refactor: rename canto-backend → backend, canto-frontend → frontend

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/indextts/utils/front.py

@@ -0,0 +1,536 @@
+# -*- coding: utf-8 -*-
+import os
+import traceback
+import re
+from typing import List, Union, overload
+import warnings
+from indextts.utils.common import tokenize_by_CJK_char, de_tokenized_by_CJK_char
+from sentencepiece import SentencePieceProcessor
+
+
+class TextNormalizer:
+    def __init__(self):
+        self.zh_normalizer = None
+        self.en_normalizer = None
+        self.char_rep_map = {
+            "：": ",",
+            "；": ",",
+            ";": ",",
+            "，": ",",
+            "。": ".",
+            "！": "!",
+            "？": "?",
+            "\n": " ",
+            "·": "-",
+            "、": ",",
+            "...": "…",
+            ",,,": "…",
+            "，，，": "…",
+            "……": "…",
+            "“": "'",
+            "”": "'",
+            '"': "'",
+            "‘": "'",
+            "’": "'",
+            "（": "'",
+            "）": "'",
+            "(": "'",
+            ")": "'",
+            "《": "'",
+            "》": "'",
+            "【": "'",
+            "】": "'",
+            "[": "'",
+            "]": "'",
+            "—": "-",
+            "～": "-",
+            "~": "-",
+            "「": "'",
+            "」": "'",
+            ":": ",",
+        }
+        self.zh_char_rep_map = {
+            "$": ".",
+            **self.char_rep_map,
+        }
+
+    def match_email(self, email):
+        # 正则表达式匹配邮箱格式：数字英文@数字英文.英文
+        pattern = r"^[a-zA-Z0-9]+@[a-zA-Z0-9]+\.[a-zA-Z]+$"
+        return re.match(pattern, email) is not None
+
+    PINYIN_TONE_PATTERN = r"(?<![a-z])((?:[bpmfdtnlgkhjqxzcsryw]|[zcs]h)?(?:[aeiouüv]|[ae]i|u[aio]|ao|ou|i[aue]|[uüv]e|[uvü]ang?|uai|[aeiuv]n|[aeio]ng|ia[no]|i[ao]ng)|ng|er)([1-5])"
+    """
+    匹配拼音声调格式：pinyin+数字，声调1-5，5表示轻声
+    例如：xuan4, jve2, ying1, zhong4, shang5
+    不匹配：beta1, voice2
+    """
+    NAME_PATTERN = r"[\u4e00-\u9fff]+(?:[-·—][\u4e00-\u9fff]+){1,2}"
+    """
+    匹配人名，格式：中文·中文，中文·中文-中文
+    例如：克里斯托弗·诺兰，约瑟夫·高登-莱维特
+    """
+
+    # 匹配常见英语缩写 's，仅用于替换为 is，不匹配所有 's
+    ENGLISH_CONTRACTION_PATTERN = r"(what|where|who|which|how|t?here|it|s?he|that|this)'s"
+
+
+    def use_chinese(self, s):
+        has_chinese = bool(re.search(r"[\u4e00-\u9fff]", s))
+        has_alpha = bool(re.search(r"[a-zA-Z]", s))
+        is_email = self.match_email(s)
+        if has_chinese or not has_alpha or is_email:
+            return True
+
+        has_pinyin = bool(re.search(TextNormalizer.PINYIN_TONE_PATTERN, s, re.IGNORECASE))
+        return has_pinyin
+
+    def load(self):
+        # print(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
+        # sys.path.append(model_dir)
+        import platform
+        if self.zh_normalizer is not None and self.en_normalizer is not None:
+            return
+        if platform.system() == "Darwin":
+            from wetext import Normalizer
+
+            self.zh_normalizer = Normalizer(remove_erhua=False, lang="zh", operator="tn")
+            self.en_normalizer = Normalizer(lang="en", operator="tn")
+        else:
+            from tn.chinese.normalizer import Normalizer as NormalizerZh
+            from tn.english.normalizer import Normalizer as NormalizerEn
+            # use new cache dir for build tagger rules with disable remove_interjections and remove_erhua
+            cache_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "tagger_cache")
+            if not os.path.exists(cache_dir):
+                os.makedirs(cache_dir)
+                with open(os.path.join(cache_dir, ".gitignore"), "w") as f:
+                    f.write("*\n")
+            self.zh_normalizer = NormalizerZh(
+                cache_dir=cache_dir, remove_interjections=False, remove_erhua=False, overwrite_cache=False
+            )
+            self.en_normalizer = NormalizerEn(overwrite_cache=False)
+
+    def normalize(self, text: str) -> str:
+        if not self.zh_normalizer or not self.en_normalizer:
+            print("Error, text normalizer is not initialized !!!")
+            return ""
+        if self.use_chinese(text):
+            text = re.sub(TextNormalizer.ENGLISH_CONTRACTION_PATTERN, r"\1 is", text, flags=re.IGNORECASE)
+            replaced_text, pinyin_list = self.save_pinyin_tones(text.rstrip())
+            
+            replaced_text, original_name_list = self.save_names(replaced_text)
+            try:
+                result = self.zh_normalizer.normalize(replaced_text)
+            except Exception:
+                result = ""
+                print(traceback.format_exc())
+            # 恢复人名
+            result = self.restore_names(result, original_name_list)
+            # 恢复拼音声调
+            result = self.restore_pinyin_tones(result, pinyin_list)
+            pattern = re.compile("|".join(re.escape(p) for p in self.zh_char_rep_map.keys()))
+            result = pattern.sub(lambda x: self.zh_char_rep_map[x.group()], result)
+        else:
+            try:
+                text = re.sub(TextNormalizer.ENGLISH_CONTRACTION_PATTERN, r"\1 is", text, flags=re.IGNORECASE)
+                result = self.en_normalizer.normalize(text)
+            except Exception:
+                result = text
+                print(traceback.format_exc())
+            pattern = re.compile("|".join(re.escape(p) for p in self.char_rep_map.keys()))
+            result = pattern.sub(lambda x: self.char_rep_map[x.group()], result)
+        return result
+
+    def correct_pinyin(self, pinyin: str):
+        """
+        将 jqx 的韵母为 u/ü 的拼音转换为 v
+        如：ju -> jv , que -> qve, xün -> xvn
+        """
+        if pinyin[0] not in "jqxJQX":
+            return pinyin
+        # 匹配 jqx 的韵母为 u/ü 的拼音
+        pattern = r"([jqx])[uü](n|e|an)*(\d)"
+        repl = r"\g<1>v\g<2>\g<3>"
+        pinyin = re.sub(pattern, repl, pinyin, flags=re.IGNORECASE)
+        return pinyin.upper()
+
+    def save_names(self, original_text):
+        """
+        替换人名为占位符 <n_a>、 <n_b>, ...
+        例如：克里斯托弗·诺兰 -> <n_a>
+        """
+        # 人名
+        name_pattern = re.compile(TextNormalizer.NAME_PATTERN, re.IGNORECASE)
+        original_name_list = re.findall(name_pattern, original_text)
+        if len(original_name_list) == 0:
+            return (original_text, None)
+        original_name_list = list(set("".join(n) for n in original_name_list))
+        transformed_text = original_text
+        # 替换占位符 <n_a>、 <n_b>, ...
+        for i, name in enumerate(original_name_list):
+            number = chr(ord("a") + i)
+            transformed_text = transformed_text.replace(name, f"<n_{number}>")
+
+        return transformed_text, original_name_list
+
+    def restore_names(self, normalized_text, original_name_list):
+        """
+        恢复人名为原来的文字
+        例如：<n_a> -> original_name_list[0]
+        """
+        if not original_name_list or len(original_name_list) == 0:
+            return normalized_text
+
+        transformed_text = normalized_text
+        # 替换为占位符 <n_a>、 <n_b>, ...
+        for i, name in enumerate(original_name_list):
+            number = chr(ord("a") + i)
+            transformed_text = transformed_text.replace(f"<n_{number}>", name)
+        return transformed_text
+
+    def save_pinyin_tones(self, original_text):
+        """
+        替换拼音声调为占位符 <pinyin_a>, <pinyin_b>, ...
+        例如：xuan4 -> <pinyin_a>
+        """
+        # 声母韵母+声调数字
+        origin_pinyin_pattern = re.compile(TextNormalizer.PINYIN_TONE_PATTERN, re.IGNORECASE)
+        original_pinyin_list = re.findall(origin_pinyin_pattern, original_text)
+        if len(original_pinyin_list) == 0:
+            return (original_text, None)
+        original_pinyin_list = list(set("".join(p) for p in original_pinyin_list))
+        transformed_text = original_text
+        # 替换为占位符 <pinyin_a>, <pinyin_b>, ...
+        for i, pinyin in enumerate(original_pinyin_list):
+            number = chr(ord("a") + i)
+            transformed_text = transformed_text.replace(pinyin, f"<pinyin_{number}>")
+
+        # print("original_text: ", original_text)
+        # print("transformed_text: ", transformed_text)
+        return transformed_text, original_pinyin_list
+
+    def restore_pinyin_tones(self, normalized_text, original_pinyin_list):
+        """
+        恢复拼音中的音调数字（1-5）为原来的拼音
+        例如：<pinyin_a> -> original_pinyin_list[0]
+        """
+        if not original_pinyin_list or len(original_pinyin_list) == 0:
+            return normalized_text
+
+        transformed_text = normalized_text
+        # 替换占位符 <pinyin_a>, <pinyin_b>, ...
+        for i, pinyin in enumerate(original_pinyin_list):
+            number = chr(ord("a") + i)
+            pinyin = self.correct_pinyin(pinyin)
+            transformed_text = transformed_text.replace(f"<pinyin_{number}>", pinyin)
+        # print("normalized_text: ", normalized_text)
+        # print("transformed_text: ", transformed_text)
+        return transformed_text
+
+
+class TextTokenizer:
+    def __init__(self, vocab_file: str, normalizer: TextNormalizer = None):
+        self.vocab_file = vocab_file
+        self.normalizer = normalizer
+
+        if self.vocab_file is None:
+            raise ValueError("vocab_file is None")
+        if not os.path.exists(self.vocab_file):
+            raise ValueError(f"vocab_file {self.vocab_file} does not exist")
+        if self.normalizer:
+            self.normalizer.load()
+        # 加载词表
+        self.sp_model = SentencePieceProcessor(model_file=self.vocab_file)
+
+        self.pre_tokenizers = [
+            # 预处理器
+            tokenize_by_CJK_char,
+        ]
+
+    @property
+    def vocab_size(self):
+        return self.sp_model.GetPieceSize()
+
+    @property
+    def unk_token(self):
+        return "<unk>"
+
+    @property
+    def pad_token(self):
+        return None
+
+    @property
+    def bos_token(self):
+        return "<s>"
+
+    @property
+    def eos_token(self):
+        return "</s>"
+
+    @property
+    def pad_token_id(self):
+        return -1
+
+    @property
+    def bos_token_id(self):
+        return 0
+
+    @property
+    def eos_token_id(self):
+        return 1
+
+    @property
+    def unk_token_id(self):
+        return self.sp_model.unk_id()
+
+    @property
+    def special_tokens_map(self):
+        return {
+            "unk_token": self.unk_token,
+            "pad_token": self.pad_token,
+            "bos_token": self.bos_token,
+            "eos_token": self.eos_token,
+        }
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        return vocab
+
+    @overload
+    def convert_ids_to_tokens(self, ids: int) -> str: ...
+
+    @overload
+    def convert_ids_to_tokens(self, ids: List[int]) -> List[str]: ...
+
+    def convert_ids_to_tokens(self, ids: Union[List[int], int]):
+        return self.sp_model.IdToPiece(ids)
+
+    def convert_tokens_to_ids(self, tokens: Union[List[str], str]) -> List[int]:
+        if isinstance(tokens, str):
+            tokens = [tokens]
+        return [self.sp_model.PieceToId(token) for token in tokens]
+
+    def tokenize(self, text: str) -> List[str]:
+        return self.encode(text, out_type=str)
+
+    def encode(self, text: str, **kwargs):
+        if len(text) == 0:
+            return []
+        if len(text.strip()) == 1:
+            return self.sp_model.Encode(text, out_type=kwargs.pop("out_type", int), **kwargs)
+        # 预处理
+        if self.normalizer:
+            text = self.normalizer.normalize(text)
+        if len(self.pre_tokenizers) > 0:
+            for pre_tokenizer in self.pre_tokenizers:
+                text = pre_tokenizer(text)
+        return self.sp_model.Encode(text, out_type=kwargs.pop("out_type", int), **kwargs)
+
+    def batch_encode(self, texts: List[str], **kwargs):
+        # 预处理
+        if self.normalizer:
+            texts = [self.normalizer.normalize(text) for text in texts]
+        if len(self.pre_tokenizers) > 0:
+            for pre_tokenizer in self.pre_tokenizers:
+                texts = [pre_tokenizer(text) for text in texts]
+        return self.sp_model.Encode(texts, out_type=kwargs.pop("out_type", int), **kwargs)
+
+    def decode(self, ids: Union[List[int], int], do_lower_case=False, **kwargs):
+        if isinstance(ids, int):
+            ids = [ids]
+        decoded = self.sp_model.Decode(ids, out_type=kwargs.pop("out_type", str), **kwargs)
+        return de_tokenized_by_CJK_char(decoded, do_lower_case=do_lower_case)
+
+    @staticmethod
+    def split_sentences_by_token(
+        tokenized_str: List[str], split_tokens: List[str], max_tokens_per_sentence: int
+    ) -> List[List[str]]:
+        """
+        将tokenize后的结果按特定token进一步分割
+        """
+        # 处理特殊情况
+        if len(tokenized_str) == 0:
+            return []
+        sentences: List[List[str]] = []
+        current_sentence = []
+        current_sentence_tokens_len = 0
+        for i in range(len(tokenized_str)):
+            token = tokenized_str[i]
+            current_sentence.append(token)
+            current_sentence_tokens_len += 1
+            if current_sentence_tokens_len <= max_tokens_per_sentence:
+                if token in split_tokens and current_sentence_tokens_len > 2:
+                    if i < len(tokenized_str) - 1:
+                        if tokenized_str[i + 1] in ["'", "▁'"]:
+                            # 后续token是'，则不切分
+                            current_sentence.append(tokenized_str[i + 1])
+                            i += 1
+                    sentences.append(current_sentence)
+                    current_sentence = []
+                    current_sentence_tokens_len = 0
+                continue
+            # 如果当前tokens的长度超过最大限制
+            if not  ("," in split_tokens or "▁," in split_tokens ) and ("," in current_sentence or "▁," in current_sentence): 
+                # 如果当前tokens中有,，则按,分割
+                sub_sentences = TextTokenizer.split_sentences_by_token(
+                    current_sentence, [",", "▁,"], max_tokens_per_sentence=max_tokens_per_sentence
+                )
+            elif "-" not in split_tokens and "-" in current_sentence:
+                # 没有,，则按-分割
+                sub_sentences = TextTokenizer.split_sentences_by_token(
+                    current_sentence, ["-"], max_tokens_per_sentence=max_tokens_per_sentence
+                )
+            else:
+                # 按照长度分割
+                sub_sentences = []
+                for j in range(0, len(current_sentence), max_tokens_per_sentence):
+                    if j + max_tokens_per_sentence < len(current_sentence):
+                        sub_sentences.append(current_sentence[j : j + max_tokens_per_sentence])
+                    else:
+                        sub_sentences.append(current_sentence[j:])
+                warnings.warn(
+                    f"The tokens length of sentence exceeds limit: {max_tokens_per_sentence}, "
+                    f"Tokens in sentence: {current_sentence}."
+                    "Maybe unexpected behavior",
+                    RuntimeWarning,
+                )
+            sentences.extend(sub_sentences)
+            current_sentence = []
+            current_sentence_tokens_len = 0
+        if current_sentence_tokens_len > 0:
+            assert current_sentence_tokens_len <= max_tokens_per_sentence
+            sentences.append(current_sentence)
+        # 如果相邻的句子加起来长度小于最大限制，则合并
+        merged_sentences = []
+        for sentence in sentences:
+            if len(sentence) == 0:
+                continue
+            if len(merged_sentences) == 0:
+                merged_sentences.append(sentence)
+            elif len(merged_sentences[-1]) + len(sentence) <= max_tokens_per_sentence:
+                merged_sentences[-1] = merged_sentences[-1] + sentence
+            else:
+                merged_sentences.append(sentence)
+        return merged_sentences
+
+    punctuation_marks_tokens = [
+        ".",
+        "!",
+        "?",
+        "▁.",
+        # "▁!", # unk
+        "▁?",
+        "▁...", # ellipsis
+    ]
+    def split_sentences(self, tokenized: List[str], max_tokens_per_sentence=120) -> List[List[str]]:
+        return TextTokenizer.split_sentences_by_token(
+            tokenized, self.punctuation_marks_tokens, max_tokens_per_sentence=max_tokens_per_sentence
+        )
+
+
+if __name__ == "__main__":
+    # 测试程序
+
+    text_normalizer = TextNormalizer()
+
+    cases = [
+        "IndexTTS 正式发布1.0版本了，效果666",
+        "晕XUAN4是一种GAN3觉",
+        "我爱你！",
+        "I love you!",
+        "“我爱你”的英语是“I love you”",
+        "2.5平方电线",
+        "共465篇，约315万字",
+        "2002年的第一场雪，下在了2003年",
+        "速度是10km/h",
+        "现在是北京时间2025年01月11日 20:00",
+        "他这条裤子是2012年买的，花了200块钱",
+        "电话：135-4567-8900",
+        "1键3连",
+        "他这条视频点赞3000+，评论1000+，收藏500+",
+        "这是1024元的手机，你要吗？",
+        "受不liao3你了",
+        "“衣裳”不读衣chang2，而是读衣shang5",
+        "最zhong4要的是：不要chong2蹈覆辙",
+        "不zuo1死就不会死",
+        "See you at 8:00 AM",
+        "8:00 AM 开会",
+        "Couting down 3, 2, 1, go!",
+        "数到3就开始：1、2、3",
+        "This sales for 2.5% off, only $12.5.",
+        "5G网络是4G网络的升级版，2G网络是3G网络的前身",
+        "苹果于2030/1/2发布新 iPhone 2X 系列手机，最低售价仅 ¥12999",
+        "这酒...里...有毒...",
+        # 异常case
+        "只有,,,才是最好的",
+        "babala2是什么？",  # babala二是什么?
+        "用beta1测试",  # 用beta一测试
+        "have you ever been to beta2?",  # have you ever been to beta two?
+        "such as XTTS, CosyVoice2, Fish-Speech, and F5-TTS",  # such as xtts,cosyvoice two,fish-speech,and f five-tts
+        "where's the money?",  # where is the money?
+        "who's there?",  # who is there?
+        "which's the best?",  # which is the best?
+        "how's it going?",  # how is it going?
+        "今天是个好日子 it's a good day",  # 今天是个好日子 it is a good day
+        # 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。",
+        # 长句子
+        "《盗梦空间》是由美国华纳兄弟影片公司出品的电影，由克里斯托弗·诺兰执导并编剧，莱昂纳多·迪卡普里奥、玛丽昂·歌迪亚、约瑟夫·高登-莱维特、艾利奥特·佩吉、汤姆·哈迪等联袂主演，2010年7月16日在美国上映，2010年9月1日在中国内地上映，2020年8月28日在中国内地重映。影片剧情游走于梦境与现实之间，被定义为“发生在意识结构内的当代动作科幻片”，讲述了由莱昂纳多·迪卡普里奥扮演的造梦师，带领特工团队进入他人梦境，从他人的潜意识中盗取机密，并重塑他人梦境的故事。",
+        "清晨拉开窗帘，阳光洒在窗台的Bloomixy花艺礼盒上——薰衣草香薰蜡烛唤醒嗅觉，永生花束折射出晨露般光泽。设计师将“自然绽放美学”融入每个细节：手工陶瓷花瓶可作首饰收纳，香薰精油含依兰依兰舒缓配方。限量款附赠《365天插花灵感手册》，让每个平凡日子都有花开仪式感。\n宴会厅灯光暗下的刹那，Glimmeria星月系列耳坠开始发光——瑞士冷珐琅工艺让蓝宝石如银河流动，钛合金骨架仅3.2g无负重感。设计师秘密：内置微型重力感应器，随步伐产生0.01mm振幅，打造“行走的星光”。七夕限定礼盒含星座定制铭牌，让爱意如星辰永恒闪耀。",
+        "电影1：“黑暗骑士”（演员：克里斯蒂安·贝尔、希斯·莱杰；导演：克里斯托弗·诺兰）；电影2：“盗梦空间”（演员：莱昂纳多·迪卡普里奥；导演：克里斯托弗·诺兰）；电影3：“钢琴家”（演员：艾德里安·布洛迪；导演：罗曼·波兰斯基）；电影4：“泰坦尼克号”（演员：莱昂纳多·迪卡普里奥；导演：詹姆斯·卡梅隆）；电影5：“阿凡达”（演员：萨姆·沃辛顿；导演：詹姆斯·卡梅隆）；电影6：“南方公园：大电影”（演员：马特·斯通、托马斯·艾恩格瑞；导演：特雷·帕克）",
+    ]
+    # 测试分词器
+    tokenizer = TextTokenizer(
+        vocab_file="checkpoints/bpe.model",
+        normalizer=text_normalizer,
+    )
+
+    codes = tokenizer.batch_encode(
+        cases,
+        out_type=int,
+    )
+
+    print(f"vocab_size: {tokenizer.vocab_size}")
+    # print(f"pad_token: {tokenizer.pad_token}, pad_token_id: {tokenizer.pad_token_id}")
+    print(f"bos_token: {tokenizer.bos_token}, bos_token_id: {tokenizer.bos_token_id}")
+    print(f"eos_token: {tokenizer.eos_token}, eos_token_id: {tokenizer.eos_token_id}")
+    print(f"unk_token: {tokenizer.unk_token}, unk_token_id: {tokenizer.unk_token_id}")
+    # 测试拼音 (8474-10201)
+    for id in range(8474, 10201):
+        pinyin = tokenizer.convert_ids_to_tokens(id)
+        if re.match(TextNormalizer.PINYIN_TONE_PATTERN, pinyin, re.IGNORECASE) is None:
+            print(f"{pinyin} should be matched")
+    for badcase in [
+        "beta1", "better1", "voice2", "bala2", "babala2", "hunger2"
+    ]:
+        if re.match(TextNormalizer.PINYIN_TONE_PATTERN, badcase, re.IGNORECASE) is not None:
+            print(f"{badcase} should not be matched!")
+    # 不应该有 unk_token_id
+    for t in set([*TextTokenizer.punctuation_marks_tokens, ",", "▁,", "-", "▁..."]):
+        tokens = tokenizer.convert_tokens_to_ids(t)
+        if tokenizer.unk_token_id in tokens:
+            print(f"Warning: {t} is unknown token")
+        print(f"`{t}`", "->", tokens, "->", tokenizer.convert_ids_to_tokens(tokens))
+    for ch in set(tokenizer.normalizer.zh_char_rep_map.values()):
+        # 测试 normalize后的字符能被分词器识别
+        print(f"`{ch}`", "->", tokenizer.sp_model.Encode(ch, out_type=str))
+        print(f"` {ch}`", "->", tokenizer.sp_model.Encode(f" {ch}", out_type=str))
+    max_tokens_per_sentence=120
+    for i in range(len(cases)):
+        print(f"原始文本: {cases[i]}")
+        print(f"Normalized: {text_normalizer.normalize(cases[i])}")
+        tokens = tokenizer.tokenize(cases[i])
+        print("Tokenzied: ", ", ".join([f"`{t}`" for t in tokens]))
+        sentences = tokenizer.split_sentences(tokens, max_tokens_per_sentence=max_tokens_per_sentence)
+        print("Splitted sentences count:", len(sentences))
+        if len(sentences) > 1:
+            for j in range(len(sentences)):
+                print(f"  {j}, count:", len(sentences[j]), ", tokens:", "".join(sentences[j]))
+                if len(sentences[j]) > max_tokens_per_sentence:
+                    print(f"Warning: sentence {j} is too long, length: {len(sentences[j])}")
+        #print(f"Token IDs (first 10): {codes[i][:10]}")
+        if tokenizer.unk_token in codes[i]:
+            print(f"Warning: `{cases[i]}` contains UNKNOWN token")
+        print(f"Decoded: {tokenizer.decode(codes[i], do_lower_case=True)}")
+        print("-" * 50)