/g2p

Code for SLT 2016 paper on Grapheme-to-Phoneme conversion using attention based encoder-decoder models

Primary LanguagePython

Jointly learning to align and convert graphemes to phonemes with neural attention models

Grapheme-to-Phoneme (G2P) conversion using attention based encoder-decoder models

Dependencies

  • Tensorflow == 1.0.0
  • Bunch
  • Editdistance

Evaluation Datasets

We used the following datasets provided by Stanley Chen (stanchen@us.ibm.com):

  • CMUDict
  • Pronlex
  • NetTalk

Note - For CMUDict, it might be a good idea to use the newer version from here - https://raw.githubusercontent.com/cmusphinx/cmudict/master/cmudict.dict

Steps

  • Prepare data:
python data_utils.py -data_dir DATA_DIR [-{train,dev,test}_file] {TRAIN,DEV,TEST}_FILE
  • Train/Eval models
python g2p.py -data_dir DATA_DIR -tb_dir BASE_MODEL_DIR [-eval]

Reference

Jointly learning to align and convert graphemes to phonemes with neural attention models by Shubham Toshniwal and Karen Livescu.

Here's the [BIBTEX] entry for citation ease.