---
language:
- km
tags:
- khmer
- ipa
- phonemization
- seq2seq
- text2text-generation
- encoder-decoder
license: apache-2.0
---

# Khmer IPA

A sequence-to-sequence model that converts Khmer script to IPA (International Phonetic Alphabet) transcriptions.
Trained from scratch using a BERT-based encoder-decoder architecture with character-level tokenizers for both input (Khmer) and output (IPA).

## Model Details

| Property | Value |
|---|---|
| Architecture | `EncoderDecoderModel` (BERT encoder + BERT decoder) |
| Hidden size | 512 |
| Layers (enc + dec) | 6 each |
| Attention heads | 8 |
| Feed-forward size | 1024 |
| Encoder vocab size | 1000 (Khmer characters) |
| Decoder vocab size | 1000 (IPA characters) |
| Max sequence length | 128 |
| Best eval loss | 0.1736 (checkpoint 26000, ~11 epochs) |

## Usage

This model uses **two separate tokenizers** — one for Khmer input and one for IPA output — stored in subfolders.

```python
from transformers import EncoderDecoderModel, AutoTokenizer

model = EncoderDecoderModel.from_pretrained("byumatrixlab/khmer-ipa")
encoder_tokenizer = AutoTokenizer.from_pretrained("byumatrixlab/khmer-ipa", subfolder="encoder_tokenizer")
decoder_tokenizer = AutoTokenizer.from_pretrained("byumatrixlab/khmer-ipa", subfolder="decoder_tokenizer")

def khmer_to_ipa(text, num_beams=4):
    inputs = encoder_tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        max_length=128,
    )
    output_ids = model.generate(
        **inputs,
        max_length=128,
        num_beams=num_beams,
        early_stopping=True,
    )
    return decoder_tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(khmer_to_ipa("សួស្តី"))
# → suǝsdǝy

print(khmer_to_ipa("ខ្ញុំជាសិស្ស"))
# → kɲomciesəh
```

### Batched inference

```python
def khmer_to_ipa_batch(texts, num_beams=4):
    inputs = encoder_tokenizer(
        texts,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=128,
    )
    output_ids = model.generate(
        **inputs,
        max_length=128,
        num_beams=num_beams,
        early_stopping=True,
    )
    return [decoder_tokenizer.decode(ids, skip_special_tokens=True) for ids in output_ids]
```

## Training Data and Repository

Developed by the BYU MATRIX Lab
Training code and data processing scripts: [MekongPhon](https://github.com/byu-matrix-lab/MekongPhon)