feat(indextts): add glossary support for custom term pronunciations

indextts/infer_v2.py

@@ -167,12 +167,18 @@ class IndexTTS2:
         print(">> bigvgan weights restored from:", bigvgan_name)
 
         self.bpe_path = os.path.join(self.model_dir, self.cfg.dataset["bpe_model"])
-        self.normalizer = TextNormalizer()
+        self.normalizer = TextNormalizer(enable_glossary=True)
         self.normalizer.load()
         print(">> TextNormalizer loaded")
         self.tokenizer = TextTokenizer(self.bpe_path, self.normalizer)
         print(">> bpe model loaded from:", self.bpe_path)
 
+        # 加载术语词汇表（如果存在）
+        self.glossary_path = os.path.join(self.model_dir, "glossary.yaml")
+        if os.path.exists(self.glossary_path):
+            self.normalizer.load_glossary_from_yaml(self.glossary_path)
+            print(">> Glossary loaded from:", self.glossary_path)
+
         emo_matrix = torch.load(os.path.join(self.model_dir, self.cfg.emo_matrix))
         self.emo_matrix = emo_matrix.to(self.device)
         self.emo_num = list(self.cfg.emo_num)

indextts/utils/common.py

@@ -62,7 +62,7 @@ def de_tokenized_by_CJK_char(line: str, do_lower_case=False) -> str:
       output = "see you!"
     """
     # replace english words in the line with placeholders
-    english_word_pattern = re.compile(r"([A-Z]+(?:[\s-][A-Z-]+)*)", re.IGNORECASE)
+    english_word_pattern = re.compile(r"([A-Z]+(?:[\s'-][A-Z-]+)*)", re.IGNORECASE)
     english_sents = english_word_pattern.findall(line)
     for i, sent in enumerate(english_sents):
         line = line.replace(sent, f"<sent_{i}>")

indextts/utils/front.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from functools import lru_cache
 import os
 import traceback
 import re
@@ -9,7 +10,7 @@ from sentencepiece import SentencePieceProcessor
 
 
 class TextNormalizer:
-    def __init__(self):
+    def __init__(self, enable_glossary=False):
         self.zh_normalizer = None
         self.en_normalizer = None
         self.char_rep_map = {
@@ -53,6 +54,18 @@ class TextNormalizer:
             "$": ".",
             **self.char_rep_map,
         }
+        self.enable_glossary = enable_glossary
+        # 术语词汇表：用户可自定义专业术语的读法
+        # 格式: {"原始术语": {"en": "英文读法", "zh": "中文读法"}}
+        self.term_glossary = {
+            # "M.2": {"en": "M dot two", "zh": "M 二"},
+            # "PCIe 5.0": {"en": "PCIE five", "zh": "PCIE 五点零"},
+            # "PCIe 4.0": {"en": "PCIE four", "zh": "PCIE 四点零"},
+            # "AHCI": "A H C I",
+            # "TTS": "T T S",
+            # "Inc.": {"en": "Ink"},
+            # ".md": {"en": "dot M D", "zh": "点 M D"},
+        }
 
     def match_email(self, email):
         # 正则表达式匹配邮箱格式：数字英文@数字英文.英文
@@ -124,6 +137,9 @@ class TextNormalizer:
             return ""
         if self.use_chinese(text):
             text = re.sub(TextNormalizer.ENGLISH_CONTRACTION_PATTERN, r"\1 is", text, flags=re.IGNORECASE)
+            # 应用术语词汇表（优先级最高，在所有保护之前）
+            if self.enable_glossary:
+                text = self.apply_glossary_terms(text, lang="zh")
             # 保护技术术语（如 GPT-5-nano）避免被中文normalizer错误处理
             replaced_text, tech_list = self.save_tech_terms(text.rstrip())
             replaced_text, pinyin_list = self.save_pinyin_tones(replaced_text)
@@ -145,6 +161,9 @@ class TextNormalizer:
         else:
             try:
                 text = re.sub(TextNormalizer.ENGLISH_CONTRACTION_PATTERN, r"\1 is", text, flags=re.IGNORECASE)
+                # 应用术语词汇表（优先级最高，在所有保护之前）
+                if self.enable_glossary:
+                    text = self.apply_glossary_terms(text, lang="en")
                 # 保护技术术语（如 GPT-5-Nano）避免被英文normalizer错误处理
                 replaced_text, tech_list = self.save_tech_terms(text)
                 result = self.en_normalizer.normalize(replaced_text)
@@ -242,6 +261,95 @@ class TextNormalizer:
         transformed_text = re.sub(r'\s*<H>\s*', '-', normalized_text)
         return transformed_text
 
+    def apply_glossary_terms(self, text, lang="zh"):
+        """
+        应用术语词汇表，将专业术语替换为对应语言的读法
+
+        Args:
+            text: 待处理文本
+            lang: 语言类型 "zh" 或 "en"
+
+        Returns:
+            处理后的文本
+
+        Example:
+            "M.2 NVMe SSD" -> (zh) "M 二 NVMe SSD"
+            "M.2 NVMe SSD" -> (en) "M dot two NVMe SSD"
+        """
+        if not self.term_glossary:
+            return text
+
+        # 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
+        # 例如："PCIe 5.0" 应该在 "PCIe" 之前匹配
+        sorted_terms = sorted(self.term_glossary.keys(), key=len, reverse=True)
+        @lru_cache(maxsize=42)
+        def get_term_pattern(term: str):
+            return re.compile(re.escape(term), re.IGNORECASE)
+        transformed_text = text
+        for term in sorted_terms:
+            term_value = self.term_glossary[term]
+            if isinstance(term_value, dict):
+                replacement = term_value.get(lang, term_value.get(lang, term))
+            else:
+                replacement = term_value
+            # 使用正则进行大小写不敏感的替换
+            pattern = get_term_pattern(term)
+            transformed_text = pattern.sub(replacement, transformed_text)
+
+        return transformed_text
+
+    def load_glossary(self, glossary_dict):
+        """
+        加载外部术语词汇表
+
+        Args:
+            glossary_dict: 术语词典，格式为 {"术语": {"en": "英文读法", "zh": "中文读法"}}
+
+        Example:
+            normalizer.load_glossary({
+                "M.2": {"en": "M dot two", "zh": "M 二"},
+                "PCIe": {"en": "PCIE", "zh": "PCIE"}
+            })
+        """
+        if glossary_dict and isinstance(glossary_dict, dict):
+            self.term_glossary.update(glossary_dict)
+
+    def load_glossary_from_yaml(self, glossary_path):
+        """
+        从 YAML 文件加载术语词汇表
+
+        Args:
+            glossary_path: YAML 文件路径
+
+        Example:
+            normalizer.load_glossary_from_yaml("checkpoints/glossary.yaml")
+
+        YAML 文件格式:
+            M.2:
+              en: M dot two
+              zh: M 二
+            NVMe: N-V-M-E  # 中英文相同读法
+        """
+        if glossary_path and os.path.exists(glossary_path):
+            import yaml
+            with open(glossary_path, 'r', encoding='utf-8') as f:
+                external_glossary = yaml.safe_load(f)
+                if external_glossary and isinstance(external_glossary, dict):
+                    self.term_glossary.update(external_glossary)
+                    return True
+        return False
+
+    def save_glossary_to_yaml(self, glossary_path):
+        """
+        保存术语词汇表到 YAML 文件
+
+        Args:
+            glossary_path: YAML 文件路径
+        """
+        import yaml
+        with open(glossary_path, 'w', encoding='utf-8') as f:
+            yaml.dump(self.term_glossary, f, allow_unicode=True, default_flow_style=False)
+
     def save_pinyin_tones(self, original_text):
         """
         替换拼音声调为占位符 <pinyin_a>, <pinyin_b>, ...
@@ -493,7 +601,7 @@ class TextTokenizer:
 if __name__ == "__main__":
     # 测试程序
 
-    text_normalizer = TextNormalizer()
+    text_normalizer = TextNormalizer(enable_glossary=True)
 
     cases = [
         "IndexTTS 正式发布1.0版本了，效果666",
@@ -535,9 +643,11 @@ if __name__ == "__main__":
         "今天是个好日子 it's a good day",  # 今天是个好日子 it is a good day
         # 术语
         "such as XTTS, CosyVoice2, Fish-Speech, and F5-TTS",  # such as xtts,cosyvoice two,fish-speech,and f five-tts
-        "GPT-5-Nano is the smallest and fastest variant in the GPT-5 model family.",
-        "GPT-5-Nano 是 GPT-5 模型家族中最小且速度最快的变体",
-        "2025/09/08 IndexTTS-2 全球发布"
+        "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
+        "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
+        "2025/09/08 IndexTTS-2 全球发布",  # 二零二五年九月八日 IndexTTS-二全球发布
+        "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
+        "we dive deep into the showdown between DisplayPort 1.4 and HDMI 2.1 to determine which is the best choice for gaming enthusiasts",
         # 人名
         "约瑟夫·高登-莱维特（Joseph Gordon-Levitt is an American actor）",
         "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。",

tools/i18n/locale/en_US.json

@@ -46,5 +46,16 @@
   "与音色参考音频相同": "Same as the voice reference",
   "情感随机采样": "Randomize emotion sampling",
   "显示实验功能": "Show experimental features",
-  "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."
+  "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.",
+  "自定义个别专业术语的读音": "Customize the pronunciation of each term or how it is \"say as\".",
+  "请至少输入一种读法": "Please enter at least one pronunciation",
+  "已添加": "Added",
+  "开启术语词汇读音": "Enable custom term pronunciations",
+  "添加术语": "Add term",
+  "暂无术语": "No custom terms added yet",
+  "请输入术语": "Please input the term",
+  "术语": "Term",
+  "已删除": "Deleted",
+  "英文读法": "English pronunciation",
+  "自定义术语词汇读音": "Customize term pronunciations"
 }

webui.py

@@ -109,6 +109,21 @@ def get_example_cases(include_experimental = False):
     # exclude emotion control mode 3 (emotion from text description)
     return [x for x in example_cases if x[1] != EMO_CHOICES_ALL[3]]
 
+def format_glossary_markdown():
+    """将词汇表转换为Markdown表格格式"""
+    if not tts.normalizer.term_glossary:
+        return i18n("暂无术语")
+
+    lines = [f"| {i18n('术语')} | {i18n('中文读法')} | {i18n('英文读法')} |"]
+    lines.append("|---|---|---|")
+
+    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")
 
+        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 +392,71 @@ 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"""
+        if not term:
+            return (
+                gr.update(value=i18n("请输入术语")),
+                gr.update()
+            )
+        if not reading_zh and not reading_en:
+            return (
+                gr.update(value=i18n("请至少输入一种读法")),
+                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
+        tts.normalizer.save_glossary_to_yaml(tts.glossary_path)
+
+        # 更新Markdown表格
+        return (
+            gr.update(value=f"{i18n('已添加')}: {term}"),
+            gr.update(value=format_glossary_markdown())
+        )
+
+    def on_delete_glossary_term(table_data):
+        """删除选中的术语（通过清空输入框中的术语来触发）"""
+        # 注意：Gradio Dataframe 的选择功能有限，这里我们通过术语输入框来指定要删除的术语
+        pass  # 实际删除功能需要结合具体的选择机制
+
+    def on_delete_by_term(term):
+        """通过术语名称删除"""
+        if not term:
+            return (
+                gr.update(value=i18n("请输入要删除的术语")),
+                gr.update()
+            )
+
+        if term in tts.normalizer.term_glossary:
+            del tts.normalizer.term_glossary[term]
+            # 自动保存
+            tts.normalizer.save_glossary_to_yaml(tts.glossary_path)
+
+            # 更新Markdown表格
+            return (
+                gr.update(value=f"{i18n('已删除')}: {term}"),
+                gr.update(value=format_glossary_markdown())
+            )
+        else:
+            return (
+                gr.update(value=f"{i18n('术语不存在')}: {term}"),
+                gr.update()
+            )
+
     def on_method_change(emo_control_method):
         if emo_control_method == 1:  # emotion reference audio
             return (gr.update(visible=True),
@@ -410,6 +514,17 @@ 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 +541,14 @@ with gr.Blocks(title="IndexTTS Demo") as demo:
                          inputs=[],
                          outputs=[gen_button])
 
+    # 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]
+    )
+
+
     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,