Add en_normalization and fix LangSegmenter (#2062)

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -7,7 +7,7 @@ sys.path.append(now_dir)
 
 import re
 import torch
-import LangSegment
+from text.LangSegmenter import LangSegmenter
 from text import chinese
 from typing import Dict, List, Tuple
 from text.cleaner import clean_text
@@ -20,7 +20,7 @@ from tools.i18n.i18n import I18nAuto, scan_language_list
 language=os.environ.get("language","Auto")
 language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
 i18n = I18nAuto(language=language)
-punctuation = set(['!', '?', '…', ',', '.', '-'," "])
+punctuation = set(['!', '?', '…', ',', '.', '-'])
 
 def get_first(text:str) -> str:
     pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
@@ -119,12 +119,7 @@ class TextPreprocessor:
     def get_phones_and_bert(self, text:str, language:str, version:str, final:bool=False):
         if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
             language = language.replace("all_","")
-            if language == "en":
+            formattext = text
-                LangSegment.setfilters(["en"])
-                formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
-            else:
-                # 因无法区别中日韩文汉字,以用户输入为准
-                formattext = text
             while "  " in formattext:
                 formattext = formattext.replace("  ", " ")
             if language == "zh":
@@ -148,19 +143,18 @@ class TextPreprocessor:
         elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
             textlist=[]
             langlist=[]
-            LangSegment.setfilters(["zh","ja","en","ko"])
             if language == "auto":
-                for tmp in LangSegment.getTexts(text):
+                for tmp in LangSegmenter.getTexts(text):
                     langlist.append(tmp["lang"])
                     textlist.append(tmp["text"])
             elif language == "auto_yue":
-                for tmp in LangSegment.getTexts(text):
+                for tmp in LangSegmenter.getTexts(text):
                     if tmp["lang"] == "zh":
                         tmp["lang"] = "yue"
                     langlist.append(tmp["lang"])
                     textlist.append(tmp["text"])
             else:
-                for tmp in LangSegment.getTexts(text):
+                for tmp in LangSegmenter.getTexts(text):
                     if tmp["lang"] == "en":
                         langlist.append(tmp["lang"])
                     else:

GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py

@@ -135,7 +135,7 @@ def cut3(inp):
 @register_method("cut4")
 def cut4(inp):
     inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip(".").split(".")]
+    opts = re.split(r'(?<!\d)\.(?!\d)', inp.strip("."))
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 

GPT_SoVITS/inference_webui.py

@@ -380,11 +380,7 @@ from text import chinese
 def get_phones_and_bert(text,language,version,final=False):
     if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
         language = language.replace("all_","")
-        if language == "en":
+        formattext = text
-            formattext = text
-        else:
-            # 因无法区别中日韩文汉字,以用户输入为准
-            formattext = text
         while "  " in formattext:
             formattext = formattext.replace("  ", " ")
         if language == "zh":
@@ -738,7 +734,7 @@ def cut3(inp):
 
 def cut4(inp):
     inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip(".").split(".")]
+    opts = re.split(r'(?<!\d)\.(?!\d)', inp.strip("."))
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 

GPT_SoVITS/text/en_normalization/expend.py

@@ -0,0 +1,275 @@
+# by https://github.com/Cosmo-klara
+
+from __future__ import print_function
+
+import re
+import inflect
+import unicodedata
+
+# 后缀计量单位替换表
+measurement_map = {
+    "m": ["meter", "meters"],
+    'km': ["kilometer", "kilometers"],
+    "km/h": ["kilometer per hour", "kilometers per hour"],
+    "ft": ["feet", "feet"],
+    "L": ["liter", "liters"],
+    "tbsp": ["tablespoon", "tablespoons"],
+    'tsp': ["teaspoon", "teaspoons"],
+    "h": ["hour", "hours"],
+    "min": ["minute", "minutes"],
+    "s": ["second", "seconds"],
+    "°C": ["degree celsius", "degrees celsius"],
+    "°F": ["degree fahrenheit", "degrees fahrenheit"]
+}
+
+
+# 识别 12,000 类型
+_inflect = inflect.engine()
+
+# 转化数字序数词
+_ordinal_number_re = re.compile(r'\b([0-9]+)\. ')
+
+# 我听说好像对于数字正则识别其实用 \d 会好一点
+
+_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
+
+# 时间识别
+_time_re = re.compile(r'\b([01]?[0-9]|2[0-3]):([0-5][0-9])\b')
+
+# 后缀计量单位识别
+_measurement_re = re.compile(r'\b([0-9]+(\.[0-9]+)?(m|km|km/h|ft|L|tbsp|tsp|h|min|s|°C|°F))\b')
+
+# 前后 £ 识别 ( 写了识别两边某一边的，但是不知道为什么失败了┭┮﹏┭┮ )
+_pounds_re_start = re.compile(r'£([0-9\.\,]*[0-9]+)')
+_pounds_re_end = re.compile(r'([0-9\.\,]*[0-9]+)£')
+
+# 前后 $ 识别
+_dollars_re_start = re.compile(r'\$([0-9\.\,]*[0-9]+)')
+_dollars_re_end = re.compile(r'([(0-9\.\,]*[0-9]+)\$')
+
+# 小数的识别
+_decimal_number_re = re.compile(r'([0-9]+\.\s*[0-9]+)')
+
+# 分数识别 (形式 "3/4" )
+_fraction_re = re.compile(r'([0-9]+/[0-9]+)')
+
+# 序数词识别
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
+
+# 数字处理
+_number_re = re.compile(r'[0-9]+')
+
+def _convert_ordinal(m):
+    """
+    标准化序数词, 例如: 1. 2. 3. 4. 5. 6. 
+    Examples:
+        input: "1. "
+        output: "1st"
+    然后在后面的 _expand_ordinal, 将其转化为 first 这类的
+    """
+    ordinal = _inflect.ordinal(m.group(1))
+    return ordinal + ", "
+
+def _remove_commas(m):
+    return m.group(1).replace(',', '')
+
+def _expand_time(m):
+    """
+    将 24 小时制的时间转换为 12 小时制的时间表示方式。
+    
+    Examples:
+        input: "13:00 / 4:00 / 13:30"
+        output: "one o'clock p.m. / four o'clock am. / one thirty p.m."
+    """
+    hours, minutes = map(int, m.group(1, 2))
+    period = 'a.m.' if hours < 12 else 'p.m.'
+    if hours > 12:
+        hours -= 12
+
+    hour_word = _inflect.number_to_words(hours)
+    minute_word = _inflect.number_to_words(minutes) if minutes != 0 else ''
+
+    if minutes == 0:
+        return f"{hour_word} o'clock {period}"
+    else:
+        return f"{hour_word} {minute_word} {period}"
+
+
+def _expand_measurement(m):
+    """
+    处理一些常见的测量单位后缀, 目前支持: m, km, km/h, ft, L, tbsp, tsp, h, min, s, °C, °F
+    如果要拓展的话修改: _measurement_re 和 measurement_map
+    """
+    sign = m.group(3)
+    ptr = 1
+    # 想不到怎么方便的取数字，又懒得改正则，诶，1.2 反正也是复数读法，干脆直接去掉 "."
+    num = int(m.group(1).replace(sign, '').replace(".",''))
+    decimal_part = m.group(2)
+    # 上面判断的漏洞，比如 0.1 的情况，在这里排除了
+    if decimal_part == None and num == 1:
+        ptr = 0
+    return m.group(1).replace(sign, " " + measurement_map[sign][ptr])
+
+
+def _expand_pounds(m):
+    """
+    没找到特别规范的说明，和美元的处理一样，其实可以把两个合并在一起
+    """
+    match = m.group(1)
+    parts = match.split('.')
+    if len(parts) > 2:
+        return match + ' pounds'    # Unexpected format
+    pounds = int(parts[0]) if parts[0] else 0
+    pence = int(parts[1].ljust(2, '0')) if len(parts) > 1 and parts[1] else 0
+    if pounds and pence:
+        pound_unit = 'pound' if pounds == 1 else 'pounds'
+        penny_unit = 'penny' if pence == 1 else 'pence'
+        return '%s %s and %s %s' % (pounds, pound_unit, pence, penny_unit)
+    elif pounds:
+        pound_unit = 'pound' if pounds == 1 else 'pounds'
+        return '%s %s' % (pounds, pound_unit)
+    elif pence:
+        penny_unit = 'penny' if pence == 1 else 'pence'
+        return '%s %s' % (pence, penny_unit)
+    else:
+        return 'zero pounds'
+
+def _expand_dollars(m):
+    """
+    change: 美分是 100 的限值, 应该要做补零的吧
+    Example:
+        input: "32.3$ / $6.24"
+        output: "thirty-two dollars and thirty cents" / "six dollars and twenty-four cents"
+    """
+    match = m.group(1)
+    parts = match.split('.')
+    if len(parts) > 2:
+        return match + ' dollars'    # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1].ljust(2, '0')) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+        cent_unit = 'cent' if cents == 1 else 'cents'
+        return '%s %s and %s %s' % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+        return '%s %s' % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = 'cent' if cents == 1 else 'cents'
+        return '%s %s' % (cents, cent_unit)
+    else:
+        return 'zero dollars'
+
+# 小数的处理
+def _expand_decimal_number(m):
+    """
+    Example:
+        input: "13.234"
+        output: "thirteen point two three four"
+    """
+    match = m.group(1)
+    parts = match.split('.')
+    words = []
+    # 遍历字符串中的每个字符
+    for char in parts[1]:
+        if char == '.':
+            words.append("point")
+        else:
+            words.append(char)
+    return parts[0] + " point " + " ".join(words)
+
+
+# 分数的处理
+def _expend_fraction(m):
+    """
+    规则1: 分子使用基数词读法, 分母用序数词读法.
+    规则2: 如果分子大于 1, 在读分母的时候使用序数词复数读法.
+    规则3: 当分母为2的时候, 分母读做 half, 并且当分子大于 1 的时候, half 也要用复数读法, 读为 halves.
+    Examples:
+    
+    | Written |	Said |
+    |:---:|:---:|
+    | 1/3 | one third |
+    | 3/4 | three fourths |
+    | 5/6 | five sixths |
+    | 1/2 | one half |
+    | 3/2 | three halves |
+    """
+    match = m.group(0)
+    numerator, denominator = map(int, match.split('/'))
+
+    numerator_part = _inflect.number_to_words(numerator)
+    if denominator == 2:
+        if numerator == 1:
+            denominator_part = 'half'
+        else:
+            denominator_part = 'halves'
+    elif denominator == 1:
+        return f'{numerator_part}'
+    else:
+        denominator_part = _inflect.ordinal(_inflect.number_to_words(denominator))
+        if numerator > 1:
+            denominator_part += 's'
+
+    return f'{numerator_part} {denominator_part}'
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return 'two thousand'
+        elif num > 2000 and num < 2010:
+            return 'two thousand ' + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + ' hundred'
+        else:
+            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+    else:
+        return _inflect.number_to_words(num, andword='')
+
+
+def normalize(text):
+    """
+    !!! 所有的处理都需要正确的输入 !!!
+    可以添加新的处理，只需要添加正则表达式和对应的处理函数即可
+    """
+
+    text = re.sub(_ordinal_number_re, _convert_ordinal, text)
+    text = re.sub(r'(?<!\d)-|-(?!\d)', ' minus ', text)
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_time_re, _expand_time, text)
+    text = re.sub(_measurement_re, _expand_measurement, text)
+    text = re.sub(_pounds_re_start, _expand_pounds, text)
+    text = re.sub(_pounds_re_end, _expand_pounds, text)
+    text = re.sub(_dollars_re_start, _expand_dollars, text)
+    text = re.sub(_dollars_re_end, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_number, text)
+    text = re.sub(_fraction_re, _expend_fraction, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+
+    text = ''.join(char for char in unicodedata.normalize('NFD', text)
+                    if unicodedata.category(char) != 'Mn')  # Strip accents
+
+    text = re.sub("%", " percent", text)
+    text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
+    text = re.sub(r"(?i)i\.e\.", "that is", text)
+    text = re.sub(r"(?i)e\.g\.", "for example", text)
+    # 增加纯大写单词拆分
+    text = re.sub(r'(?<!^)(?<![\s])([A-Z])', r' \1', text)
+    return text
+
+
+if __name__ == '__main__':
+    # 我觉得其实可以把切分结果展示出来（只读，或者修改不影响传给TTS的实际text）
+    # 然后让用户确认后再输入给 TTS，可以让用户检查自己有没有不标准的输入
+    print(normalize("1. test ordinal number 1st"))
+    print(normalize("32.3$, $6.24, 1.1£, £7.14."))
+    print(normalize("3/23, 1/2, 3/2, 1/3, 6/1"))
+    print(normalize("1st, 22nd"))
+    print(normalize("a test 20h, 1.2s, 1L, 0.1km"))
+    print(normalize("a test of time 4:00, 13:00, 13:30"))
+    print(normalize("a test of temperature 4°F, 23°C, -19°C"))

GPT_SoVITS/text/english.py

@@ -10,7 +10,7 @@ from text.symbols2 import symbols
 
 import unicodedata
 from builtins import str as unicode
-from g2p_en.expand import normalize_numbers
+from text.en_normalization.expend import normalize
 from nltk.tokenize import TweetTokenizer
 word_tokenize = TweetTokenizer().tokenize
 from nltk import pos_tag
@@ -22,6 +22,17 @@ CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
 CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
 NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
 
+
+# 适配中文及 g2p_en 标点
+rep_map = {
+    "[;:：，；]": ",",
+    '["’]': "'",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+}
+
+
 arpa = {
     "AH0",
     "S",
@@ -220,32 +231,16 @@ def get_namedict():
 
 def text_normalize(text):
     # todo: eng text normalize
-    # 适配中文及 g2p_en 标点
+
-    rep_map = {
+    # 效果相同，和 chinese.py 保持一致
-        "[;:：，；]": ",",
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
-        '["’]': "'",
+    text = pattern.sub(lambda x: rep_map[x.group()], text)
-        "。": ".",
+
-        "！": "!",
-        "？": "?",
-    }
-    for p, r in rep_map.items():
-        text = re.sub(p, r, text)
-
-    # 来自 g2p_en 文本格式化处理
-    # 增加大写兼容
-    # 增加纯大写单词拆分
     text = unicode(text)
-    text = normalize_numbers(text)
+    text = normalize(text)
-    text = ''.join(char for char in unicodedata.normalize('NFD', text)
-                    if unicodedata.category(char) != 'Mn')  # Strip accents
-    text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
-    text = re.sub(r"(?i)i\.e\.", "that is", text)
-    text = re.sub(r"(?i)e\.g\.", "for example", text)
-    text = re.sub(r'(?<!^)(?<![\s])([A-Z])', r' \1', text)
 
     # 避免重复标点引起的参考泄露
     text = replace_consecutive_punctuation(text)
-
     return text