# coding: utf-8
import re
import string
import numpy as np
from text import cleaners
from text.symbols import symbols, en_symbols, PAD, EOS
from text.korean import jamo_to_korean
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)} # 80개
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
isEn=False
# Regular expression matching text enclosed in curly braces:
_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
puncuation_table = str.maketrans({key: None for key in string.punctuation})
def convert_to_en_symbols():
'''Converts built-in korean symbols to english, to be used for english training
'''
global _symbol_to_id, _id_to_symbol, isEn
if not isEn:
print(" [!] Converting to english mode")
_symbol_to_id = {s: i for i, s in enumerate(en_symbols)}
_id_to_symbol = {i: s for i, s in enumerate(en_symbols)}
isEn=True
def remove_puncuations(text):
return text.translate(puncuation_table)
def text_to_sequence(text, cleaner_names=['korean_cleaners'], as_token=False):
return _text_to_sequence(text, cleaner_names, as_token)
def _text_to_sequence(text, cleaner_names, as_token):
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
Args:
text: string to convert to a sequence
cleaner_names: names of the cleaner functions to run the text through
Returns:
List of integers corresponding to the symbols in the text
'''
sequence = []
# Check for curly braces and treat their contents as ARPAbet:
while len(text):
m = _curly_re.match(text)
if not m:
sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
break
sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
sequence += _arpabet_to_sequence(m.group(2))
text = m.group(3)
# Append EOS token
sequence.append(_symbol_to_id[EOS]) # [14, 29, 45, 2, 27, 62, 20, 21, 4, 39, 45, 1]
if as_token:
return sequence_to_text(sequence, combine_jamo=True)
else:
return np.array(sequence, dtype=np.int32)
def sequence_to_text(sequence, skip_eos_and_pad=False, combine_jamo=False):
'''Converts a sequence of IDs back to a string'''
result = ''
for symbol_id in sequence:
if symbol_id in _id_to_symbol:
s = _id_to_symbol[symbol_id]
# Enclose ARPAbet back in curly braces:
if len(s) > 1 and s[0] == '@':
s = '{%s}' % s[1:]
if not skip_eos_and_pad or s not in [EOS, PAD]:
result += s
result = result.replace('}{', ' ')
if combine_jamo:
return jamo_to_korean(result)
else:
return result
def _clean_text(text, cleaner_names):
for name in cleaner_names:
cleaner = getattr(cleaners, name)
if not cleaner:
raise Exception('Unknown cleaner: %s' % name)
text = cleaner(text) # '존경하는' --> ['ᄌ', 'ᅩ', 'ᆫ', 'ᄀ', 'ᅧ', 'ᆼ', 'ᄒ', 'ᅡ', 'ᄂ', 'ᅳ', 'ᆫ', '~']
return text
def _symbols_to_sequence(symbols):
return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
def _arpabet_to_sequence(text):
return _symbols_to_sequence(['@' + s for s in text.split()])
def _should_keep_symbol(s):
return s in _symbol_to_id and s is not '_' and s is not '~'
