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 at least version 1.0.0. Please see the installation guide for details
You can install tensorflow with the following command:
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0-cp27-none-linux_x86_64.whl
The package itself uses setuptools, so you can follow standard installation process:
sudo python setup.py install
You can also run the tests
python setup.py test
The runnable script g2p-seq2seq is installed in /usr/local/bin folder by default (you can adjust it with setup.py options if needed) . You need to make sure you have this folder included in your PATH so you can run this script from command line.
A pretrained model 2-layer LSTM with 512 hidden units is available for download on cmusphinx website. Unpack the model after download. The model is trained on CMU English dictionary
wget -O g2p-seq2seq-cmudict.tar.gz https://sourceforge.net/projects/cmusphinx/files/G2P%20Models/g2p-seq2seq-cmudict.tar.gz/download
tar xf g2p-seq2seq-cmudict.tar.gz
The easiest way to check how the tool works is to run it the interactive mode and type the words
$ g2p-seq2seq --interactive --model g2p-seq2seq-cmudict
Creating 2 layers of 512 units.
Reading model parameters from g2p-seq2seq-cmudict
> hello
HH EH L OW
>
To generate pronunciations for an English word list with a trained model, run
g2p-seq2seq --decode your_wordlist --model model_folder_path
The wordlist is a text file with one word per line
To evaluate Word Error Rate of the trained model, run
g2p-seq2seq --evaluate your_test_dictionary --model model_folder_path
The test dictionary should be a dictionary in standard format.
To train G2P you need a dictionary (word and phone sequence per line). See an example dictionary
g2p-seq2seq --train train_dictionary.dic --model model_folder_path
You can set up maximum training steps:
"--max_steps" - Maximum number of training steps (Default: 0).
If 0 train until no improvement is observed
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: 2).
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)
If you need to continue train saved model just launch the following code:
g2p-seq2seq --train train_dictionary.dic --model model_folder_path
And, if you want to start training from scratch:
"--reinit" - Rewrite model in model_folder_path
| System | WER (CMUdict PRONALSYL 2007), % | WER (CMUdict latest*), % |
|---|---|---|
| Baseline WFST (Phonetisaurus) | 24.4 | 33.89 |
| LSTM num_layers=2, size=64 | 31.3 | ~39 |
| LSTM num_layers=2, size=512 | 23.3 | ~31 |
| * These results pointed out for dictionary without stress. |
[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.