Mintplex-Labs/g2p-en
1
0
Fork 0
mirror of https://github.com/Mintplex-Labs/g2p-en.git synced 2026-07-01 18:25:51 -04:00
Code Issues Packages Projects Releases Wiki Activity

update information to upload pip

Browse Source
This commit is contained in:
kjs
2019-04-08 19:13:00 +09:00
parent 69d9d6e755
commit 87697b824f
9 changed files with 53 additions and 53 deletions
Download Patch File Download Diff File Expand all files Collapse all files
MANIFEST.in
+1 -1
View File
@@ -1,2 +1,2 @@
include g2p_en/homographs.en
include g2p_en/logdir/*
include g2p_en/checkpoint20.npz
README.md
+14 -9
View File
@@ -53,15 +53,20 @@ nltk package will be automatically downloaded at your first run.
## Usage
from g2p_en import g2p
text = "I refuse to collect the refuse around here."
print(g2p(text))
>>>['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', 'EH1', 'R', '.']
text = "I am an activationist."
print(g2p(text))
>>>['AY1', 'M', ' ', 'AE1', 'N', ' ', 'AE2', 'K', 'T', 'AH0', 'V', 'EY1', 'SH', 'AH0', 'N', 'IH0', 'S', 'T']
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.
README.rst
+14 -9
View File
@@ -82,15 +82,20 @@ Usage
::
from g2p_en import g2p
text = "I refuse to collect the refuse around here."
print(g2p(text))
>>>['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', 'EH1', 'R', '.']
text = "I am an activationist."
print(g2p(text))
>>>['AY1', 'M', ' ', 'AE1', 'N', ' ', 'AE2', 'K', 'T', 'AH0', 'V', 'EY1', 'SH', 'AH0', 'N', 'IH0', 'S', 'T']
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.
g2p_en.egg-info/PKG-INFO
+18 -21
View File
@@ -1,6 +1,6 @@
Metadata-Version: 1.1
Name: g2p-en
Version: 1.0.0
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
@@ -10,6 +10,8 @@ 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
@@ -51,18 +53,16 @@ Description: g2p\_en: A Simple Python Module for English Grapheme To Phoneme Con
Environment
-----------
- python 2.x or 3.x
- python 3.x
Dependencies
------------
- numpy >= 1.13.1
- tensorflow >= 1.3.0
- nltk >= 3.2.4
- python -m nltk.downloader "averaged\_perceptron\_tagger" "cmudict"
- inflect >= 0.3.1
- Distance >= 0.1.3
- future >= 0.16.0
Installation
------------
@@ -91,24 +91,21 @@ Description: g2p\_en: A Simple Python Module for English Grapheme To Phoneme Con
::
from g2p_en import g2p
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', ' ', '.']
text = "I refuse to collect the refuse around here."
print(g2p(text))
>>>[u'AY1', ' ', u'R', u'IH0', u'F', u'Y', u'UW1', u'Z', ' ', u'T', u'UW1', ' ', u'K', u'AH0', u'L', u'EH1', u'K', u'T', ' ', u'DH', u'AH0', ' ', u'R', u'EH1', u'F', u'Y', u'UW2', u'Z', ' ', u'ER0', u'AW1', u'N', u'D', ' ', u'HH', u'EH1', u'R']
text = "I am an activationist."
print(g2p(text))
>>>[u'AY1', u'M', ' ', u'AE1', u'N', ' ', u'AE2', u'K', u'T', u'AH0', u'V', u'EY1', u'SH', u'AH0', u'N', u'IH0', u'S', u'T']
If you need to convert lots of texts, you can use the global tf session.
::
import g2p_en as g2p
with g2p.Session():
phs = [g2p.g2p(text) for text in texts]
May, 2018.
g2p_en.egg-info/SOURCES.txt
+2 -7
View File
@@ -1,19 +1,14 @@
MANIFEST.in
README.md
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/train.py
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
g2p_en/logdir/checkpoint
g2p_en/logdir/model_epoch_14_gs_27956.data-00000-of-00001
g2p_en/logdir/model_epoch_14_gs_27956.index
g2p_en/logdir/model_epoch_14_gs_27956.meta
g2p_en.egg-info/top_level.txt
g2p_en.egg-info/requires.txt
-2
View File
@@ -1,6 +1,4 @@
numpy>=1.13.1
tensorflow>=1.3.0
nltk>=3.2.4
inflect>=0.3.1
distance>=0.1.3
future>=0.16.0
g2p_en/__init__.py
+1 -1
View File
@@ -1 +1 @@
from .g2p import g2p, Session
from .g2p import G2p
g2p_en/g2p.py
+2 -2
View File
@@ -14,7 +14,7 @@ import re
import os
import unicodedata
from builtins import str as unicode
from expand import normalize_numbers
from .expand import normalize_numbers
try:
nltk.data.find('taggers/averaged_perceptron_tagger.zip')
@@ -71,7 +71,7 @@ class G2p(object):
self.homograph2features = construct_homograph_dictionary()
def load_variables(self):
self.variables = np.load('checkpoint20.npz')
self.variables = np.load(os.path.join(dirname,'checkpoint20.npz'))
self.enc_emb = self.variables["enc_emb"] # (29, 64). (len(graphemes), emb)
self.enc_w_ih = self.variables["enc_w_ih"] # (3*128, 64)
self.enc_w_hh = self.variables["enc_w_hh"] # (3*128, 128)
setup.py
+1 -1
View File
@@ -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 = '2.0.0',
version = '2.0.1',
description = 'A Simple Python Module for English Grapheme To Phoneme Conversion',
long_description=long_description,
author = 'Kyubyong Park & Jongseok Kim',