The tool does Grapheme-to-Phoneme (G2P) conversion using recurrent neural network (RNN) with long short-term memory units (LSTM). LSTM sequence-to-sequence models were successfully applied in various tasks, including machine translation [1] and grapheme-to-phoneme [2].

This implementation is based on python TensorFlow, which allows an efficient training on both CPU and GPU.

The tool requires TensorFlow. Please see the installation guide for details

A 2-layer LSTM with 64 hidden units is available for download on cmusphinx website. Unpack the model after download. It is trained on CMU English dictionary

The easiest way to check how the tool works is to run it the interactive mode

  python g2p.py --interactive --model [model_folder_path]

then type the words

To generate pronunciations for an English word list with a trained model, run

  python g2p.py --decode [your_wordlist] --model [model_folder_path]

The wordlist is a text file: one word per line

To count Word Error Rate of the trained model, run

  python g2p.py --count_wer [your_wordlist] --model [model_folder_path]

The wordlist is a text file: one word per line

To train G2P you need a dictionary (word and phone sequence per line). See an example dictionary

  python g2p.py --train [train_dictionary.dic] --model [model_folder_path]

It is a good idea to play with the following parameters:

  "--size" - Size of each model layer (Default: 64).
     We observed much better results with 512 units, but the training becomes slow

  "--num_layers" - Number of layers in the model (Default: 1). 
     For example, you can try 1 if the train set is not large enough, 
     or 3 to hopefully get better results

  "--learning_rate" - Initial Learning rate (Default: 0.5). 

  "--learning_rate_decay_factor" - Learning rate decays by this much (Default: 0.8)

You can manually point out Development and Test datasets:

  "--valid" - Development dictionary (Default: created from train_dictionary.dic)
  "--test" - Test dictionary (Default: created from train_dictionary.dic)

[1] Ilya Sutskever, Vinyals Oriol and V. Le Quoc. "Sequence to sequence learning with neural networks." In Advances in neural information processing systems, pp. 3104-3112. 2014.

[2] Yao, Kaisheng, and Geoffrey Zweig. "Sequence-to-sequence neural net models for grapheme-to-phoneme conversion." arXiv preprint arXiv:1506.00196, 2015.