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Add fixed accent myg2p

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litagin02
2024-01-06 22:33:10 +09:00
parent 09c0673ded
commit d130ae7110
1 changed files with 190 additions and 9 deletions
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199
text/japanese.py
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@@ -3,15 +3,14 @@
import re
import unicodedata
import jaconv
import pyopenjtalk
from num2words import num2words
from transformers import AutoTokenizer
from common.log import logger
from text import punctuation, symbols
from num2words import num2words
import pyopenjtalk
import jaconv
def hiragana2p(text: str) -> str:
"""
@@ -590,15 +589,26 @@ rep_map = {
}
def _numeric_feature_by_regex(regex, s):
match = re.search(regex, s)
if match is None:
return -50
return int(match.group(1))
def replace_punctuation(text):
"""句読点等を正規化し、日本語と句読点記号以外を削除"""
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
# 句読点を辞書で置換
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
replaced_text = re.sub(
# ↓ ひらがな、カタカナ、漢字
r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+ "".join(punctuation)
+ r"]+",
# ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている
+ "".join(punctuation) + r"]+",
# 上述以外の文字を削除
"",
replaced_text,
)
@@ -607,14 +617,186 @@ def replace_punctuation(text):
def text_normalize(text):
"""
日本語のテキストを正規化する。
結果は、ちょうど次の文字のみからなる：
- ひらがな
- カタカナ（全角長音記号「ー」が入る！）
- 漢字
- `.` （句点`。`や`…`の一部や改行等）
- `,` （読点`、`や`:`等）
- `?` （疑問符`？`）
- `!` （感嘆符`！`）
- `'` （`「`や`」`等）
- `-` （`～`や`-`等、これいる？）
注意点:
- 三点リーダー`…`は`...`に変換される（`なるほど…。` → `なるほど....`）
- `～`は`-`に変換される！（`あ～、そっかー。` → `あ-, なるほどー.`）
- 数字は漢字に変換される（`1,100円` → `千百円`、`52.34` → `五十二点三四`）
- 読点や疑問符等の位置・個数等は保持される（`??あ、、！！！` → `??あ,,!!!`）
"""
res = unicodedata.normalize("NFKC", text)
res = japanese_convert_numbers_to_words(res)
res = japanese_convert_numbers_to_words(res) # 「100円」→「百円」等
# res = "".join([i for i in res if is_japanese_character(i)])
res = replace_punctuation(res)
res = res.replace("゙", "")
return res
def pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) -> list[str]:
"""Extract phoneme + prosoody symbol sequence from input full-context labels.
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.
Args:
text (str): Input text.
drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
Returns:
List[str]: List of phoneme + prosody symbols.
Examples:
>>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
>>> pyopenjtalk_g2p_prosody("こんにちは。")
['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
.. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
"""
labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
N = len(labels)
phones = []
for n in range(N):
lab_curr = labels[n]
# current phoneme
p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
# deal unvoiced vowels as normal vowels
if drop_unvoiced_vowels and p3 in "AEIOU":
p3 = p3.lower()
# deal with sil at the beginning and the end of text
if p3 == "sil":
assert n == 0 or n == N - 1
if n == 0:
phones.append("^")
elif n == N - 1:
# check question form or not
e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
if e3 == 0:
phones.append("$")
elif e3 == 1:
phones.append("?")
continue
elif p3 == "pau":
phones.append("_")
continue
else:
phones.append(p3)
# accent type and position info (forward or backward)
a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
# number of mora in accent phrase
f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
# accent phrase border
if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
phones.append("#")
# pitch falling
elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
phones.append("]")
# pitch rising
elif a2 == 1 and a2_next == 2:
phones.append("[")
return phones
def fix_tone(tones: list[tuple[str, int]]) -> list[tuple[str, int]]:
# Fix tone to use only 0 and 1
tone_values = set(tone for _, tone in tones)
if len(tone_values) == 1:
assert tone_values == {0}, tone_values
return tones
elif len(tone_values) == 2:
if tone_values == {0, 1}:
return tones
elif tone_values == {-1, 0}:
return [(letter, 0 if tone == -1 else 1) for letter, tone in tones]
else:
raise ValueError(f"Unexpected tone values: {tone_values}")
else:
raise ValueError(f"Unexpected tone values: {tone_values}")
def g2p_for_segment(text: str) -> list[tuple[str, int]]:
"""
句読点等の記号を含まないテキストに対して、音素とアクセント（0か1）のペアのリストを返す
"""
prosodies = pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True)
result: list[tuple[str, int]] = []
current_phrase = []
current_tone = 0
for i, letter in enumerate(prosodies):
# 特殊記号の処理
# 文頭記号、無視する
if letter == "^":
assert i == 0, "Unexpected ^"
# アクセント句の終わりに来る記号
elif letter in ("$", "?", "_", "#"):
# 保持しているフレーズを、アクセント数値を0-1に修正し結果に追加
result.extend(fix_tone(current_phrase))
# 末尾に来る終了記号、無視（文中の疑問文は`_`になる）
if letter in ("$", "?"):
assert i == len(prosodies) - 1, "Unexpected $ or ?"
# pause記号、句読点等で発生するが、そもそも入力に無いはずなのでエラー起こす
elif letter == "_":
raise ValueError("Unexpected _")
# ここまでで記号は`#`（アクセント句境界）のみ
# 新しいフレーズを準備
current_phrase = []
current_tone = 0
# アクセント上昇記号
elif letter == "[":
current_tone += 1
# アクセント下降記号
elif letter == "]":
current_tone -= 1
# それ以外は通常の音素
else:
current_phrase.append((letter, current_tone))
return result
def myg2p(text: str):
# 例: "こんにちは、世界ー。。元気？！"
text = text_normalize(text)
# こんにちは,世界ー..元気?!
# punctuationで`text`を分割
pattern = "(" + "|".join(re.escape(p) for p in punctuation) + ")"
sep_text: list[str] = [s for s in re.split(pattern, text) if s]
# ["こんにちは", ",", "世界ー", ".", ".", "元気", "?", "!"]
result = []
for segment in sep_text:
if segment in punctuation:
result.append((segment, 0))
else:
result.extend(g2p_for_segment(segment))
result = [("_", 0)] + result + [("_", 0)]
return [phoneme for phoneme, _ in result], [tone for _, tone in result]
def distribute_phone(n_phone, n_word):
phones_per_word = [0] * n_word
for task in range(n_phone):
@@ -688,7 +870,6 @@ def g2p(norm_text):
sep_tokenized.append(tokenizer.tokenize(i))
else:
sep_tokenized.append([i])
sep_phonemes = handle_long([kata2phoneme(i) for i in sep_kata])
# 异常处理，MeCab不认识的词的话会一路传到这里来，然后炸掉。目前来看只有那些超级稀有的生僻词会出现这种情况
for i in sep_phonemes: