xlm-roberta-urdu-turn-detection

Urdu Turn Detection Model (XLM-RoBERTa-Base)

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This model is a fine-tuned version of xlm-roberta-base for Urdu Turn Detection (End-of-Turn). It serves as a high-accuracy teacher model for smaller distilled variants (like DistilBERT) or as a robust standalone model for server side inference.

This model detects End-of-Turn (EoT) in Urdu speech transcripts, classifying each sentence as:

• Complete → The speaker has finished their turn
• Incomplete → The speaker is pausing / trailing off / not yet done

While this may appear similar to Voice Activity Detection (VAD), it solves a different and more linguistically complex problem:

🔍 VAD vs. Turn Detection (Key Difference)

VAD only detects whether sound is present or absent — i.e.,
➡️ “Is the speaker currently making noise or not?”

It cannot determine whether a sentence is logically complete, because it relies only on raw audio features (energy, frequency, silence gaps).

Turn Detection, however, is a semantic task:
➡️ “Has the speaker finished their thought, or are they about to continue?”

This requires understanding grammar, syntax, pause structures, and sentence completeness — something VAD does not and cannot evaluate.

Example:

Thus, this model complements VAD:

• VAD → detects audio boundaries
• Turn Detection → detects linguistic boundaries

It is finetuned on a 10,000 sample Urdu dataset and optimized for real-time deployment in conversational AI, ASR pipelines, and voice assistants.

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Model Details

• Base Model: xlm-roberta-base
• Task: Binary Text Classification (Complete vs. Incomplete)
• Language: Urdu (اردو)
• Dataset: 10,000 samples (Real-world + High-quality Synthetic)

Performance

Training Metrics

Technical Specifications

Training Configuration

Trainable Parameters

• Total Parameters: ~278M
• Model Size: ~1.1 GB

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "PuristanLabs1/xlm-roberta-urdu-turn-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "میں گھر جا رہا ہوں" # Complete
# text = "اگر میں وہاں ہوتا..." # Incomplete

inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    label = torch.argmax(logits, dim=-1).item()

print("Complete" if label == 1 else "Incomplete")