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 dictionaries. It requires a pronunciation dictionary with each line consisting of the orthographic transcription followed by the phonetic transcription. The model is generated using the Phonetisaurus software, which generates FST (finite state transducer) files. 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:
bin/mfa_train_g2p /path/to/dictionary/file /path/to/output/dictionary
This should be used if there are instances of a single orthographic character corresponding to more than 2 phones (common in Korean hangul, Chinese character orthography, etc.).
Montreal-Forced-Aligner/examples/ you will find a small Chinese dictionary. It is too small to generate a usable model, but can provide a helpful example. Inputting
bin/mfa_train_g2p Montreal-Forced-Aligner/examples/chinese_dict.txt Montreal-Forced-Aligner/examples/CH_test_model
This should take no more than a few seconds, and should produce a model which could be used for generating dictionaries
Because there is so little data in chinese_dict.txt, the model produced will not be very accurate. Thus any dictionary generated from it will also be inaccurate.