David Portes
commited on
Commit
·
65ce00b
1
Parent(s):
bc2a85a
text_to_seq
Browse files- text_to_sequence.py +309 -0
text_to_sequence.py
ADDED
@@ -0,0 +1,309 @@
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1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
# *****************************************************************************
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3 |
+
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
4 |
+
#
|
5 |
+
# Redistribution and use in source and binary forms, with or without
|
6 |
+
# modification, are permitted provided that the following conditions are met:
|
7 |
+
# * Redistributions of source code must retain the above copyright
|
8 |
+
# notice, this list of conditions and the following disclaimer.
|
9 |
+
# * Redistributions in binary form must reproduce the above copyright
|
10 |
+
# notice, this list of conditions and the following disclaimer in the
|
11 |
+
# documentation and/or other materials provided with the distribution.
|
12 |
+
# * Neither the name of the NVIDIA CORPORATION nor the
|
13 |
+
# names of its contributors may be used to endorse or promote products
|
14 |
+
# derived from this software without specific prior written permission.
|
15 |
+
#
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+
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
|
17 |
+
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
18 |
+
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
19 |
+
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
|
20 |
+
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
|
21 |
+
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
22 |
+
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
|
23 |
+
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
24 |
+
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
25 |
+
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
26 |
+
#
|
27 |
+
# *****************************************************************************
|
28 |
+
import re
|
29 |
+
|
30 |
+
|
31 |
+
valid_symbols = [
|
32 |
+
"AA",
|
33 |
+
"AA0",
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34 |
+
"AA1",
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35 |
+
"AA2",
|
36 |
+
"AE",
|
37 |
+
"AE0",
|
38 |
+
"AE1",
|
39 |
+
"AE2",
|
40 |
+
"AH",
|
41 |
+
"AH0",
|
42 |
+
"AH1",
|
43 |
+
"AH2",
|
44 |
+
"AO",
|
45 |
+
"AO0",
|
46 |
+
"AO1",
|
47 |
+
"AO2",
|
48 |
+
"AW",
|
49 |
+
"AW0",
|
50 |
+
"AW1",
|
51 |
+
"AW2",
|
52 |
+
"AY",
|
53 |
+
"AY0",
|
54 |
+
"AY1",
|
55 |
+
"AY2",
|
56 |
+
"B",
|
57 |
+
"CH",
|
58 |
+
"D",
|
59 |
+
"DH",
|
60 |
+
"EH",
|
61 |
+
"EH0",
|
62 |
+
"EH1",
|
63 |
+
"EH2",
|
64 |
+
"ER",
|
65 |
+
"ER0",
|
66 |
+
"ER1",
|
67 |
+
"ER2",
|
68 |
+
"EY",
|
69 |
+
"EY0",
|
70 |
+
"EY1",
|
71 |
+
"EY2",
|
72 |
+
"F",
|
73 |
+
"G",
|
74 |
+
"HH",
|
75 |
+
"IH",
|
76 |
+
"IH0",
|
77 |
+
"IH1",
|
78 |
+
"IH2",
|
79 |
+
"IY",
|
80 |
+
"IY0",
|
81 |
+
"IY1",
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82 |
+
"IY2",
|
83 |
+
"JH",
|
84 |
+
"K",
|
85 |
+
"L",
|
86 |
+
"M",
|
87 |
+
"N",
|
88 |
+
"NG",
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89 |
+
"OW",
|
90 |
+
"OW0",
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91 |
+
"OW1",
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92 |
+
"OW2",
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93 |
+
"OY",
|
94 |
+
"OY0",
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95 |
+
"OY1",
|
96 |
+
"OY2",
|
97 |
+
"P",
|
98 |
+
"R",
|
99 |
+
"S",
|
100 |
+
"SH",
|
101 |
+
"T",
|
102 |
+
"TH",
|
103 |
+
"UH",
|
104 |
+
"UH0",
|
105 |
+
"UH1",
|
106 |
+
"UH2",
|
107 |
+
"UW",
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108 |
+
"UW0",
|
109 |
+
"UW1",
|
110 |
+
"UW2",
|
111 |
+
"V",
|
112 |
+
"W",
|
113 |
+
"Y",
|
114 |
+
"Z",
|
115 |
+
"ZH",
|
116 |
+
]
|
117 |
+
|
118 |
+
|
119 |
+
"""
|
120 |
+
Defines the set of symbols used in text input to the model.
|
121 |
+
The default is a set of ASCII characters that works well for English. For other data, you can modify _characters. See TRAINING_DATA.md for details.
|
122 |
+
"""
|
123 |
+
|
124 |
+
|
125 |
+
_pad = "_"
|
126 |
+
_punctuation = "!'(),.:;? "
|
127 |
+
_special = "-"
|
128 |
+
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz^*"
|
129 |
+
|
130 |
+
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same
|
131 |
+
# as uppercase letters):
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132 |
+
_arpabet = ["@" + s for s in valid_symbols]
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133 |
+
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134 |
+
# Export all symbols:
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135 |
+
symbols = (
|
136 |
+
[_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
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137 |
+
)
|
138 |
+
|
139 |
+
|
140 |
+
# Mappings from symbol to numeric ID and vice versa:
|
141 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
142 |
+
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
143 |
+
|
144 |
+
# Regular expression matching text enclosed in curly braces:
|
145 |
+
_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
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146 |
+
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147 |
+
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148 |
+
# Regular expression matching whitespace:
|
149 |
+
_whitespace_re = re.compile(r"\s+")
|
150 |
+
|
151 |
+
# List of (regular expression, replacement) pairs for abbreviations:
|
152 |
+
_abbreviations = [
|
153 |
+
(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
|
154 |
+
for x in [
|
155 |
+
("mrs", "misess"),
|
156 |
+
("mr", "mister"),
|
157 |
+
("dr", "doctor"),
|
158 |
+
("st", "saint"),
|
159 |
+
("co", "company"),
|
160 |
+
("jr", "junior"),
|
161 |
+
("maj", "major"),
|
162 |
+
("gen", "general"),
|
163 |
+
("drs", "doctors"),
|
164 |
+
("rev", "reverend"),
|
165 |
+
("lt", "lieutenant"),
|
166 |
+
("hon", "honorable"),
|
167 |
+
("sgt", "sergeant"),
|
168 |
+
("capt", "captain"),
|
169 |
+
("esq", "esquire"),
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170 |
+
("ltd", "limited"),
|
171 |
+
("col", "colonel"),
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172 |
+
("ft", "fort"),
|
173 |
+
]
|
174 |
+
]
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175 |
+
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176 |
+
|
177 |
+
def expand_abbreviations(text):
|
178 |
+
"""expand abbreviations pre-defined
|
179 |
+
"""
|
180 |
+
for regex, replacement in _abbreviations:
|
181 |
+
text = re.sub(regex, replacement, text)
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182 |
+
return text
|
183 |
+
|
184 |
+
|
185 |
+
# def expand_numbers(text):
|
186 |
+
# return normalize_numbers(text)
|
187 |
+
|
188 |
+
|
189 |
+
def lowercase(text):
|
190 |
+
"""lowercase the text
|
191 |
+
"""
|
192 |
+
return text.lower()
|
193 |
+
|
194 |
+
|
195 |
+
def collapse_whitespace(text):
|
196 |
+
"""Replaces whitespace by " " in the text
|
197 |
+
"""
|
198 |
+
return re.sub(_whitespace_re, " ", text)
|
199 |
+
|
200 |
+
|
201 |
+
def convert_to_ascii(text):
|
202 |
+
"""Converts text to ascii
|
203 |
+
"""
|
204 |
+
text_encoded = text.encode("ascii", "ignore")
|
205 |
+
return text_encoded.decode()
|
206 |
+
|
207 |
+
|
208 |
+
def basic_cleaners(text):
|
209 |
+
"""Basic pipeline that lowercases and collapses whitespace without transliteration.
|
210 |
+
"""
|
211 |
+
text = lowercase(text)
|
212 |
+
text = collapse_whitespace(text)
|
213 |
+
return text
|
214 |
+
|
215 |
+
|
216 |
+
def transliteration_cleaners(text):
|
217 |
+
"""Pipeline for non-English text that transliterates to ASCII.
|
218 |
+
"""
|
219 |
+
text = convert_to_ascii(text)
|
220 |
+
text = lowercase(text)
|
221 |
+
text = collapse_whitespace(text)
|
222 |
+
return text
|
223 |
+
|
224 |
+
|
225 |
+
def english_cleaners(text):
|
226 |
+
"""Pipeline for English text, including number and abbreviation expansion.
|
227 |
+
"""
|
228 |
+
text = convert_to_ascii(text)
|
229 |
+
text = lowercase(text)
|
230 |
+
text = expand_abbreviations(text)
|
231 |
+
text = collapse_whitespace(text)
|
232 |
+
return text
|
233 |
+
|
234 |
+
|
235 |
+
def text_to_sequence(text, cleaner_names):
|
236 |
+
"""Returns a list of integers corresponding to the symbols in the text.
|
237 |
+
Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
238 |
+
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
|
239 |
+
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
|
240 |
+
|
241 |
+
Arguments
|
242 |
+
---------
|
243 |
+
text : str
|
244 |
+
string to convert to a sequence
|
245 |
+
cleaner_names : list
|
246 |
+
names of the cleaner functions to run the text through
|
247 |
+
|
248 |
+
"""
|
249 |
+
sequence = []
|
250 |
+
|
251 |
+
# Check for curly braces and treat their contents as ARPAbet:
|
252 |
+
while len(text):
|
253 |
+
m = _curly_re.match(text)
|
254 |
+
if not m:
|
255 |
+
sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
|
256 |
+
break
|
257 |
+
sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
|
258 |
+
sequence += _arpabet_to_sequence(m.group(2))
|
259 |
+
text = m.group(3)
|
260 |
+
|
261 |
+
return sequence
|
262 |
+
|
263 |
+
|
264 |
+
def sequence_to_text(sequence):
|
265 |
+
"""Converts a sequence of IDs back to a string
|
266 |
+
"""
|
267 |
+
result = ""
|
268 |
+
for symbol_id in sequence:
|
269 |
+
if symbol_id in _id_to_symbol:
|
270 |
+
s = _id_to_symbol[symbol_id]
|
271 |
+
# Enclose ARPAbet back in curly braces:
|
272 |
+
if len(s) > 1 and s[0] == "@":
|
273 |
+
s = "{%s}" % s[1:]
|
274 |
+
result += s
|
275 |
+
return result.replace("}{", " ")
|
276 |
+
|
277 |
+
|
278 |
+
def _clean_text(text, cleaner_names):
|
279 |
+
"""apply different cleaning pipeline according to cleaner_names
|
280 |
+
"""
|
281 |
+
for name in cleaner_names:
|
282 |
+
if name == "english_cleaners":
|
283 |
+
cleaner = english_cleaners
|
284 |
+
if name == "transliteration_cleaners":
|
285 |
+
cleaner = transliteration_cleaners
|
286 |
+
if name == "basic_cleaners":
|
287 |
+
cleaner = basic_cleaners
|
288 |
+
if not cleaner:
|
289 |
+
raise Exception("Unknown cleaner: %s" % name)
|
290 |
+
text = cleaner(text)
|
291 |
+
return text
|
292 |
+
|
293 |
+
|
294 |
+
def _symbols_to_sequence(symbols):
|
295 |
+
"""convert symbols to sequence
|
296 |
+
"""
|
297 |
+
return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
|
298 |
+
|
299 |
+
|
300 |
+
def _arpabet_to_sequence(text):
|
301 |
+
"""Prepend "@" to ensure uniqueness
|
302 |
+
"""
|
303 |
+
return _symbols_to_sequence(["@" + s for s in text.split()])
|
304 |
+
|
305 |
+
|
306 |
+
def _should_keep_symbol(s):
|
307 |
+
"""whether to keep a certain symbol
|
308 |
+
"""
|
309 |
+
return s in _symbol_to_id and s != "_" and s != "~"
|