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Implement character mapping functionality for text normalization across multiple languages, including Cantonese, Chinese, English, Japanese, and Korean. Introduce text_normalize_with_map methods to return normalized text along with character mappings. Add a new utility module for building character mappings.
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白菜工厂1145号员工
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# reference: https://huggingface.co/spaces/Naozumi0512/Bert-VITS2-Cantonese-Yue/blob/main/text/chinese.py
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# reference: https://huggingface.co/spaces/Naozumi0512/Bert-VITS2-Cantonese-Yue/blob/main/text/chinese.py
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import re
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import re
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import cn2an
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import cn2an
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import ToJyutping
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import ToJyutping
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from text.symbols import punctuation
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from text.symbols import punctuation
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from text.zh_normalization.text_normlization import TextNormalizer
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from text.zh_normalization.text_normlization import TextNormalizer
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normalizer = lambda x: cn2an.transform(x, "an2cn")
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normalizer = lambda x: cn2an.transform(x, "an2cn")
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INITIALS = [
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INITIALS = [
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"aa",
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"aa",
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"aai",
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"aai",
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"aak",
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"aak",
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"aap",
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"aap",
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"aat",
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"aat",
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"aau",
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"aau",
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"ai",
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"ai",
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"au",
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"au",
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"ap",
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"ap",
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"at",
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"at",
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"ak",
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"ak",
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"a",
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"a",
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"p",
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"p",
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"b",
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"b",
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"e",
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"e",
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"ts",
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"ts",
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"t",
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"t",
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"dz",
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"dz",
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"d",
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"d",
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"kw",
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"kw",
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"k",
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"k",
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"gw",
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"gw",
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"g",
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"g",
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"f",
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"f",
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"h",
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"h",
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"l",
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"l",
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"m",
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"m",
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"ng",
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"ng",
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"n",
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"n",
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"s",
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"s",
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"y",
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"y",
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"w",
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"w",
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"c",
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"c",
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"z",
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"z",
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"j",
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"j",
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"ong",
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"ong",
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"on",
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"on",
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"ou",
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"ou",
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"oi",
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"oi",
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"ok",
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"ok",
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"o",
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"o",
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"uk",
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"uk",
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"ung",
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"ung",
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]
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]
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INITIALS += ["sp", "spl", "spn", "sil"]
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INITIALS += ["sp", "spl", "spn", "sil"]
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rep_map = {
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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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"。": ".",
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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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"\n": ".",
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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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"$": ".",
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"$": ".",
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"“": "'",
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"“": "'",
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"”": "'",
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"”": "'",
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'"': "'",
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'"': "'",
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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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"）": "'",
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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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"《": "'",
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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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"[": "'",
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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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"～": "-",
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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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}
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}
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def replace_punctuation(text):
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def replace_punctuation(text):
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# text = text.replace("嗯", "恩").replace("呣", "母")
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# text = text.replace("嗯", "恩").replace("呣", "母")
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pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
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pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
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replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
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replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
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replaced_text = re.sub(r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text)
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replaced_text = re.sub(r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text)
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return replaced_text
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return replaced_text
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def text_normalize(text):
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def text_normalize(text):
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tx = TextNormalizer()
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tx = TextNormalizer()
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sentences = tx.normalize(text)
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sentences = tx.normalize(text)
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dest_text = ""
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dest_text = ""
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for sentence in sentences:
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for sentence in sentences:
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dest_text += replace_punctuation(sentence)
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dest_text += replace_punctuation(sentence)
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return dest_text
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return dest_text
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punctuation_set = set(punctuation)
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def text_normalize_with_map(text):
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"""
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带字符映射的粤语标准化函数
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def jyuping_to_initials_finals_tones(jyuping_syllables):
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initials_finals = []
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Returns:
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tones = []
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normalized_text: 标准化后的文本
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word2ph = []
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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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for syllable in jyuping_syllables:
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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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if syllable in punctuation:
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"""
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initials_finals.append(syllable)
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from .char_mapping_utils import build_char_mapping
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tones.append(0)
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word2ph.append(1) # Add 1 for punctuation
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# 先进行标准化
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elif syllable == "_":
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normalized_text = text_normalize(text)
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initials_finals.append(syllable)
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tones.append(0)
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# 构建字符映射
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word2ph.append(1) # Add 1 for underscore
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mappings = build_char_mapping(text, normalized_text)
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else:
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try:
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return normalized_text, mappings
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tone = int(syllable[-1])
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syllable_without_tone = syllable[:-1]
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except ValueError:
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punctuation_set = set(punctuation)
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tone = 0
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syllable_without_tone = syllable
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def jyuping_to_initials_finals_tones(jyuping_syllables):
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for initial in INITIALS:
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initials_finals = []
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if syllable_without_tone.startswith(initial):
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tones = []
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if syllable_without_tone.startswith("nga"):
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word2ph = []
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initials_finals.extend(
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[
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for syllable in jyuping_syllables:
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syllable_without_tone[:2],
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if syllable in punctuation:
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syllable_without_tone[2:] or syllable_without_tone[-1],
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initials_finals.append(syllable)
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]
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tones.append(0)
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)
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word2ph.append(1) # Add 1 for punctuation
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# tones.extend([tone, tone])
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elif syllable == "_":
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tones.extend([-1, tone])
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initials_finals.append(syllable)
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word2ph.append(2)
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tones.append(0)
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else:
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word2ph.append(1) # Add 1 for underscore
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final = syllable_without_tone[len(initial) :] or initial[-1]
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else:
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initials_finals.extend([initial, final])
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try:
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# tones.extend([tone, tone])
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tone = int(syllable[-1])
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tones.extend([-1, tone])
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syllable_without_tone = syllable[:-1]
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word2ph.append(2)
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except ValueError:
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break
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tone = 0
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assert len(initials_finals) == len(tones)
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syllable_without_tone = syllable
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###魔改为辅音+带音调的元音
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for initial in INITIALS:
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phones = []
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if syllable_without_tone.startswith(initial):
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for a, b in zip(initials_finals, tones):
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if syllable_without_tone.startswith("nga"):
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if b not in [-1, 0]: ###防止粤语和普通话重合开头加Y，如果是标点，不加。
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initials_finals.extend(
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todo = "%s%s" % (a, b)
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[
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else:
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syllable_without_tone[:2],
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todo = a
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syllable_without_tone[2:] or syllable_without_tone[-1],
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if todo not in punctuation_set:
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]
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todo = "Y%s" % todo
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)
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phones.append(todo)
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# tones.extend([tone, tone])
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tones.extend([-1, tone])
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# return initials_finals, tones, word2ph
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word2ph.append(2)
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return phones, word2ph
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else:
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final = syllable_without_tone[len(initial) :] or initial[-1]
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initials_finals.extend([initial, final])
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def get_jyutping(text):
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# tones.extend([tone, tone])
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jyutping_array = []
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tones.extend([-1, tone])
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punct_pattern = re.compile(r"^[{}]+$".format(re.escape("".join(punctuation))))
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word2ph.append(2)
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break
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syllables = ToJyutping.get_jyutping_list(text)
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assert len(initials_finals) == len(tones)
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for word, syllable in syllables:
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###魔改为辅音+带音调的元音
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if punct_pattern.match(word):
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phones = []
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puncts = re.split(r"([{}])".format(re.escape("".join(punctuation))), word)
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for a, b in zip(initials_finals, tones):
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for punct in puncts:
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if b not in [-1, 0]: ###防止粤语和普通话重合开头加Y，如果是标点，不加。
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if len(punct) > 0:
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todo = "%s%s" % (a, b)
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jyutping_array.append(punct)
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else:
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else:
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todo = a
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# match multple jyutping eg: liu4 ge3, or single jyutping eg: liu4
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if todo not in punctuation_set:
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if not re.search(r"^([a-z]+[1-6]+[ ]?)+$", syllable):
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todo = "Y%s" % todo
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raise ValueError(f"Failed to convert {word} to jyutping: {syllable}")
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phones.append(todo)
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jyutping_array.append(syllable)
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# return initials_finals, tones, word2ph
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return jyutping_array
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return phones, word2ph
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def get_bert_feature(text, word2ph):
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def get_jyutping(text):
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from text import chinese_bert
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jyutping_array = []
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punct_pattern = re.compile(r"^[{}]+$".format(re.escape("".join(punctuation))))
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return chinese_bert.get_bert_feature(text, word2ph)
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syllables = ToJyutping.get_jyutping_list(text)
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def g2p(text):
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for word, syllable in syllables:
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# word2ph = []
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if punct_pattern.match(word):
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jyuping = get_jyutping(text)
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puncts = re.split(r"([{}])".format(re.escape("".join(punctuation))), word)
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# print(jyuping)
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for punct in puncts:
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# phones, tones, word2ph = jyuping_to_initials_finals_tones(jyuping)
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if len(punct) > 0:
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phones, word2ph = jyuping_to_initials_finals_tones(jyuping)
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jyutping_array.append(punct)
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# phones = ["_"] + phones + ["_"]
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else:
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# tones = [0] + tones + [0]
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# match multple jyutping eg: liu4 ge3, or single jyutping eg: liu4
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# word2ph = [1] + word2ph + [1]
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if not re.search(r"^([a-z]+[1-6]+[ ]?)+$", syllable):
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return phones, word2ph
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raise ValueError(f"Failed to convert {word} to jyutping: {syllable}")
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jyutping_array.append(syllable)
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if __name__ == "__main__":
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return jyutping_array
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# text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
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text = "佢個鋤頭太短啦。"
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text = text_normalize(text)
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def get_bert_feature(text, word2ph):
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# phones, tones, word2ph = g2p(text)
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from text import chinese_bert
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phones, word2ph = g2p(text)
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# print(phones, tones, word2ph)
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return chinese_bert.get_bert_feature(text, word2ph)
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print(phones, word2ph)
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def g2p(text):
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# word2ph = []
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jyuping = get_jyutping(text)
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# print(jyuping)
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# phones, tones, word2ph = jyuping_to_initials_finals_tones(jyuping)
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phones, word2ph = jyuping_to_initials_finals_tones(jyuping)
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# phones = ["_"] + phones + ["_"]
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# tones = [0] + tones + [0]
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# word2ph = [1] + word2ph + [1]
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return phones, word2ph
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if __name__ == "__main__":
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# text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
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text = "佢個鋤頭太短啦。"
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text = text_normalize(text)
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# phones, tones, word2ph = g2p(text)
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phones, word2ph = g2p(text)
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# print(phones, tones, word2ph)
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print(phones, word2ph)
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144
GPT_SoVITS/text/char_mapping_utils.py
Normal file
144
GPT_SoVITS/text/char_mapping_utils.py
Normal file
@ -0,0 +1,144 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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通用字符映射工具
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用于在文本标准化后，建立原始文本到标准化文本的字符级映射
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"""
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def build_char_mapping(original_text, normalized_text):
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"""
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通过字符对齐算法，构建原始文本到标准化文本的映射
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Args:
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original_text: 原始文本
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normalized_text: 标准化后的文本
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Returns:
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dict: {
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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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"""
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# 使用动态规划找到最优对齐
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m, n = len(original_text), len(normalized_text)
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# dp[i][j] 表示 original_text[:i] 和 normalized_text[:j] 的对齐代价
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# 0 = 匹配, 1 = 替换, 插入, 删除
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dp = [[float('inf')] * (n + 1) for _ in range(m + 1)]
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# 记录路径
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path = [[None] * (n + 1) for _ in range(m + 1)]
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# 初始化
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for i in range(m + 1):
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dp[i][0] = i
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if i > 0:
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path[i][0] = ('del', i-1, -1)
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for j in range(n + 1):
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dp[0][j] = j
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if j > 0:
|
||||||
|
path[0][j] = ('ins', -1, j-1)
|
||||||
|
|
||||||
|
# 动态规划
|
||||||
|
for i in range(1, m + 1):
|
||||||
|
for j in range(1, n + 1):
|
||||||
|
orig_char = original_text[i-1]
|
||||||
|
norm_char = normalized_text[j-1]
|
||||||
|
|
||||||
|
# 匹配代价（相同字符代价为0，不同字符代价为1）
|
||||||
|
match_cost = 0 if orig_char == norm_char else 1
|
||||||
|
|
||||||
|
# 三种操作的代价
|
||||||
|
costs = [
|
||||||
|
(dp[i-1][j-1] + match_cost, 'match' if match_cost == 0 else 'replace', i-1, j-1),
|
||||||
|
(dp[i-1][j] + 1, 'del', i-1, j),
|
||||||
|
(dp[i][j-1] + 1, 'ins', i, j-1),
|
||||||
|
]
|
||||||
|
|
||||||
|
min_cost, op, pi, pj = min(costs, key=lambda x: x[0])
|
||||||
|
dp[i][j] = min_cost
|
||||||
|
path[i][j] = (op, pi, pj)
|
||||||
|
|
||||||
|
# 回溯路径，构建映射
|
||||||
|
orig_to_norm = [-1] * len(original_text)
|
||||||
|
norm_to_orig = [-1] * len(normalized_text)
|
||||||
|
|
||||||
|
i, j = m, n
|
||||||
|
alignments = []
|
||||||
|
|
||||||
|
while i > 0 or j > 0:
|
||||||
|
if path[i][j] is None:
|
||||||
|
break
|
||||||
|
|
||||||
|
op, pi, pj = path[i][j]
|
||||||
|
|
||||||
|
if op in ['match', 'replace']:
|
||||||
|
# 原始字符i-1 对应 标准化字符j-1
|
||||||
|
alignments.append((i-1, j-1))
|
||||||
|
i, j = pi, pj
|
||||||
|
elif op == 'del':
|
||||||
|
# 原始字符i-1 被删除，映射到当前标准化位置（如果存在）
|
||||||
|
if j > 0:
|
||||||
|
alignments.append((i-1, j-1))
|
||||||
|
i = pi
|
||||||
|
elif op == 'ins':
|
||||||
|
# 标准化字符j-1 是插入的，没有对应的原始字符
|
||||||
|
j = pj
|
||||||
|
|
||||||
|
# 根据对齐结果建立映射
|
||||||
|
for orig_idx, norm_idx in alignments:
|
||||||
|
if orig_idx >= 0 and orig_idx < len(original_text):
|
||||||
|
if orig_to_norm[orig_idx] == -1:
|
||||||
|
orig_to_norm[orig_idx] = norm_idx
|
||||||
|
|
||||||
|
if norm_idx >= 0 and norm_idx < len(normalized_text):
|
||||||
|
if norm_to_orig[norm_idx] == -1:
|
||||||
|
norm_to_orig[norm_idx] = orig_idx
|
||||||
|
|
||||||
|
return {
|
||||||
|
"orig_to_norm": orig_to_norm,
|
||||||
|
"norm_to_orig": norm_to_orig
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def test_char_mapping():
|
||||||
|
"""测试字符映射功能"""
|
||||||
|
test_cases = [
|
||||||
|
("50元", "五十元"),
|
||||||
|
("3.5度", "三点五度"),
|
||||||
|
("GPT-4", "GPT minus four"), # 也可以测试英文
|
||||||
|
]
|
||||||
|
|
||||||
|
for orig, norm in test_cases:
|
||||||
|
print(f"原始: '{orig}'")
|
||||||
|
print(f"标准化: '{norm}'")
|
||||||
|
|
||||||
|
mappings = build_char_mapping(orig, norm)
|
||||||
|
orig_to_norm = mappings["orig_to_norm"]
|
||||||
|
norm_to_orig = mappings["norm_to_orig"]
|
||||||
|
|
||||||
|
print(f"原始→标准化映射:")
|
||||||
|
for i, c in enumerate(orig):
|
||||||
|
norm_idx = orig_to_norm[i]
|
||||||
|
if norm_idx >= 0 and norm_idx < len(norm):
|
||||||
|
print(f" [{i}]'{c}' → [{norm_idx}]'{norm[norm_idx]}'")
|
||||||
|
else:
|
||||||
|
print(f" [{i}]'{c}' → 无映射")
|
||||||
|
|
||||||
|
print(f"标准化→原始映射:")
|
||||||
|
for i, c in enumerate(norm):
|
||||||
|
orig_idx = norm_to_orig[i]
|
||||||
|
if orig_idx >= 0 and orig_idx < len(orig):
|
||||||
|
print(f" [{i}]'{c}' ← [{orig_idx}]'{orig[orig_idx]}'")
|
||||||
|
else:
|
||||||
|
print(f" [{i}]'{c}' ← 无对应")
|
||||||
|
|
||||||
|
print()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test_char_mapping()
|
||||||
|
|
||||||
@ -181,6 +181,27 @@ def text_normalize(text):
|
|||||||
return dest_text
|
return dest_text
|
||||||
|
|
||||||
|
|
||||||
|
def text_normalize_with_map(text):
|
||||||
|
"""
|
||||||
|
带字符映射的中文标准化函数（旧版）
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
normalized_text: 标准化后的文本
|
||||||
|
char_mappings: 字典，包含:
|
||||||
|
- "orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
|
||||||
|
- "norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
|
||||||
|
"""
|
||||||
|
from .char_mapping_utils import build_char_mapping
|
||||||
|
|
||||||
|
# 先进行标准化
|
||||||
|
normalized_text = text_normalize(text)
|
||||||
|
|
||||||
|
# 构建字符映射
|
||||||
|
mappings = build_char_mapping(text, normalized_text)
|
||||||
|
|
||||||
|
return normalized_text, mappings
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
|
text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
|
||||||
text = "呣呣呣～就是…大人的鼹鼠党吧？"
|
text = "呣呣呣～就是…大人的鼹鼠党吧？"
|
||||||
|
|||||||
@ -326,6 +326,27 @@ def text_normalize(text):
|
|||||||
return dest_text
|
return dest_text
|
||||||
|
|
||||||
|
|
||||||
|
def text_normalize_with_map(text):
|
||||||
|
"""
|
||||||
|
带字符映射的中文标准化函数
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
normalized_text: 标准化后的文本
|
||||||
|
char_mappings: 字典，包含:
|
||||||
|
- "orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
|
||||||
|
- "norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
|
||||||
|
"""
|
||||||
|
from .char_mapping_utils import build_char_mapping
|
||||||
|
|
||||||
|
# 先进行标准化
|
||||||
|
normalized_text = text_normalize(text)
|
||||||
|
|
||||||
|
# 构建字符映射
|
||||||
|
mappings = build_char_mapping(text, normalized_text)
|
||||||
|
|
||||||
|
return normalized_text, mappings
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
|
text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
|
||||||
text = "呣呣呣～就是…大人的鼹鼠党吧？"
|
text = "呣呣呣～就是…大人的鼹鼠党吧？"
|
||||||
|
|||||||
@ -61,6 +61,87 @@ def clean_text(text, language, version=None):
|
|||||||
return phones, word2ph, norm_text
|
return phones, word2ph, norm_text
|
||||||
|
|
||||||
|
|
||||||
|
def clean_text_with_mapping(text, language, version=None):
|
||||||
|
"""
|
||||||
|
带字符映射的文本清洗函数
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text: 原始文本
|
||||||
|
language: 语言代码 (zh, ja, en, ko, yue)
|
||||||
|
version: 模型版本 (v1, v2)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tuple: (phones, word2ph, norm_text, char_mappings)
|
||||||
|
- phones: 音素序列
|
||||||
|
- word2ph: 词到音素的映射
|
||||||
|
- norm_text: 标准化后的文本
|
||||||
|
- char_mappings: 字符映射字典 {"orig_to_norm": [...], "norm_to_orig": [...]}
|
||||||
|
"""
|
||||||
|
if version is None:
|
||||||
|
version = os.environ.get("version", "v2")
|
||||||
|
if version == "v1":
|
||||||
|
symbols = symbols_v1.symbols
|
||||||
|
language_module_map = {"zh": "chinese", "ja": "japanese", "en": "english"}
|
||||||
|
else:
|
||||||
|
symbols = symbols_v2.symbols
|
||||||
|
language_module_map = {"zh": "chinese2", "ja": "japanese", "en": "english", "ko": "korean", "yue": "cantonese"}
|
||||||
|
|
||||||
|
if language not in language_module_map:
|
||||||
|
language = "en"
|
||||||
|
text = " "
|
||||||
|
|
||||||
|
# 特殊符号处理暂不支持映射
|
||||||
|
for special_s, special_l, target_symbol in special:
|
||||||
|
if special_s in text and language == special_l:
|
||||||
|
phones, word2ph, norm_text = clean_special(text, language, special_s, target_symbol, version)
|
||||||
|
# 返回空映射
|
||||||
|
char_mappings = {
|
||||||
|
"orig_to_norm": list(range(len(text))),
|
||||||
|
"norm_to_orig": list(range(len(norm_text)))
|
||||||
|
}
|
||||||
|
return phones, word2ph, norm_text, char_mappings
|
||||||
|
|
||||||
|
language_module = __import__("text." + language_module_map[language], fromlist=[language_module_map[language]])
|
||||||
|
|
||||||
|
# 使用带映射的标准化函数
|
||||||
|
if hasattr(language_module, "text_normalize_with_map"):
|
||||||
|
norm_text, char_mappings = language_module.text_normalize_with_map(text)
|
||||||
|
elif hasattr(language_module, "text_normalize"):
|
||||||
|
# 如果没有带映射的版本，使用普通版本并构建映射
|
||||||
|
norm_text = language_module.text_normalize(text)
|
||||||
|
from .char_mapping_utils import build_char_mapping
|
||||||
|
char_mappings = build_char_mapping(text, norm_text)
|
||||||
|
else:
|
||||||
|
# 不进行标准化
|
||||||
|
norm_text = text
|
||||||
|
char_mappings = {
|
||||||
|
"orig_to_norm": list(range(len(text))),
|
||||||
|
"norm_to_orig": list(range(len(text)))
|
||||||
|
}
|
||||||
|
|
||||||
|
# 处理音素
|
||||||
|
if language == "zh" or language == "yue":
|
||||||
|
phones, word2ph = language_module.g2p(norm_text)
|
||||||
|
assert len(phones) == sum(word2ph)
|
||||||
|
assert len(norm_text) == len(word2ph)
|
||||||
|
else:
|
||||||
|
# Try per-language word2ph helpers
|
||||||
|
if hasattr(language_module, "g2p_with_word2ph"):
|
||||||
|
try:
|
||||||
|
phones, word2ph = language_module.g2p_with_word2ph(norm_text, keep_punc=False)
|
||||||
|
except Exception:
|
||||||
|
phones = language_module.g2p(norm_text)
|
||||||
|
word2ph = None
|
||||||
|
else:
|
||||||
|
phones = language_module.g2p(norm_text)
|
||||||
|
word2ph = None
|
||||||
|
if language == "en" and len(phones) < 4:
|
||||||
|
phones = [","] + phones
|
||||||
|
|
||||||
|
phones = ["UNK" if ph not in symbols else ph for ph in phones]
|
||||||
|
return phones, word2ph, norm_text, char_mappings
|
||||||
|
|
||||||
|
|
||||||
def clean_special(text, language, special_s, target_symbol, version=None):
|
def clean_special(text, language, special_s, target_symbol, version=None):
|
||||||
if version is None:
|
if version is None:
|
||||||
version = os.environ.get("version", "v2")
|
version = os.environ.get("version", "v2")
|
||||||
|
|||||||
@ -238,6 +238,214 @@ def _expand_number(m):
|
|||||||
return _inflect.number_to_words(num, andword="")
|
return _inflect.number_to_words(num, andword="")
|
||||||
|
|
||||||
|
|
||||||
|
class CharMapper:
|
||||||
|
"""
|
||||||
|
字符映射追踪器，用于记录文本标准化过程中字符位置的变化
|
||||||
|
|
||||||
|
核心思想：维护从原始文本到当前文本的映射
|
||||||
|
- orig_to_curr[i] 表示原始文本第i个字符对应当前文本的位置
|
||||||
|
"""
|
||||||
|
def __init__(self, text):
|
||||||
|
self.original_text = text
|
||||||
|
self.text = text
|
||||||
|
# 初始化：每个原始字符映射到自己
|
||||||
|
self.orig_to_curr = list(range(len(text)))
|
||||||
|
|
||||||
|
def apply_sub(self, pattern, replacement_func):
|
||||||
|
"""
|
||||||
|
应用正则替换并更新映射
|
||||||
|
|
||||||
|
关键：需要通过旧映射来更新新映射
|
||||||
|
支持捕获组的特殊处理（如大写字母拆分）
|
||||||
|
"""
|
||||||
|
new_text = ""
|
||||||
|
# curr_to_new[i] 表示当前文本第i个字符在新文本中的位置
|
||||||
|
curr_to_new = [-1] * len(self.text)
|
||||||
|
|
||||||
|
pos = 0
|
||||||
|
for match in pattern.finditer(self.text):
|
||||||
|
# 处理匹配前的未变化文本
|
||||||
|
for i in range(pos, match.start()):
|
||||||
|
curr_to_new[i] = len(new_text)
|
||||||
|
new_text += self.text[i]
|
||||||
|
|
||||||
|
# 处理匹配的部分
|
||||||
|
replacement = replacement_func(match)
|
||||||
|
replacement_start_pos = len(new_text)
|
||||||
|
|
||||||
|
# 特殊处理：如果替换文本包含原始文本的字符（例如 "A" -> " A"）
|
||||||
|
# 尝试找到对应关系
|
||||||
|
match_text = match.group(0)
|
||||||
|
if len(match.groups()) > 0 and match.group(1) in replacement:
|
||||||
|
# 有捕获组，尝试精确映射
|
||||||
|
# 例如: "(?<!^)(?<![\s])([A-Z])" 匹配 "P"，替换为 " P"
|
||||||
|
captured = match.group(1)
|
||||||
|
replacement_idx = replacement.find(captured)
|
||||||
|
if replacement_idx >= 0:
|
||||||
|
# 捕获的字符在替换文本中
|
||||||
|
for i in range(match.start(), match.end()):
|
||||||
|
char = self.text[i]
|
||||||
|
if char in captured:
|
||||||
|
# 这个字符在捕获组中，映射到替换文本中的对应位置
|
||||||
|
char_idx_in_replacement = replacement.find(char, replacement_idx)
|
||||||
|
if char_idx_in_replacement >= 0:
|
||||||
|
curr_to_new[i] = replacement_start_pos + char_idx_in_replacement
|
||||||
|
else:
|
||||||
|
curr_to_new[i] = replacement_start_pos
|
||||||
|
else:
|
||||||
|
curr_to_new[i] = replacement_start_pos
|
||||||
|
else:
|
||||||
|
# 捕获的字符不在替换文本中，都映射到起始位置
|
||||||
|
for i in range(match.start(), match.end()):
|
||||||
|
curr_to_new[i] = replacement_start_pos
|
||||||
|
else:
|
||||||
|
# 没有捕获组或不包含原始字符，匹配部分的所有字符都映射到替换文本的起始位置
|
||||||
|
for i in range(match.start(), match.end()):
|
||||||
|
curr_to_new[i] = replacement_start_pos
|
||||||
|
|
||||||
|
new_text += replacement
|
||||||
|
pos = match.end()
|
||||||
|
|
||||||
|
# 处理剩余文本
|
||||||
|
for i in range(pos, len(self.text)):
|
||||||
|
curr_to_new[i] = len(new_text)
|
||||||
|
new_text += self.text[i]
|
||||||
|
|
||||||
|
# 更新原始到当前的映射：orig -> old_curr -> new_curr
|
||||||
|
new_orig_to_curr = []
|
||||||
|
for orig_idx in range(len(self.original_text)):
|
||||||
|
old_curr_idx = self.orig_to_curr[orig_idx]
|
||||||
|
if old_curr_idx >= 0 and old_curr_idx < len(curr_to_new):
|
||||||
|
new_orig_to_curr.append(curr_to_new[old_curr_idx])
|
||||||
|
else:
|
||||||
|
new_orig_to_curr.append(-1)
|
||||||
|
|
||||||
|
self.text = new_text
|
||||||
|
self.orig_to_curr = new_orig_to_curr
|
||||||
|
|
||||||
|
def apply_char_filter(self, keep_pattern):
|
||||||
|
"""
|
||||||
|
应用字符过滤（只保留符合模式的字符）并更新映射
|
||||||
|
|
||||||
|
keep_pattern: 正则表达式字符串，如 "[ A-Za-z'.,?!-]"
|
||||||
|
"""
|
||||||
|
new_text = ""
|
||||||
|
curr_to_new = []
|
||||||
|
|
||||||
|
for i, char in enumerate(self.text):
|
||||||
|
if re.match(keep_pattern, char):
|
||||||
|
curr_to_new.append(len(new_text))
|
||||||
|
new_text += char
|
||||||
|
else:
|
||||||
|
# 字符被删除
|
||||||
|
if new_text:
|
||||||
|
curr_to_new.append(len(new_text) - 1)
|
||||||
|
else:
|
||||||
|
curr_to_new.append(-1)
|
||||||
|
|
||||||
|
# 更新原始映射
|
||||||
|
new_orig_to_curr = []
|
||||||
|
for orig_idx in range(len(self.original_text)):
|
||||||
|
old_curr_idx = self.orig_to_curr[orig_idx]
|
||||||
|
if old_curr_idx >= 0 and old_curr_idx < len(curr_to_new):
|
||||||
|
new_orig_to_curr.append(curr_to_new[old_curr_idx])
|
||||||
|
else:
|
||||||
|
new_orig_to_curr.append(-1)
|
||||||
|
|
||||||
|
self.text = new_text
|
||||||
|
self.orig_to_curr = new_orig_to_curr
|
||||||
|
|
||||||
|
def get_norm_to_orig(self):
|
||||||
|
"""
|
||||||
|
构建标准化文本到原始文本的反向映射
|
||||||
|
"""
|
||||||
|
if not self.text:
|
||||||
|
return []
|
||||||
|
|
||||||
|
norm_to_orig = [-1] * len(self.text)
|
||||||
|
for orig_idx, norm_idx in enumerate(self.orig_to_curr):
|
||||||
|
if 0 <= norm_idx < len(self.text):
|
||||||
|
# 如果多个原始字符映射到同一个标准化位置，取第一个
|
||||||
|
if norm_to_orig[norm_idx] == -1:
|
||||||
|
norm_to_orig[norm_idx] = orig_idx
|
||||||
|
|
||||||
|
return norm_to_orig
|
||||||
|
|
||||||
|
|
||||||
|
def normalize_with_map(text):
|
||||||
|
"""
|
||||||
|
带字符映射的标准化函数
|
||||||
|
|
||||||
|
返回:
|
||||||
|
normalized_text: 标准化后的文本
|
||||||
|
char_mappings: 字典，包含:
|
||||||
|
- "orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
|
||||||
|
- "norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
|
||||||
|
"""
|
||||||
|
mapper = CharMapper(text)
|
||||||
|
|
||||||
|
# 按照 normalize() 的顺序应用所有转换
|
||||||
|
mapper.apply_sub(_ordinal_number_re, _convert_ordinal)
|
||||||
|
mapper.apply_sub(re.compile(r"(?<!\d)-|-(?!\d)"), lambda m: " minus ")
|
||||||
|
mapper.apply_sub(_comma_number_re, _remove_commas)
|
||||||
|
mapper.apply_sub(_time_re, _expand_time)
|
||||||
|
mapper.apply_sub(_measurement_re, _expand_measurement)
|
||||||
|
mapper.apply_sub(_pounds_re_start, _expand_pounds)
|
||||||
|
mapper.apply_sub(_pounds_re_end, _expand_pounds)
|
||||||
|
mapper.apply_sub(_dollars_re_start, _expand_dollars)
|
||||||
|
mapper.apply_sub(_dollars_re_end, _expand_dollars)
|
||||||
|
mapper.apply_sub(_decimal_number_re, _expand_decimal_number)
|
||||||
|
mapper.apply_sub(_fraction_re, _expend_fraction)
|
||||||
|
mapper.apply_sub(_ordinal_re, _expand_ordinal)
|
||||||
|
mapper.apply_sub(_number_re, _expand_number)
|
||||||
|
|
||||||
|
# Strip accents - 需要手动处理映射
|
||||||
|
normalized_nfd = unicodedata.normalize("NFD", mapper.text)
|
||||||
|
new_text = ""
|
||||||
|
curr_to_new = []
|
||||||
|
for i, char in enumerate(normalized_nfd):
|
||||||
|
if unicodedata.category(char) != "Mn":
|
||||||
|
curr_to_new.append(len(new_text))
|
||||||
|
new_text += char
|
||||||
|
else:
|
||||||
|
# 重音符号被删除，映射到前一个字符
|
||||||
|
if new_text:
|
||||||
|
curr_to_new.append(len(new_text) - 1)
|
||||||
|
else:
|
||||||
|
curr_to_new.append(-1)
|
||||||
|
|
||||||
|
# 更新原始映射 - 需要处理 NFD 可能改变字符数的情况
|
||||||
|
# 简化处理：假设 NFD 不会显著改变字符数（对于英文通常是这样）
|
||||||
|
if len(curr_to_new) >= len(mapper.text):
|
||||||
|
# NFD 展开了一些字符
|
||||||
|
new_orig_to_curr = []
|
||||||
|
for orig_idx in range(len(mapper.original_text)):
|
||||||
|
old_curr_idx = mapper.orig_to_curr[orig_idx]
|
||||||
|
if old_curr_idx >= 0 and old_curr_idx < len(curr_to_new):
|
||||||
|
new_orig_to_curr.append(curr_to_new[old_curr_idx])
|
||||||
|
else:
|
||||||
|
new_orig_to_curr.append(-1)
|
||||||
|
mapper.orig_to_curr = new_orig_to_curr
|
||||||
|
mapper.text = new_text
|
||||||
|
|
||||||
|
# 继续其他替换
|
||||||
|
mapper.apply_sub(re.compile("%"), lambda m: " percent")
|
||||||
|
|
||||||
|
# 删除非法字符 - 使用 apply_char_filter
|
||||||
|
mapper.apply_char_filter(r"[ A-Za-z'.,?!\-]")
|
||||||
|
|
||||||
|
mapper.apply_sub(re.compile(r"(?i)i\.e\."), lambda m: "that is")
|
||||||
|
mapper.apply_sub(re.compile(r"(?i)e\.g\."), lambda m: "for example")
|
||||||
|
mapper.apply_sub(re.compile(r"(?<!^)(?<![\s])([A-Z])"), lambda m: " " + m.group(1))
|
||||||
|
|
||||||
|
norm_to_orig = mapper.get_norm_to_orig()
|
||||||
|
|
||||||
|
return mapper.text, {
|
||||||
|
"orig_to_norm": mapper.orig_to_curr,
|
||||||
|
"norm_to_orig": norm_to_orig
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
def normalize(text):
|
def normalize(text):
|
||||||
"""
|
"""
|
||||||
!!! 所有的处理都需要正确的输入 !!!
|
!!! 所有的处理都需要正确的输入 !!!
|
||||||
|
|||||||
@ -245,6 +245,32 @@ def text_normalize(text):
|
|||||||
return text
|
return text
|
||||||
|
|
||||||
|
|
||||||
|
def text_normalize_with_map(text):
|
||||||
|
"""
|
||||||
|
带字符映射的英文标准化函数
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
normalized_text: 标准化后的文本
|
||||||
|
char_mappings: 字典，包含:
|
||||||
|
- "orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
|
||||||
|
- "norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
|
||||||
|
"""
|
||||||
|
from .en_normalization.expend import normalize_with_map
|
||||||
|
|
||||||
|
# 先进行标点替换
|
||||||
|
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
||||||
|
text = pattern.sub(lambda x: rep_map[x.group()], text)
|
||||||
|
text = unicode(text)
|
||||||
|
|
||||||
|
# 使用带映射的标准化函数
|
||||||
|
normalized_text, mappings = normalize_with_map(text)
|
||||||
|
|
||||||
|
# 避免重复标点引起的参考泄露
|
||||||
|
normalized_text = replace_consecutive_punctuation(normalized_text)
|
||||||
|
|
||||||
|
return normalized_text, mappings
|
||||||
|
|
||||||
|
|
||||||
class en_G2p(G2p):
|
class en_G2p(G2p):
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
|
|||||||
@ -179,6 +179,27 @@ def text_normalize(text):
|
|||||||
return text
|
return text
|
||||||
|
|
||||||
|
|
||||||
|
def text_normalize_with_map(text):
|
||||||
|
"""
|
||||||
|
带字符映射的日文标准化函数
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
normalized_text: 标准化后的文本
|
||||||
|
char_mappings: 字典，包含:
|
||||||
|
- "orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
|
||||||
|
- "norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
|
||||||
|
"""
|
||||||
|
from .char_mapping_utils import build_char_mapping
|
||||||
|
|
||||||
|
# 先进行标准化
|
||||||
|
normalized_text = text_normalize(text)
|
||||||
|
|
||||||
|
# 构建字符映射
|
||||||
|
mappings = build_char_mapping(text, normalized_text)
|
||||||
|
|
||||||
|
return normalized_text, mappings
|
||||||
|
|
||||||
|
|
||||||
# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
|
# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
|
||||||
def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True):
|
def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True):
|
||||||
"""Extract phoneme + prosoody symbol sequence from input full-context labels.
|
"""Extract phoneme + prosoody symbol sequence from input full-context labels.
|
||||||
|
|||||||
@ -357,6 +357,22 @@ def g2p_with_word2ph(text, keep_punc=False):
|
|||||||
word2ph.append(max(1, len(phs_valid)))
|
word2ph.append(max(1, len(phs_valid)))
|
||||||
return phones_all, word2ph
|
return phones_all, word2ph
|
||||||
|
|
||||||
|
def text_normalize_with_map(text):
|
||||||
|
"""
|
||||||
|
韩文不需要标准化，直接返回原始文本和恒等映射
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
normalized_text: 原始文本（未改变）
|
||||||
|
char_mappings: 字典，包含恒等映射
|
||||||
|
"""
|
||||||
|
# 韩文不进行标准化，直接返回恒等映射
|
||||||
|
mappings = {
|
||||||
|
"orig_to_norm": list(range(len(text))),
|
||||||
|
"norm_to_orig": list(range(len(text)))
|
||||||
|
}
|
||||||
|
return text, mappings
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
text = "안녕하세요"
|
text = "안녕하세요"
|
||||||
print(g2p(text))
|
print(g2p(text))
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user