remove info files, change version

2026-07-01 18:25:51 -04:00 · 2019-04-08 19:22:44 +09:00
parent 87697b824f
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-Metadata-Version: 1.1
-Name: g2p-en
-Version: 2.0.1
-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.
-        
-        Training (Note that pretrained model is already included)
-        ---------------------------------------------------------
-        
-        ::
-        
-            python train.py
-        
-        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
-Platform: UNKNOWN
@@ -1,14 +0,0 @@
-MANIFEST.in
-README.rst
-setup.cfg
-setup.py
-g2p_en/__init__.py
-g2p_en/checkpoint20.npz
-g2p_en/expand.py
-g2p_en/g2p.py
-g2p_en/homographs.en
-g2p_en.egg-info/PKG-INFO
-g2p_en.egg-info/SOURCES.txt
-g2p_en.egg-info/dependency_links.txt
-g2p_en.egg-info/requires.txt
-g2p_en.egg-info/top_level.txt
@@ -1 +0,0 @@
-
@@ -1,4 +0,0 @@
-numpy>=1.13.1
-nltk>=3.2.4
-inflect>=0.3.1
-distance>=0.1.3
@@ -1 +0,0 @@
-g2p_en
@@ -12,7 +12,7 @@ with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
 setup(
  name = 'g2p_en',
  packages = ['g2p_en'], # this must be the same as the name above
-  version = '2.0.1',
+  version = '2.0.0',
  description = 'A Simple Python Module for English Grapheme To Phoneme Conversion',
  long_description=long_description,
  author = 'Kyubyong Park & Jongseok Kim',