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Refactor: improve regular expression performance during NLP

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Improve performance by pre-compiling regular expressions that are executed many times.
This commit is contained in:
tsukumi
2024-05-12 00:26:46 +09:00
parent 2d6baa3928
commit 44693e7d6b
5 changed files with 171 additions and 150 deletions
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73
style_bert_vits2/nlp/chinese/normalizer.py
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@@ -5,6 +5,41 @@ import cn2an
from style_bert_vits2.nlp.symbols import PUNCTUATIONS from style_bert_vits2.nlp.symbols import PUNCTUATIONS
__REPLACE_MAP = {
"：": ",",
"；": ",",
"，": ",",
"。": ".",
"！": "!",
"？": "?",
"\n": ".",
"·": ",",
"、": ",",
"...": "…",
"$": ".",
"“": "'",
"”": "'",
'"': "'",
"‘": "'",
"’": "'",
"（": "'",
"）": "'",
"(": "'",
")": "'",
"《": "'",
"》": "'",
"【": "'",
"】": "'",
"[": "'",
"]": "'",
"—": "-",
"～": "-",
"~": "-",
"「": "'",
"」": "'",
}
def normalize_text(text: str) -> str: def normalize_text(text: str) -> str:
numbers = re.findall(r"\d+(?:\.?\d+)?", text) numbers = re.findall(r"\d+(?:\.?\d+)?", text)
for number in numbers: for number in numbers:
@@ -15,44 +50,10 @@ def normalize_text(text: str) -> str:
def replace_punctuation(text: str) -> str: def replace_punctuation(text: str) -> str:
REPLACE_MAP = {
"：": ",",
"；": ",",
"，": ",",
"。": ".",
"！": "!",
"？": "?",
"\n": ".",
"·": ",",
"、": ",",
"...": "…",
"$": ".",
"“": "'",
"”": "'",
'"': "'",
"‘": "'",
"’": "'",
"（": "'",
"）": "'",
"(": "'",
")": "'",
"《": "'",
"》": "'",
"【": "'",
"】": "'",
"[": "'",
"]": "'",
"—": "-",
"～": "-",
"~": "-",
"「": "'",
"」": "'",
}
text = text.replace("嗯", "恩").replace("呣", "母") text = text.replace("嗯", "恩").replace("呣", "母")
pattern = re.compile("|".join(re.escape(p) for p in REPLACE_MAP.keys())) pattern = re.compile("|".join(re.escape(p) for p in __REPLACE_MAP.keys()))
replaced_text = pattern.sub(lambda x: REPLACE_MAP[x.group()], text) replaced_text = pattern.sub(lambda x: __REPLACE_MAP[x.group()], text)
replaced_text = re.sub( replaced_text = re.sub(
r"[^\u4e00-\u9fa5" + "".join(PUNCTUATIONS) + r"]+", "", replaced_text r"[^\u4e00-\u9fa5" + "".join(PUNCTUATIONS) + r"]+", "", replaced_text

65
style_bert_vits2/nlp/japanese/g2p.py
View File

@@ -5,7 +5,7 @@ from style_bert_vits2.constants import Languages
from style_bert_vits2.logging import logger from style_bert_vits2.logging import logger
from style_bert_vits2.nlp import bert_models from style_bert_vits2.nlp import bert_models
from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
from style_bert_vits2.nlp.japanese.mora_list import MORA_KATA_TO_MORA_PHONEMES from style_bert_vits2.nlp.japanese.mora_list import MORA_KATA_TO_MORA_PHONEMES, VOWELS
from style_bert_vits2.nlp.japanese.normalizer import replace_punctuation from style_bert_vits2.nlp.japanese.normalizer import replace_punctuation
from style_bert_vits2.nlp.symbols import PUNCTUATIONS from style_bert_vits2.nlp.symbols import PUNCTUATIONS
@@ -144,7 +144,7 @@ def text_to_sep_kata(
## 例外を送出しない場合 ## 例外を送出しない場合
## 読めない文字は「'」として扱う ## 読めない文字は「'」として扱う
logger.warning( logger.warning(
f"Cannot read: {word} in:\n{norm_text}, replaced with \"'\"" f'Cannot read: {word} in:\n{norm_text}, replaced with "\'"'
) )
# word の文字数分「'」を追加 # word の文字数分「'」を追加
yomi = "'" * len(word) yomi = "'" * len(word)
@@ -428,15 +428,23 @@ def __g2phone_tone_wo_punct(text: str) -> list[tuple[str, int]]:
return result return result
__PYOPENJTALK_G2P_PROSODY_A1_PATTERN = re.compile(r"/A:([0-9\-]+)\+")
__PYOPENJTALK_G2P_PROSODY_A2_PATTERN = re.compile(r"\+(\d+)\+")
__PYOPENJTALK_G2P_PROSODY_A3_PATTERN = re.compile(r"\+(\d+)/")
__PYOPENJTALK_G2P_PROSODY_E3_PATTERN = re.compile(r"!(\d+)_")
__PYOPENJTALK_G2P_PROSODY_F1_PATTERN = re.compile(r"/F:(\d+)_")
__PYOPENJTALK_G2P_PROSODY_P3_PATTERN = re.compile(r"\-(.*?)\+")
def __pyopenjtalk_g2p_prosody( def __pyopenjtalk_g2p_prosody(
text: str, drop_unvoiced_vowels: bool = True text: str, drop_unvoiced_vowels: bool = True
) -> list[str]: ) -> list[str]:
""" """
ESPnet の実装から引用、変更点無し。「ん」は「N」なことに注意。 ESPnet の実装から引用、概ね変更点無し。「ん」は「N」なことに注意。
ref: https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py ref: https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------
Extract phoneme + prosoody symbol sequence from input full-context labels. Extract phoneme + prosody symbol sequence from input full-context labels.
The algorithm is based on `Prosodic features control by symbols as input of The algorithm is based on `Prosodic features control by symbols as input of
sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks. sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
@@ -457,8 +465,8 @@ def __pyopenjtalk_g2p_prosody(
modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104 modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
""" """
def _numeric_feature_by_regex(regex: str, s: str) -> int: def _numeric_feature_by_regex(pattern: re.Pattern[str], s: str) -> int:
match = re.search(regex, s) match = pattern.search(s)
if match is None: if match is None:
return -50 return -50
return int(match.group(1)) return int(match.group(1))
@@ -471,7 +479,7 @@ def __pyopenjtalk_g2p_prosody(
lab_curr = labels[n] lab_curr = labels[n]
# current phoneme # current phoneme
p3 = re.search(r"\-(.*?)\+", lab_curr).group(1) # type: ignore p3 = __PYOPENJTALK_G2P_PROSODY_P3_PATTERN.search(lab_curr).group(1) # type: ignore
# deal unvoiced vowels as normal vowels # deal unvoiced vowels as normal vowels
if drop_unvoiced_vowels and p3 in "AEIOU": if drop_unvoiced_vowels and p3 in "AEIOU":
p3 = p3.lower() p3 = p3.lower()
@@ -483,7 +491,9 @@ def __pyopenjtalk_g2p_prosody(
phones.append("^") phones.append("^")
elif n == N - 1: elif n == N - 1:
# check question form or not # check question form or not
e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr) e3 = _numeric_feature_by_regex(
__PYOPENJTALK_G2P_PROSODY_E3_PATTERN, lab_curr
)
if e3 == 0: if e3 == 0:
phones.append("$") phones.append("$")
elif e3 == 1: elif e3 == 1:
@@ -496,14 +506,16 @@ def __pyopenjtalk_g2p_prosody(
phones.append(p3) phones.append(p3)
# accent type and position info (forward or backward) # accent type and position info (forward or backward)
a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr) a1 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_A1_PATTERN, lab_curr)
a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr) a2 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_A2_PATTERN, lab_curr)
a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr) a3 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_A3_PATTERN, lab_curr)
# number of mora in accent phrase # number of mora in accent phrase
f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr) f1 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_F1_PATTERN, lab_curr)
a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1]) a2_next = _numeric_feature_by_regex(
__PYOPENJTALK_G2P_PROSODY_A2_PATTERN, labels[n + 1]
)
# accent phrase border # accent phrase border
if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl": if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
phones.append("#") phones.append("#")
@@ -560,9 +572,6 @@ def __handle_long(sep_phonemes: list[list[str]]) -> list[list[str]]:
list[list[str]]: 長音記号を処理した音素のリストのリスト list[list[str]]: 長音記号を処理した音素のリストのリスト
""" """
# 母音の集合 (便宜上「ん」を含める)
VOWELS = {"a", "i", "u", "e", "o", "N"}
for i in range(len(sep_phonemes)): for i in range(len(sep_phonemes)):
if len(sep_phonemes[i]) == 0: if len(sep_phonemes[i]) == 0:
# 空白文字等でリストが空の場合 # 空白文字等でリストが空の場合
@@ -588,6 +597,15 @@ def __handle_long(sep_phonemes: list[list[str]]) -> list[list[str]]:
return sep_phonemes return sep_phonemes
__KATAKANA_PATTERN = re.compile(r"[\u30A0-\u30FF]+")
__MORA_PATTERN = re.compile(
"|".join(
map(re.escape, sorted(MORA_KATA_TO_MORA_PHONEMES.keys(), key=len, reverse=True))
)
)
__LONG_PATTERN = re.compile(r"(\w)(ー*)")
def __kata_to_phoneme_list(text: str) -> list[str]: def __kata_to_phoneme_list(text: str) -> list[str]:
""" """
原則カタカナの `text` を受け取り、それをそのままいじらずに音素記号のリストに変換。 原則カタカナの `text` を受け取り、それをそのままいじらずに音素記号のリストに変換。
@@ -610,23 +628,20 @@ def __kata_to_phoneme_list(text: str) -> list[str]:
if set(text).issubset(set(PUNCTUATIONS)): if set(text).issubset(set(PUNCTUATIONS)):
return list(text) return list(text)
# `text` がカタカナ（`ー`含む）のみからなるかどうかをチェック # `text` がカタカナ（`ー`含む）のみからなるかどうかをチェック
if re.fullmatch(r"[\u30A0-\u30FF]+", text) is None: if __KATAKANA_PATTERN.fullmatch(text) is None:
raise ValueError(f"Input must be katakana only: {text}") raise ValueError(f"Input must be katakana only: {text}")
sorted_keys = sorted(MORA_KATA_TO_MORA_PHONEMES.keys(), key=len, reverse=True)
pattern = "|".join(map(re.escape, sorted_keys))
def mora2phonemes(mora: str) -> str: def mora2phonemes(mora: str) -> str:
cosonant, vowel = MORA_KATA_TO_MORA_PHONEMES[mora] consonant, vowel = MORA_KATA_TO_MORA_PHONEMES[mora]
if cosonant is None: if consonant is None:
return f" {vowel}" return f" {vowel}"
return f" {cosonant} {vowel}" return f" {consonant} {vowel}"
spaced_phonemes = re.sub(pattern, lambda m: mora2phonemes(m.group()), text) spaced_phonemes = __MORA_PATTERN.sub(lambda m: mora2phonemes(m.group()), text)
# 長音記号「ー」の処理 # 長音記号「ー」の処理
long_pattern = r"(\w)(ー*)"
long_replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2)) # type: ignore long_replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2)) # type: ignore
spaced_phonemes = re.sub(long_pattern, long_replacement, spaced_phonemes) spaced_phonemes = __LONG_PATTERN.sub(long_replacement, spaced_phonemes)
return spaced_phonemes.strip().split(" ") return spaced_phonemes.strip().split(" ")

10
style_bert_vits2/nlp/japanese/g2p_utils.py
View File

@@ -1,5 +1,6 @@
from style_bert_vits2.nlp.japanese.g2p import g2p from style_bert_vits2.nlp.japanese.g2p import g2p
from style_bert_vits2.nlp.japanese.mora_list import ( from style_bert_vits2.nlp.japanese.mora_list import (
CONSONANTS,
MORA_KATA_TO_MORA_PHONEMES, MORA_KATA_TO_MORA_PHONEMES,
MORA_PHONEMES_TO_MORA_KATA, MORA_PHONEMES_TO_MORA_KATA,
) )
@@ -33,15 +34,6 @@ def phone_tone2kata_tone(phone_tone: list[tuple[str, int]]) -> list[tuple[str, i
カタカナと音高のリスト。 カタカナと音高のリスト。
""" """
# 子音の集合
CONSONANTS = set(
[
consonant
for consonant, _ in MORA_KATA_TO_MORA_PHONEMES.values()
if consonant is not None
]
)
phone_tone = phone_tone[1:] # 最初の("_", 0)を無視 phone_tone = phone_tone[1:] # 最初の("_", 0)を無視
phones = [phone for phone, _ in phone_tone] phones = [phone for phone, _ in phone_tone]
tones = [tone for _, tone in phone_tone] tones = [tone for _, tone in phone_tone]

12
style_bert_vits2/nlp/japanese/mora_list.py
View File

@@ -234,3 +234,15 @@ MORA_KATA_TO_MORA_PHONEMES: dict[str, tuple[Optional[str], str]] = {
kana: (consonant, vowel) kana: (consonant, vowel)
for [kana, consonant, vowel] in __MORA_LIST_MINIMUM + __MORA_LIST_ADDITIONAL for [kana, consonant, vowel] in __MORA_LIST_MINIMUM + __MORA_LIST_ADDITIONAL
} }
# 子音の集合
CONSONANTS = set(
[
consonant
for consonant, _ in MORA_KATA_TO_MORA_PHONEMES.values()
if consonant is not None
]
)
# 母音の集合 (便宜上「ん」を含める)
VOWELS = {"a", "i", "u", "e", "o", "N"}

161
style_bert_vits2/nlp/japanese/normalizer.py
View File

@@ -6,6 +6,81 @@ from num2words import num2words
from style_bert_vits2.nlp.symbols import PUNCTUATIONS from style_bert_vits2.nlp.symbols import PUNCTUATIONS
# 記号類の正規化マップ
__REPLACE_MAP = {
"：": ",",
"；": ",",
"，": ",",
"。": ".",
"！": "!",
"？": "?",
"\n": ".",
"．": ".",
"…": "...",
"···": "...",
"・・・": "...",
"·": ",",
"・": ",",
"、": ",",
"$": ".",
"“": "'",
"”": "'",
'"': "'",
"‘": "'",
"’": "'",
"（": "'",
"）": "'",
"(": "'",
")": "'",
"《": "'",
"》": "'",
"【": "'",
"】": "'",
"[": "'",
"]": "'",
# NFKC 正規化後のハイフン・ダッシュの変種を全て通常半角ハイフン - \u002d に変換
"\u02d7": "\u002d", # ˗, Modifier Letter Minus Sign
"\u2010": "\u002d", # ‐, Hyphen,
# "\u2011": "\u002d", # ‑, Non-Breaking Hyphen, NFKC により \u2010 に変換される
"\u2012": "\u002d", # ‒, Figure Dash
"\u2013": "\u002d", # –, En Dash
"\u2014": "\u002d", # —, Em Dash
"\u2015": "\u002d", # ―, Horizontal Bar
"\u2043": "\u002d", # ⁃, Hyphen Bullet
"\u2212": "\u002d", # −, Minus Sign
"\u23af": "\u002d", # ⎯, Horizontal Line Extension
"\u23e4": "\u002d", # ⏤, Straightness
"\u2500": "\u002d", # ─, Box Drawings Light Horizontal
"\u2501": "\u002d", # ━, Box Drawings Heavy Horizontal
"\u2e3a": "\u002d", # ⸺, Two-Em Dash
"\u2e3b": "\u002d", # ⸻, Three-Em Dash
# "～": "-", # これは長音記号「ー」として扱うよう変更
# "~": "-", # これも長音記号「ー」として扱うよう変更
"「": "'",
"」": "'",
}
# 記号類の正規化パターン
__REPLACE_PATTERN = re.compile("|".join(re.escape(p) for p in __REPLACE_MAP.keys()))
# 句読点等の正規化パターン
__PUNCTUATION_CLEANUP_PATTERN = re.compile(
# ↓ ひらがな、カタカナ、漢字
r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
# ↓ 半角アルファベット（大文字と小文字）
+ r"\u0041-\u005A\u0061-\u007A"
# ↓ 全角アルファベット（大文字と小文字）
+ r"\uFF21-\uFF3A\uFF41-\uFF5A"
# ↓ ギリシャ文字
+ r"\u0370-\u03FF\u1F00-\u1FFF"
# ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている
+ "".join(PUNCTUATIONS) + r"]+", # fmt: skip
)
# 数字・通貨記号の正規化パターン
__CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
__CURRENCY_PATTERN = re.compile(r"([$¥£€])([0-9.]*[0-9])")
__NUMBER_PATTERN = re.compile(r"[0-9]+(\.[0-9]+)?")
__NUMBER_WITH_SEPARATOR_PATTERN = re.compile("[0-9]{1,3}(,[0-9]{3})+")
def normalize_text(text: str) -> str: def normalize_text(text: str) -> str:
""" """
日本語のテキストを正規化する。 日本語のテキストを正規化する。
@@ -62,80 +137,11 @@ def replace_punctuation(text: str) -> str:
str: 正規化されたテキスト str: 正規化されたテキスト
""" """
# 記号類の正規化変換マップ
REPLACE_MAP = {
"：": ",",
"；": ",",
"，": ",",
"。": ".",
"！": "!",
"？": "?",
"\n": ".",
"．": ".",
"…": "...",
"···": "...",
"・・・": "...",
"·": ",",
"・": ",",
"、": ",",
"$": ".",
"“": "'",
"”": "'",
'"': "'",
"‘": "'",
"’": "'",
"（": "'",
"）": "'",
"(": "'",
")": "'",
"《": "'",
"》": "'",
"【": "'",
"】": "'",
"[": "'",
"]": "'",
# NFKC 正規化後のハイフン・ダッシュの変種を全て通常半角ハイフン - \u002d に変換
"\u02d7": "\u002d", # ˗, Modifier Letter Minus Sign
"\u2010": "\u002d", # ‐, Hyphen,
# "\u2011": "\u002d", # ‑, Non-Breaking Hyphen, NFKC により \u2010 に変換される
"\u2012": "\u002d", # ‒, Figure Dash
"\u2013": "\u002d", # –, En Dash
"\u2014": "\u002d", # —, Em Dash
"\u2015": "\u002d", # ―, Horizontal Bar
"\u2043": "\u002d", # ⁃, Hyphen Bullet
"\u2212": "\u002d", # −, Minus Sign
"\u23af": "\u002d", # ⎯, Horizontal Line Extension
"\u23e4": "\u002d", # ⏤, Straightness
"\u2500": "\u002d", # ─, Box Drawings Light Horizontal
"\u2501": "\u002d", # ━, Box Drawings Heavy Horizontal
"\u2e3a": "\u002d", # ⸺, Two-Em Dash
"\u2e3b": "\u002d", # ⸻, Three-Em Dash
# "～": "-", # これは長音記号「ー」として扱うよう変更
# "~": "-", # これも長音記号「ー」として扱うよう変更
"「": "'",
"」": "'",
}
pattern = re.compile("|".join(re.escape(p) for p in REPLACE_MAP.keys()))
# 句読点を辞書で置換 # 句読点を辞書で置換
replaced_text = pattern.sub(lambda x: REPLACE_MAP[x.group()], text) replaced_text = __REPLACE_PATTERN.sub(lambda x: __REPLACE_MAP[x.group()], text)
replaced_text = re.sub( # 上述以外の文字を削除
# ↓ ひらがな、カタカナ、漢字 replaced_text = __PUNCTUATION_CLEANUP_PATTERN.sub("", replaced_text)
r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
# ↓ 半角アルファベット（大文字と小文字）
+ r"\u0041-\u005A\u0061-\u007A"
# ↓ 全角アルファベット（大文字と小文字）
+ r"\uFF21-\uFF3A\uFF41-\uFF5A"
# ↓ ギリシャ文字
+ r"\u0370-\u03FF\u1F00-\u1FFF"
# ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている
+ "".join(PUNCTUATIONS) + r"]+",
# 上述以外の文字を削除
"",
replaced_text,
)
return replaced_text return replaced_text
@@ -151,13 +157,8 @@ def __convert_numbers_to_words(text: str) -> str:
str: 変換されたテキスト str: 変換されたテキスト
""" """
NUMBER_WITH_SEPARATOR_PATTERN = re.compile("[0-9]{1,3}(,[0-9]{3})+") res = __NUMBER_WITH_SEPARATOR_PATTERN.sub(lambda m: m[0].replace(",", ""), text)
CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"} res = __CURRENCY_PATTERN.sub(lambda m: m[2] + __CURRENCY_MAP.get(m[1], m[1]), res)
CURRENCY_PATTERN = re.compile(r"([$¥£€])([0-9.]*[0-9])") res = __NUMBER_PATTERN.sub(lambda m: num2words(m[0], lang="ja"), res)
NUMBER_PATTERN = re.compile(r"[0-9]+(\.[0-9]+)?")
res = NUMBER_WITH_SEPARATOR_PATTERN.sub(lambda m: m[0].replace(",", ""), text)
res = CURRENCY_PATTERN.sub(lambda m: m[2] + CURRENCY_MAP.get(m[1], m[1]), res)
res = NUMBER_PATTERN.sub(lambda m: num2words(m[0], lang="ja"), res)
return res return res