Training a new G2P model

Another tool included with MFA allows you to train a G2P (Grapheme to Phoneme) model automatically from a given pronunciation dictionary. This type of model can be used for Generating a dictionary. It requires a pronunciation dictionary with each line consisting of the orthographic transcription followed by the phonetic transcription. The model is generated using the Pynini package, which generates FST (finite state transducer) files. The implementation is based on that in the Sigmorphon 2020 G2P task baseline. The G2P model output will be a .zip file like the acoustic model generated from alignment.

To train a model from a pronunciation dictionary, the following command is used:

mfa train_g2p dictionary_path output_model_path

The dictionary_path should be a full path to a pronunciation dictionary to train the model from. The output_model_path is the path to save the resulting G2P model.

Extra options (see G2P Configuration for full configuration details):

-t DIRECTORY
--temp_directory DIRECTORY

Temporary directory root to use for training, default is ~/Documents/MFA

-j NUMBER
--num_jobs NUMBER

Number of jobs to use; defaults to 3, set higher if you have more processors available and would like to train the G2P model faster

--config_path

Path to a configuration yaml for G2P model training (see Default G2P training config file for an example yaml file)

--validate

Run a validation on the dictionary with 90% of the data as training and 10% as test. It will output the percentage accuracy of pronunciations generated.

-v
--verbose

Print more messages to the command line output (see also, the log files in the MFA temporary directory for the training)

-c
--clean

Forces removal of temporary files under ~/Documents/MFA or the specified temporary directory prior to training the model.

Note

See Example 3: Train Mandarin G2P model for an example of how to train a G2P model with a premade toy example.