ChatTTS-Forge / modules /normalization.py
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from functools import lru_cache
from modules.utils.zh_normalization.text_normlization import *
import emojiswitch
from modules.utils.markdown import markdown_to_text
from modules import models
import re
@lru_cache(maxsize=64)
def is_chinese(text):
# 中文字符的 Unicode 范围是 \u4e00-\u9fff
chinese_pattern = re.compile(r"[\u4e00-\u9fff]")
return bool(chinese_pattern.search(text))
@lru_cache(maxsize=64)
def is_eng(text):
eng_pattern = re.compile(r"[a-zA-Z]")
return bool(eng_pattern.search(text))
@lru_cache(maxsize=64)
def guess_lang(text):
if is_chinese(text):
return "zh"
if is_eng(text):
return "en"
return "zh"
post_normalize_pipeline = []
pre_normalize_pipeline = []
def post_normalize():
def decorator(func):
post_normalize_pipeline.append(func)
return func
return decorator
def pre_normalize():
def decorator(func):
pre_normalize_pipeline.append(func)
return func
return decorator
def apply_pre_normalize(text):
for func in pre_normalize_pipeline:
text = func(text)
return text
def apply_post_normalize(text):
for func in post_normalize_pipeline:
text = func(text)
return text
def is_markdown(text):
markdown_patterns = [
r"(^|\s)#[^#]", # 标题
r"\*\*.*?\*\*", # 加粗
r"\*.*?\*", # 斜体
r"!\[.*?\]\(.*?\)", # 图片
r"\[.*?\]\(.*?\)", # 链接
r"`[^`]+`", # 行内代码
r"```[\s\S]*?```", # 代码块
r"(^|\s)\* ", # 无序列表
r"(^|\s)\d+\. ", # 有序列表
r"(^|\s)> ", # 引用
r"(^|\s)---", # 分隔线
]
for pattern in markdown_patterns:
if re.search(pattern, text, re.MULTILINE):
return True
return False
character_map = {
":": ",",
";": ",",
"!": "。",
"(": ",",
")": ",",
"【": ",",
"】": ",",
"『": ",",
"』": ",",
"「": ",",
"」": ",",
"《": ",",
"》": ",",
"-": ",",
"‘": " ",
"“": " ",
"’": " ",
"”": " ",
'"': " ",
"'": " ",
":": ",",
";": ",",
"!": ".",
"(": ",",
")": ",",
"[": ",",
"]": ",",
">": ",",
"<": ",",
"-": ",",
"~": " ",
"~": " ",
"/": " ",
}
character_to_word = {
" & ": " and ",
}
## ---------- post normalize ----------
@post_normalize()
def apply_character_to_word(text):
for k, v in character_to_word.items():
text = text.replace(k, v)
return text
@post_normalize()
def apply_character_map(text):
translation_table = str.maketrans(character_map)
return text.translate(translation_table)
@post_normalize()
def apply_emoji_map(text):
lang = guess_lang(text)
return emojiswitch.demojize(text, delimiters=("", ""), lang=lang)
@post_normalize()
def insert_spaces_between_uppercase(s):
# 使用正则表达式在每个相邻的大写字母之间插入空格
return re.sub(
r"(?<=[A-Z])(?=[A-Z])|(?<=[a-z])(?=[A-Z])|(?<=[\u4e00-\u9fa5])(?=[A-Z])|(?<=[A-Z])(?=[\u4e00-\u9fa5])",
" ",
s,
)
@post_normalize()
def replace_unk_tokens(text):
"""
把不在字典里的字符替换为 " , "
"""
chat_tts = models.load_chat_tts()
if "tokenizer" not in chat_tts.pretrain_models:
# 这个地方只有在 huggingface spaces 中才会触发
# 因为 hugggingface 自动处理模型卸载加载,所以如果拿不到就算了...
return text
tokenizer = chat_tts.pretrain_models["tokenizer"]
vocab = tokenizer.get_vocab()
vocab_set = set(vocab.keys())
# 添加所有英语字符
vocab_set.update(set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"))
vocab_set.update(set(" \n\r\t"))
replaced_chars = [char if char in vocab_set else " , " for char in text]
output_text = "".join(replaced_chars)
return output_text
## ---------- pre normalize ----------
@pre_normalize()
def apply_markdown_to_text(text):
if is_markdown(text):
text = markdown_to_text(text)
return text
# 将 "xxx" => \nxxx\n
# 将 'xxx' => \nxxx\n
@pre_normalize()
def replace_quotes(text):
repl = r"\n\1\n"
patterns = [
['"', '"'],
["'", "'"],
["“", "”"],
["‘", "’"],
]
for p in patterns:
text = re.sub(rf"({p[0]}[^{p[0]}{p[1]}]+?{p[1]})", repl, text)
return text
def ensure_suffix(a: str, b: str, c: str):
a = a.strip()
if not a.endswith(b):
a += c
return a
email_domain_map = {
"outlook.com": "Out look",
"hotmail.com": "Hot mail",
"yahoo.com": "雅虎",
}
# 找到所有 email 并将 name 分割为单个字母,@替换为 at ,. 替换为 dot,常见域名替换为单词
#
# 例如:
# zhzluke96@outlook.com => z h z l u k e 9 6 at out look dot com
def email_detect(text):
email_pattern = re.compile(r"([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})")
def replace(match):
email = match.group(1)
name, domain = email.split("@")
name = " ".join(name)
if domain in email_domain_map:
domain = email_domain_map[domain]
domain = domain.replace(".", " dot ")
return f"{name} at {domain}"
return email_pattern.sub(replace, text)
def sentence_normalize(sentence_text: str):
# https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
tx = TextNormalizer()
# 匹配 \[.+?\] 的部分
pattern = re.compile(r"(\[.+?\])|([^[]+)")
def normalize_part(part):
sentences = tx.normalize(part) if guess_lang(part) == "zh" else [part]
dest_text = ""
for sentence in sentences:
sentence = apply_post_normalize(sentence)
dest_text += sentence
return dest_text
def replace(match):
if match.group(1):
return f" {match.group(1)} "
else:
return normalize_part(match.group(2))
result = pattern.sub(replace, sentence_text)
# NOTE: 加了会有杂音...
# if is_end:
# 加这个是为了防止吞字
# result = ensure_suffix(result, "[uv_break]", "。。。[uv_break]。。。")
return result
def text_normalize(text, is_end=False):
text = apply_pre_normalize(text)
lines = text.split("\n")
lines = [line.strip() for line in lines]
lines = [line for line in lines if line]
lines = [sentence_normalize(line) for line in lines]
content = "\n".join(lines)
return content
if __name__ == "__main__":
test_cases = [
"ChatTTS是专门为对话场景设计的文本转语音模型,例如LLM助手对话任务。它支持英文和中文两种语言。最大的模型使用了10万小时以上的中英文数据进行训练。在HuggingFace中开源的版本为4万小时训练且未SFT的版本.",
" [oral_9] [laugh_0] [break_0] 电 [speed_0] 影 [speed_0] 中 梁朝伟 [speed_9] 扮演的陈永仁的编号27149",
" 明天有62%的概率降雨",
"大🍌,一条大🍌,嘿,你的感觉真的很奇妙 [lbreak]",
"""
# 你好,世界
```js
console.log('1')
```
**加粗**
*一条文本*
""",
"""
在沙漠、岩石、雪地上行走了很长的时间以后,小王子终于发现了一条大路。所有的大路都是通往人住的地方的。
“你们好。”小王子说。
这是一个玫瑰盛开的花园。
“你好。”玫瑰花说道。
小王子瞅着这些花,它们全都和他的那朵花一样。
“你们是什么花?”小王子惊奇地问。
“我们是玫瑰花。”花儿们说道。
“啊!”小王子说……。
""",
"""
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.
🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:
📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
""",
"""
120米
有12%的概率会下雨
""",
]
for i, test_case in enumerate(test_cases):
print(f"case {i}:\n", {"x": text_normalize(test_case, is_end=True)})