Metadata-Version: 1.1
Name: g2p-en
Version: 2.1.0
Summary: A Simple Python Module for English Grapheme To Phoneme Conversion
Home-page: https://github.com/Kyubyong/g2p
Author: Kyubyong Park & Jongseok Kim
Author-email: kbpark.linguist@gmail.com
License: Apache Software License
Download-URL: https://github.com/Kyubyong/g2p/archive/1.0.0.tar.gz
Description: g2p\_en: A Simple Python Module for English Grapheme To Phoneme Conversion
        ==========================================================================
        
        [Update] * We removed TensorFlow from the dependencies. After all, it changes its APIs quite often, and we don't expect you to have a GPU. Instead, NumPy is used for inference.
        
        This module is designed to convert English graphemes (spelling) to
        phonemes (pronunciation). It is considered essential in several tasks
        such as speech synthesis. Unlike many languages like Spanish or German
        where pronunciation of a word can be inferred from its spelling, English
        words are often far from people's expectations. Therefore, it will be
        the best idea to consult a dictionary if we want to know the
        pronunciation of some word. However, there are at least two tentative
        issues in this approach. First, you can't disambiguate the pronunciation
        of homographs, words which have multiple pronunciations. (See ``a``
        below.) Second, you can't check if the word is not in the dictionary.
        (See ``b`` below.)
        
        -
        
           \a.  I refuse to collect the refuse around here. (rɪ\|fju:z as verb vs. \|refju:s as noun)
        
        -
           \b.  I am an activationist. (activationist: newly coined word which means ``n. A person who designs and implements programs of treatment or therapy that use recreation and activities to help people whose functional abilities are affected by illness or disability.`` from `WORD SPY <https://wordspy.com/index.php?word=activationist>`__
        
        For the first homograph issue, fortunately many homographs can be
        disambiguated using their part-of-speech, if not all. When it comes to
        the words not in the dictionary, however, we should make our best guess
        using our knowledge. In this project, we employ a deep learning seq2seq
        framework based on TensorFlow.
        
        Algorithm
        ---------
        
        1. Spells out arabic numbers and some currency symbols. (e.g. $200 ->
           two hundred dollars) (This is borrowed from `Keith Ito's
           code <https://github.com/keithito/tacotron/blob/master/text/numbers.py>`__)
        2. Attempts to retrieve the correct pronunciation for homographs based
           on their POS)
        3. Looks up `The CMU Pronouncing
           Dictionary <http://www.speech.cs.cmu.edu/cgi-bin/cmudict>`__ 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 <https://github.com/ozmig77>`__
        
Keywords: g2p,g2p_en,g2pE
Platform: UNKNOWN
