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https://github.com/RVC-Boss/GPT-SoVITS.git
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322 lines
11 KiB
Python
322 lines
11 KiB
Python
# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
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import re
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import os
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import hashlib
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try:
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import pyopenjtalk
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current_file_path = os.path.dirname(__file__)
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# 防止win下无法读取模型
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if os.name == "nt":
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python_dir = os.getcwd()
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OPEN_JTALK_DICT_DIR = pyopenjtalk.OPEN_JTALK_DICT_DIR.decode("utf-8")
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if not (re.match(r"^[A-Za-z0-9_/\\:.\-]*$", OPEN_JTALK_DICT_DIR)):
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if OPEN_JTALK_DICT_DIR[: len(python_dir)].upper() == python_dir.upper():
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OPEN_JTALK_DICT_DIR = os.path.join(os.path.relpath(OPEN_JTALK_DICT_DIR, python_dir))
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else:
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import shutil
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if not os.path.exists("TEMP"):
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os.mkdir("TEMP")
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if not os.path.exists(os.path.join("TEMP", "ja")):
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os.mkdir(os.path.join("TEMP", "ja"))
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if os.path.exists(os.path.join("TEMP", "ja", "open_jtalk_dic")):
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shutil.rmtree(os.path.join("TEMP", "ja", "open_jtalk_dic"))
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shutil.copytree(
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pyopenjtalk.OPEN_JTALK_DICT_DIR.decode("utf-8"),
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os.path.join("TEMP", "ja", "open_jtalk_dic"),
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)
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OPEN_JTALK_DICT_DIR = os.path.join("TEMP", "ja", "open_jtalk_dic")
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pyopenjtalk.OPEN_JTALK_DICT_DIR = OPEN_JTALK_DICT_DIR.encode("utf-8")
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if not (re.match(r"^[A-Za-z0-9_/\\:.\-]*$", current_file_path)):
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if current_file_path[: len(python_dir)].upper() == python_dir.upper():
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current_file_path = os.path.join(os.path.relpath(current_file_path, python_dir))
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else:
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if not os.path.exists("TEMP"):
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os.mkdir("TEMP")
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if not os.path.exists(os.path.join("TEMP", "ja")):
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os.mkdir(os.path.join("TEMP", "ja"))
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if not os.path.exists(os.path.join("TEMP", "ja", "ja_userdic")):
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os.mkdir(os.path.join("TEMP", "ja", "ja_userdic"))
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shutil.copyfile(
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os.path.join(current_file_path, "ja_userdic", "userdict.csv"),
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os.path.join("TEMP", "ja", "ja_userdic", "userdict.csv"),
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)
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current_file_path = os.path.join("TEMP", "ja")
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def get_hash(fp: str) -> str:
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hash_md5 = hashlib.md5()
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with open(fp, "rb") as f:
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for chunk in iter(lambda: f.read(4096), b""):
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hash_md5.update(chunk)
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return hash_md5.hexdigest()
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USERDIC_CSV_PATH = os.path.join(current_file_path, "ja_userdic", "userdict.csv")
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USERDIC_BIN_PATH = os.path.join(current_file_path, "ja_userdic", "user.dict")
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USERDIC_HASH_PATH = os.path.join(current_file_path, "ja_userdic", "userdict.md5")
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# 如果没有用户词典，就生成一个；如果有，就检查md5，如果不一样，就重新生成
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if os.path.exists(USERDIC_CSV_PATH):
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if (
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not os.path.exists(USERDIC_BIN_PATH)
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or get_hash(USERDIC_CSV_PATH) != open(USERDIC_HASH_PATH, "r", encoding="utf-8").read()
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):
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pyopenjtalk.mecab_dict_index(USERDIC_CSV_PATH, USERDIC_BIN_PATH)
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with open(USERDIC_HASH_PATH, "w", encoding="utf-8") as f:
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f.write(get_hash(USERDIC_CSV_PATH))
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if os.path.exists(USERDIC_BIN_PATH):
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pyopenjtalk.update_global_jtalk_with_user_dict(USERDIC_BIN_PATH)
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except Exception:
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# print(e)
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import pyopenjtalk
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# failed to load user dictionary, ignore.
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pass
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from text.symbols import punctuation
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# Regular expression matching Japanese without punctuation marks:
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_japanese_characters = re.compile(
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r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
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)
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# Regular expression matching non-Japanese characters or punctuation marks:
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_japanese_marks = re.compile(
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r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
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)
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# List of (symbol, Japanese) pairs for marks:
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_symbols_to_japanese = [(re.compile("%s" % x[0]), x[1]) for x in [("％", "パーセント")]]
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# List of (consonant, sokuon) pairs:
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_real_sokuon = [
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(re.compile("%s" % x[0]), x[1])
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for x in [
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(r"Q([↑↓]*[kg])", r"k#\1"),
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(r"Q([↑↓]*[tdjʧ])", r"t#\1"),
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(r"Q([↑↓]*[sʃ])", r"s\1"),
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(r"Q([↑↓]*[pb])", r"p#\1"),
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]
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]
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# List of (consonant, hatsuon) pairs:
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_real_hatsuon = [
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(re.compile("%s" % x[0]), x[1])
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for x in [
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(r"N([↑↓]*[pbm])", r"m\1"),
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(r"N([↑↓]*[ʧʥj])", r"n^\1"),
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(r"N([↑↓]*[tdn])", r"n\1"),
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(r"N([↑↓]*[kg])", r"ŋ\1"),
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]
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]
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def post_replace_ph(ph):
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rep_map = {
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"：": ",",
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"；": ",",
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"，": ",",
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"。": ".",
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"！": "!",
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"？": "?",
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"\n": ".",
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"·": ",",
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"、": ",",
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"...": "…",
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}
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if ph in rep_map.keys():
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ph = rep_map[ph]
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return ph
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def replace_consecutive_punctuation(text):
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punctuations = "".join(re.escape(p) for p in punctuation)
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pattern = f"([{punctuations}])([{punctuations}])+"
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result = re.sub(pattern, r"\1", text)
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return result
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def symbols_to_japanese(text):
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for regex, replacement in _symbols_to_japanese:
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text = re.sub(regex, replacement, text)
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return text
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def preprocess_jap(text, with_prosody=False):
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"""Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html"""
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text = symbols_to_japanese(text)
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# English words to lower case, should have no influence on japanese words.
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text = text.lower()
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sentences = re.split(_japanese_marks, text)
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marks = re.findall(_japanese_marks, text)
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text = []
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for i, sentence in enumerate(sentences):
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if re.match(_japanese_characters, sentence):
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if with_prosody:
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text += pyopenjtalk_g2p_prosody(sentence)[1:-1]
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else:
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p = pyopenjtalk.g2p(sentence)
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text += p.split(" ")
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if i < len(marks):
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if marks[i] == " ": # 防止意外的UNK
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continue
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text += [marks[i].replace(" ", "")]
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return text
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def text_normalize(text):
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# todo: jap text normalize
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# 避免重复标点引起的参考泄露
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text = replace_consecutive_punctuation(text)
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return text
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def text_normalize_with_map(text):
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"""
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带字符映射的日文标准化函数
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Returns:
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normalized_text: 标准化后的文本
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char_mappings: 字典，包含:
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- "orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
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- "norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
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"""
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from .char_mapping_utils import build_char_mapping
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# 先进行标准化
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normalized_text = text_normalize(text)
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# 构建字符映射
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mappings = build_char_mapping(text, normalized_text)
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return normalized_text, mappings
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# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
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def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True):
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"""Extract phoneme + prosoody symbol sequence from input full-context labels.
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The algorithm is based on `Prosodic features control by symbols as input of
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sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
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Args:
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text (str): Input text.
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drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
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Returns:
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List[str]: List of phoneme + prosody symbols.
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Examples:
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>>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
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>>> pyopenjtalk_g2p_prosody("こんにちは。")
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['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
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.. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
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modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
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"""
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labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
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N = len(labels)
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phones = []
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for n in range(N):
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lab_curr = labels[n]
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# current phoneme
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p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
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# deal unvoiced vowels as normal vowels
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if drop_unvoiced_vowels and p3 in "AEIOU":
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p3 = p3.lower()
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# deal with sil at the beginning and the end of text
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if p3 == "sil":
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assert n == 0 or n == N - 1
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if n == 0:
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phones.append("^")
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elif n == N - 1:
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# check question form or not
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e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
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if e3 == 0:
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phones.append("$")
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elif e3 == 1:
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phones.append("?")
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continue
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elif p3 == "pau":
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phones.append("_")
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continue
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else:
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phones.append(p3)
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# accent type and position info (forward or backward)
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a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
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a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
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a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
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# number of mora in accent phrase
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f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
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a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
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# accent phrase border
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if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
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phones.append("#")
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# pitch falling
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elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
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phones.append("]")
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# pitch rising
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elif a2 == 1 and a2_next == 2:
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phones.append("[")
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return phones
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# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
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def _numeric_feature_by_regex(regex, s):
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match = re.search(regex, s)
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if match is None:
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return -50
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return int(match.group(1))
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def g2p(norm_text, with_prosody=True):
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phones = preprocess_jap(norm_text, with_prosody)
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phones = [post_replace_ph(i) for i in phones]
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# todo: implement tones and word2ph
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return phones
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# Helper for alignment: build phones and word2ph by per-character g2p (ignoring prosody markers)
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def g2p_with_word2ph(text, keep_punc=False):
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"""
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Returns (phones, word2ph)
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- Per-character g2p; ignore prosody markers like '[', ']','^', '$', '#', '_'
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- Punctuation counted as 1 if keep_punc else skipped
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"""
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norm_text = text_normalize(text)
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phones_all = []
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word2ph = []
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prosody_markers = {'[', ']', '^', '$', '#', '_'}
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punc_set = set(punctuation)
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for ch in norm_text:
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if ch.isspace() or ch in punc_set:
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if keep_punc:
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word2ph.append(1)
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continue
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phs = preprocess_jap(ch, with_prosody=True)
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phs = [post_replace_ph(p) for p in phs if p not in prosody_markers and p not in punc_set]
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phones_all.extend(phs)
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word2ph.append(max(1, len(phs)))
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return phones_all, word2ph
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if __name__ == "__main__":
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phones = g2p("Hello.こんにちは！今日もNiCe天気ですね！tokyotowerに行きましょう！")
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print(phones)
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