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---
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}
}
```
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