lao-ipa-nontonal

Lao IPA (Non-tonal)

A sequence-to-sequence model that converts Lao script to IPA (International Phonetic Alphabet) transcriptions, without tone markers. Trained from scratch using a BERT-based encoder-decoder architecture with character-level tokenizers for both input (Lao) and output (IPA).

For a version with tone markers, see byumatrixlab/lao-ipa-tonal.

Model Details

Usage

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

from transformers import EncoderDecoderModel, AutoTokenizer

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

def lao_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(lao_to_ipa("ສະບາຍດີ"))

print(lao_to_ipa("ຂອບໃຈ"))

Batched inference

def lao_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

Citation

If you use this model, please cite:

@inproceedings{shurtz2026mekongphon,
  title={MekongPhon: A Large-Scale Parallel IPA Corpus for Lao and Khmer},
  author={Shurtz, Ammon and Richardson, Christian and Richardson, Stephen D},
  booktitle={Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
  volume={11},
  number={16},
  pages={1650--1658},
  year={2026}
}