# Referenced from https://github.com/hccho2/Tacotron-Wavenet-Vocoder-Korean
import re
[docs]class KoreanCleaner:
@classmethod
def _normalize_numbers(cls, text):
number_to_kor = {
"0": "영",
"1": "일",
"2": "이",
"3": "삼",
"4": "사",
"5": "오",
"6": "육",
"7": "칠",
"8": "팔",
"9": "구",
}
new_text = "".join(
number_to_kor[char] if char in number_to_kor.keys() else char
for char in text
)
return new_text
@classmethod
def _normalize_english_text(cls, text):
upper_alphabet_to_kor = {
"A": "에이",
"B": "비",
"C": "씨",
"D": "디",
"E": "이",
"F": "에프",
"G": "지",
"H": "에이치",
"I": "아이",
"J": "제이",
"K": "케이",
"L": "엘",
"M": "엠",
"N": "엔",
"O": "오",
"P": "피",
"Q": "큐",
"R": "알",
"S": "에스",
"T": "티",
"U": "유",
"V": "브이",
"W": "더블유",
"X": "엑스",
"Y": "와이",
"Z": "지",
}
new_text = re.sub("[a-z]+", lambda x: str.upper(x.group()), text)
new_text = "".join(
upper_alphabet_to_kor[char]
if char in upper_alphabet_to_kor.keys()
else char
for char in new_text
)
return new_text
[docs] @classmethod
def normalize_text(cls, text):
# stage 0 : text strip
text = text.strip()
# stage 1 : normalize numbers
text = cls._normalize_numbers(text)
# stage 2 : normalize english text
text = cls._normalize_english_text(text)
return text