---
tags:
- khmer
- nlp
- punctuation-restoration
- inverse-text-normalization
- asr
- xlm-roberta
license: mit
language:
- km
---

# KhmerTagger: Inverse Text Normalization for Khmer ASR

KhmerTagger is a model for inverse text normalization (ITN) of Khmer Automatic Speech Recognition (ASR) outputs. It performs punctuation restoration and number recognition to improve readability of raw ASR text.

## Model Description

The model is based on XLM-RoBERTa as the encoder, with a bidirectional LSTM layer and two classification heads:
- **Punctuation head**: Predicts punctuation marks (space, comma, question mark, exclamation mark, etc.)
- **Number head**: Identifies and tags numeric entities in the text

## Usage

```python
from transformers import XLMRobertaTokenizer
import torch
from model import KhmerTagger

# Load tokenizer
tokenizer = XLMRobertaTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")

# Load model
model = KhmerTagger(n_punct_features=5, n_num_features=3)
model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu", weights_only=True))
model.eval()

# Your inference code here...
```

## Training

The model was trained on 1.5 million tokens of Khmer news data and achieved 97.2% accuracy on the validation set.

## Citation

```bibtex
@misc{khmertagger2025,
  author = {Seanghay Yath},
  title = {KhmerTagger: Inverse Text Normalization for Khmer Automatic Speech Recognition},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/seanghay/khmertagger}},
  note = {Open source project for Khmer punctuation restoration and number recognition using XLM-ROBERTa}
}
```