Algorithm

1. Spells out arabic numbers and some currency symbols. (e.g. $200 -> two hundred dollars) (This is borrowed from Keith Ito’s code)

2. Attempts to retrieve the correct pronunciation for homographs based on their POS)

3. Looks up The CMU Pronouncing Dictionary for non-homographs.

4. For OOVs, we predict their pronunciations using our neural net model.

Environment

• python 3.x

Dependencies

• numpy >= 1.13.1

• nltk >= 3.2.4

• python -m nltk.downloader “averaged_perceptron_tagger” “cmudict”

• inflect >= 0.3.1

• Distance >= 0.1.3

Installation

pip install g2p_en

OR

python setup.py install

nltk package will be automatically downloaded at your first run.

Usage

from g2p_en import G2p

texts = ["I have $250 in my pocket.", # number -> spell-out
         "popular pets, e.g. cats and dogs", # e.g. -> for example
         "I refuse to collect the refuse around here.", # homograph
         "I'm an activationist."] # newly coined word
g2p = G2p()
for text in texts:
    out = g2p(text)
    print(out)
>>> ['AY1', ' ', 'HH', 'AE1', 'V', ' ', 'T', 'UW1', ' ', 'HH', 'AH1', 'N', 'D', 'R', 'AH0', 'D', ' ', 'F', 'IH1', 'F', 'T', 'IY0', ' ', 'D', 'AA1', 'L', 'ER0', 'Z', ' ', 'IH0', 'N', ' ', 'M', 'AY1', ' ', 'P', 'AA1', 'K', 'AH0', 'T', ' ', '.']
>>> ['P', 'AA1', 'P', 'Y', 'AH0', 'L', 'ER0', ' ', 'P', 'EH1', 'T', 'S', ' ', ',', ' ', 'F', 'AO1', 'R', ' ', 'IH0', 'G', 'Z', 'AE1', 'M', 'P', 'AH0', 'L', ' ', 'K', 'AE1', 'T', 'S', ' ', 'AH0', 'N', 'D', ' ', 'D', 'AA1', 'G', 'Z']
>>> ['AY1', ' ', 'R', 'IH0', 'F', 'Y', 'UW1', 'Z', ' ', 'T', 'UW1', ' ', 'K', 'AH0', 'L', 'EH1', 'K', 'T', ' ', 'DH', 'AH0', ' ', 'R', 'EH1', 'F', 'Y', 'UW2', 'Z', ' ', 'ER0', 'AW1', 'N', 'D', ' ', 'HH', 'IY1', 'R', ' ', '.']
>>> ['AY1', ' ', 'AH0', 'M', ' ', 'AE1', 'N', ' ', 'AE2', 'K', 'T', 'IH0', 'V', 'EY1', 'SH', 'AH0', 'N', 'IH0', 'S', 'T', ' ', '.']

May, 2018.

Kyubyong Park & Jongseok Kim