Merge pull request #557 from yrom/feat/glossary-support
feature: 用户可自定义专业术语的读法，如 M.2 → "M dot two" (EN) / "M 二" (ZH)
This commit is contained in:
Vanka0051
GitHub
commit
1698b32033
@ -167,12 +167,18 @@ class IndexTTS2:
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print(">> bigvgan weights restored from:", bigvgan_name)
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self.bpe_path = os.path.join(self.model_dir, self.cfg.dataset["bpe_model"])
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self.normalizer = TextNormalizer()
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self.normalizer = TextNormalizer(enable_glossary=True)
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self.normalizer.load()
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print(">> TextNormalizer loaded")
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self.tokenizer = TextTokenizer(self.bpe_path, self.normalizer)
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print(">> bpe model loaded from:", self.bpe_path)
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# 加载术语词汇表（如果存在）
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self.glossary_path = os.path.join(self.model_dir, "glossary.yaml")
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if os.path.exists(self.glossary_path):
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self.normalizer.load_glossary_from_yaml(self.glossary_path)
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print(">> Glossary loaded from:", self.glossary_path)
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emo_matrix = torch.load(os.path.join(self.model_dir, self.cfg.emo_matrix))
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self.emo_matrix = emo_matrix.to(self.device)
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self.emo_num = list(self.cfg.emo_num)
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@ -62,22 +62,23 @@ def de_tokenized_by_CJK_char(line: str, do_lower_case=False) -> str:
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output = "see you!"
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"""
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# replace english words in the line with placeholders
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english_word_pattern = re.compile(r"([A-Z]+(?:[\s-][A-Z-]+)*)", re.IGNORECASE)
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english_word_pattern = re.compile(r"([A-Z]+(?:[\s'-][A-Z-]+)*)", re.IGNORECASE)
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english_sents = english_word_pattern.findall(line)
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for i, sent in enumerate(english_sents):
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line = line.replace(sent, f"<sent_{i}>")
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words = line.split()
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# restore english sentences
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sent_placeholder_pattern = re.compile(r"^.*?(<sent_(\d+)>)")
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sent_placeholder_pattern = re.compile(r"(<sent_(\d+)>)")
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for i in range(len(words)):
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m = sent_placeholder_pattern.match(words[i])
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if m:
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all_matches = sent_placeholder_pattern.findall(words[i])
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if len(all_matches) > 1:
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# restore the english word
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placeholder_index = int(m.group(2))
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words[i] = words[i].replace(m.group(1), english_sents[placeholder_index])
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if do_lower_case:
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words[i] = words[i].lower()
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for h,j in all_matches:
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placeholder_index = int(j)
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words[i] = words[i].replace(h, english_sents[placeholder_index])
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if do_lower_case:
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words[i] = words[i].lower()
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return "".join(words)
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@ -1,4 +1,5 @@
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# -*- coding: utf-8 -*-
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from functools import lru_cache
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import os
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import traceback
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import re
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@ -9,7 +10,7 @@ from sentencepiece import SentencePieceProcessor
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class TextNormalizer:
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def __init__(self):
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def __init__(self, enable_glossary=False):
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self.zh_normalizer = None
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self.en_normalizer = None
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self.char_rep_map = {
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@ -53,6 +54,24 @@ class TextNormalizer:
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"$": ".",
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**self.char_rep_map,
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}
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self.enable_glossary = enable_glossary
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# 术语词汇表：用户可自定义专业术语的读法
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# 格式: {"原始术语": {"en": "英文读法", "zh": "中文读法"}}
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# "M.2": {"en": "M dot two", "zh": "M 二"},
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# "PCIe 5.0": {"en": "PCIE five", "zh": "PCIE 五点零"},
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# "PCIe 4.0": {"en": "PCIE four", "zh": "PCIE 四点零"},
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# "AHCI": "A H C I",
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# "TTS": "T T S",
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# "Inc.": {"en": "Ink"},
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# ".json": {"en": " dot Jay-Son", "zh": "点 Jay-Son"},
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# "C++": {"en": "C plus plus", "zh": "C 加加"},
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# "C#": "C sharp"
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# self.term_glossary = {
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# "C++": {"en": "C plus plus", "zh": "C 加加"},
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# "C#": "C sharp",
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# "CMake": "C Make",
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# }
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self.term_glossary = dict()
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def match_email(self, email):
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# 正则表达式匹配邮箱格式：数字英文@数字英文.英文
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@ -71,6 +90,14 @@ class TextNormalizer:
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例如：克里斯托弗·诺兰，约瑟夫·高登-莱维特
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"""
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TECH_TERM_PATTERN = r"[A-Za-z][A-Za-z0-9]*(?:-[A-Za-z0-9]+)+"
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"""
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匹配技术术语，格式：字母开头+(字母或数字)*+(-字母或数字)+
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例如：GPT-5-nano, F5-TTS, Fish-Speech, GPT-5, CosyVoice-2
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必须以字母开头，避免匹配纯数字（如电话号码 135-4567-8900）
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用于保护连字符结构，防止中文normalizer将连字符解析为减号（如"负五减"）
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"""
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# 匹配常见英语缩写 's，仅用于替换为 is，不匹配所有 's
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ENGLISH_CONTRACTION_PATTERN = r"(what|where|who|which|how|t?here|it|s?he|that|this)'s"
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@ -116,7 +143,12 @@ class TextNormalizer:
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return ""
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if self.use_chinese(text):
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text = re.sub(TextNormalizer.ENGLISH_CONTRACTION_PATTERN, r"\1 is", text, flags=re.IGNORECASE)
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replaced_text, pinyin_list = self.save_pinyin_tones(text.rstrip())
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# 应用术语词汇表（优先级最高，在所有保护之前）
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if self.enable_glossary:
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text = self.apply_glossary_terms(text, lang="zh")
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# 保护技术术语（如 GPT-5-nano）避免被中文normalizer错误处理
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replaced_text, tech_list = self.save_tech_terms(text.rstrip())
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replaced_text, pinyin_list = self.save_pinyin_tones(replaced_text)
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replaced_text, original_name_list = self.save_names(replaced_text)
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try:
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@ -128,12 +160,21 @@ class TextNormalizer:
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result = self.restore_names(result, original_name_list)
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# 恢复拼音声调
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result = self.restore_pinyin_tones(result, pinyin_list)
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# 恢复技术术语
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result = self.restore_tech_terms(result, tech_list)
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pattern = re.compile("|".join(re.escape(p) for p in self.zh_char_rep_map.keys()))
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result = pattern.sub(lambda x: self.zh_char_rep_map[x.group()], result)
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else:
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try:
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text = re.sub(TextNormalizer.ENGLISH_CONTRACTION_PATTERN, r"\1 is", text, flags=re.IGNORECASE)
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result = self.en_normalizer.normalize(text)
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# 应用术语词汇表（优先级最高，在所有保护之前）
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if self.enable_glossary:
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text = self.apply_glossary_terms(text, lang="en")
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# 保护技术术语（如 GPT-5-Nano）避免被英文normalizer错误处理
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replaced_text, tech_list = self.save_tech_terms(text)
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result = self.en_normalizer.normalize(replaced_text)
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# 恢复技术术语
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result = self.restore_tech_terms(result, tech_list)
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except Exception:
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result = text
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print(traceback.format_exc())
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@ -188,6 +229,133 @@ class TextNormalizer:
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transformed_text = transformed_text.replace(f"<n_{number}>", name)
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return transformed_text
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def save_tech_terms(self, original_text):
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"""
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保护技术术语中的连字符，防止被中文normalizer解析为减号
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策略：将术语中的连字符替换为特殊占位符<H>，数字仍可被正常处理
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例如：GPT-5-nano -> GPT<H>5<H>nano，然后 5 被转换为 五
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最终恢复为：GPT-五-nano
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"""
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tech_pattern = re.compile(TextNormalizer.TECH_TERM_PATTERN)
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original_tech_list = tech_pattern.findall(original_text)
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if len(original_tech_list) == 0:
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return (original_text, None)
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# 去重并按长度降序排列（避免短匹配先替换导致问题）
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original_tech_list = sorted(set(original_tech_list), key=len, reverse=True)
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transformed_text = original_text
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# 将术语中的连字符替换为占位符 <H>
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for term in original_tech_list:
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# 将 GPT-5-nano 替换为 GPT<H>5<H>nano
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protected_term = term.replace("-", "<H>")
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transformed_text = transformed_text.replace(term, protected_term)
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return transformed_text, original_tech_list
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def restore_tech_terms(self, normalized_text, original_tech_list):
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"""
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恢复技术术语中的连字符
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将占位符 <H> 恢复为连字符 -
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同时清理 normalizer 可能在占位符周围添加的多余空格
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"""
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if not original_tech_list or len(original_tech_list) == 0:
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return normalized_text
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# 清理 <H> 周围可能的空格，然后恢复为连字符
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# 处理模式: " <H> " -> "-", " <H>" -> "-", "<H> " -> "-", "<H>" -> "-"
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transformed_text = re.sub(r'\s*<H>\s*', '-', normalized_text)
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return transformed_text
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def apply_glossary_terms(self, text, lang="zh"):
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"""
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应用术语词汇表，将专业术语替换为对应语言的读法
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Args:
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text: 待处理文本
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lang: 语言类型 "zh" 或 "en"
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Returns:
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处理后的文本
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Example:
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"M.2 NVMe SSD" -> (zh) "M 二 NVMe SSD"
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"M.2 NVMe SSD" -> (en) "M dot two NVMe SSD"
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"""
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if not self.term_glossary:
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return text
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# 按术语长度降序排列，避免短术语先匹配导致长术语无法匹配
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# 例如："PCIe 5.0" 应该在 "PCIe" 之前匹配
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sorted_terms = sorted(self.term_glossary.keys(), key=len, reverse=True)
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@lru_cache(maxsize=42)
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def get_term_pattern(term: str):
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return re.compile(re.escape(term), re.IGNORECASE)
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transformed_text = text
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for term in sorted_terms:
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term_value = self.term_glossary[term]
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if isinstance(term_value, dict):
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replacement = term_value.get(lang, term_value.get(lang, term))
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else:
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replacement = term_value
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# 使用正则进行大小写不敏感的替换
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pattern = get_term_pattern(term)
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transformed_text = pattern.sub(replacement, transformed_text)
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return transformed_text
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def load_glossary(self, glossary_dict):
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"""
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加载外部术语词汇表
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Args:
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glossary_dict: 术语词典，格式为 {"术语": {"en": "英文读法", "zh": "中文读法"}}
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Example:
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normalizer.load_glossary({
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"M.2": {"en": "M dot two", "zh": "M 二"},
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"PCIe": {"en": "PCIE", "zh": "PCIE"}
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})
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"""
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if glossary_dict and isinstance(glossary_dict, dict):
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self.term_glossary.update(glossary_dict)
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def load_glossary_from_yaml(self, glossary_path):
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"""
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从 YAML 文件加载术语词汇表
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Args:
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glossary_path: YAML 文件路径
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Example:
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normalizer.load_glossary_from_yaml("checkpoints/glossary.yaml")
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YAML 文件格式:
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M.2:
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en: M dot two
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zh: M 二
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NVMe: N-V-M-E # 中英文相同读法
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"""
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if glossary_path and os.path.exists(glossary_path):
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import yaml
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with open(glossary_path, 'r', encoding='utf-8') as f:
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external_glossary = yaml.safe_load(f)
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if external_glossary and isinstance(external_glossary, dict):
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self.term_glossary = external_glossary
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return True
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return False
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def save_glossary_to_yaml(self, glossary_path):
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"""
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保存术语词汇表到 YAML 文件
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Args:
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glossary_path: YAML 文件路径
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"""
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import yaml
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with open(glossary_path, 'w', encoding='utf-8') as f:
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yaml.dump(self.term_glossary, f, allow_unicode=True, default_flow_style=False)
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def save_pinyin_tones(self, original_text):
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"""
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替换拼音声调为占位符 <pinyin_a>, <pinyin_b>, ...
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@ -439,7 +607,7 @@ class TextTokenizer:
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if __name__ == "__main__":
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# 测试程序
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text_normalizer = TextNormalizer()
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text_normalizer = TextNormalizer(enable_glossary=True)
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cases = [
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"IndexTTS 正式发布1.0版本了，效果666",
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@ -474,12 +642,18 @@ if __name__ == "__main__":
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"babala2是什么？", # babala二是什么?
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"用beta1测试", # 用beta一测试
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"have you ever been to beta2?", # have you ever been to beta two?
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"such as XTTS, CosyVoice2, Fish-Speech, and F5-TTS", # such as xtts,cosyvoice two,fish-speech,and f five-tts
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"where's the money?", # where is the money?
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"who's there?", # who is there?
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"which's the best?", # which is the best?
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"how's it going?", # how is it going?
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"今天是个好日子 it's a good day", # 今天是个好日子 it is a good day
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# 术语
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"such as XTTS, CosyVoice2, Fish-Speech, and F5-TTS", # such as xtts,cosyvoice two,fish-speech,and f five-tts
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"GPT-5-Nano is the smallest and fastest variant in the GPT-5 model family.", # GPT-five-Nano is the smallest and fastest variant in the GPT-five model family
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"GPT-5-Nano 是 GPT-5 模型家族中最小且速度最快的变体", # GPT-五-Nano 是 GPT-五 系统中最小且速度最快的变体
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"2025/09/08 IndexTTS-2 全球发布", # 二零二五年九月八日 IndexTTS-二全球发布
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"Here are some highly-rated M.2 NVMe SSDs: Samsung 9100 PRO PCIe 5.0 SSD M.2, $139.99", # Here are some highly-rated M dot two NVMe SSD's, Samsung nine thousand one hundred PRO PCIE five SSD M dot two . one hundred and thirty nine dollars and ninety nine cents
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"we dive deep into the showdown between DisplayPort 1.4 and HDMI 2.1 to determine which is the best choice for gaming enthusiasts",
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# 人名
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"约瑟夫·高登-莱维特（Joseph Gordon-Levitt is an American actor）",
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"蒂莫西·唐纳德·库克（英文名：Timothy Donald Cook），通称蒂姆·库克（Tim Cook），美国商业经理、工业工程师和工业开发商，现任苹果公司首席执行官。",
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@ -46,5 +46,20 @@
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"与音色参考音频相同": "Same as the voice reference",
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"情感随机采样": "Randomize emotion sampling",
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"显示实验功能": "Show experimental features",
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"提示：此功能为实验版，结果尚不稳定，我们正在持续优化中。": "Note: This feature is currently experimental and may not produce satisfactory results. We're dedicated to improving its performance in a future release."
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"提示：此功能为实验版，结果尚不稳定，我们正在持续优化中。": "Note: This feature is currently experimental and may not produce satisfactory results. We're dedicated to improving its performance in a future release.",
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"自定义个别专业术语的读音": "Customize the pronunciation of each term or how it is \"say as\".",
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"请至少输入一种读法": "Please enter at least one pronunciation",
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"已添加": "Added",
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"开启术语词汇读音": "Enable custom term pronunciations",
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"添加术语": "Add term",
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"暂无术语": "No custom terms added yet",
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"请输入术语": "Please input the term",
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"术语": "Term",
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"已删除": "Deleted",
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"英文读法": "English pronunciation",
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"自定义术语词汇读音": "Customize term pronunciations",
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"中文读法": "Chinese pronunciation",
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"词汇表已更新": "Glossary updated successfully",
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"保存词汇表时出错": "Error saving glossary",
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"加载词汇表时出错": "Error loading glossary"
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}
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119
webui.py
119
webui.py
@ -109,6 +109,21 @@ def get_example_cases(include_experimental = False):
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# exclude emotion control mode 3 (emotion from text description)
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return [x for x in example_cases if x[1] != EMO_CHOICES_ALL[3]]
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def format_glossary_markdown():
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"""将词汇表转换为Markdown表格格式"""
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if not tts.normalizer.term_glossary:
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return i18n("暂无术语")
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lines = [f"| {i18n('术语')} | {i18n('中文读法')} | {i18n('英文读法')} |"]
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lines.append("|---|---|---|")
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|
||||
for term, reading in tts.normalizer.term_glossary.items():
|
||||
zh = reading.get("zh", "") if isinstance(reading, dict) else reading
|
||||
en = reading.get("en", "") if isinstance(reading, dict) else reading
|
||||
lines.append(f"| {term} | {zh} | {en} |")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
def gen_single(emo_control_method,prompt, text,
|
||||
emo_ref_path, emo_weight,
|
||||
vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8,
|
||||
@ -195,8 +210,9 @@ with gr.Blocks(title="IndexTTS Demo") as demo:
|
||||
gen_button = gr.Button(i18n("生成语音"), key="gen_button",interactive=True)
|
||||
output_audio = gr.Audio(label=i18n("生成结果"), visible=True,key="output_audio")
|
||||
|
||||
experimental_checkbox = gr.Checkbox(label=i18n("显示实验功能"), value=False)
|
||||
|
||||
with gr.Row():
|
||||
experimental_checkbox = gr.Checkbox(label=i18n("显示实验功能"), value=False)
|
||||
glossary_checkbox = gr.Checkbox(label=i18n("开启术语词汇读音"), value=tts.normalizer.enable_glossary)
|
||||
with gr.Accordion(i18n("功能设置")):
|
||||
# 情感控制选项部分
|
||||
with gr.Row():
|
||||
@ -246,6 +262,29 @@ with gr.Blocks(title="IndexTTS Demo") as demo:
|
||||
with gr.Row(visible=False) as emo_weight_group:
|
||||
emo_weight = gr.Slider(label=i18n("情感权重"), minimum=0.0, maximum=1.0, value=0.65, step=0.01)
|
||||
|
||||
# 术语词汇表管理
|
||||
with gr.Accordion(i18n("自定义术语词汇读音"), open=False, visible=tts.normalizer.enable_glossary) as glossary_accordion:
|
||||
gr.Markdown(i18n("自定义个别专业术语的读音"))
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1):
|
||||
glossary_term = gr.Textbox(
|
||||
label=i18n("术语"),
|
||||
placeholder="IndexTTS2",
|
||||
)
|
||||
glossary_reading_zh = gr.Textbox(
|
||||
label=i18n("中文读法"),
|
||||
placeholder="Index T-T-S 二",
|
||||
)
|
||||
glossary_reading_en = gr.Textbox(
|
||||
label=i18n("英文读法"),
|
||||
placeholder="Index T-T-S two",
|
||||
)
|
||||
btn_add_term = gr.Button(i18n("添加术语"), scale=1)
|
||||
with gr.Column(scale=2):
|
||||
glossary_table = gr.Markdown(
|
||||
value=format_glossary_markdown()
|
||||
)
|
||||
|
||||
with gr.Accordion(i18n("高级生成参数设置"), open=False, visible=True) as advanced_settings_group:
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1):
|
||||
@ -353,6 +392,48 @@ with gr.Blocks(title="IndexTTS Demo") as demo:
|
||||
segments_preview: gr.update(value=df),
|
||||
}
|
||||
|
||||
# 术语词汇表事件处理函数
|
||||
def on_add_glossary_term(term, reading_zh, reading_en):
|
||||
"""添加术语到词汇表并自动保存"""
|
||||
term = term.rstrip()
|
||||
reading_zh = reading_zh.rstrip()
|
||||
reading_en = reading_en.rstrip()
|
||||
|
||||
if not term:
|
||||
gr.Warning(i18n("请输入术语"))
|
||||
return gr.update()
|
||||
|
||||
if not reading_zh and not reading_en:
|
||||
gr.Warning(i18n("请至少输入一种读法"))
|
||||
return gr.update()
|
||||
|
||||
|
||||
# 构建读法数据
|
||||
if reading_zh and reading_en:
|
||||
reading = {"zh": reading_zh, "en": reading_en}
|
||||
elif reading_zh:
|
||||
reading = {"zh": reading_zh}
|
||||
elif reading_en:
|
||||
reading = {"en": reading_en}
|
||||
else:
|
||||
reading = reading_zh or reading_en
|
||||
|
||||
# 添加到词汇表
|
||||
tts.normalizer.term_glossary[term] = reading
|
||||
|
||||
# 自动保存到文件
|
||||
try:
|
||||
tts.normalizer.save_glossary_to_yaml(tts.glossary_path)
|
||||
gr.Info(i18n("词汇表已更新"), duration=1)
|
||||
except Exception as e:
|
||||
gr.Error(i18n("保存词汇表时出错"))
|
||||
print(f"Error details: {e}")
|
||||
return gr.update()
|
||||
|
||||
# 更新Markdown表格
|
||||
return gr.update(value=format_glossary_markdown())
|
||||
|
||||
|
||||
def on_method_change(emo_control_method):
|
||||
if emo_control_method == 1: # emotion reference audio
|
||||
return (gr.update(visible=True),
|
||||
@ -410,6 +491,17 @@ with gr.Blocks(title="IndexTTS Demo") as demo:
|
||||
outputs=[emo_control_method, example_table]
|
||||
)
|
||||
|
||||
def on_glossary_checkbox_change(is_enabled):
|
||||
"""控制术语词汇表的可见性"""
|
||||
tts.normalizer.enable_glossary = is_enabled
|
||||
return gr.update(visible=is_enabled)
|
||||
|
||||
glossary_checkbox.change(
|
||||
on_glossary_checkbox_change,
|
||||
inputs=[glossary_checkbox],
|
||||
outputs=[glossary_accordion]
|
||||
)
|
||||
|
||||
input_text_single.change(
|
||||
on_input_text_change,
|
||||
inputs=[input_text_single, max_text_tokens_per_segment],
|
||||
@ -426,6 +518,29 @@ with gr.Blocks(title="IndexTTS Demo") as demo:
|
||||
inputs=[],
|
||||
outputs=[gen_button])
|
||||
|
||||
def on_demo_load():
|
||||
"""页面加载时重新加载glossary数据"""
|
||||
try:
|
||||
tts.normalizer.load_glossary_from_yaml(tts.glossary_path)
|
||||
except Exception as e:
|
||||
gr.Error(i18n("加载词汇表时出错"))
|
||||
print(f"Failed to reload glossary on page load: {e}")
|
||||
return gr.update(value=format_glossary_markdown())
|
||||
|
||||
# 术语词汇表事件绑定
|
||||
btn_add_term.click(
|
||||
on_add_glossary_term,
|
||||
inputs=[glossary_term, glossary_reading_zh, glossary_reading_en],
|
||||
outputs=[glossary_table]
|
||||
)
|
||||
|
||||
# 页面加载时重新加载glossary
|
||||
demo.load(
|
||||
on_demo_load,
|
||||
inputs=[],
|
||||
outputs=[glossary_table]
|
||||
)
|
||||
|
||||
gen_button.click(gen_single,
|
||||
inputs=[emo_control_method,prompt_audio, input_text_single, emo_upload, emo_weight,
|
||||
vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user