio boundary detection
• Turn Detection → meaning boundary detection

This model is designed specifically for dialogue systems, ASR post-processing, and real-time voice agents, where understanding when the user has finished their thought is critical.

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⚡ About This Quantized Model

This model is obtained by applying Dynamic INT8 Quantization on top of the finetuned PuristanLabs1/urdu-turn-detection-distilbert EoT classifier.
Quantization reduces model size, memory footprint, and CPU latency, while retaining high accuracy.

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📉 Quantization Summary

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📊 Quantized Model Performance (INT8)

Evaluation on 1,000 held-out Urdu samples:

🔬 Detailed Classification Report

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🆚 Base vs Quantized Model

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🔧 Usage — Fast Inference with Quantized Model

import torch
from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification

model_name = "PuristanLabs1/urdu-turn-detection-distilbert-quantized"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load config
config = AutoConfig.from_pretrained(model_name)

# Build model architecture
model = AutoModelForSequenceClassification.from_config(config)

# Apply dynamic quantization
model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

# Load quantized weights
state_dict = torch.hub.load_state_dict_from_url(
    f"https://huggingface.co/{model_name}/resolve/main/quantized_model.pt",
    map_location="cpu",
)
model.load_state_dict(state_dict)

# Inference
text = "میں گھر جا رہا ہوں"
inputs = tokenizer(text, return_tensors="pt")

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

label = "Complete" if score > 0.5 else "Incomplete"
print(label, score)

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📚 Dataset & Training

• Total samples: 10,000 Urdu utterances
• Balanced: 50% Complete, 50% Incomplete
• Includes:
  • 2,825 real-world human-validated samples
  • 7,175 synthetic high-quality Urdu sentences
• Script: Nastaliq/Arabic Urdu only (no Roman Urdu)

Training was identical to the base model; only inference layers were quantized.

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🎯 Intended Use Cases

• Voice assistants (detect user turn-end)
• Dialogue agents / Conversational AI
• Real-time ASR segmentation
• Voice bots & call center automation
• Turn-taking prediction in multi-speaker systems

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⚠️ Limitations

• Text-only model (no acoustic cues)
• Ambiguous short utterances (e.g., “ہاں؟”, “کیا؟”) may remain context-dependent
• Not a replacement for VAD; best used together with VAD in pipelines

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📝 License

MIT License