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b309ea1b · chenzk · b309ea1b · b309ea1b · b309ea1b · b309ea1b
Commit b309ea1b authored May 07, 2024 by chenzk
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--- a/text/langdetect/profiles/ur
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\ No newline at end of file
--- a/text/langdetect/utils/__init__.py
+++ b/text/langdetect/utils/__init__.py
--- a/text/langdetect/utils/lang_profile.py
+++ b/text/langdetect/utils/lang_profile.py
+from collections import defaultdict
+import re
+
+import six
+from six.moves import xrange
+
+from .ngram import NGram
+
+
+class LangProfile(object):
+    MINIMUM_FREQ = 2
+    LESS_FREQ_RATIO = 100000
+
+    ROMAN_CHAR_RE = re.compile(r'^[A-Za-z]$')
+    ROMAN_SUBSTR_RE = re.compile(r'.*[A-Za-z].*')
+
+    def __init__(self, name=None, freq=None, n_words=None):
+        self.freq = defaultdict(int)
+        if freq is not None:
+            self.freq.update(freq)
+
+        if n_words is None:
+            n_words = [0] * NGram.N_GRAM
+
+        self.name = name
+        self.n_words = n_words
+
+    def add(self, gram):
+        '''Add n-gram to profile.'''
+        if self.name is None or gram is None:  # Illegal
+            return
+        length = len(gram)
+        if length < 1 or length > NGram.N_GRAM:  # Illegal
+            return
+        self.n_words[length - 1] += 1
+        self.freq[gram] += 1
+
+    def omit_less_freq(self):
+        '''Eliminate below less frequency n-grams and noise Latin alphabets.'''
+        if self.name is None:  # Illegal
+            return
+        threshold = max(self.n_words[0] // self.LESS_FREQ_RATIO, self.MINIMUM_FREQ)
+
+        roman = 0
+        for key, count in list(six.iteritems(self.freq)):
+            if count <= threshold:
+                self.n_words[len(key)-1] -= count
+                del self.freq[key]
+            elif self.ROMAN_CHAR_RE.match(key):
+                roman += count
+
+        # roman check
+        if roman < self.n_words[0] // 3:
+            for key, count in list(six.iteritems(self.freq)):
+                if self.ROMAN_SUBSTR_RE.match(key):
+                    self.n_words[len(key)-1] -= count
+                    del self.freq[key]
+
+    def update(self, text):
+        '''Update the language profile with (fragmented) text.
+        Extract n-grams from text and add their frequency into the profile.
+        '''
+        if text is None:
+            return
+        text = NGram.normalize_vi(text)
+        gram = NGram()
+        for ch in text:
+            gram.add_char(ch)
+            for n in xrange(1, NGram.N_GRAM+1):
+                self.add(gram.get(n))
--- a/text/langdetect/utils/messages.properties
+++ b/text/langdetect/utils/messages.properties
+NGram.CJK_KANJI_EXCLUDE=\u0020\uFF08\uFF09
+NGram.LATIN1_EXCLUDE=\u00A0\u00AB\u00B0\u00BB
+NGram.KANJI_1_0=\u4F7C\u6934
+NGram.KANJI_1_2=\u88CF\u95B2
+NGram.KANJI_1_4=\u7027\u7DCB
+NGram.KANJI_1_8=\u4E80\u4E9C\u4EEE\u5263\u5264\u5270\u52C5\u52E7\u52F2\u53B3\u5449\u58CA\u58CC\u5968\u59C9\u59EB\u5D8B\u5DE3\u5E30\u6075\u622F\u623B\u6255\u629C\u629E\u62DD\u62E1\u633F\u635C\u63FA\u6442\u6589\u658E\u6669\u66A6\u66FD\u6804\u685C\u6B69\u6B6F\u6BBB\u6C37\u6C5A\u6D44\u6E09\u6E0B\u6E13\u6EDD\u713C\u72A0\u731F\u7363\u7A32\u7A42\u7A93\u7ADC\u7C8B\u7C9B\u7DD1\u7E01\u7E04\u7E26\u7E4A\u7E4B\u7E70\u8074\u8107\u8133\u81D3\u820E\u8217\u8358\u83D3\u85AC\u8987\u899A\u8B21\u8B72\u8B83\u8CDB\u9045\u90F7\u91C8\u9271\u9283\u92AD\u9665\u967A\u96A0\u96A3\u96B7\u970A\u983C\u9854\u9855\u99C6\u9A12\u9ED9\u9F62
+NGram.KANJI_1_11=\u67D8\u831C
+NGram.KANJI_1_12=\u5742\u57FC\u5800
+NGram.KANJI_1_13=\u4E3C\u4E98\u4FE3\u4FF5\u5072\u51A8\u53A9\u5451\u546A\u5504\u5516\u55A9\u55B0\u5618\u5642\u565B\u567A\u56A2\u57F4\u5840\u5841\u58F1\u59F6\u5A2F\u5B22\u5B8D\u5DCC\u5EFB\u5F10\u60A9\u60E3\u61D0\u62F6\u63B4\u63BB\u63C3\u6681\u685F\u6955\u6962\u696F\u698A\u698E\u69FB\u6A2B\u6A7F\u6B53\u6BD8\u6D99\u6E07\u7460\u7473\u7560\u7573\u758E\u7690\u7815\u783A\u7962\u7A4F\u7A63\u7AEA\u7BED\u7CA7\u7D18\u7D3A\u7E4D\u8061\u8218\u8276\u82C5\u8597\u85AB\u86CD\u874B\u88FE\u8ACF\u8B90\u8D0B\u8FBF\u9013\u9061\u914E\u9154\u918D\u9190\u91A4\u91B8\u9262\u929A\u92ED\u92F3\u932C\u96EB\u96F0\u976D\u97EE\u981A\u99C4\u9A28\u9AC4\u9B8E\u9C10\u9D0E\u9D5C\u9D8F\u9E78\u9EB9\u9EBA\u9EBF
+NGram.KANJI_1_14=\u5F66\u7984\u7985
+NGram.KANJI_1_16=\u5861\u7B25\u844E\u9419\u9D07
+NGram.KANJI_1_18=\u5039\u514E\u51E7\u51EA\u5301\u5302\u5859\u58F7\u59AC\u5C2D\u5CA8\u5EFC\u6357\u64B9\u67CA\u6802\u6834\u68BC\u6900\u6919\u691B\u69D9\u6AE8\u6D9C\u6E8C\u6F09\u6F45\u701E\u7026\u7114\u72DB\u7577\u75E9\u783F\u7895\u7A50\u7AC3\u7B48\u7B86\u7BAA\u7C7E\u7C82\u7C8D\u7CCE\u7D2C\u7F6B\u7FEB\u8557\u85AE\u86CE\u877F\u8997\u8ACC\u8CB0\u8CCE\u8FE9\u9197\u920E\u9266\u927E\u92F2\u9306\u9453\u9784\u982C\u9834\u99C8\u9BF5\u9C2F\u9D2C
+NGram.KANJI_1_22=\u6762\u6A17\u887F
+NGram.KANJI_1_27=\u4E21\u4E57\u4ECF\u4F1D\u4FA1\u4FF3\u5024\u50CD\u5150\u5186\u51E6\u52B4\u52B9\u5358\u53CE\u55B6\u56E3\u56F2\u56F3\u570F\u5727\u5869\u5897\u58F2\u5909\u5B9F\u5BDB\u5BFE\u5C02\u5DFB\u5E2F\u5E81\u5E83\u5EC3\u5F3E\u5F93\u5FB3\u5FB4\u5FDC\u60AA\u6226\u6238\u6271\u62E0\u6319\u63B2\u6483\u64AE\u67A0\u67FB\u691C\u697D\u69D8\u6A29\u6B73\u6B74\u6BCE\u6C17\u6CA2\u6D5C\u6E08\u6E80\u702C\u7523\u767A\u770C\u7D4C\u7D75\u7D76\u7D99\u7D9A\u7DCF\u8535\u8846\u89A7\u89B3\u8A33\u8AAC\u8AAD\u8C4A\u8EE2\u8EFD\u8FBA\u8FBC\u9244\u9332\u95A2\u95D8\u96D1\u99C5\u9A13\u9ED2
+NGram.KANJI_1_29=\u4F0E\u4FFA\u5036\u53E1\u54B2\u5506\u583A\u5C3B\u5CAC\u5CE0\u5CEF\u6803\u68B6\u6A0B\u6A8E\u73C2\u7551\u7826\u7881\u79B0\u7B39\u8429\u8599\u8FBB\u9162\u95C7\u9688\u96BC\u9AEA\u9DF2
+NGram.KANJI_1_31=\u5553\u938C
+NGram.KANJI_1_35=\u51B4\u564C\u57DC\u5B2C\u6822\u685D\u690B\u6973\u6C93\u7511\u7887\u7A17\u83D6\u847A\u8494\u8526\u854E\u85C1\u86F8\u88B4\u93A7\u9B92\u9C39\u9C48\u9C52
+NGram.KANJI_2_0=\u4E2B\u4EC3\u4F09\u4F57\u4F6F\u4F70\u4FD1\u4FDA\u500C\u5043\u516E\u5189\u5241\u530D\u5310\u5412\u54AB\u54AF\u5514\u5556\u55B1\u561F\u573B\u586D\u587D\u58C5\u58D1\u5914\u5A62\u5A6A\u5AE6\u5B40\u5B5B\u5B70\u5BB8\u5CD2\u5D01\u5D34\u5E11\u5EA0\u5F0B\u5F2D\u5F87\u607F\u621B\u6221\u6289\u63A3\u6452\u646D\u64D8\u652B\u6600\u6631\u6641\u66F7\u6773\u67B8\u67DD\u67DE\u6829\u68FB\u69AD\u6A47\u6C10\u6C68\u6C74\u6C85\u6CD3\u6D31\u6D93\u6D94\u6DB8\u6DBF\u6DC5\u6E6E\u6EA7\u6EB4\u6EC2\u6F2A\u6F2F\u6FB9\u6FC2\u6FDB\u6FEE\u70AF\u70FD\u7166\u726F\u729B\u739F\u73DE\u740A\u746D\u749C\u749F\u74E0\u759D\u75A3\u75CD\u75DE\u7600\u7620\u7688\u7738\u7762\u776B\u777D\u77E3\u781D\u7837\u78A3\u7946\u7B60\u7F44\u7F54\u7F5F\u7FAF\u8026\u807F\u80C4\u80DB\u80ED\u81E7\u824B\u82B7\u82E3\u8392\u846D\u84D3\u8548\u85B9\u86DE\u873F\u8753\u8782\u87AB\u87B3\u87D1\u87E0\u87FE\u8821\u88D8\u88E8\u8913\u891A\u892B\u8983\u8C3F\u8C49\u8C82\u8D6D\u8DE4\u8E1D\u8E1E\u8E7C\u8FE5\u8FE8\u9005\u9035\u9050\u9082\u9083\u9095\u90E2\u911E\u91AE\u91B4\u93D6\u9621\u968D\u96B9\u96D2\u9711\u9713\u973E\u9AB0\u9AB7\u9AE6\u9B03\u9B23\u9EDC\u9EEF
+NGram.KANJI_2_1=\u4E82\u4F48\u4F54\u50F9\u5167\u528D\u52DE\u532F\u537B\u53C3\u5433\u555F\u55AE\u56B4\u570D\u5716\u58D3\u58DE\u5920\u5967\u5A1B\u5BEB\u5BEC\u5C08\u5C0D\u5C46\u5C6C\u5CFD\u5E36\u5E6B\u5EC8\u5EF3\u5F48\u5F91\u5F9E\u5FB5\u6046\u60E1\u61F7\u6232\u6236\u64C7\u64CA\u64D4\u64DA\u64F4\u651D\u6578\u65B7\u6649\u6A13\u6A23\u6A6B\u6A94\u6AA2\u6B0A\u6B50\u6B61\u6B72\u6B77\u6B78\u6C92\u6EAB\u6EFF\u6FD5\u6FDF\u71DF\u722D\u72C0\u734E\u737B\u746A\u7522\u773E\u78BC\u7A69\u7C3D\u7CB5\u7D55\u7D72\u7DA0\u7DAB\u7DE3\u7E5E\u7E6A\u7E7C\u7E8C\u8072\u807D\u8085\u812B\u8166\u8173\u81D8\u8209\u820A\u8332\u838A\u840A\u85E5\u860B\u8655\u865B\u88DD\u89BA\u89BD\u89C0\u8AAA\u8B6F\u8B7D\u8B8A\u8B93\u8C50\u8CF4\u8E64\u8F15\u8F49\u8FA6\u8FAD\u9109\u9130\u91AB\u91CB\u92B7\u9304\u9322\u95CA\u96A8\u96AA\u96B1\u96B8\u96D6\u96D9\u96DC\u9748\u975C\u986F\u9918\u99DB\u9A57\u9B25\u9EA5\u9EC3\u9EDE\u9F52
+NGram.KANJI_2_4=\u514C\u51AA\u5614\u56AE\u56C2\u582F\u58FA\u5B0C\u5D11\u5DD2\u5DD6\u5E40\u5E5F\u5EEC\u6137\u6417\u6488\u64F2\u652A\u6582\u6689\u689F\u68D7\u69D3\u6A97\u6AB8\u6ABB\u6AC3\u6ADA\u6B7F\u6BB2\u6EA5\u6EC4\u6EF2\u7009\u701D\u7028\u703E\u7165\u71BE\u721B\u7463\u7464\u7469\u7515\u7526\u75FA\u7621\u779E\u79B1\u7A1F\u7AC4\u7AC7\u7B8F\u7BE9\u7D2E\u7D68\u7D8F\u7DB8\u7DBA\u7E46\u7E79\u7F4C\u7F88\u8070\u8073\u8076\u81BE\u82BB\u83A2\u858A\u8591\u861A\u8778\u87EC\u8805\u880D\u893B\u8A1B\u8A25\u8A36\u8A85\u8AA6\u8B17\u8B28\u8CB6\u8CE4\u8D16\u8D1B\u8ECB\u9112\u9214\u9249\u93AC\u9594\u9598\u95BB\u95D5\u965E\u96B4\u97DC\u9821\u9824\u9921\u9952\u9A55\u9A5B\u9B1A\u9C13\u9D09\u9DAF\u9E1A\u9E75\u9F67
+NGram.KANJI_2_9=\u4E9F\u4F6C\u4FDE\u4FFE\u5029\u5140\u51A2\u5345\u539D\u53FB\u54C7\u5599\u560E\u561B\u563B\u566C\u5676\u5729\u574D\u57E4\u595A\u598D\u5A1F\u5A25\u5A77\u5AB2\u5AD6\u5BF0\u5C2C\u5CEA\u5E37\u5F08\u6059\u606A\u6096\u609A\u62A8\u6555\u6556\u66E6\u675E\u68E3\u69BB\u6BCB\u6BD3\u6C1F\u6C26\u6C81\u6DC4\u6DDE\u6E32\u6E44\u6E4D\u6F33\u6F7C\u6FA7\u701A\u701B\u715C\u741B\u7428\u7480\u74A8\u7504\u752C\u768B\u76CE\u78CA\u78FA\u79BA\u7C27\u8046\u81FB\u8331\u8393\u83C1\u8403\u8438\u843C\u8446\u85B0\u87D2\u8862\u8DC6\u9074\u9131\u9672\u96EF\u9704\u9706\u977C\u9ABC\u9E92\u9ECF
+NGram.KANJI_2_10=\u51BD\u5704\u7350\u73A5
+NGram.KANJI_2_11=\u4E15\u4EA2\u4F5A\u50D6\u5349\u53DF\u5484\u5958\u5B34\u5B5A\u5C91\u5E1B\u5F77\u61CB\u61FF\u620C\u620D\u622E\u6248\u6538\u660A\u664F\u678B\u67E9\u69B7\u69C3\u6CB1\u6CD7\u6D5A\u6DAA\u6DC7\u7099\u71EE\u7325\u7425\u7455\u747E\u749E\u75B5\u7678\u7693\u76C2\u77B0\u77BF\u78CB\u7957\u795A\u797A\u7A79\u7B08\u7B75\u7BB4\u7F9A\u7FB2\u7FDF\u80E5\u81BA\u8340\u837C\u8398\u8559\u85A8\u86DF\u8734\u8882\u88F4\u8936\u900D\u907D\u9642\u96C9\u9AFB\u9E9D\u9EBE
+NGram.KANJI_2_12=\u5F57\u7940
+NGram.KANJI_2_13=\u5191\u7791\u792C\u7D46
+NGram.KANJI_2_15=\u5713\u58FD\u5D17\u5D19\u5DBC\u5F4C\u6191\u64A5\u687F\u69AE\u6AFB\u6EEC\u6F3F\u6FE4\u6FF1\u6FFE\u700B\u74CA\u76E1\u76E7\u7926\u792B\u79AE\u7AA9\u7C43\u7C4C\u7C64\u7DBD\u81A0\u856D\u8594\u8606\u8A62\u8AF7\u8CC8\u8CE3\u8D99\u8F1B\u8F3B\u9059\u9127\u9264\u947D\u95A9\u97CB\u980C\u9838\u9846\u99AE\u9A19\u9B06\u9B91\u9F4A\u9F4B
+NGram.KANJI_2_16=\u4E69\u4EC4\u4EDF\u4EF3\u4F0B\u4F5E\u5000\u5028\u50E5\u513B\u5157\u51DC\u52D7\u530F\u5379\u53F5\u5471\u5477\u5555\u555C\u557B\u5594\u55B2\u55C9\u560D\u5616\u562E\u5630\u5653\u5657\u566F\u56A8\u56B6\u5820\u5880\u58CE\u58D9\u5950\u5969\u596D\u599E\u59B3\u59CD\u59D2\u5A40\u5AA7\u5ABC\u5AD7\u5AD8\u5B0B\u5B24\u5B38\u5B53\u5C5C\u5D06\u5D47\u5D94\u5D9D\u5E57\u5EC4\u5F46\u5FAC\u60BD\u60D8\u6123\u615D\u615F\u6175\u618A\u61AB\u61E3\u623E\u6308\u636B\u645F\u6519\u6595\u6698\u66B8\u67D9\u6840\u695D\u696E\u6979\u69C1\u69E8\u6AEC\u6AFA\u6B5F\u6CAC\u6CE0\u6CEF\u6D0C\u6D36\u6DD2\u6DD9\u6DE6\u6DEC\u6E5F\u6FA0\u6FEC\u7156\u71C4\u71DC\u71EC\u71FC\u720D\u7230\u7292\u7296\u72A2\u72CE\u7357\u737A\u7380\u7386\u73A8\u73EE\u743F\u74A6\u74CF\u74D4\u74DA\u755A\u75A5\u75B3\u75C2\u75E0\u75F1\u75FF\u7601\u7609\u7646\u7658\u769A\u76B0\u774F\u775C\u778B\u77BD\u77C7\u7843\u787F\u78F4\u79C8\u7A88\u7A95\u7AFD\u7B1E\u7B67\u7B9D\u7BCC\u7C0D\u7C11\u7C37\u7C40\u7C6E\u7CB3\u7CBD\u7D09\u7D31\u7D40\u7D5B\u7D70\u7D91\u7D9E\u7DB0\u7DD9\u7DF9\u7E08\u7E11\u7E1D\u7E35\u7E52\u7FB6\u7FBF\u7FEE\u8012\u801C\u8028\u8052\u8123\u8188\u81C3\u81DA\u81FE\u8210\u82BE\u83A0\u83D4\u8407\u8435\u8477\u849E\u84C6\u84CA\u85F9\u867A\u86B5\u86B6\u86C4\u8706\u8707\u870A\u8768\u87BB\u8831\u8839\u8879\u8921\u8938\u8964\u89A6\u89AC\u8A10\u8A3E\u8AC2\u8ADB\u8AF3\u8B2B\u8B41\u8B4E\u8B5F\u8B6B\u8B92\u8C55\u8C62\u8C73\u8C8A\u8C8D\u8CB2\u8CB3\u8CD2\u8CE1\u8CFB\u8D0D\u8E34\u8E7A\u8E8A\u8ED4\u8EFE\u8F0A\u8F1C\u8F1E\u8F26\u8FAE\u9088\u90C3\u90FE\u9134\u9148\u91D9\u91E9\u9238\u9239\u923D\u924D\u925A\u9296\u92AC\u92BB\u9315\u9319\u931A\u9321\u9370\u9394\u93A2\u93D8\u93E4\u943A\u9477\u9582\u958E\u95A1\u95C8\u95CC\u95D4\u9658\u966C\u970F\u973D\u9744\u975B\u9766\u97A3\u97A6\u97C1\u97C6\u980A\u9837\u9853\u9870\u98AF\u98B3\u98BA\u98E9\u98ED\u9912\u991B\u991E\u993D\u993F\u99D1\u99DF\u9A01\u9A3E\u9A43\u9A4D\u9ACF\u9AE1\u9B22\u9B58\u9C25\u9C3E\u9C54\u9C56\u9D15\u9D23\u9D89\u9DC2\u9DD3\u9E82\u9E8B\u9EA9\u9EE0\u9EF7\u9F07\u9F2F\u9F34\u9F3E\u9F5F\u9F6C
+NGram.KANJI_2_18=\u5155\u520E\u55DF\u56C0\u56C1\u5793\u5FD6\u5FF8\u6029\u60FA\u613E\u6147\u615A\u62C8\u6384\u6883\u6894\u68F9\u6AA3\u6AAE\u6AC2\u6E63\u7032\u70A4\u7146\u71FB\u7228\u72F7\u7370\u7441\u74BF\u75B8\u75E3\u7622\u76CD\u7768\u79E3\u7A60\u7B6E\u7BC1\u7C5F\u7D06\u7E2F\u7E39\u8146\u81CF\u8703\u8729\u8737\u87EF\u88D2\u8A22\u8AC4\u8AF6\u8E59\u8F33\u8F42\u9169\u91B1\u9278\u93C3\u93DD\u9460\u946A\u9785\u9AD1\u9B4D\u9B4E\u9C31\u9D12\u9ECC
+NGram.KANJI_2_21=\u502A\u544E\u59AE\u59EC\u5D1B\u66A8\u6BD7\u6C76\u6E1D\u70EF\u742A\u7459\u7FE1\u82EF\u8343\u85C9\u8A79\u90DD
+NGram.KANJI_2_22=\u4EDE\u4F7B\u504C\u50EE\u52E3\u52F0\u536E\u54A9\u54BB\u54BF\u54C2\u54E6\u550F\u556A\u55E8\u564E\u5664\u5671\u568F\u56DD\u572F\u57A0\u5809\u5924\u59A3\u59A4\u59E3\u5A13\u5A23\u5B51\u5B73\u5C50\u5C8C\u6035\u60C6\u6106\u6215\u62CE\u62FD\u64ED\u6549\u6554\u655D\u659B\u65CE\u65D6\u6615\u6624\u665E\u6677\u669D\u66E9\u6772\u677C\u696B\u6A84\u6AA0\u6BFD\u6C16\u6C86\u6C94\u6CD6\u6D2E\u6D39\u6F78\u6FB6\u705E\u70CA\u7168\u723B\u7256\u7284\u73B3\u740D\u742F\u7498\u74A9\u752D\u75F3\u7634\u768E\u76B4\u76E5\u77A0\u77DC\u781F\u782D\u7AA0\u7BFE\u7FF1\u80AB\u8174\u81EC\u8202\u8222\u8228\u82DC\u8306\u83FD\u8469\u84FF\u859C\u8617\u86B1\u8722\u8C89\u8D67\u8DCE\u8E49\u8E76\u8E87\u8FE2\u8FE4\u8FF8\u9016\u905B\u9174\u982B\u98E7\u9955\u9B32
+NGram.KANJI_2_23=\u4F8F\u5055\u524C\u548E\u5583\u594E\u5CB7\u5ED6\u5F5D\u6021\u66B9\u66F0\u6C55\u6C7E\u6C82\u6E2D\u6EC7\u6ED5\u70B3\u71B9\u72C4\u73C0\u7426\u745C\u748B\u7696\u777F\u79A7\u79B9\u7F8C\u8153\u8339\u8386\u8725\u90B5\u9102\u962E\u9716\u97F6
+NGram.KANJI_2_28=\u5733\u57D4\u838E\u8FEA
+NGram.KANJI_2_29=\u50ED\u5F29\u62EE\u6A9C\u7BC6\u80F1\u8129\u8171\u822B\u8AEB
+NGram.KANJI_2_30=\u4EB3\u4F15\u4FB7\u5006\u509A\u50A2\u5102\u5109\u5115\u5137\u5138\u513C\u524B\u524E\u5277\u528A\u52E6\u52FB\u5331\u5436\u5443\u54FD\u5538\u555E\u55C6\u55C7\u5679\u5690\u5695\u56C9\u56D1\u56EA\u588A\u58E2\u5AFB\u5B2A\u5B43\u5B7F\u5BE2\u5C37\u5D27\u5D84\u5D87\u5DD4\u5EC1\u5EDD\u5F12\u5FA0\u60F1\u616B\u61F5\u61F6\u61FE\u62DA\u6371\u6399\u63C0\u6451\u647B\u6493\u64BB\u64BF\u64C4\u64F1\u64F7\u650F\u652C\u665D\u6684\u6688\u66EC\u672E\u68E7\u69A6\u69ED\u69F3\u6A01\u6AAF\u6AE5\u6BA4\u6BAE\u6BAF\u6BC6\u6C08\u6C2C\u6C59\u6D87\u6EBC\u6ECC\u6EF7\u6F6F\u6F80\u6F86\u6FD8\u6FF0\u6FFA\u7006\u7018\u7030\u7051\u7192\u71C9\u71D9\u71F4\u71FE\u7274\u7377\u74A3\u750C\u7613\u7627\u7661\u7662\u7665\u766E\u7671\u7672\u76BA\u775E\u776A\u778C\u78E7\u7955\u7A08\u7AC5\u7B4D\u7C2B\u7C6C\u7CF0\u7D02\u7D1C\u7D73\u7DA2\u7DB5\u7DDE\u7E09\u7E0A\u7E37\u7E43\u7E61\u7E7D\u7E93\u7F3D\u7FF9\u81A9\u8271\u83F8\u84C0\u8514\u85BA\u86A9\u86FB\u879E\u8814\u8836\u889E\u8932\u896A\u896F\u8993\u89B2\u8A15\u8A16\u8A1D\u8A5B\u8A6C\u8A6D\u8A7C\u8AA1\u8AA3\u8AA5\u8B0A\u8B4F\u8B59\u8B96\u8C48\u8C54\u8CBD\u8CFA\u8D13\u8E89\u8E8B\u8EAA\u8EC0\u8EDB\u8EFC\u8F12\u8F1F\u8F3E\u8F45\u8FFA\u9015\u9183\u919E\u91A3\u91D7\u91F5\u9209\u9215\u923E\u9240\u9251\u9257\u927B\u9293\u92A8\u92C5\u92C7\u92F0\u9333\u935A\u9382\u938A\u9398\u93B3\u93D7\u93DF\u93E2\u93FD\u942B\u942E\u9433\u9463\u9470\u9472\u947E\u95D0\u96CB\u97C3\u97CC\u981C\u9839\u986B\u98B6\u98EA\u9909\u991A\u9935\u993E\u9951\u99A5\u99B1\u99D9\u99DD\u99F1\u9A2B\u9A62\u9A65\u9AAF\u9AD2\u9AEF\u9B0D\u9B28\u9B77\u9BFD\u9C49\u9C5F\u9C78\u9D3F\u9D72\u9DD7\u9E1B\u9EB4\u9EF4\u9F66\u9F94
+NGram.KANJI_2_31=\u5DBD\u63C6\u6E3E\u7587\u8AF1\u8B5A\u9695
+NGram.KANJI_2_32=\u53A5\u589F\u5CD9\u7109\u7F79\u8006\u8654\u8944\u968B\u96CD
+NGram.KANJI_2_35=\u4F47\u4F91\u4FCE\u4FDF\u527D\u535E\u55DA\u56A5\u5879\u5A11\u5B7A\u5CAB\u5CF4\u5EBE\u5F7F\u5FA8\u601B\u606B\u60B8\u610D\u6134\u619A\u61FA\u6369\u6523\u65CC\u66C4\u6727\u6968\u6A05\u6A48\u6B59\u6BEC\u6D35\u6D38\u6E19\u701F\u7064\u711C\u716C\u71A8\u71E7\u7258\u743A\u746F\u75BD\u75D9\u75F2\u7669\u766C\u76DE\u7729\u77BC\u78EC\u792A\u7A37\u7A62\u7BE6\u7C2A\u7C50\u7D07\u7DD8\u7E5A\u7F8B\u7FD5\u7FF3\u8151\u81CD\u8317\u83F4\u85EA\u85FA\u8823\u895E\u89F4\u8A0C\u8A41\u8AA8\u8ACD\u8B10\u8CC1\u8D05\u8D73\u8E4A\u8E85\u8E91\u8EFB\u8F13\u9087\u914A\u91C9\u923F\u93B0\u9403\u95A8\u95AD\u9730\u9865\u9903\u9945\u9949\u99AD\u99E2\u9A6A\u9D26\u9E1E\u9EDD\u9F2C\u9F72
+NGram.KANJI_2_36=\u4E9E\u4F86\u5011\u50B3\u5152\u5169\u5340\u5718\u5B78\u5BE6\u5BF6\u5C07\u5EE3\u61C9\u6230\u6703\u689D\u6A02\u6C23\u7063\u7368\u756B\u7576\u767C\u7A31\u7D93\u7E23\u7E3D\u81FA\u8207\u842C\u85DD\u865F\u8B49\u8B80\u8CFD\u908A\u9435\u95DC\u965D\u9AD4\u9EE8
+NGram.KANJI_2_37=\u5480\u5580\u5C39\u67EF\u68B5\u6D85\u8521\u90B1
+NGram.KANJI_2_38=\u4E1F\u4F96\u4FE0\u50F1\u5118\u522A\u5291\u52C1\u52DB\u52F3\u52F5\u52F8\u53B2\u55CE\u562F\u580A\u5862\u58AE\u58D8\u58DF\u58E9\u58EF\u5925\u593E\u599D\u5ABD\u5C62\u5EC2\u5EDA\u5EE2\u5F4E\u5F65\u6085\u6158\u61FC\u6200\u62CB\u633E\u6416\u6436\u6490\u64CB\u64E0\u64FA\u6514\u651C\u6524\u6558\u6583\u66B1\u66C6\u66C9\u66E0\u6A11\u6A1E\u6A38\u6A62\u6AB3\u6B16\u6B98\u6BBC\u6C2B\u6DDA\u6DE8\u6DEA\u6DFA\u6EEF\u6EFE\u6F32\u6F51\u6F5B\u700F\u71D2\u7210\u7246\u7260\u72A7\u72F9\u7375\u7378\u758A\u760B\u76DC\u76EA\u77DA\u77FD\u78DA\u7919\u797F\u79AA\u7A05\u7A4C\u7ACA\u7C72\u7D81\u7DDD\u7E31\u7E69\u7E6B\u7E73\u7E96\u7E9C\u81BD\u81C9\u81DF\u8259\u8277\u8396\u83A7\u8523\u8525\u860A\u863F\u8667\u87A2\u87F2\u881F\u883B\u89F8\u8B20\u8B74\u8B9A\u8C4E\u8C6C\u8C93\u8CEC\u8D0A\u8D0F\u8D95\u8E10\u8F4E\u8FAF\u8FF4\u905E\u9072\u9081\u908F\u91AC\u91C0\u91C1\u91D0\u921E\u9223\u9245\u929C\u92B3\u92C1\u9336\u934A\u93C8\u9444\u9452\u947C\u947F\u9592\u95B1\u95C6\u95D6\u95E1\u95E2\u96DE\u9742\u978F\u984F\u9871\u98B1\u98C4\u99ED\u9A37\u9A45\u9A5F\u9AEE\u9B27\u9BCA\u9C77\u9D51\u9D5D\u9E79\u9E7C\u9E7D\u9EB5\u9EBC\u9F61\u9F63\u9F90\u9F9C
+NGram.KANJI_3_1=\u5283\u7562\u7DEC\u88E1\u8F2F
+NGram.KANJI_3_2=\u5009\u502B\u5049\u5075\u507D\u5091\u5098\u50B5\u50B7\u50BE\u5100\u5104\u511F\u518A\u525B\u5289\u5442\u5805\u589C\u58C7\u5922\u596A\u5A66\u5B6B\u5BE7\u5BE9\u5DBA\u5E63\u5E7E\u5FB9\u6163\u616E\u6176\u61B2\u61B6\u61F8\u639B\u63DA\u63EE\u640D\u64B2\u64C1\u64EC\u6557\u6575\u6607\u66AB\u68C4\u6A39\u6C96\u6CC1\u6E1B\u6E6F\u6E9D\u6EC5\u6F01\u6F64\u6FC3\u7058\u707D\u7344\u7642\u76E4\u7832\u790E\u7B46\u7D05\u7D0B\u7D14\u7D19\u7D1B\u7D39\u7D61\u7DB1\u7DCA\u7DD2\u7DE0\u7DE9\u7DEF\u7DF4\u7E2E\u7E3E\u8105\u8108\u81E8\u8266\u84CB\u84EE\u85A9\u885D\u88DC\u8972\u8A02\u8A0E\u8A13\u8A17\u8A2A\u8A34\u8A3A\u8A3C\u8A69\u8A73\u8A95\u8AA0\u8AA4\u8AB2\u8AC7\u8ACB\u8B00\u8B1B\u8B1D\u8B5C\u8C9D\u8C9E\u8CA2\u8CA8\u8CA9\u8CAB\u8CAC\u8CB7\u8CBF\u8CC0\u8CDE\u8CE2\u8CFC\u8D08\u8DE1\u8E8D\u8ECC\u8EDF\u8EF8\u8F14\u8F1D\u8F2A\u8F44\u9055\u9069\u9077\u907C\u90F5\u91DD\u9285\u92FC\u9326\u932F\u9375\u9396\u93AE\u93E1\u9451\u9589\u95A3\u9663\u9670\u9673\u96BB\u9801\u9802\u9803\u9806\u9808\u9810\u983B\u984D\u9858\u9867\u98EF\u98F2\u98FE\u990A\u99D0\u9A0E\u9A5A\u9B5A\u9CE5\u9DB4\u9E97\u9F8D
+NGram.KANJI_3_3=\u543E\u5BEE\u5F18\u6590\u725F\u83C5\u85E9\u9E93
+NGram.KANJI_3_4=\u5016\u53AD\u5606\u5629\u58BE\u5F14\u6065\u6144\u646F\u647A\u67F5\u6953\u6C3E\u6F2C\u6F97\u6FB1\u7169\u71E6\u71ED\u74BD\u79BF\u7A1C\u7A4E\u7AAF\u7CDE\u7D17\u7D43\u7E55\u7FA8\u807E\u8139\u8490\u8569\u856A\u87FB\u8A23\u8AB9\u8AE6\u8AFA\u8B2C\u8CD1\u91D8\u92F8\u9318\u96DB\u99B4\u9BC9\u9C2D\u9CF6\u9D61\u9DFA
+NGram.KANJI_3_5=\u4E26\u4F75\u4FC2\u500B\u5074\u5099\u512A\u5225\u5247\u5275\u5287\u52D5\u52D9\u52DD\u52E2\u5354\u54E1\u554F\u5712\u57F7\u5831\u5834\u5BAE\u5C0E\u5C64\u5CA1\u5CF6\u5E2B\u5E79\u5EAB\u5F35\u5F37\u5F8C\u5FA9\u611B\u614B\u63A1\u63DB\u6642\u66F8\u6771\u696D\u6975\u69CB\u6A19\u6A4B\u6A5F\u6BBA\u6C7A\u6E2C\u6E96\u6F22\u70BA\u7121\u71B1\u7372\u73FE\u74B0\u7570\u76E3\u78BA\u7A2E\u7A4D\u7AF6\u7BC0\u7BC4\u7BC9\u7C21\u7D00\u7D04\u7D0D\u7D1A\u7D30\u7D42\u7D44\u7D50\u7D66\u7D71\u7DAD\u7DDA\u7DE8\u7E54\u7F85\u7FA9\u7FD2\u8056\u805E\u8077\u8208\u83EF\u8449\u8853\u885B\u88FD\u8907\u898B\u898F\u8996\u89AA\u8A08\u8A18\u8A2D\u8A31\u8A55\u8A5E\u8A66\u8A71\u8A72\u8A8C\u8A8D\u8A9E\u8ABF\u8AD6\u8AF8\u8B58\u8B70\u8B77\u8CA0\u8CA1\u8CB4\u8CBB\u8CC7\u8CEA\u8ECA\u8ECD\u8F03\u8F09\u8F38\u8FB2\u9023\u9031\u9032\u904A\u904B\u904E\u9054\u9060\u9078\u907A\u9084\u9280\u9577\u9580\u958B\u9593\u9678\u967D\u968A\u968E\u969B\u96E2\u96E3\u96F2\u96FB\u97D3\u97FF\u9805\u9818\u982D\u984C\u985E\u98A8\u98DB\u9928\u99AC\u9BAE
+NGram.KANJI_3_8=\u5F6B\u6C4E\u7B87\u8A70
+NGram.KANJI_3_9=\u540B\u5B5C\u826E
+NGram.KANJI_3_11=\u4F83\u4FF8\u51CB\u52BE\u53F1\u548B\u558B\u5CB1\u5D69\u5F3C\u620E\u621F\u64E2\u67DA\u6854\u69CC\u6A35\u6C8C\u6E1A\u6F15\u6FE0\u717D\u7252\u7AFA\u82D3\u83DF\u8431\u9041\u9149\u9798
+NGram.KANJI_3_12=\u4ED5\u55E3\u572D\u57A3\u587E\u5983\u5A9B\u5C90\u5E61\u672D\u6960\u6F5F\u72D9\u72E9\u757F\u7949\u7950\u7E82\u7FCC\u82B8\u90B8\u91DC\u961C\u9B45
+NGram.KANJI_3_13=\u55AB\u6249\u643E\u6841\u68B1\u725D\u7B8B\u7C95\u7E1E\u7F36\u8A03\u8A6B\u8E74\u95A4
+NGram.KANJI_3_15=\u50AD\u50D1\u5132\u51F1\u55AC\u5617\u5687\u584A\u59EA\u5B30\u5BF5\u5C0B\u5C4D\u5EDF\u6182\u61A4\u64AB\u64FE\u66A2\u6897\u694A\u69CD\u6B3D\u6BC0\u6D29\u6F38\u7015\u7149\u71C8\u723A\u7336\u7345\u755D\u76C3\u78A9\u798D\u7AAE\u7DFB\u7E2B\u7F75\u7F77\u81E5\u834A\u852D\u85CD\u8755\u8A3B\u8A54\u8AE7\u8B02\u8B39\u8CAA\u8CE6\u8DA8\u8E5F\u8F5F\u905C\u912D\u919C\u92D2\u932B\u937E\u9418\u9583\u9812\u985B\u9905\u99B3\u99C1\u99D5\u9A30\u9CF3\u9D3B\u9D6C
+NGram.KANJI_3_16=\u6D6C\u72FD\u77A5\u8956\u9C0D
+NGram.KANJI_3_18=\u5919\u5F4A\u6063\u63AC\u649A\u6715\u6AD3\u71D0\u758B\u834F\u85F7\u88DF\u8F61\u93D1\u98F4\u9D60
+NGram.KANJI_3_19=\u4F50\u7DB2\u962A
+NGram.KANJI_3_22=\u5E96\u75D4\u91C6
+NGram.KANJI_3_23=\u5E9A\u6C40\u821C\u839E\u8FED\u9EDB
+NGram.KANJI_3_27=\u5F01\u66DC
+NGram.KANJI_3_29=\u5023\u5208\u531D\u536F\u53E9\u54C9\u598A\u59BE\u5A20\u5D6F\u5DF3\u66C7\u66D6\u66F3\u6775\u6A3D\u6ADB\u6B86\u6C72\u6E25\u73EA\u7435\u760D\u7656\u7825\u78D0\u7A14\u7A6B\u7B20\u7BE0\u7CF8\u7DAC\u7DBB\u7DBE\u80E4\u80F4\u837B\u8466\u8568\u867B\u8A63\u91E7\u9320\u935B\u9591\u965B\u98E2\u990C\u9913\u9BAB
+NGram.KANJI_3_30=\u60B6\u8AD2\u8CC2\u9237\u9328\u934D\u9397\u9830
+NGram.KANJI_3_31=\u4FB6\u50D5\u51CD\u559A\u55AA\u5674\u5857\u585A\u5875\u58B3\u596E\u59E6\u5A41\u5D50\u5E25\u5E33\u5F59\u61C7\u61F2\u6368\u6383\u65AC\u68DF\u68F2\u6A3A\u6B04\u6DBC\u6DF5\u6E26\u6E4A\u6E67\u6F54\u6F70\u6FC1\u6FEB\u7159\u727D\u7652\u77EF\u78EF\u798E\u7A40\u7AAA\u7BE4\u7C60\u7CE7\u7CFE\u7D21\u7D33\u7D5E\u7D79\u7DB4\u7DBF\u7E1B\u7E8F\u7F70\u814E\u816B\u8178\u819A\u84BC\u85A6\u865C\u8766\u8A1F\u8A50\u8A60\u8A6E\u8A87\u8A98\u8AB0\u8ADC\u8AED\u8AEE\u8B0E\u8B19\u8CA7\u8CAF\u8CB8\u8CBC\u8CC3\u8CC4\u8CCA\u8CDC\u8CE0\u8CED\u8ED2\u8F29\u8F3F\u91E3\u920D\u9234\u925B\u9298\u9310\u934B\u958F\u95A5\u9727\u97FB\u9811\u984E\u98FC\u98FD\u99D2\u99FF\u9B31\u9BE8\u9C57\u9CE9\u9CF4\u9D28\u9DF9
+NGram.KANJI_3_32=\u4E1E\u502D\u51A5\u5321\u58EC\u5A3C\u5BC5\u5CE8\u61A9\u620A\u65A1\u6714\u6853\u6893\u6C50\u6C5D\u7436\u745A\u745B\u773A\u7941\u7947\u8543\u865E\u8C5A\u914B\u99A8\u9AB8
+NGram.KANJI_3_35=\u4E99\u5BA5\u5DFD\u608C\u60C7\u60DA\u6190\u61A7\u6753\u6777\u6787\u6B4E\u6F23\u6FE1\u6FEF\u7337\u7827\u786F\u7893\u7ABA\u7B94\u7BB8\u7C3E\u7D62\u7E6D\u80B1\u81BF\u81C6\u821B\u82E7\u83F0\u84D1\u86ED\u8888\u8B01\u8B04\u8F4D\u9291\u92E4\u932E\u9354\u936C\u939A\u9957\u9AED\u9BAA\u9BAD\u9BD6\u9BDB\u9C3B\u9D1B
+NGram.KANJI_3_36=\u50C5\u53E2\u5EE0\u65BC\u70CF\u723E\u7D10\u7D9C\u806F\u8607\u862D\u8A0A\u8AFE\u8CD3\u9019\u9813\u9B6F
+NGram.KANJI_3_37=\u4EA8\u4F3D\u5384\u5EFF\u60DF\u66DD\u6E5B\u8087\u82D1\u8FE6\u9640\u9E9F
+NGram.KANJI_3_38=\u5147\u525D\u5678\u617E\u6372\u79A6\u8ABC\u92EA\u9438\u9817
+NGram.KANJI_4_0=\u6D3C\u718F\u74EE\u8712
+NGram.KANJI_4_9=\u4F84\u54C6\u5565\u68F1\u6D82\u83C7
+NGram.KANJI_4_10=\u4FE9\u4FED\u51FF\u523D\u5300\u5364\u538C\u5450\u5455\u545C\u54D1\u54D7\u5578\u56A3\u58F6\u592F\u5CE6\u5D2D\u5E90\u6073\u607C\u60EB\u61D2\u62E2\u62E3\u631A\u6320\u6323\u6361\u63B7\u63B8\u63BA\u6405\u65A9\u65F7\u6619\u6655\u67A3\u67E0\u6805\u6808\u6866\u6868\u6869\u6A71\u6BE1\u6C79\u6CA5\u6CDE\u6DA4\u6DA7\u6DA9\u6E85\u70DB\u70E6\u70EB\u7115\u724D\u7410\u759F\u75AE\u75EA\u75F9\u762B\u763E\u76B1\u77EB\u783E\u79C3\u7A8D\u7A9C\u7B5D\u7BF1\u7EC5\u7ED2\u7EDE\u7EE3\u7EF7\u7EF8\u7EFD\u7F00\u7F0E\u7F15\u7F1A\u7F20\u7F24\u7F28\u7FA1\u7FD8\u8038\u803B\u804B\u80AE\u817B\u82C7\u8327\u835E\u8367\u83BA\u8424\u864F\u8681\u8682\u8715\u8717\u8721\u8747\u874E\u8845\u886C\u889C\u88E4\u89C5\u8BB6\u8BB9\u8BC0\u8BC5\u8BE1\u8BEB\u8BEC\u8BF5\u8C0E\u8C1A\u8D2E\u8D31\u8D43\u8D4E\u8D58\u8F67\u8F7F\u9489\u9499\u949D\u94A0\u94A5\u94AE\u94BE\u94D0\u94DB\u94F2\u9508\u950C\u951A\u9525\u952D\u952F\u9530\u953B\u9540\u9550\u9570\u9576\u95F0\u960E\u9668\u96CF\u97E7\u9885\u988A\u98A4\u9965\u9975\u997A\u997F\u9985\u998D\u998F\u9A6E\u9A6F\u9A74\u9A79\u9A7C\u9A82\u9A87\u9CA4\u9CC4\u9CCD\u9CD6\u9E20\u9E25\u9E35\u9E3D\u9E45\u9E49\u9E4A\u9E66
+NGram.KANJI_4_16=\u576F\u579B\u6345\u78B4\u79EB\u79F8
+NGram.KANJI_4_17=\u4E13\u4E1A\u4E1C\u4E24\u4E25\u4E2A\u4E3E\u4E49\u4E50\u4E66\u4E9A\u4EA7\u4EBF\u4ECE\u4EEC\u4EF7\u4F17\u4F20\u5170\u5173\u519B\u51B3\u51E4\u51FB\u5219\u521B\u522B\u529E\u52A1\u52A8\u52BF\u534F\u5355\u536B\u5386\u53BF\u53D1\u53D8\u542F\u5458\u54CD\u56E2\u56ED\u56F4\u56FE\u573A\u5904\u590D\u5934\u5B81\u5B9E\u5BF9\u5BFC\u5C14\u5C9B\u5E26\u5E7F\u5E94\u5F00\u5F20\u5F3A\u603B\u6218\u65E0\u65F6\u663E\u672F\u6743\u6784\u6807\u6C14\u6C49\u707E\u70ED\u73AF\u73B0\u7535\u76D1\u786E\u79CD\u79EF\u7B80\u7C7B\u7EA2\u7EA6\u7EA7\u7EAA\u7EBF\u7EC4\u7EC7\u7ED3\u7EDF\u7EE7\u7EED\u7EF4\u7F16\u7F57\u804C\u8054\u817E\u8282\u82CF\u83B7\u8425\u89C1\u89C2\u89C4\u89C6\u8BA1\u8BA4\u8BAE\u8BAF\u8BB0\u8BB8\u8BBA\u8BBE\u8BC1\u8BC4\u8BD1\u8BDD\u8BE5\u8BED\u8BF4\u8C03\u8D22\u8D23\u8D28\u8D39\u8D44\u8D5B\u8F66\u8F6C\u8F83\u8FBE\u8FC7\u8FD0\u8FD8\u8FD9\u8FDB\u8FDE\u9009\u94C1\u957F\u95E8\u95EE\u95F4\u95FB\u961F\u9633\u9645\u9646\u96BE\u9879\u9884\u9886\u9898\u98CE\u9A6C\u9F99
+NGram.KANJI_4_18=\u51DB\u67B7
+NGram.KANJI_4_22=\u4FA5\u545B\u5499\u5520\u5570\u56F1\u5A76\u5C96\u60AF\u60ED\u618B\u61A8\u62A0\u62A1\u62E7\u6363\u6390\u63B0\u6400\u6402\u6512\u6748\u70C1\u732C\u765E\u7663\u76CF\u7741\u781A\u7980\u79C6\u79FD\u7AA5\u7B0B\u7B8D\u7BA9\u7BAB\u7BD3\u7CAA\u7EAB\u7ECA\u7EE2\u7F2D\u7F30\u8110\u8113\u81CA\u835A\u8360\u84D6\u852B\u87E5\u8869\u8A8A\u8BA5\u8BF2\u8C05\u8C12\u8D30\u8D4A\u8D61\u8DF7\u8E6D\u8E8F\u8F95\u8F99\u8FAB\u94B3\u94C6\u94E3\u9504\u954A\u9563\u95FA\u9893\u9981\u9992\u9AA1\u9CAB\u9E2F\u9E33\u9EB8
+NGram.KANJI_4_24=\u4E22\u4E8F\u4F1E\u4FA3\u5151\u517D\u51BB\u51D1\u5220\u529D\u52CB\u5367\u5389\u5395\u53E0\u53F9\u5413\u548F\u5524\u575E\u575F\u5784\u5792\u57A6\u57AB\u58F3\u5986\u5988\u5A04\u5A07\u5BA0\u5C18\u5C82\u5DE9\u5E10\u5E1C\u5F2F\u60E9\u6124\u629B\u6321\u6324\u635E\u63FD\u6401\u644A\u6491\u655B\u658B\u6635\u67AB\u67DC\u680B\u692D\u6984\u6A31\u6B7C\u6BD9\u6C22\u6CA6\u6CA7\u6CEA\u6CFB\u6CFC\u6D46\u6D47\u6D4A\u6D51\u6DA1\u6E0A\u6E83\u6EE4\u6EE5\u6F9C\u6FD2\u70C2\u7237\u727A\u730E\u7574\u75AF\u7792\u7816\u7845\u78B1\u7A77\u7A91\u7A9D\u7AD6\u7B3C\u7B5B\u7CAE\u7EA4\u7EB1\u7EBA\u7ECE\u7ED1\u7EF0\u7EF3\u7F14\u7F1D\u7F34\u7F62\u8042\u806A\u80A0\u80A4\u80BE\u80BF\u80C0\u810F\u8138\u8231\u8270\u829C\u82CD\u8350\u83B9\u841D\u8574\u8680\u8BB3\u8BBC\u8BBD\u8BC8\u8BF1\u8BFD\u8C0A\u8C0D\u8C1C\u8C24\u8C26\u8C2C\u8C2D\u8C34\u8D1E\u8D2C\u8D3C\u8D41\u8D42\u8D4C\u8D50\u8D5A\u8F69\u8F88\u8F90\u8FA9\u915D\u9171\u9493\u949E\u94A7\u94A9\u94BB\u94C3\u94C5\u94DD\u94F8\u9505\u9510\u9523\u9524\u95EF\u95F7\u95F9\u9600\u9610\u96F3\u97F5\u987D\u9882\u9888\u9896\u98D8\u9971\u9972\u9976\u997C\u9A84\u9A86\u9A8F\u9A97\u9A9A\u9AA4\u9CB8\u9CDE\u9E26\u9E43\u9E64\u9E70\u9F7F\u9F9F
+NGram.KANJI_4_28=\u534E\u62A5\u7ECF\u7F51
+NGram.KANJI_4_34=\u4E34\u4E3D\u4E4C\u4E54\u4E60\u4E61\u4E70\u4EB2\u4EC5\u4EEA\u4F18\u4F1F\u4F24\u4F26\u4FA7\u50A8\u513F\u5174\u517B\u518C\u519C\u51B5\u51CF\u5218\u521A\u5267\u52B3\u5356\u5382\u5385\u538B\u53A6\u5434\u5706\u5723\u5757\u575A\u575B\u575D\u5907\u591F\u593A\u5956\u5B59\u5BA1\u5BAB\u5BBD\u5BBE\u5BFB\u5C42\u5C81\u5E01\u5E08\u5E86\u5E93\u5F02\u5F39\u5F52\u5F55\u5F7B\u6000\u6001\u6076\u620F\u6237\u6267\u6269\u626C\u62A2\u62A4\u62DF\u62E5\u62E9\u6325\u635F\u6362\u6444\u6653\u6682\u6740\u6742\u6768\u6781\u6811\u6837\u6865\u68C0\u6B22\u6BC1\u6BD5\u6C47\u6C9F\u6CAA\u6CFD\u6D4B\u6DA8\u6E10\u6EE1\u6EE8\u706D\u7075\u70DF\u7231\u739B\u7597\u76D6\u76D8\u77FF\u7801\u7840\u79BB\u7A33\u7ADE\u7B14\u7B7E\u7CA4\u7D27\u7EB3\u7EBD\u7EC3\u7EC6\u7EC8\u7ECD\u7ED5\u7ED9\u7EDC\u7EDD\u7EE9\u7EFC\u7EFF\u7F13\u7F29\u8083\u80DC\u8111\u814A\u8230\u827A\u8363\u836F\u8428\u84DD\u867D\u8865\u88AD\u89C8\u8BA2\u8BA8\u8BA9\u8BAD\u8BB2\u8BBF\u8BC6\u8BCD\u8BD5\u8BEF\u8BF7\u8BF8\u8BFA\u8BFB\u8C08\u8D1D\u8D1F\u8D21\u8D25\u8D27\u8D2D\u8D2F\u8D35\u8D38\u8DC3\u8F6E\u8F6F\u8F7B\u8F7D\u8F86\u8F91\u8F93\u8F96\u8FB9\u8FBD\u8FC1\u8FDC\u8FDD\u9002\u9057\u90BB\u90D1\u91CA\u9488\u949F\u94A2\u94B1\u94F6\u9500\u9526\u9547\u9614\u9634\u9635\u9636\u9648\u9655\u9669\u9690\u97E9\u9875\u9876\u987A\u987B\u987E\u987F\u9891\u989D\u98DE\u9986\u9A7B\u9A8C\u9C81\u9C9C\u9F50
+NGram.KANJI_4_39=\u4E1B\u4E1D\u4E27\u4EA9\u4ED1\u4ED3\u4F2A\u4FA6\u4FA8\u503A\u503E\u507F\u5188\u51AF\u51C0\u51C9\u51ED\u51EF\u5242\u5251\u52B2\u5362\u53A2\u5415\u5417\u5428\u55B7\u5760\u5899\u5939\u594B\u5987\u5A31\u5A74\u5BAA\u5C1D\u5C7F\u5C97\u5CAD\u5E05\u5E2E\u5E99\u5E9E\u5E9F\u5F03\u5FC6\u5FE7\u60AC\u60CA\u60EF\u626B\u6270\u629A\u62E6\u62E8\u6446\u6447\u654C\u67AA\u680F\u6863\u68A6\u6C64\u6D01\u6D53\u6D9D\u6DA6\u6E14\u6E17\u6EDA\u6EE9\u707F\u70BC\u70E7\u7275\u72B9\u72EE\u72F1\u743C\u7545\u76D0\u7855\u7978\u7B79\u7BEE\u7EA0\u7EAC\u7EAF\u7EB2\u7EB5\u7EB7\u7EB8\u7EB9\u7ED8\u7EEA\u7EF5\u7F05\u7F06\u7F18\u7F5A\u80C1\u80F6\u8109\u8206\u8273\u82F9\u8346\u8361\u83B2\u8427\u8651\u867E\u8854\u89C9\u8BC9\u8BCA\u8BD7\u8BDA\u8BDE\u8BE2\u8BE6\u8BFE\u8C01\u8C0B\u8C10\u8C13\u8C22\u8C23\u8C28\u8C31\u8D24\u8D26\u8D29\u8D2A\u8D2B\u8D34\u8D37\u8D3A\u8D3E\u8D3F\u8D4B\u8D4F\u8D54\u8D56\u8D5E\u8D60\u8D62\u8D75\u8D76\u8D8B\u8F68\u8F70\u8F74\u8F85\u8F89\u8FC8\u8FDF\u900A\u9012\u903B\u9093\u90AE\u917F\u9274\u94A6\u94DC\u94ED\u94FA\u94FE\u9501\u950B\u9519\u9521\u952E\u955C\u95EA\u95ED\u95F2\u95F8\u95FD\u9601\u9605\u9647\u96B6\u96FE\u9877\u9881\u9887\u9897\u989C\u98A0\u996D\u996E\u9970\u9A70\u9A71\u9A73\u9A76\u9A7E\u9A91\u9C7C\u9E1F\u9E21\u9E23\u9E2D\u9E3F\u9E4F\u9F84
+NGram.KANJI_5_10=\u5239\u8EAF
+NGram.KANJI_5_11=\u51C4\u8471
+NGram.KANJI_5_12=\u6DC0\u7C98
+NGram.KANJI_5_13=\u5631\u5815\u8695
+NGram.KANJI_5_14=\u4E71\u4FA0\u5265\u52B1\u5374\u53A8\u53D9\u58EE\u5BDD\u5BFF\u5C3D\u5C4A\u5CE1\u5F25\u5F84\u604B\u60A6\u60E7\u60E8\u631F\u636E\u643A\u663C\u664B\u67A2\u6816\u697C\u6B8B\u6BB4\u6D45\u6E7F\u6EDE\u6F5C\u706F\u7089\u72ED\u732A\u732B\u76D7\u793C\u7977\u7A0E\u7A83\u80C6\u811A\u8131\u82A6\u830E\u848B\u865A\u866B\u86EE\u89E6\u8A89\u8DF5\u8E0A\u8E2A\u8F9E\u9065\u968F\u9759\u9EA6
+NGram.KANJI_5_18=\u601C\u75D2
+NGram.KANJI_5_26=\u4E07\u4E0E\u4E89\u4F1A\u4F53\u515A\u5185\u5199\u533A\u533B\u53C2\u53CC\u53F7\u58F0\u5965\u5B66\u5B9D\u5C06\u5C5E\u5F53\u62C5\u6570\u65AD\u65E7\u6761\u6765\u6A2A\u6B27\u6CA1\u6E29\u6E7E\u70B9\u72B6\u72EC\u732E\u753B\u79F0\u88C5\u9EC4
+NGram.KANJI_5_29=\u693F\u82EB
+NGram.KANJI_5_34=\u53F6\u6D9B\u83B1
+NGram.KANJI_5_39=\u5C61\u788D
+NGram.KANJI_6_0=\u4E10\u4E52\u4EC6\u4F88\u4FD0\u51F3\u533E\u53ED\u53EE\u5406\u541D\u5429\u5435\u5440\u5490\u5495\u54B1\u54C4\u54FC\u557C\u55D3\u5669\u56E4\u5777\u5992\u59E8\u5B7D\u5BDE\u5BE5\u5C79\u5C94\u5DCD\u5E18\u5E1A\u5E54\u5FF1\u604D\u6064\u60F6\u6127\u6177\u6233\u6252\u625B\u6273\u6296\u62C2\u62C7\u62F4\u638F\u6396\u63E3\u63EA\u6413\u6479\u64A9\u64C2\u659F\u667E\u6760\u6845\u6963\u6A90\u6B83\u6C13\u6C5E\u6D8E\u6D95\u6DCC\u6ED4\u6F13\u6F3E\u6FA1\u7076\u70D8\u710A\u71CE\u7239\u72E1\u73B7\u7599\u759A\u75A4\u75CA\u7629\u7682\u76C5\u76EF\u778E\u77AA\u787C\u7889\u788C\u78BE\u79E7\u7A96\u7A98\u7B77\u7C7D\u7CB1\u7D0A\u7D6E\u7F94\u7FCE\u8116\u814B\u814C\u819B\u828D\u82DF\u8301\u83E0\u85D5\u8611\u86A3\u8708\u8822\u8C4C\u8DB4\u8DEA\u8E42\u8E66\u8E72\u8EBA\u901B\u9157\u970E\u97ED
+NGram.KANJI_6_3=\u62FC\u88D4\u9B4F
+NGram.KANJI_6_9=\u4ED7\u4F63\u4FCF\u5018\u50BB\u50F5\u5154\u5201\u522E\u5254\u527F\u5306\u5462\u5492\u5496\u54A8\u54AA\u554A\u5561\u5564\u5566\u5885\u5938\u5AC2\u5AE9\u5CED\u5F64\u6084\u608D\u60A8\u60D5\u61C2\u61C8\u6254\u626F\u62AC\u6346\u634D\u640F\u6454\u6487\u6495\u64D2\u6746\u6789\u68B3\u68F5\u695E\u6986\u6995\u69A8\u6A44\u6AAC\u6B79\u6C28\u6C2E\u6CF5\u6DE4\u6E34\u6E3A\u6E89\u6F29\u70AB\u70AC\u7130\u715E\u7184\u71AC\u7238\u7281\u72E0\u74E3\u74F7\u7529\u7578\u761F\u7626\u76D4\u775B\u7779\u7784\u77BB\u780C\u780D\u7838\u7898\u78C5\u78F7\u7AED\u7B28\u7BE1\u7C07\u7CD5\u7CD9\u7CEF\u7F38\u800D\u8084\u809A\u8165\u816E\u832B\u8334\u840D\u8774\u886B\u888D\u88D9\u88F9\u8C41\u8D81\u8D9F\u8E22\u8E29\u8EB2\u8F9C\u9165\u918B\u9631\u964B\u964C\u9661\u9709\u9739\u9776\u9AD3\u9ED4
+NGram.KANJI_6_10=\u4E53\u5582\u5600\u6342\u7B06
+NGram.KANJI_6_11=\u5288\u543C\u5475\u5486\u54EE\u5598\u56BC\u5962\u5A36\u5A9A\u5B75\u5BA6\u5C38\u5C4E\u5F8A\u5F98\u627C\u62CC\u62D7\u63C9\u6930\u6954\u69D0\u6BEF\u6C90\u6CBD\u6CBE\u6F31\u6F88\u70D9\u7329\u75BC\u75F0\u7737\u77D7\u7B19\u7FB9\u803F\u80D6\u813E\u81C0\u8205\u8309\u83BD\u846B\u8517\u868C\u8759\u8815\u8859\u8B6C\u8E81\u8EAC\u90A2\u9698\u9B44
+NGram.KANJI_6_12=\u722C\u7FD4
+NGram.KANJI_6_16=\u5228\u5315\u542E\u54CE\u5509\u5527\u5543\u55B3\u55E1\u5636\u568E\u5FFF\u61E6\u6376\u642A\u6726\u74E4\u76F9\u7736\u7BD9\u8019\u80F0\u80F3\u812F\u818A\u8200\u8214\u8638\u869C\u86C0\u86C6\u86D4\u87C6\u88B1\u8902\u8C7A\u8E4B\u9119
+NGram.KANJI_6_18=\u67D2\u6ED3\u87C0\u87CB\u8DDB\u901E\u9163
+NGram.KANJI_6_20=\u4F5B\u52D2\u54C8\u62FF\u66FC\u6D59\u704C\u7586\u9ECE
+NGram.KANJI_6_21=\u4E48\u4EFF\u4F19\u4FF1\u5021\u5077\u5195\u5212\u5269\u5401\u541E\u5427\u54EA\u5587\u558A\u55BB\u566A\u573E\u574E\u5783\u57AE\u584C\u58E4\u5960\u5976\u59CA\u5A1C\u5DE2\u5F99\u600E\u6015\u6263\u626D\u6293\u62C6\u62D6\u62EF\u62F1\u6316\u632A\u6380\u6389\u63D2\u641E\u64C5\u64CE\u65F1\u6664\u6735\u6770\u67EC\u6846\u684C\u68AD\u6B47\u6B49\u6B67\u6C1B\u6C27\u6C2F\u6C5B\u6C89\u6DF9\u6EAF\u70AE\u70E4\u731C\u7334\u73BB\u7470\u76FC\u788E\u789F\u78B0\u78B3\u7A0D\u7A3B\u7A57\u7CB9\u7F69\u8335\u8354\u84BF\u8DCC\u8DD1\u904F\u90A8\u9189\u9677\u9738\u978B
+NGram.KANJI_6_22=\u5162\u53E8\u542D\u5501\u552C\u5639\u563F\u56B7\u6043\u60B4\u6194\u61CA\u634E\u63CD\u6414\u64AC\u6DAE\u6E43\u6F66\u7095\u7316\u733E\u7728\u7830\u78D5\u7ABF\u7FE9\u8018\u80EF\u8198\u8693\u86AA\u86AF\u874C\u8783\u879F\u8892\u8E6C
+NGram.KANJI_6_23=\u4FD8\u4FEF\u501A\u5085\u5180\u526A\u5323\u54ED\u5634\u56CA\u58A9\u58F9\u5955\u5978\u59DA\u5A49\u5B55\u5BC7\u5BE8\u5D4C\u5E62\u6467\u64BC\u6500\u655E\u6572\u658C\u6670\u68CD\u68D5\u68E0\u6912\u6A0A\u6BB7\u6C9B\u6D3D\u6DC6\u6E23\u6F8E\u7011\u7092\u714C\u73AB\u7405\u7624\u76D2\u7960\u79C9\u7A20\u7BF7\u7F50\u804A\u8086\u81C2\u8292\u82DE\u852C\u857E\u859B\u8760\u8C6B\u8DBE\u8E48\u8F9F\u96A7
+NGram.KANJI_6_25=\u4E8E\u5DF2\u5FB7\u7AD9
+NGram.KANJI_6_28=\u4E58\u4ECD\u4EFD\u4F30\u4F60\u4F69\u503C\u5047\u51B0\u51F0\u5361\u5377\u53E6\u54E5\u552E\u5708\u5740\u5761\u57C3\u5821\u589E\u5979\u59C6\u5B69\u5B83\u5E15\u5E76\u5F17\u5F88\u6208\u622A\u624E\u627E\u62D4\u62DC\u63ED\u641C\u6536\u6548\u65C1\u665A\u6668\u67E5\u6B65\u6BCF\u6C61\u6CDB\u6D4E\u6D89\u6DB5\u6E38\u6EAA\u6FB3\u70B8\u745F\u7538\u7A97\u7F3A\u7F55\u805A\u8258\u827E\u82AC\u8303\u83F2\u8482\u85CF\u8DDF\u903E\u9080\u970D\u9760\u9ED1\u9ED8
+NGram.KANJI_6_29=\u634F\u6518\u7B50\u809B
+NGram.KANJI_6_30=\u54A7\u57C2\u5AB3\u60CB\u6886\u8378\u85D0\u8671
+NGram.KANJI_6_32=\u5080\u5121\u51A4\u54AC\u55DC\u592D\u5DEB\u6292\u68D8\u69B4\u6A59\u6E24\u7FC5\u80DA\u8180\u86DB\u8700\u8DCB\u9761
+NGram.KANJI_6_34=\u4E30\u51E0\u542C\u613F
+NGram.KANJI_6_35=\u4E56\u547B\u55FD\u5C41\u606C\u6115\u6CAE\u7119\u795F\u7CDC\u86C9\u86F9\u8713\u873B\u8757\u8925\u892A\u96F9
+NGram.KANJI_6_37=\u51B2\u5308\u5398\u54B8\u59DC\u5C4F\u5D14\u5F6D\u60E0\u6241\u6350\u699C\u6BEB\u6C6A\u6CC4\u6DEE\u6F58\u6F6D\u7199\u77EE\u7ADF\u8058\u820D\u8212\u8389\u8587\u884D\u8881\u8FA8\u8FF9\u96D5
+NGram.KANJI_6_39=\u574F\u6251\u6302
+NGram.KANJI_7_0=\u52FA\u5544\u60F0\u6994\u86A4\u86E4
+NGram.KANJI_7_3=\u4E59\u4E7E\u4EAD\u4EF0\u4EF2\u4F0F\u4F10\u4FAF\u4FCA\u500D\u501F\u5076\u508D\u50E7\u5112\u5146\u5192\u51AC\u51DD\u51FD\u5200\u5237\u524A\u52A3\u52C3\u52C7\u52DF\u5351\u5352\u5353\u5378\u537F\u53E5\u5439\u54FA\u574A\u5782\u57CB\u5893\u58C1\u5915\u5937\u5949\u5951\u5974\u59B9\u5A18\u5A5A\u5ACC\u5B54\u5B5D\u5B64\u5B8F\u5BBF\u5BD2\u5C3A\u5C6F\u5CB3\u5D07\u5DE7\u5E84\u5E8A\u5F26\u5F69\u5F70\u5F90\u5FAA\u5FCD\u6012\u6016\u602A\u60A0\u60B2\u60BC\u6148\u6162\u6170\u6291\u6298\u62AB\u62BC\u62BD\u62D2\u62D3\u62D8\u62F3\u6311\u638C\u6398\u63E1\u642C\u6458\u64A4\u654F\u656C\u659C\u65E2\u65E8\u65EC\u6606\u6614\u6676\u6691\u6696\u66F9\u6749\u676F\u679A\u679D\u67CF\u67D4\u67F1\u67F3\u67F4\u6817\u6842\u6843\u6851\u68A8\u68CB\u68D2\u6B20\u6B32\u6BBF\u6C57\u6C88\u6CCA\u6D17\u6D1E\u6D69\u6D6E\u6D78\u6DE1\u6DFB\u6E58\u6EB6\u6F0F\u6F20\u7070\u708E\u70AD\u7126\u718A\u71C3\u7267\u72C2\u731B\u7384\u73A9\u73CD\u7434\u75AB\u75DB\u76C6\u76FE\u773C\u7891\u78C1\u795D\u7965\u79D2\u79DF\u79E6\u7A00\u7B11\u7B51\u7B54\u7C89\u7C92\u7CD6\u7D2B\u7F8A\u7FBD\u7FFC\u8010\u80A5\u80CE\u8150\u8179\u819C\u8247\u829D\u82B3\u82D7\u82E6\u8302\u8336\u8352\u83CA\u83CC\u83DC\u845B\u846C\u84B2\u84B8\u84C4\u8584\u864E\u86C7\u8861\u8863\u8870\u888B\u8896\u88D5\u8986\u8C46\u8DA3\u8E0F\u8F9B\u8FC5\u8FEB\u8FF7\u9003\u9006\u902E\u9042\u9063\u90ED\u963B\u9676\u96EA\u9756\u9B3C\u9B42\u9F3B
+NGram.KANJI_7_6=\u4E01\u4E03\u4E45\u4E5D\u4E88\u4E92\u4EA1\u4ECB\u4EE4\u4F01\u4F0A\u4F2F\u4F3C\u4F4E\u4F4F\u4F55\u4F8B\u4F9D\u4FBF\u4FEE\u505C\u50CF\u516B\u516D\u5175\u5177\u5178\u5207\u520A\u5224\u526F\u529F\u52A9\u5343\u5348\u535A\u5370\u53BB\u53CB\u53F3\u5409\u542B\u544A\u547C\u5584\u5747\u5802\u590F\u592B\u5931\u5947\u597D\u5A01\u5A92\u5B63\u5B8C\u5B97\u5BA2\u5BA3\u5BA4\u5BB3\u5BB9\u5BC6\u5BCC\u5BDF\u5C04\u5C1A\u5C45\u5C4B\u5CB8\u5DE6\u5E0C\u5E1D\u5E2D\u5E55\u5E8F\u5E95\u5E97\u5EA7\u5EB7\u5EF6\u5F8B\u5FAE\u5FC5\u5FD7\u5FF5\u601D\u6025\u606F\u60F3\u611F\u623F\u6253\u6279\u627F\u6295\u6297\u62EC\u6388\u6392\u63F4\u6545\u6551\u6574\u6599\u65C5\u65E9\u6613\u6620\u6625\u666E\u666F\u66B4\u66F4\u670D\u671B\u6728\u672B\u6751\u677E\u67B6\u6838\u6839\u6848\u68EE\u690D\u6982\u6A21\u6B4C\u6B62\u6B66\u6BB5\u6BCD\u6C0F\u6C38\u6C42\u6CBF\u6CE2\u6CE8\u6D0B\u6D3E\u6D88\u6DF1\u6E05\u6E56\u706B\u7167\u7206\u7236\u7247\u7387\u7530\u7537\u7559\u7565\u7591\u75C5\u767B\u767D\u767E\u7687\u76DB\u76DF\u771F\u7763\u77ED\u7834\u79FB\u7A81\u7AE0\u7AEF\u7B56\u7B97\u7C4D\u7CBE\u7D20\u7D22\u7F72\u7FA4\u8001\u8003\u81F4\u822A\u826F\u82B1\u8349\u843D\u878D\u8857\u89D2\u8B66\u8C37\u8D70\u8D85\u8D8A\u8DB3\u8FF0\u8FFD\u9001\u901F\u90A3\u90A6\u914D\u91CE\u9632\u963F\u9644\u964D\u9664\u96C4\u96E8\u9752\u9769\u98DF
+NGram.KANJI_7_7=\u4E09\u4E0A\u4E0B\u4E0D\u4E16\u4E3B\u4E8B\u4E8C\u4EE3\u4EE5\u4F4D\u4F5C\u4F7F\u5165\u5168\u516C\u5171\u51FA\u5206\u5229\u5236\u524D\u529B\u52A0\u5316\u5317\u5357\u539F\u53CA\u53F0\u5408\u540C\u540D\u548C\u5730\u57FA\u5916\u591A\u5929\u5B50\u5B9A\u5BB6\u5C0F\u5C71\u5DDE\u5DE5\u5E02\u5E73\u5EA6\u5EFA\u5F0F\u6027\u6210\u6240\u6307\u653F\u6587\u65B0\u65B9\u660E\u6700\u6709\u671F\u672C\u6B21\u6B63\u6C11\u6CBB\u6CD5\u6D77\u7269\u7279\u7406\u751F\u7528\u7531\u754C\u76EE\u76F8\u793E\u79D1\u7ACB\u7B2C\u7B49\u7CFB\u8005\u80FD\u81EA\u82F1\u884C\u8868\u897F\u8981\u901A\u9053\u90E8\u90FD\u91CD\u9AD8
+NGram.KANJI_7_9=\u4E4D\u4F36\u5319\u6A61\u6DCB\u7194
+NGram.KANJI_7_11=\u4E5E\u4F43\u5026\u50FB\u515C\u5243\u5420\u5446\u54B3\u54BD\u553E\u55A7\u5703\u5984\u5AC9\u5B09\u5C51\u5DFE\u5ED3\u5F1B\u6055\u618E\u62D9\u65A7\u6652\u6977\u6EBA\u707C\u75D8\u79E4\u7AFF\u7B4F\u7CA5\u808B\u8098\u80B4\u8235\u82DB\u849C\u8549\u868A\u86FE\u8718\u914C
+NGram.KANJI_7_12=\u4E08\u4E38\u4F8D\u50DA\u5203\u5256\u52C9\u52D8\u52FE\u5320\u533F\u5375\u53D4\u540F\u54E8\u56DA\u5806\u5996\u5999\u59A5\u59A8\u59FF\u5AE1\u5BB0\u5BF8\u5C09\u5C3F\u5C48\u5C65\u5D29\u5E06\u5E4C\u5EB5\u5EB6\u5EB8\u5F13\u5FCC\u5FD8\u6052\u606D\u609F\u60D1\u614E\u6247\u62B1\u6349\u64E6\u6577\u65ED\u6674\u6734\u67C4\u6850\u690E\u6A58\u6B3A\u6B89\u6C41\u6CBC\u6CCC\u6CF3\u6D74\u6DAF\u6DF3\u6ECB\u6F02\u6F84\u71E5\u7261\u7272\u72AC\u72FC\u733F\u7409\u755C\u76F2\u7720\u77AC\u77E2\u7802\u786B\u78E8\u7901\u7948\u79E9\u7A1A\u7A74\u7AE3\u7B4B\u7B52\u7BB1\u7C3F\u8015\u8096\u809D\u80A2\u80A9\u80AA\u80BA\u80F8\u8102\u810A\u8154\u8155\u8170\u817A\u81A8\u81ED\u820C\u8236\u82BD\u8305\u83E9\u83F1\u840C\u85FB\u8650\u8702\u8A93\u8E44\u8FB0\u9038\u9091\u90AA\u916C\u9175\u9177\u9685\u96C0\u96C7\u96CC\u97AD
+NGram.KANJI_7_13=\u63D6\u803D
+NGram.KANJI_7_16=\u602F\u7566
+NGram.KANJI_7_18=\u634C\u7C38
+NGram.KANJI_7_19=\u4E18\u4E73\u4E95\u4EAB\u4EC1\u4ED8\u4ED9\u4F11\u4F34\u4F38\u4F59\u4FB5\u4FC3\u4FD7\u5012\u5019\u5065\u50AC\u5144\u5145\u514D\u517C\u51A0\u51B7\u5211\u5238\u523A\u523B\u5272\u52E4\u5360\u5371\u539A\u541B\u5426\u5438\u5473\u54F2\u5510\u552F\u5531\u559C\u5609\u56F0\u56FA\u591C\u5948\u594F\u59BB\u59D3\u5B85\u5B87\u5B88\u5B99\u5B9C\u5BC4\u5BFA\u5C0A\u5C3E\u5CA9\u5D0E\u5DE1\u5DE8\u5DEE\u5DF1\u5E45\u5E78\u5E7B\u5E7C\u5EAD\u5EF7\u5F1F\u5F31\u5F79\u5F7C\u5F85\u5F92\u5FA1\u5FE0\u6050\u60A3\u6212\u62DB\u632F\u6355\u63A2\u63AA\u63CF\u642D\u6469\u64CD\u653B\u6563\u660C\u662D\u667A\u6697\u66FF\u6750\u675F\u677F\u6790\u67D3\u682A\u6885\u68B0\u6B8A\u6B96\u6BDB\u6C60\u6CB9\u6CC9\u6D25\u6D66\u6DB2\u6DF7\u6E21\u6ED1\u6F2B\u6F6E\u6FC0\u7235\u725B\u72AF\u7389\u7532\u7533\u756A\u75BE\u75C7\u76AE\u76CA\u7740\u786C\u7956\u7968\u796D\u7981\u79C0\u79C1\u79CB\u79D8\u7A3F\u7AE5\u7AF9\u7E41\u7F6A\u7FFB\u8089\u80CC\u80DE\u81E3\u821E\u8239\u82E5\u8328\u8377\u85E4\u8840\u88C1\u88C2\u8C6A\u8D64\u8DDD\u8FCE\u8FD4\u9000\u9014\u907F\u90CA\u90CE\u90E1\u9152\u9178\u9686\u9694\u969C\u9707\u9732\u9AA8\u9B54\u9E7F\u9EBB
+NGram.KANJI_7_20=\u4E39\u4E43\u4EAE\u4F73\u504F\u505A\u51C6\u51CC\u52AA\u5339\u5347\u53EB\u53EC\u5448\u5766\u57F9\u5854\u585E\u58A8\u5B8B\u5C01\u5CF0\u5E72\u5EC9\u5F80\u5F81\u5FBD\u5FEB\u6069\u6211\u624D\u628A\u62B5\u62CD\u6309\u63A7\u64AD\u6566\u6597\u65CB\u65D7\u6628\u6717\u6731\u674E\u675C\u683D\u6881\u6B3E\u6BD2\u6C7D\u6C99\u6CE5\u6CF0\u6D1B\u6D2A\u70C8\u719F\u724C\u7259\u73E0\u73ED\u745E\u74E6\u7518\u751A\u7686\u770B\u7B26\u8033\u80A1\u80E1\u821F\u83AB\u8499\u8D74\u8DE8\u900F\u9010\u9047\u904D\u906D\u9675\u96C5\u96F6\u96F7\u9700\u9F13
+NGram.KANJI_7_21=\u5764\u59D0\u5A03\u6062\u6108\u68C9\u7164\u79BE\u7BAD\u903C
+NGram.KANJI_7_23=\u4EA5\u50B2\u532A\u5366\u543B\u54E9\u5632\u59D1\u5BB5\u5DF7\u5F6A\u5F6C\u5FFD\u6070\u6168\u61BE\u63A0\u63A9\u6478\u65A4\u68A7\u6A1F\u6CAB\u70F9\u711A\u723D\u7262\u72F8\u751C\u754F\u75B9\u76C8\u7709\u7897\u7CCA\u7F9E\u8299\u82AD\u82B9\u82D4\u8304\u84C9\u84EC\u854A\u85AF\u86D9\u8FA3\u9187\u97A0
+NGram.KANJI_7_25=\u4E14\u4E5F\u4F46\u514B\u5176\u5230\u5373\u53EA\u540E\u5982\u5C3C\u5DF4\u6216\u62C9\u65AF\u66FE\u6B64\u6D32\u6D6A\u7BC7\u800C
+NGram.KANJI_7_28=\u4E4E\u4E9B\u4EA6\u4EC0\u4FC4\u5403\u5957\u5C24\u6089\u6258\u67D0\u758F\u7FF0\u8D6B
+NGram.KANJI_7_29=\u4FAE\u5944\u5A29\u6101\u62ED\u6328\u637B\u6666\u6687\u66AE\u673D\u6756\u67FF\u6813\u68A2\u699B\u7078\u708A\u7396\u7422\u7525\u75E2\u76BF\u7766\u77B3\u7A3C\u7A92\u819D\u81FC\u8237\u8338\u8511\u88F3\u8FC2
+NGram.KANJI_7_32=\u4E11\u4F3A\u4F51\u5197\u51B6\u51F9\u52FF\u541F\u5507\u5589\u5993\u5A7F\u5AC1\u5B9B\u5BC2\u5BE1\u5F04\u5F0A\u5F27\u6020\u6028\u6068\u6094\u6109\u611A\u614C\u621A\u62B9\u62D0\u62F7\u62FE\u632B\u633D\u6367\u660F\u6627\u6643\u66D9\u674F\u6795\u67AF\u67D1\u6876\u68DA\u68FA\u6905\u69FD\u6A80\u6B6A\u6CB8\u6CE3\u6DD1\u6DEB\u6E9C\u6EA2\u6EF4\u6F06\u714E\u716E\u722A\u7280\u74A7\u752B\u75B2\u75D5\u75F4\u77AD\u77E9\u785D\u79BD\u7A3D\u7A9F\u7B1B\u7B95\u7C9F\u7CDF\u80C3\u8106\u817F\u818F\u81B3\u828B\u82A5\u82AF\u840E\u851A\u853D\u8776\u87F9\u8877\u8910\u8912\u8C79\u8D66\u8FB1\u9017\u90C1\u916A\u9699\u96C1\u971C\u9774\u978D
+NGram.KANJI_7_33=\u4E4B\u4E86\u4E94\u4EA4\u4EAC\u4ECA\u4ED6\u4EF6\u4EFB\u4F9B\u4FDD\u4FE1\u5143\u5148\u5149\u518D\u5217\u521D\u5305\u5341\u534A\u53C8\u53CD\u53D6\u53D7\u53E3\u53E4\u53EF\u53F2\u53F8\u5404\u5411\u5468\u547D\u54C1\u5546\u5668\u56DB\u56DE\u56E0\u571F\u578B\u57CE\u57DF\u5883\u58EB\u592A\u592E\u5973\u59CB\u59D4\u5B57\u5B58\u5B89\u5B98\u5C11\u5C31\u5C40\u5C55\u5DDD\u5E03\u5E38\u5E9C\u5F15\u5F62\u5F71\u5F97\u5FC3\u60C5\u610F\u624B\u6280\u6301\u63A5\u63A8\u63D0\u652F\u6539\u653E\u6559\u65BD\u65CF\u661F\u66F2\u671D\u672A\u6797\u679C\u6821\u683C\u6B7B\u6BD4\u6C34\u6C5F\u6CB3\u6D3B\u6D41\u6E2F\u6E90\u6F14\u7136\u7248\u738B\u7403\u76F4\u7701\u77E5\u77F3\u7814\u793A\u795E\u798F\u7A0B\u7A76\u7A7A\u7BA1\u7C73\u7F6E\u7F8E\u80B2\u81F3\u822C\u8272\u8457\u88AB\u89E3\u8A00\u8C61\u8D77\u8DEF\u8EAB\u8FD1\u9020\u91CC\u91CF\u91D1\u9650\u9662\u96C6\u975E\u9762\u97F3\u9996\u9999
+NGram.KANJI_7_35=\u55C5\u57A2\u58D5\u59E5\u637A\u74E2\u7CE0\u895F
+NGram.KANJI_7_37=\u4E19\u4E32\u4E4F\u4E91\u4EC7\u4ED4\u4F0D\u5141\u51E1\u51F6\u51F8\u52AB\u535C\u53C9\u53DB\u540A\u5410\u54C0\u559D\u5750\u5751\u576A\u57E0\u5824\u582A\u5830\u5835\u5851\u5858\u586B\u5954\u59FB\u5A46\u5B5F\u5BB4\u5BD3\u5C16\u5C60\u5CFB\u5D16\u5E16\u5E3D\u5E7D\u5E87\u5ECA\u5FD9\u60DC\u60F9\u6155\u6167\u6234\u626E\u6276\u6284\u633A\u6377\u6492\u649E\u64B0\u6562\u6591\u65A5\u65E6\u65FA\u6602\u670B\u676D\u68AF\u695A\u6B23\u6BC5\u6C70\u6C83\u6CE1\u6D8C\u6DD8\u6E20\u71D5\u72D0\u72D7\u73B2\u73CA\u7433\u7483\u74DC\u74F6\u7554\u764C\u7761\u77DB\u78A7\u7A46\u7A7F\u7A84\u7C97\u7D2F\u7FC1\u7FE0\u8000\u8017\u808C\u80AF\u8404\u8461\u8463\u8475\u8513\u85AA\u8679\u86CB\u871C\u87BA\u88F8\u8C8C\u8DF3\u8FC4\u901D\u9022\u906E\u9075\u9192\u91C7\u966A\u971E\u9910\u9B41\u9F0E\u9F20
+TO_NORMALIZE_VI_CHARS=AEIOUYaeiouy\u00c2\u00ca\u00d4\u00e2\u00ea\u00f4\u0102\u0103\u01a0\u01a1\u01af\u01b0
+DMARK_CLASS=\u0300\u0301\u0303\u0309\u0323
+NORMALIZED_VI_CHARS_0300=\u00C0\u00C8\u00CC\u00D2\u00D9\u1EF2\u00E0\u00E8\u00EC\u00F2\u00F9\u1EF3\u1EA6\u1EC0\u1ED2\u1EA7\u1EC1\u1ED3\u1EB0\u1EB1\u1EDC\u1EDD\u1EEA\u1EEB
+NORMALIZED_VI_CHARS_0301=\u00C1\u00C9\u00CD\u00D3\u00DA\u00DD\u00E1\u00E9\u00ED\u00F3\u00FA\u00FD\u1EA4\u1EBE\u1ED0\u1EA5\u1EBF\u1ED1\u1EAE\u1EAF\u1EDA\u1EDB\u1EE8\u1EE9
+NORMALIZED_VI_CHARS_0303=\u00C3\u1EBC\u0128\u00D5\u0168\u1EF8\u00E3\u1EBD\u0129\u00F5\u0169\u1EF9\u1EAA\u1EC4\u1ED6\u1EAB\u1EC5\u1ED7\u1EB4\u1EB5\u1EE0\u1EE1\u1EEE\u1EEF
+NORMALIZED_VI_CHARS_0309=\u1EA2\u1EBA\u1EC8\u1ECE\u1EE6\u1EF6\u1EA3\u1EBB\u1EC9\u1ECF\u1EE7\u1EF7\u1EA8\u1EC2\u1ED4\u1EA9\u1EC3\u1ED5\u1EB2\u1EB3\u1EDE\u1EDF\u1EEC\u1EED
+NORMALIZED_VI_CHARS_0323=\u1EA0\u1EB8\u1ECA\u1ECC\u1EE4\u1EF4\u1EA1\u1EB9\u1ECB\u1ECD\u1EE5\u1EF5\u1EAC\u1EC6\u1ED8\u1EAD\u1EC7\u1ED9\u1EB6\u1EB7\u1EE2\u1EE3\u1EF0\u1EF1
--- a/text/langdetect/utils/messages.py
+++ b/text/langdetect/utils/messages.py
+from os import path
+
+
+class Messages(object):
+    MESSAGES_FILENAME = path.join(path.dirname(__file__), 'messages.properties')
+
+    def __init__(self):
+        self.messages = {}
+        with open(self.MESSAGES_FILENAME, 'r') as f:
+            for line in f:
+                key, _, value = line.strip().partition('=')
+                self.messages[key] = value.encode().decode('unicode_escape')
+
+    def get_string(self, key):
+        return self.messages.get(key, '!%s!' % key)
+
+
+_messages = None
+def get_string(key):
+    global _messages
+    if _messages is None:
+        _messages = Messages()
+    return _messages.get_string(key)
--- a/text/langdetect/utils/ngram.py
+++ b/text/langdetect/utils/ngram.py
+import re
+
+import six
+
+from . import messages
+from .unicode_block import (
+    unicode_block,
+    UNICODE_BASIC_LATIN,
+    UNICODE_LATIN_1_SUPPLEMENT,
+    UNICODE_LATIN_EXTENDED_B,
+    UNICODE_GENERAL_PUNCTUATION,
+    UNICODE_ARABIC,
+    UNICODE_LATIN_EXTENDED_ADDITIONAL,
+    UNICODE_HIRAGANA,
+    UNICODE_KATAKANA,
+    UNICODE_BOPOMOFO,
+    UNICODE_BOPOMOFO_EXTENDED,
+    UNICODE_CJK_UNIFIED_IDEOGRAPHS,
+    UNICODE_HANGUL_SYLLABLES,
+)
+
+
+class NGram(object):
+    LATIN1_EXCLUDED = messages.get_string('NGram.LATIN1_EXCLUDE')
+    N_GRAM = 3
+
+    def __init__(self):
+        self.grams = ' '
+        self.capitalword = False
+
+    def add_char(self, ch):
+        '''Append a character into ngram buffer.'''
+        ch = self.normalize(ch)
+        last_char = self.grams[-1]
+        if last_char == ' ':
+            self.grams = ' '
+            self.capitalword = False
+            if ch == ' ':
+                return
+        elif len(self.grams) >= self.N_GRAM:
+            self.grams = self.grams[1:]
+        self.grams += ch
+
+        if ch.isupper():
+            if last_char.isupper():
+                self.capitalword = True
+        else:
+            self.capitalword = False
+
+    def get(self, n):
+        '''Get n-gram.'''
+        if self.capitalword:
+            return
+        if n < 1 or n > self.N_GRAM or len(self.grams) < n:
+            return
+        if n == 1:
+            ch = self.grams[-1]
+            if ch == ' ':
+                return
+            return ch
+        else:
+            return self.grams[-n:]
+
+    @classmethod
+    def normalize(cls, ch):
+        block = unicode_block(ch)
+        if block == UNICODE_BASIC_LATIN:
+            if ch < 'A' or ('Z' < ch < 'a') or 'z' < ch:
+                ch = ' '
+        elif block == UNICODE_LATIN_1_SUPPLEMENT:
+            if cls.LATIN1_EXCLUDED.find(ch) >= 0:
+                ch = ' '
+        elif block == UNICODE_LATIN_EXTENDED_B:
+            # normalization for Romanian
+            if ch == six.u('\u0219'):  # Small S with comma below => with cedilla
+                ch = six.u('\u015f')
+            if ch == six.u('\u021b'):  # Small T with comma below => with cedilla
+                ch = six.u('\u0163')
+        elif block == UNICODE_GENERAL_PUNCTUATION:
+            ch = ' '
+        elif block == UNICODE_ARABIC:
+            if ch == six.u('\u06cc'):
+                ch = six.u('\u064a')  # Farsi yeh => Arabic yeh
+        elif block == UNICODE_LATIN_EXTENDED_ADDITIONAL:
+            if ch >= six.u('\u1ea0'):
+                ch = six.u('\u1ec3')
+        elif block == UNICODE_HIRAGANA:
+            ch = six.u('\u3042')
+        elif block == UNICODE_KATAKANA:
+            ch = six.u('\u30a2')
+        elif block in (UNICODE_BOPOMOFO, UNICODE_BOPOMOFO_EXTENDED):
+            ch = six.u('\u3105')
+        elif block == UNICODE_CJK_UNIFIED_IDEOGRAPHS:
+            ch = cls.CJK_MAP.get(ch, ch)
+        elif block == UNICODE_HANGUL_SYLLABLES:
+            ch = six.u('\uac00')
+        return ch
+
+    @classmethod
+    def normalize_vi(cls, text):
+        '''Normalizer for Vietnamese.
+        Normalize Alphabet + Diacritical Mark(U+03xx) into U+1Exx.
+        '''
+        def repl(m):
+            alphabet = cls.TO_NORMALIZE_VI_CHARS.find(m.group(1))
+            dmark = cls.DMARK_CLASS.find(m.group(2))  # Diacritical Mark
+            return cls.NORMALIZED_VI_CHARS[dmark][alphabet]
+        return cls.ALPHABET_WITH_DMARK.sub(repl, text)
+
+    NORMALIZED_VI_CHARS = [
+        messages.get_string('NORMALIZED_VI_CHARS_0300'),
+        messages.get_string('NORMALIZED_VI_CHARS_0301'),
+        messages.get_string('NORMALIZED_VI_CHARS_0303'),
+        messages.get_string('NORMALIZED_VI_CHARS_0309'),
+        messages.get_string('NORMALIZED_VI_CHARS_0323')]
+    TO_NORMALIZE_VI_CHARS = messages.get_string('TO_NORMALIZE_VI_CHARS')
+    DMARK_CLASS = messages.get_string('DMARK_CLASS')
+    ALPHABET_WITH_DMARK = re.compile(
+        '([' + TO_NORMALIZE_VI_CHARS + '])([' + DMARK_CLASS + '])',
+        re.UNICODE)
+
+    # CJK Kanji Normalization Mapping
+    CJK_CLASS = [
+        messages.get_string('NGram.KANJI_1_0'),
+        messages.get_string('NGram.KANJI_1_2'),
+        messages.get_string('NGram.KANJI_1_4'),
+        messages.get_string('NGram.KANJI_1_8'),
+        messages.get_string('NGram.KANJI_1_11'),
+        messages.get_string('NGram.KANJI_1_12'),
+        messages.get_string('NGram.KANJI_1_13'),
+        messages.get_string('NGram.KANJI_1_14'),
+        messages.get_string('NGram.KANJI_1_16'),
+        messages.get_string('NGram.KANJI_1_18'),
+        messages.get_string('NGram.KANJI_1_22'),
+        messages.get_string('NGram.KANJI_1_27'),
+        messages.get_string('NGram.KANJI_1_29'),
+        messages.get_string('NGram.KANJI_1_31'),
+        messages.get_string('NGram.KANJI_1_35'),
+        messages.get_string('NGram.KANJI_2_0'),
+        messages.get_string('NGram.KANJI_2_1'),
+        messages.get_string('NGram.KANJI_2_4'),
+        messages.get_string('NGram.KANJI_2_9'),
+        messages.get_string('NGram.KANJI_2_10'),
+        messages.get_string('NGram.KANJI_2_11'),
+        messages.get_string('NGram.KANJI_2_12'),
+        messages.get_string('NGram.KANJI_2_13'),
+        messages.get_string('NGram.KANJI_2_15'),
+        messages.get_string('NGram.KANJI_2_16'),
+        messages.get_string('NGram.KANJI_2_18'),
+        messages.get_string('NGram.KANJI_2_21'),
+        messages.get_string('NGram.KANJI_2_22'),
+        messages.get_string('NGram.KANJI_2_23'),
+        messages.get_string('NGram.KANJI_2_28'),
+        messages.get_string('NGram.KANJI_2_29'),
+        messages.get_string('NGram.KANJI_2_30'),
+        messages.get_string('NGram.KANJI_2_31'),
+        messages.get_string('NGram.KANJI_2_32'),
+        messages.get_string('NGram.KANJI_2_35'),
+        messages.get_string('NGram.KANJI_2_36'),
+        messages.get_string('NGram.KANJI_2_37'),
+        messages.get_string('NGram.KANJI_2_38'),
+        messages.get_string('NGram.KANJI_3_1'),
+        messages.get_string('NGram.KANJI_3_2'),
+        messages.get_string('NGram.KANJI_3_3'),
+        messages.get_string('NGram.KANJI_3_4'),
+        messages.get_string('NGram.KANJI_3_5'),
+        messages.get_string('NGram.KANJI_3_8'),
+        messages.get_string('NGram.KANJI_3_9'),
+        messages.get_string('NGram.KANJI_3_11'),
+        messages.get_string('NGram.KANJI_3_12'),
+        messages.get_string('NGram.KANJI_3_13'),
+        messages.get_string('NGram.KANJI_3_15'),
+        messages.get_string('NGram.KANJI_3_16'),
+        messages.get_string('NGram.KANJI_3_18'),
+        messages.get_string('NGram.KANJI_3_19'),
+        messages.get_string('NGram.KANJI_3_22'),
+        messages.get_string('NGram.KANJI_3_23'),
+        messages.get_string('NGram.KANJI_3_27'),
+        messages.get_string('NGram.KANJI_3_29'),
+        messages.get_string('NGram.KANJI_3_30'),
+        messages.get_string('NGram.KANJI_3_31'),
+        messages.get_string('NGram.KANJI_3_32'),
+        messages.get_string('NGram.KANJI_3_35'),
+        messages.get_string('NGram.KANJI_3_36'),
+        messages.get_string('NGram.KANJI_3_37'),
+        messages.get_string('NGram.KANJI_3_38'),
+        messages.get_string('NGram.KANJI_4_0'),
+        messages.get_string('NGram.KANJI_4_9'),
+        messages.get_string('NGram.KANJI_4_10'),
+        messages.get_string('NGram.KANJI_4_16'),
+        messages.get_string('NGram.KANJI_4_17'),
+        messages.get_string('NGram.KANJI_4_18'),
+        messages.get_string('NGram.KANJI_4_22'),
+        messages.get_string('NGram.KANJI_4_24'),
+        messages.get_string('NGram.KANJI_4_28'),
+        messages.get_string('NGram.KANJI_4_34'),
+        messages.get_string('NGram.KANJI_4_39'),
+        messages.get_string('NGram.KANJI_5_10'),
+        messages.get_string('NGram.KANJI_5_11'),
+        messages.get_string('NGram.KANJI_5_12'),
+        messages.get_string('NGram.KANJI_5_13'),
+        messages.get_string('NGram.KANJI_5_14'),
+        messages.get_string('NGram.KANJI_5_18'),
+        messages.get_string('NGram.KANJI_5_26'),
+        messages.get_string('NGram.KANJI_5_29'),
+        messages.get_string('NGram.KANJI_5_34'),
+        messages.get_string('NGram.KANJI_5_39'),
+        messages.get_string('NGram.KANJI_6_0'),
+        messages.get_string('NGram.KANJI_6_3'),
+        messages.get_string('NGram.KANJI_6_9'),
+        messages.get_string('NGram.KANJI_6_10'),
+        messages.get_string('NGram.KANJI_6_11'),
+        messages.get_string('NGram.KANJI_6_12'),
+        messages.get_string('NGram.KANJI_6_16'),
+        messages.get_string('NGram.KANJI_6_18'),
+        messages.get_string('NGram.KANJI_6_20'),
+        messages.get_string('NGram.KANJI_6_21'),
+        messages.get_string('NGram.KANJI_6_22'),
+        messages.get_string('NGram.KANJI_6_23'),
+        messages.get_string('NGram.KANJI_6_25'),
+        messages.get_string('NGram.KANJI_6_28'),
+        messages.get_string('NGram.KANJI_6_29'),
+        messages.get_string('NGram.KANJI_6_30'),
+        messages.get_string('NGram.KANJI_6_32'),
+        messages.get_string('NGram.KANJI_6_34'),
+        messages.get_string('NGram.KANJI_6_35'),
+        messages.get_string('NGram.KANJI_6_37'),
+        messages.get_string('NGram.KANJI_6_39'),
+        messages.get_string('NGram.KANJI_7_0'),
+        messages.get_string('NGram.KANJI_7_3'),
+        messages.get_string('NGram.KANJI_7_6'),
+        messages.get_string('NGram.KANJI_7_7'),
+        messages.get_string('NGram.KANJI_7_9'),
+        messages.get_string('NGram.KANJI_7_11'),
+        messages.get_string('NGram.KANJI_7_12'),
+        messages.get_string('NGram.KANJI_7_13'),
+        messages.get_string('NGram.KANJI_7_16'),
+        messages.get_string('NGram.KANJI_7_18'),
+        messages.get_string('NGram.KANJI_7_19'),
+        messages.get_string('NGram.KANJI_7_20'),
+        messages.get_string('NGram.KANJI_7_21'),
+        messages.get_string('NGram.KANJI_7_23'),
+        messages.get_string('NGram.KANJI_7_25'),
+        messages.get_string('NGram.KANJI_7_28'),
+        messages.get_string('NGram.KANJI_7_29'),
+        messages.get_string('NGram.KANJI_7_32'),
+        messages.get_string('NGram.KANJI_7_33'),
+        messages.get_string('NGram.KANJI_7_35'),
+        messages.get_string('NGram.KANJI_7_37')]
+
+    CJK_MAP = {}
+
+    @classmethod
+    def _init_cjk_map(cls):
+        for cjk_list in cls.CJK_CLASS:
+            representative = cjk_list[0]
+            for ch in cjk_list:
+                cls.CJK_MAP[ch] = representative
+
+NGram._init_cjk_map()
--- a/text/langdetect/utils/unicode_block.py
+++ b/text/langdetect/utils/unicode_block.py
+UNICODE_BASIC_LATIN = 1
+UNICODE_LATIN_1_SUPPLEMENT = 2
+UNICODE_LATIN_EXTENDED_A = 3
+UNICODE_LATIN_EXTENDED_B = 4
+UNICODE_IPA_EXTENSIONS = 5
+UNICODE_SPACING_MODIFIER_LETTERS = 6
+UNICODE_COMBINING_DIACRITICAL_MARKS = 7
+UNICODE_GREEK_AND_COPTIC = 8
+UNICODE_CYRILLIC = 9
+UNICODE_CYRILLIC_SUPPLEMENT = 10
+UNICODE_ARMENIAN = 11
+UNICODE_HEBREW = 12
+UNICODE_ARABIC = 13
+UNICODE_SYRIAC = 14
+UNICODE_ARABIC_SUPPLEMENT = 15
+UNICODE_THAANA = 16
+UNICODE_NKO = 17
+UNICODE_SAMARITAN = 18
+UNICODE_MANDAIC = 19
+UNICODE_ARABIC_EXTENDED_A = 20
+UNICODE_DEVANAGARI = 21
+UNICODE_BENGALI = 22
+UNICODE_GURMUKHI = 23
+UNICODE_GUJARATI = 24
+UNICODE_ORIYA = 25
+UNICODE_TAMIL = 26
+UNICODE_TELUGU = 27
+UNICODE_KANNADA = 28
+UNICODE_MALAYALAM = 29
+UNICODE_SINHALA = 30
+UNICODE_THAI = 31
+UNICODE_LAO = 32
+UNICODE_TIBETAN = 33
+UNICODE_MYANMAR = 34
+UNICODE_GEORGIAN = 35
+UNICODE_HANGUL_JAMO = 36
+UNICODE_ETHIOPIC = 37
+UNICODE_ETHIOPIC_SUPPLEMENT = 38
+UNICODE_CHEROKEE = 39
+UNICODE_UNIFIED_CANADIAN_ABORIGINAL_SYLLABICS = 40
+UNICODE_OGHAM = 41
+UNICODE_RUNIC = 42
+UNICODE_TAGALOG = 43
+UNICODE_HANUNOO = 44
+UNICODE_BUHID = 45
+UNICODE_TAGBANWA = 46
+UNICODE_KHMER = 47
+UNICODE_MONGOLIAN = 48
+UNICODE_UNIFIED_CANADIAN_ABORIGINAL_SYLLABICS_EXTENDED = 49
+UNICODE_LIMBU = 50
+UNICODE_TAI_LE = 51
+UNICODE_NEW_TAI_LUE = 52
+UNICODE_KHMER_SYMBOLS = 53
+UNICODE_BUGINESE = 54
+UNICODE_TAI_THAM = 55
+UNICODE_BALINESE = 56
+UNICODE_SUNDANESE = 57
+UNICODE_BATAK = 58
+UNICODE_LEPCHA = 59
+UNICODE_OL_CHIKI = 60
+UNICODE_SUNDANESE_SUPPLEMENT = 61
+UNICODE_VEDIC_EXTENSIONS = 62
+UNICODE_PHONETIC_EXTENSIONS = 63
+UNICODE_PHONETIC_EXTENSIONS_SUPPLEMENT = 64
+UNICODE_COMBINING_DIACRITICAL_MARKS_SUPPLEMENT = 65
+UNICODE_LATIN_EXTENDED_ADDITIONAL = 66
+UNICODE_GREEK_EXTENDED = 67
+UNICODE_GENERAL_PUNCTUATION = 68
+UNICODE_SUPERSCRIPTS_AND_SUBSCRIPTS = 69
+UNICODE_CURRENCY_SYMBOLS = 70
+UNICODE_COMBINING_DIACRITICAL_MARKS_FOR_SYMBOLS = 71
+UNICODE_LETTERLIKE_SYMBOLS = 72
+UNICODE_NUMBER_FORMS = 73
+UNICODE_ARROWS = 74
+UNICODE_MATHEMATICAL_OPERATORS = 75
+UNICODE_MISCELLANEOUS_TECHNICAL = 76
+UNICODE_CONTROL_PICTURES = 77
+UNICODE_OPTICAL_CHARACTER_RECOGNITION = 78
+UNICODE_ENCLOSED_ALPHANUMERICS = 79
+UNICODE_BOX_DRAWING = 80
+UNICODE_BLOCK_ELEMENTS = 81
+UNICODE_GEOMETRIC_SHAPES = 82
+UNICODE_MISCELLANEOUS_SYMBOLS = 83
+UNICODE_DINGBATS = 84
+UNICODE_MISCELLANEOUS_MATHEMATICAL_SYMBOLS_A = 85
+UNICODE_SUPPLEMENTAL_ARROWS_A = 86
+UNICODE_BRAILLE_PATTERNS = 87
+UNICODE_SUPPLEMENTAL_ARROWS_B = 88
+UNICODE_MISCELLANEOUS_MATHEMATICAL_SYMBOLS_B = 89
+UNICODE_SUPPLEMENTAL_MATHEMATICAL_OPERATORS = 90
+UNICODE_MISCELLANEOUS_SYMBOLS_AND_ARROWS = 91
+UNICODE_GLAGOLITIC = 92
+UNICODE_LATIN_EXTENDED_C = 93
+UNICODE_COPTIC = 94
+UNICODE_GEORGIAN_SUPPLEMENT = 95
+UNICODE_TIFINAGH = 96
+UNICODE_ETHIOPIC_EXTENDED = 97
+UNICODE_CYRILLIC_EXTENDED_A = 98
+UNICODE_SUPPLEMENTAL_PUNCTUATION = 99
+UNICODE_CJK_RADICALS_SUPPLEMENT = 100
+UNICODE_KANGXI_RADICALS = 101
+UNICODE_IDEOGRAPHIC_DESCRIPTION_CHARACTERS = 102
+UNICODE_CJK_SYMBOLS_AND_PUNCTUATION = 103
+UNICODE_HIRAGANA = 104
+UNICODE_KATAKANA = 105
+UNICODE_BOPOMOFO = 106
+UNICODE_HANGUL_COMPATIBILITY_JAMO = 107
+UNICODE_KANBUN = 108
+UNICODE_BOPOMOFO_EXTENDED = 109
+UNICODE_CJK_STROKES = 110
+UNICODE_KATAKANA_PHONETIC_EXTENSIONS = 111
+UNICODE_ENCLOSED_CJK_LETTERS_AND_MONTHS = 112
+UNICODE_CJK_COMPATIBILITY = 113
+UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_A = 114
+UNICODE_YIJING_HEXAGRAM_SYMBOLS = 115
+UNICODE_CJK_UNIFIED_IDEOGRAPHS = 116
+UNICODE_YI_SYLLABLES = 117
+UNICODE_YI_RADICALS = 118
+UNICODE_LISU = 119
+UNICODE_VAI = 120
+UNICODE_CYRILLIC_EXTENDED_B = 121
+UNICODE_BAMUM = 122
+UNICODE_MODIFIER_TONE_LETTERS = 123
+UNICODE_LATIN_EXTENDED_D = 124
+UNICODE_SYLOTI_NAGRI = 125
+UNICODE_COMMON_INDIC_NUMBER_FORMS = 126
+UNICODE_PHAGS_PA = 127
+UNICODE_SAURASHTRA = 128
+UNICODE_DEVANAGARI_EXTENDED = 129
+UNICODE_KAYAH_LI = 130
+UNICODE_REJANG = 131
+UNICODE_HANGUL_JAMO_EXTENDED_A = 132
+UNICODE_JAVANESE = 133
+UNICODE_CHAM = 134
+UNICODE_MYANMAR_EXTENDED_A = 135
+UNICODE_TAI_VIET = 136
+UNICODE_MEETEI_MAYEK_EXTENSIONS = 137
+UNICODE_ETHIOPIC_EXTENDED_A = 138
+UNICODE_MEETEI_MAYEK = 139
+UNICODE_HANGUL_SYLLABLES = 140
+UNICODE_HANGUL_JAMO_EXTENDED_B = 141
+UNICODE_HIGH_SURROGATES = 142
+UNICODE_HIGH_PRIVATE_USE_SURROGATES = 143
+UNICODE_LOW_SURROGATES = 144
+UNICODE_PRIVATE_USE_AREA = 145
+UNICODE_CJK_COMPATIBILITY_IDEOGRAPHS = 146
+UNICODE_ALPHABETIC_PRESENTATION_FORMS = 147
+UNICODE_ARABIC_PRESENTATION_FORMS_A = 148
+UNICODE_VARIATION_SELECTORS = 149
+UNICODE_VERTICAL_FORMS = 150
+UNICODE_COMBINING_HALF_MARKS = 151
+UNICODE_CJK_COMPATIBILITY_FORMS = 152
+UNICODE_SMALL_FORM_VARIANTS = 153
+UNICODE_ARABIC_PRESENTATION_FORMS_B = 154
+UNICODE_HALFWIDTH_AND_FULLWIDTH_FORMS = 155
+UNICODE_SPECIALS = 156
+UNICODE_LINEAR_B_SYLLABARY = 157
+UNICODE_LINEAR_B_IDEOGRAMS = 158
+UNICODE_AEGEAN_NUMBERS = 159
+UNICODE_ANCIENT_GREEK_NUMBERS = 160
+UNICODE_ANCIENT_SYMBOLS = 161
+UNICODE_PHAISTOS_DISC = 162
+UNICODE_LYCIAN = 163
+UNICODE_CARIAN = 164
+UNICODE_OLD_ITALIC = 165
+UNICODE_GOTHIC = 166
+UNICODE_UGARITIC = 167
+UNICODE_OLD_PERSIAN = 168
+UNICODE_DESERET = 169
+UNICODE_SHAVIAN = 170
+UNICODE_OSMANYA = 171
+UNICODE_CYPRIOT_SYLLABARY = 172
+UNICODE_IMPERIAL_ARAMAIC = 173
+UNICODE_PHOENICIAN = 174
+UNICODE_LYDIAN = 175
+UNICODE_MEROITIC_HIEROGLYPHS = 176
+UNICODE_MEROITIC_CURSIVE = 177
+UNICODE_KHAROSHTHI = 178
+UNICODE_OLD_SOUTH_ARABIAN = 179
+UNICODE_AVESTAN = 180
+UNICODE_INSCRIPTIONAL_PARTHIAN = 181
+UNICODE_INSCRIPTIONAL_PAHLAVI = 182
+UNICODE_OLD_TURKIC = 183
+UNICODE_RUMI_NUMERAL_SYMBOLS = 184
+UNICODE_BRAHMI = 185
+UNICODE_KAITHI = 186
+UNICODE_SORA_SOMPENG = 187
+UNICODE_CHAKMA = 188
+UNICODE_SHARADA = 189
+UNICODE_TAKRI = 190
+UNICODE_CUNEIFORM = 191
+UNICODE_CUNEIFORM_NUMBERS_AND_PUNCTUATION = 192
+UNICODE_EGYPTIAN_HIEROGLYPHS = 193
+UNICODE_BAMUM_SUPPLEMENT = 194
+UNICODE_MIAO = 195
+UNICODE_KANA_SUPPLEMENT = 196
+UNICODE_BYZANTINE_MUSICAL_SYMBOLS = 197
+UNICODE_MUSICAL_SYMBOLS = 198
+UNICODE_ANCIENT_GREEK_MUSICAL_NOTATION = 199
+UNICODE_TAI_XUAN_JING_SYMBOLS = 200
+UNICODE_COUNTING_ROD_NUMERALS = 201
+UNICODE_MATHEMATICAL_ALPHANUMERIC_SYMBOLS = 202
+UNICODE_ARABIC_MATHEMATICAL_ALPHABETIC_SYMBOLS = 203
+UNICODE_MAHJONG_TILES = 204
+UNICODE_DOMINO_TILES = 205
+UNICODE_PLAYING_CARDS = 206
+UNICODE_ENCLOSED_ALPHANUMERIC_SUPPLEMENT = 207
+UNICODE_ENCLOSED_IDEOGRAPHIC_SUPPLEMENT = 208
+UNICODE_MISCELLANEOUS_SYMBOLS_AND_PICTOGRAPHS = 209
+UNICODE_EMOTICONS = 210
+UNICODE_TRANSPORT_AND_MAP_SYMBOLS = 211
+UNICODE_ALCHEMICAL_SYMBOLS = 212
+UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_B = 213
+UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_C = 214
+UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_D = 215
+UNICODE_CJK_COMPATIBILITY_IDEOGRAPHS_SUPPLEMENT = 216
+UNICODE_TAGS = 217
+UNICODE_VARIATION_SELECTORS_SUPPLEMENT = 218
+UNICODE_SUPPLEMENTARY_PRIVATE_USE_AREA_A = 219
+UNICODE_SUPPLEMENTARY_PRIVATE_USE_AREA_B = 220
+
+
+_unicode_blocks = [
+    (UNICODE_BASIC_LATIN, 0x0000, 0x007F),
+    (UNICODE_LATIN_1_SUPPLEMENT, 0x0080, 0x00FF),
+    (UNICODE_LATIN_EXTENDED_A, 0x0100, 0x017F),
+    (UNICODE_LATIN_EXTENDED_B, 0x0180, 0x024F),
+    (UNICODE_IPA_EXTENSIONS, 0x0250, 0x02AF),
+    (UNICODE_SPACING_MODIFIER_LETTERS, 0x02B0, 0x02FF),
+    (UNICODE_COMBINING_DIACRITICAL_MARKS, 0x0300, 0x036F),
+    (UNICODE_GREEK_AND_COPTIC, 0x0370, 0x03FF),
+    (UNICODE_CYRILLIC, 0x0400, 0x04FF),
+    (UNICODE_CYRILLIC_SUPPLEMENT, 0x0500, 0x052F),
+    (UNICODE_ARMENIAN, 0x0530, 0x058F),
+    (UNICODE_HEBREW, 0x0590, 0x05FF),
+    (UNICODE_ARABIC, 0x0600, 0x06FF),
+    (UNICODE_SYRIAC, 0x0700, 0x074F),
+    (UNICODE_ARABIC_SUPPLEMENT, 0x0750, 0x077F),
+    (UNICODE_THAANA, 0x0780, 0x07BF),
+    (UNICODE_NKO, 0x07C0, 0x07FF),
+    (UNICODE_SAMARITAN, 0x0800, 0x083F),
+    (UNICODE_MANDAIC, 0x0840, 0x085F),
+    (UNICODE_ARABIC_EXTENDED_A, 0x08A0, 0x08FF),
+    (UNICODE_DEVANAGARI, 0x0900, 0x097F),
+    (UNICODE_BENGALI, 0x0980, 0x09FF),
+    (UNICODE_GURMUKHI, 0x0A00, 0x0A7F),
+    (UNICODE_GUJARATI, 0x0A80, 0x0AFF),
+    (UNICODE_ORIYA, 0x0B00, 0x0B7F),
+    (UNICODE_TAMIL, 0x0B80, 0x0BFF),
+    (UNICODE_TELUGU, 0x0C00, 0x0C7F),
+    (UNICODE_KANNADA, 0x0C80, 0x0CFF),
+    (UNICODE_MALAYALAM, 0x0D00, 0x0D7F),
+    (UNICODE_SINHALA, 0x0D80, 0x0DFF),
+    (UNICODE_THAI, 0x0E00, 0x0E7F),
+    (UNICODE_LAO, 0x0E80, 0x0EFF),
+    (UNICODE_TIBETAN, 0x0F00, 0x0FFF),
+    (UNICODE_MYANMAR, 0x1000, 0x109F),
+    (UNICODE_GEORGIAN, 0x10A0, 0x10FF),
+    (UNICODE_HANGUL_JAMO, 0x1100, 0x11FF),
+    (UNICODE_ETHIOPIC, 0x1200, 0x137F),
+    (UNICODE_ETHIOPIC_SUPPLEMENT, 0x1380, 0x139F),
+    (UNICODE_CHEROKEE, 0x13A0, 0x13FF),
+    (UNICODE_UNIFIED_CANADIAN_ABORIGINAL_SYLLABICS, 0x1400, 0x167F),
+    (UNICODE_OGHAM, 0x1680, 0x169F),
+    (UNICODE_RUNIC, 0x16A0, 0x16FF),
+    (UNICODE_TAGALOG, 0x1700, 0x171F),
+    (UNICODE_HANUNOO, 0x1720, 0x173F),
+    (UNICODE_BUHID, 0x1740, 0x175F),
+    (UNICODE_TAGBANWA, 0x1760, 0x177F),
+    (UNICODE_KHMER, 0x1780, 0x17FF),
+    (UNICODE_MONGOLIAN, 0x1800, 0x18AF),
+    (UNICODE_UNIFIED_CANADIAN_ABORIGINAL_SYLLABICS_EXTENDED, 0x18B0, 0x18FF),
+    (UNICODE_LIMBU, 0x1900, 0x194F),
+    (UNICODE_TAI_LE, 0x1950, 0x197F),
+    (UNICODE_NEW_TAI_LUE, 0x1980, 0x19DF),
+    (UNICODE_KHMER_SYMBOLS, 0x19E0, 0x19FF),
+    (UNICODE_BUGINESE, 0x1A00, 0x1A1F),
+    (UNICODE_TAI_THAM, 0x1A20, 0x1AAF),
+    (UNICODE_BALINESE, 0x1B00, 0x1B7F),
+    (UNICODE_SUNDANESE, 0x1B80, 0x1BBF),
+    (UNICODE_BATAK, 0x1BC0, 0x1BFF),
+    (UNICODE_LEPCHA, 0x1C00, 0x1C4F),
+    (UNICODE_OL_CHIKI, 0x1C50, 0x1C7F),
+    (UNICODE_SUNDANESE_SUPPLEMENT, 0x1CC0, 0x1CCF),
+    (UNICODE_VEDIC_EXTENSIONS, 0x1CD0, 0x1CFF),
+    (UNICODE_PHONETIC_EXTENSIONS, 0x1D00, 0x1D7F),
+    (UNICODE_PHONETIC_EXTENSIONS_SUPPLEMENT, 0x1D80, 0x1DBF),
+    (UNICODE_COMBINING_DIACRITICAL_MARKS_SUPPLEMENT, 0x1DC0, 0x1DFF),
+    (UNICODE_LATIN_EXTENDED_ADDITIONAL, 0x1E00, 0x1EFF),
+    (UNICODE_GREEK_EXTENDED, 0x1F00, 0x1FFF),
+    (UNICODE_GENERAL_PUNCTUATION, 0x2000, 0x206F),
+    (UNICODE_SUPERSCRIPTS_AND_SUBSCRIPTS, 0x2070, 0x209F),
+    (UNICODE_CURRENCY_SYMBOLS, 0x20A0, 0x20CF),
+    (UNICODE_COMBINING_DIACRITICAL_MARKS_FOR_SYMBOLS, 0x20D0, 0x20FF),
+    (UNICODE_LETTERLIKE_SYMBOLS, 0x2100, 0x214F),
+    (UNICODE_NUMBER_FORMS, 0x2150, 0x218F),
+    (UNICODE_ARROWS, 0x2190, 0x21FF),
+    (UNICODE_MATHEMATICAL_OPERATORS, 0x2200, 0x22FF),
+    (UNICODE_MISCELLANEOUS_TECHNICAL, 0x2300, 0x23FF),
+    (UNICODE_CONTROL_PICTURES, 0x2400, 0x243F),
+    (UNICODE_OPTICAL_CHARACTER_RECOGNITION, 0x2440, 0x245F),
+    (UNICODE_ENCLOSED_ALPHANUMERICS, 0x2460, 0x24FF),
+    (UNICODE_BOX_DRAWING, 0x2500, 0x257F),
+    (UNICODE_BLOCK_ELEMENTS, 0x2580, 0x259F),
+    (UNICODE_GEOMETRIC_SHAPES, 0x25A0, 0x25FF),
+    (UNICODE_MISCELLANEOUS_SYMBOLS, 0x2600, 0x26FF),
+    (UNICODE_DINGBATS, 0x2700, 0x27BF),
+    (UNICODE_MISCELLANEOUS_MATHEMATICAL_SYMBOLS_A, 0x27C0, 0x27EF),
+    (UNICODE_SUPPLEMENTAL_ARROWS_A, 0x27F0, 0x27FF),
+    (UNICODE_BRAILLE_PATTERNS, 0x2800, 0x28FF),
+    (UNICODE_SUPPLEMENTAL_ARROWS_B, 0x2900, 0x297F),
+    (UNICODE_MISCELLANEOUS_MATHEMATICAL_SYMBOLS_B, 0x2980, 0x29FF),
+    (UNICODE_SUPPLEMENTAL_MATHEMATICAL_OPERATORS, 0x2A00, 0x2AFF),
+    (UNICODE_MISCELLANEOUS_SYMBOLS_AND_ARROWS, 0x2B00, 0x2BFF),
+    (UNICODE_GLAGOLITIC, 0x2C00, 0x2C5F),
+    (UNICODE_LATIN_EXTENDED_C, 0x2C60, 0x2C7F),
+    (UNICODE_COPTIC, 0x2C80, 0x2CFF),
+    (UNICODE_GEORGIAN_SUPPLEMENT, 0x2D00, 0x2D2F),
+    (UNICODE_TIFINAGH, 0x2D30, 0x2D7F),
+    (UNICODE_ETHIOPIC_EXTENDED, 0x2D80, 0x2DDF),
+    (UNICODE_CYRILLIC_EXTENDED_A, 0x2DE0, 0x2DFF),
+    (UNICODE_SUPPLEMENTAL_PUNCTUATION, 0x2E00, 0x2E7F),
+    (UNICODE_CJK_RADICALS_SUPPLEMENT, 0x2E80, 0x2EFF),
+    (UNICODE_KANGXI_RADICALS, 0x2F00, 0x2FDF),
+    (UNICODE_IDEOGRAPHIC_DESCRIPTION_CHARACTERS, 0x2FF0, 0x2FFF),
+    (UNICODE_CJK_SYMBOLS_AND_PUNCTUATION, 0x3000, 0x303F),
+    (UNICODE_HIRAGANA, 0x3040, 0x309F),
+    (UNICODE_KATAKANA, 0x30A0, 0x30FF),
+    (UNICODE_BOPOMOFO, 0x3100, 0x312F),
+    (UNICODE_HANGUL_COMPATIBILITY_JAMO, 0x3130, 0x318F),
+    (UNICODE_KANBUN, 0x3190, 0x319F),
+    (UNICODE_BOPOMOFO_EXTENDED, 0x31A0, 0x31BF),
+    (UNICODE_CJK_STROKES, 0x31C0, 0x31EF),
+    (UNICODE_KATAKANA_PHONETIC_EXTENSIONS, 0x31F0, 0x31FF),
+    (UNICODE_ENCLOSED_CJK_LETTERS_AND_MONTHS, 0x3200, 0x32FF),
+    (UNICODE_CJK_COMPATIBILITY, 0x3300, 0x33FF),
+    (UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_A, 0x3400, 0x4DBF),
+    (UNICODE_YIJING_HEXAGRAM_SYMBOLS, 0x4DC0, 0x4DFF),
+    (UNICODE_CJK_UNIFIED_IDEOGRAPHS, 0x4E00, 0x9FFF),
+    (UNICODE_YI_SYLLABLES, 0xA000, 0xA48F),
+    (UNICODE_YI_RADICALS, 0xA490, 0xA4CF),
+    (UNICODE_LISU, 0xA4D0, 0xA4FF),
+    (UNICODE_VAI, 0xA500, 0xA63F),
+    (UNICODE_CYRILLIC_EXTENDED_B, 0xA640, 0xA69F),
+    (UNICODE_BAMUM, 0xA6A0, 0xA6FF),
+    (UNICODE_MODIFIER_TONE_LETTERS, 0xA700, 0xA71F),
+    (UNICODE_LATIN_EXTENDED_D, 0xA720, 0xA7FF),
+    (UNICODE_SYLOTI_NAGRI, 0xA800, 0xA82F),
+    (UNICODE_COMMON_INDIC_NUMBER_FORMS, 0xA830, 0xA83F),
+    (UNICODE_PHAGS_PA, 0xA840, 0xA87F),
+    (UNICODE_SAURASHTRA, 0xA880, 0xA8DF),
+    (UNICODE_DEVANAGARI_EXTENDED, 0xA8E0, 0xA8FF),
+    (UNICODE_KAYAH_LI, 0xA900, 0xA92F),
+    (UNICODE_REJANG, 0xA930, 0xA95F),
+    (UNICODE_HANGUL_JAMO_EXTENDED_A, 0xA960, 0xA97F),
+    (UNICODE_JAVANESE, 0xA980, 0xA9DF),
+    (UNICODE_CHAM, 0xAA00, 0xAA5F),
+    (UNICODE_MYANMAR_EXTENDED_A, 0xAA60, 0xAA7F),
+    (UNICODE_TAI_VIET, 0xAA80, 0xAADF),
+    (UNICODE_MEETEI_MAYEK_EXTENSIONS, 0xAAE0, 0xAAFF),
+    (UNICODE_ETHIOPIC_EXTENDED_A, 0xAB00, 0xAB2F),
+    (UNICODE_MEETEI_MAYEK, 0xABC0, 0xABFF),
+    (UNICODE_HANGUL_SYLLABLES, 0xAC00, 0xD7AF),
+    (UNICODE_HANGUL_JAMO_EXTENDED_B, 0xD7B0, 0xD7FF),
+    (UNICODE_HIGH_SURROGATES, 0xD800, 0xDB7F),
+    (UNICODE_HIGH_PRIVATE_USE_SURROGATES, 0xDB80, 0xDBFF),
+    (UNICODE_LOW_SURROGATES, 0xDC00, 0xDFFF),
+    (UNICODE_PRIVATE_USE_AREA, 0xE000, 0xF8FF),
+    (UNICODE_CJK_COMPATIBILITY_IDEOGRAPHS, 0xF900, 0xFAFF),
+    (UNICODE_ALPHABETIC_PRESENTATION_FORMS, 0xFB00, 0xFB4F),
+    (UNICODE_ARABIC_PRESENTATION_FORMS_A, 0xFB50, 0xFDFF),
+    (UNICODE_VARIATION_SELECTORS, 0xFE00, 0xFE0F),
+    (UNICODE_VERTICAL_FORMS, 0xFE10, 0xFE1F),
+    (UNICODE_COMBINING_HALF_MARKS, 0xFE20, 0xFE2F),
+    (UNICODE_CJK_COMPATIBILITY_FORMS, 0xFE30, 0xFE4F),
+    (UNICODE_SMALL_FORM_VARIANTS, 0xFE50, 0xFE6F),
+    (UNICODE_ARABIC_PRESENTATION_FORMS_B, 0xFE70, 0xFEFF),
+    (UNICODE_HALFWIDTH_AND_FULLWIDTH_FORMS, 0xFF00, 0xFFEF),
+    (UNICODE_SPECIALS, 0xFFF0, 0xFFFF),
+    (UNICODE_LINEAR_B_SYLLABARY, 0x10000, 0x1007F),
+    (UNICODE_LINEAR_B_IDEOGRAMS, 0x10080, 0x100FF),
+    (UNICODE_AEGEAN_NUMBERS, 0x10100, 0x1013F),
+    (UNICODE_ANCIENT_GREEK_NUMBERS, 0x10140, 0x1018F),
+    (UNICODE_ANCIENT_SYMBOLS, 0x10190, 0x101CF),
+    (UNICODE_PHAISTOS_DISC, 0x101D0, 0x101FF),
+    (UNICODE_LYCIAN, 0x10280, 0x1029F),
+    (UNICODE_CARIAN, 0x102A0, 0x102DF),
+    (UNICODE_OLD_ITALIC, 0x10300, 0x1032F),
+    (UNICODE_GOTHIC, 0x10330, 0x1034F),
+    (UNICODE_UGARITIC, 0x10380, 0x1039F),
+    (UNICODE_OLD_PERSIAN, 0x103A0, 0x103DF),
+    (UNICODE_DESERET, 0x10400, 0x1044F),
+    (UNICODE_SHAVIAN, 0x10450, 0x1047F),
+    (UNICODE_OSMANYA, 0x10480, 0x104AF),
+    (UNICODE_CYPRIOT_SYLLABARY, 0x10800, 0x1083F),
+    (UNICODE_IMPERIAL_ARAMAIC, 0x10840, 0x1085F),
+    (UNICODE_PHOENICIAN, 0x10900, 0x1091F),
+    (UNICODE_LYDIAN, 0x10920, 0x1093F),
+    (UNICODE_MEROITIC_HIEROGLYPHS, 0x10980, 0x1099F),
+    (UNICODE_MEROITIC_CURSIVE, 0x109A0, 0x109FF),
+    (UNICODE_KHAROSHTHI, 0x10A00, 0x10A5F),
+    (UNICODE_OLD_SOUTH_ARABIAN, 0x10A60, 0x10A7F),
+    (UNICODE_AVESTAN, 0x10B00, 0x10B3F),
+    (UNICODE_INSCRIPTIONAL_PARTHIAN, 0x10B40, 0x10B5F),
+    (UNICODE_INSCRIPTIONAL_PAHLAVI, 0x10B60, 0x10B7F),
+    (UNICODE_OLD_TURKIC, 0x10C00, 0x10C4F),
+    (UNICODE_RUMI_NUMERAL_SYMBOLS, 0x10E60, 0x10E7F),
+    (UNICODE_BRAHMI, 0x11000, 0x1107F),
+    (UNICODE_KAITHI, 0x11080, 0x110CF),
+    (UNICODE_SORA_SOMPENG, 0x110D0, 0x110FF),
+    (UNICODE_CHAKMA, 0x11100, 0x1114F),
+    (UNICODE_SHARADA, 0x11180, 0x111DF),
+    (UNICODE_TAKRI, 0x11680, 0x116CF),
+    (UNICODE_CUNEIFORM, 0x12000, 0x123FF),
+    (UNICODE_CUNEIFORM_NUMBERS_AND_PUNCTUATION, 0x12400, 0x1247F),
+    (UNICODE_EGYPTIAN_HIEROGLYPHS, 0x13000, 0x1342F),
+    (UNICODE_BAMUM_SUPPLEMENT, 0x16800, 0x16A3F),
+    (UNICODE_MIAO, 0x16F00, 0x16F9F),
+    (UNICODE_KANA_SUPPLEMENT, 0x1B000, 0x1B0FF),
+    (UNICODE_BYZANTINE_MUSICAL_SYMBOLS, 0x1D000, 0x1D0FF),
+    (UNICODE_MUSICAL_SYMBOLS, 0x1D100, 0x1D1FF),
+    (UNICODE_ANCIENT_GREEK_MUSICAL_NOTATION, 0x1D200, 0x1D24F),
+    (UNICODE_TAI_XUAN_JING_SYMBOLS, 0x1D300, 0x1D35F),
+    (UNICODE_COUNTING_ROD_NUMERALS, 0x1D360, 0x1D37F),
+    (UNICODE_MATHEMATICAL_ALPHANUMERIC_SYMBOLS, 0x1D400, 0x1D7FF),
+    (UNICODE_ARABIC_MATHEMATICAL_ALPHABETIC_SYMBOLS, 0x1EE00, 0x1EEFF),
+    (UNICODE_MAHJONG_TILES, 0x1F000, 0x1F02F),
+    (UNICODE_DOMINO_TILES, 0x1F030, 0x1F09F),
+    (UNICODE_PLAYING_CARDS, 0x1F0A0, 0x1F0FF),
+    (UNICODE_ENCLOSED_ALPHANUMERIC_SUPPLEMENT, 0x1F100, 0x1F1FF),
+    (UNICODE_ENCLOSED_IDEOGRAPHIC_SUPPLEMENT, 0x1F200, 0x1F2FF),
+    (UNICODE_MISCELLANEOUS_SYMBOLS_AND_PICTOGRAPHS, 0x1F300, 0x1F5FF),
+    (UNICODE_EMOTICONS, 0x1F600, 0x1F64F),
+    (UNICODE_TRANSPORT_AND_MAP_SYMBOLS, 0x1F680, 0x1F6FF),
+    (UNICODE_ALCHEMICAL_SYMBOLS, 0x1F700, 0x1F77F),
+    (UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_B, 0x20000, 0x2A6DF),
+    (UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_C, 0x2A700, 0x2B73F),
+    (UNICODE_CJK_UNIFIED_IDEOGRAPHS_EXTENSION_D, 0x2B740, 0x2B81F),
+    (UNICODE_CJK_COMPATIBILITY_IDEOGRAPHS_SUPPLEMENT, 0x2F800, 0x2FA1F),
+    (UNICODE_TAGS, 0xE0000, 0xE007F),
+    (UNICODE_VARIATION_SELECTORS_SUPPLEMENT, 0xE0100, 0xE01EF),
+    (UNICODE_SUPPLEMENTARY_PRIVATE_USE_AREA_A, 0xF0000, 0xFFFFF),
+    (UNICODE_SUPPLEMENTARY_PRIVATE_USE_AREA_B, 0x100000, 0x10FFFF),
+]
+
+NUM_BLOCKS = len(_unicode_blocks)
+
+
+def unicode_block(ch):
+    '''Return the Unicode block name for ch, or None if ch has no block.'''
+    cp = ord(ch)
+    # special case basic latin
+    if cp <= 0x7F:
+        return UNICODE_BASIC_LATIN
+    # binary search for the correct block
+    be, en = 0, NUM_BLOCKS - 1
+    while be <= en:
+        mid = (be+en) >> 1
+        name, start, end = _unicode_blocks[mid]
+        if start <= cp <= end:
+            return name
+        if cp < start:
+            en = mid-1
+        else:
+            be = mid+1
--- a/text/mandarin.py
+++ b/text/mandarin.py
+import re
+from pypinyin import lazy_pinyin, Style
+from .custom_pypinyin_dict import phrase_pinyin_data
+phrase_pinyin_data.load()
+import jieba
+from .cn2an import an2cn
+
+# 标点符号正则
+punc_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "~",
+    "「": "'",
+    "」": "'",
+    "『": "'", 
+    "』": "'",
+}
+punc_table = str.maketrans(punc_map)
+
+# 数字正则化
+number_pattern = re.compile(r'\d+(?:\.?\d+)?')
+def replace_number(match):
+    return an2cn(match.group())
+def normalize_number(text):
+    return number_pattern.sub(replace_number, text)
+
+# get symbols of phones
+def load_pinyin_symbols(path):
+    pinyin_dict={}
+    temp = []
+    with open(path, "r", encoding='utf-8') as f:
+        content = f.readlines()
+    for line in content:
+        cuts = line.strip().split(',')
+        pinyin = cuts[0]
+        phones = cuts[1].split(' ')
+        pinyin_dict[pinyin] = phones
+        temp.extend(phones)
+    temp = list(set(temp))
+    tone = []
+    for phone in temp:
+        for i in range(1, 6):
+            phone2 = phone + str(i)
+            tone.append(phone2)
+    print(sorted(tone, key=lambda x: len(x)))
+    return pinyin_dict
+
+def load_pinyin_dict(path):
+    pinyin_dict = {}
+    with open(path, "r", encoding='utf-8') as f:
+        for line in f:
+            key, value = line.strip().split(',', 1)
+            pinyin_dict[key] = value.split()
+    return pinyin_dict
+pinyin_dict = load_pinyin_dict('text/cnm3/ds_CNM3.txt')
+
+def chinese_to_cnm3(text: str):
+    text = text.translate(punc_table)
+    text = normalize_number(text)
+    words = jieba.lcut(text, cut_all=False)
+    phones = []
+    for word in words:
+        pinyin_list = lazy_pinyin(word, style=Style.TONE3, neutral_tone_with_five=True)
+        for pinyin in pinyin_list:
+            if pinyin[-1].isdigit():
+                tone = pinyin[-1]
+                syllable = pinyin[:-1]
+                phone = pinyin_dict[syllable]
+                phones.extend([ph + tone for ph in phone])
+            elif pinyin[-1].isalpha():
+                pass
+            else:
+                phones.extend(pinyin)
+    return phones
\ No newline at end of file
--- a/text/symbols.py
+++ b/text/symbols.py
+'''
+Defines the set of symbols used in text input to the model.
+'''
+
+# japanese_cleaners
+# _pad        = '_'
+# _punctuation = ',.!?-'
+# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
+
+
+'''# japanese_cleaners2
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
+'''
+
+
+'''# korean_cleaners
+_pad        = '_'
+_punctuation = ',.!?…~'
+_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
+'''
+
+'''# chinese_cleaners
+_pad        = '_'
+_punctuation = '，。！？—…'
+_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
+'''
+
+# # zh_ja_mixture_cleaners
+# _pad        = '_'
+# _punctuation = ',.!?-~…'
+# _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
+
+
+'''# sanskrit_cleaners
+_pad        = '_'
+_punctuation = '।'
+_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
+'''
+
+'''# cjks_cleaners
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
+'''
+
+'''# thai_cleaners
+_pad        = '_'
+_punctuation = '.!? '
+_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
+'''
+
+# # cjke_cleaners2
+_pad        = '_'
+_punctuation = ',.!?-~…' + "'"
+_IPA_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
+_CNM3_letters = ['y1', 'y2', 'y3', 'y4', 'y5', 'n1', 'n2', 'n3', 'n4', 'n5', 'p1', 'p2', 'p3', 'p4', 'p5', 'x1', 'x2', 'x3', 'x4', 'x5', 'k1', 'k2', 'k3', 'k4', 'k5', 'l1', 'l2', 'l3', 'l4', 'l5', 'q1', 'q2', 'q3', 'q4', 'q5', 'w1', 'w2', 'w3', 'w4', 'w5', 'E1', 'E2', 'E3', 'E4', 'E5', 'b1', 'b2', 'b3', 'b4', 'b5', 'c1', 'c2', 'c3', 'c4', 'c5', 'z1', 'z2', 'z3', 'z4', 'z5', 'e1', 'e2', 'e3', 'e4', 'e5', 'f1', 'f2', 'f3', 'f4', 'f5', 's1', 's2', 's3', 's4', 's5', 'j1', 'j2', 'j3', 'j4', 'j5', 'o1', 'o2', 'o3', 'o4', 'o5', 'i1', 'i2', 'i3', 'i4', 'i5', 'd1', 'd2', 'd3', 'd4', 'd5', 'm1', 'm2', 'm3', 'm4', 'm5', 't1', 't2', 't3', 't4', 't5', 'h1', 'h2', 'h3', 'h4', 'h5', 'g1', 'g2', 'g3', 'g4', 'g5', 'v1', 'v2', 'v3', 'v4', 'v5', 'r1', 'r2', 'r3', 'r4', 'r5', 'a1', 'a2', 'a3', 'a4', 'a5', 'u1', 'u2', 'u3', 'u4', 'u5', 'I01', 'I02', 'I03', 'I04', 'I05', 'i01', 'i02', 'i03', 'i04', 'i05', 'uo1', 'uo2', 'uo3', 'uo4', 'uo5', 'o01', 'o02', 'o03', 'o04', 'o05', 'U01', 'U02', 'U03', 'U04', 'U05', 'v01', 'v02', 'v03', 'v04', 'v05', 'er1', 'er2', 'er3', 'er4', 'er5', 'A01', 'A02', 'A03', 'A04', 'A05', 'ai1', 'ai2', 'ai3', 'ai4', 'ai5', 'e01', 'e02', 'e03', 'e04', 'e05', 'sh1', 'sh2', 'sh3', 'sh4', 'sh5', 'an1', 'an2', 'an3', 'an4', 'an5', 'ou1', 'ou2', 'ou3', 'ou4', 'ou5', 'ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'a01', 'a02', 'a03', 'a04', 'a05', 'N01', 'N02', 'N03', 'N04', 'N05', 'ao1', 'ao2', 'ao3', 'ao4', 'ao5', 've1', 've2', 've3', 've4', 've5', 'ir1', 'ir2', 'ir3', 'ir4', 'ir5', 'ng1', 'ng2', 'ng3', 'ng4', 'ng5', 'ua1', 'ua2', 'ua3', 'ua4', 'ua5', 'zh1', 'zh2', 'zh3', 'zh4', 'zh5', 'O01', 'O02', 'O03', 'O04', 'O05', 'ie1', 'ie2', 'ie3', 'ie4', 'ie5', 'E01', 'E02', 'E03', 'E04', 'E05', 'ia1', 'ia2', 'ia3', 'ia4', 'ia5', 'iE01', 'iE02', 'iE03', 'iE04', 'iE05', 'ang1', 'ang2', 'ang3', 'ang4', 'ang5', 'ng01', 'ng02', 'ng03', 'ng04', 'ng05', 'io01', 'io02', 'io03', 'io04', 'io05', 'iA01', 'iA02', 'iA03', 'iA04', 'iA05', 'uA01', 'uA02', 'uA03', 'uA04', 'uA05', 'ong1', 'ong2', 'ong3', 'ong4', 'ong5', 'oo01', 'oo02', 'oo03', 'oo04', 'oo05', 'uE01', 'uE02', 'uE03', 'uE04', 'uE05', 'vE01', 'vE02', 'vE03', 'vE04', 'vE05', 'ue01', 'ue02', 'ue03', 'ue04', 'ue05', 'ua01', 'ua02', 'ua03', 'ua04', 'ua05', 'iO01', 'iO02', 'iO03', 'iO04', 'iO05']
+_additional = ['<sil>', '<asp>']
+# _CNM3_letters = []
+
+
+'''# shanghainese_cleaners
+_pad        = '_'
+_punctuation = ',.!?…'
+_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
+'''
+
+'''# chinese_dialect_cleaners
+_pad        = '_'
+_punctuation = ',.!?~…─'
+_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
+'''
+
+# Export all symbols:
+symbols = [_pad] + list(_punctuation) + list(_IPA_letters) + _CNM3_letters + _additional
+
+# Special symbol ids
+SPACE_ID = symbols.index(" ")
--- a/train.py
+++ b/train.py
+import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+
+from dataclasses import asdict
+from tqdm import tqdm
+
+from models.model import StableTTS
+from datas.dataset import StableDataset, collate_fn
+from datas.sampler import DistributedBucketSampler
+from text import symbols
+from config import MelConfig, ModelConfig, TrainConfig
+from utils.scheduler import get_cosine_schedule_with_warmup
+from utils.load import continue_training
+from utils.plot import plot_alignment_to_numpy
+
+torch.backends.cudnn.benchmark = True
+    
+def setup(rank, world_size):
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '12356'
+    dist.init_process_group("gloo" if os.name == "nt" else "nccl", rank=rank, world_size=world_size)
+
+def cleanup():
+    dist.destroy_process_group()
+    
+def _init_config(model_config: ModelConfig, mel_config: MelConfig, train_config: TrainConfig):
+    
+    if not os.path.exists(train_config.model_save_path):
+        print(f'Creating {train_config.model_save_path}')
+        os.makedirs(train_config.model_save_path, exist_ok=True)
+
+def train(rank, world_size):
+    setup(rank, world_size)
+    torch.cuda.set_device(rank)
+
+    model_config = ModelConfig()
+    mel_config = MelConfig()
+    train_config = TrainConfig()
+    
+    _init_config(model_config, mel_config, train_config)
+    
+    model = StableTTS(len(symbols), mel_config.n_mels, **asdict(model_config)).to(rank)
+    
+    model = DDP(model, device_ids=[rank])
+
+    train_dataset = StableDataset(train_config.train_dataset_path, mel_config.hop_length)
+    train_sampler = DistributedBucketSampler(train_dataset, train_config.batch_size, [32,300,400,500,600,700,800,900,1000], num_replicas=world_size, rank=rank)
+    train_dataloader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=4, pin_memory=True, collate_fn=collate_fn, persistent_workers=True)
+    
+    if rank == 0:
+        writer = SummaryWriter(train_config.log_dir)
+
+    optimizer = optim.AdamW(model.parameters(), lr=train_config.learning_rate)
+    scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=int(train_config.warmup_steps), num_training_steps=train_config.num_epochs * len(train_dataloader))
+    
+    # load latest checkpoints if possible
+    current_epoch = continue_training(train_config.model_save_path, model, optimizer)
+
+    model.train()
+    for epoch in range(current_epoch, train_config.num_epochs):  # loop over the train_dataset multiple times
+        train_dataloader.batch_sampler.set_epoch(epoch)
+        if rank == 0:
+            dataloader = tqdm(train_dataloader)
+        else:
+            dataloader = train_dataloader
+            
+        for batch_idx, datas in enumerate(dataloader):
+            datas = [data.to(rank, non_blocking=True) for data in datas]
+            x, x_lengths, y, y_lengths = datas
+            optimizer.zero_grad()
+            dur_loss, diff_loss, prior_loss, attn = model(x, x_lengths, y, y_lengths)
+            loss = dur_loss + diff_loss + prior_loss
+            loss.backward()
+            optimizer.step()
+            scheduler.step()
+            
+            if rank == 0 and batch_idx % train_config.log_interval == 0:
+                steps = epoch * len(dataloader) + batch_idx
+                writer.add_scalar("training/diff_loss", diff_loss.item(), steps)
+                writer.add_scalar("training/dur_loss", dur_loss.item(), steps)
+                writer.add_scalar("training/prior_loss", prior_loss.item(), steps)
+                writer.add_scalar("learning_rate/learning_rate", scheduler.get_last_lr()[0], steps)
+                # writer.add_image("training/attention", plot_alignment_to_numpy(attn[0,0].data.cpu().numpy()), steps, dataformats='HWC')
+            
+        if rank == 0 and epoch % train_config.save_interval == 0:
+            torch.save(model.module.state_dict(), os.path.join(train_config.model_save_path, f'checkpoint_{epoch}.pt'))
+            torch.save(optimizer.state_dict(), os.path.join(train_config.model_save_path, f'optimizer_{epoch}.pt'))
+        print(f"Rank {rank}, Epoch {epoch}, Loss {loss.item()}")
+
+    cleanup()
+    
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    world_size = torch.cuda.device_count()
+    #print(world_size)
+    torch.multiprocessing.spawn(train, args=(world_size,), nprocs=world_size, join=True)
--- a/utils/__init__.py
+++ b/utils/__init__.py
--- a/utils/audio.py
+++ b/utils/audio.py
+from dataclasses import dataclass, asdict
+import torch
+from torch import Tensor
+import torch.nn as nn
+import torchaudio
+import torchaudio.transforms
+
+from config import MelConfig
+
+class LogMelSpectrogram(nn.Module):
+    def __init__(self, config: MelConfig):
+        super().__init__()
+        self.spec = torchaudio.transforms.MelSpectrogram(**asdict(config))
+        
+    def forward(self, x: Tensor) -> Tensor:
+        return self.compress(self.spec(x))
+        
+    def compress(self, x: Tensor) -> Tensor:
+        return torch.log(torch.clamp(x, min=1e-5))
+
+    def decompress(self, x: Tensor) -> Tensor:
+        return torch.exp(x)
+
+    
+def load_and_resample_audio(audio_path, target_sr, device='cpu') -> Tensor:
+    try:
+        y, sr = torchaudio.load(audio_path)
+    except Exception as e:
+        print(str(e))
+        return None
+    
+    y.to(device)
+    # Convert to mono
+    if y.size(0) > 1:
+        y = y[0, :].unsqueeze(0) # shape: [2, time] -> [time] -> [1, time]
+        
+    # resample audio to target sample_rate
+    if sr != target_sr:
+        y = torchaudio.functional.resample(y, sr, target_sr)
+    return y
\ No newline at end of file
--- a/utils/load.py
+++ b/utils/load.py
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+def continue_training(checkpoint_path, model: DDP, optimizer: optim.Optimizer) -> int:
+    """load the latest checkpoints and optimizers"""
+    model_dict = {}
+    optimizer_dict = {}
+    
+    # globt all the checkpoints in the directory
+    for file in os.listdir(checkpoint_path):
+        if file.endswith(".pt"):
+            name, epoch_str = file.rsplit('_', 1)
+            epoch = int(epoch_str.split('.')[0])
+            
+            if name.startswith("checkpoint"):
+                model_dict[epoch] = file
+            elif name.startswith("optimizer"):
+                optimizer_dict[epoch] = file
+    
+    # get the largest epoch
+    common_epochs = set(model_dict.keys()) & set(optimizer_dict.keys())
+    if common_epochs:
+        max_epoch = max(common_epochs)
+        model_path = os.path.join(checkpoint_path, model_dict[max_epoch])
+        optimizer_path = os.path.join(checkpoint_path, optimizer_dict[max_epoch])
+        
+        # load model and optimizer
+        model.module.load_state_dict(torch.load(model_path, map_location='cpu'))
+        optimizer.load_state_dict(torch.load(optimizer_path, map_location='cpu'))
+        
+        print(f'resume model and optimizer from {max_epoch} epoch')
+        return max_epoch + 1
+    
+    else:
+        # load pretrained checkpoint
+        if model_dict:
+            model_path = os.path.join(checkpoint_path, model_dict[max(model_dict.keys())])
+            model.module.load_state_dict(torch.load(model_path, map_location='cpu'))
+            
+        return 0
\ No newline at end of file
--- a/utils/plot.py
+++ b/utils/plot.py
+import os
+import glob
+import logging
+import json
+import numpy as np
+import torch
+import torchaudio
+
+MATPLOTLIB_FLAG = False
+
+def plot_alignment_to_numpy(alignment, info=None):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+
+  fig, ax = plt.subplots(figsize=(6, 4))
+  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+                  interpolation='none')
+  fig.colorbar(im, ax=ax)
+  xlabel = 'Decoder timestep'
+  if info is not None:
+      xlabel += '\n\n' + info
+  plt.xlabel(xlabel)
+  plt.ylabel('Encoder timestep')
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
\ No newline at end of file
--- a/utils/scheduler.py
+++ b/utils/scheduler.py
+# modified from transformers.optimization
+import math
+from functools import partial
+
+import torch
+from torch import nn
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau
+
+def _get_constant_lambda(_=None):
+    return 1
+
+
+def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
+    """
+    Create a schedule with a constant learning rate, using the learning rate set in optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    return LambdaLR(optimizer, _get_constant_lambda, last_epoch=last_epoch)
+
+
+def get_reduce_on_plateau_schedule(optimizer: Optimizer, **kwargs):
+    """
+    Create a schedule with a constant learning rate that decreases when a metric has stopped improving.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        kwargs (`dict`, *optional*):
+            Extra parameters to be passed to the scheduler. See `torch.optim.lr_scheduler.ReduceLROnPlateau`
+            for possible parameters.
+
+    Return:
+        `torch.optim.lr_scheduler.ReduceLROnPlateau` with the appropriate schedule.
+    """
+
+    return ReduceLROnPlateau(optimizer, **kwargs)
+
+
+def _get_constant_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int):
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1.0, num_warmup_steps))
+    return 1.0
+
+
+def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
+    """
+    Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
+    increases linearly between 0 and the initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    lr_lambda = partial(_get_constant_schedule_with_warmup_lr_lambda, num_warmup_steps=num_warmup_steps)
+    return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
+
+
+def _get_linear_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int, num_training_steps: int):
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+    return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps)))
+
+
+def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
+    """
+    Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
+    a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        num_training_steps (`int`):
+            The total number of training steps.
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    lr_lambda = partial(
+        _get_linear_schedule_with_warmup_lr_lambda,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+    )
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def _get_cosine_schedule_with_warmup_lr_lambda(
+    current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_cycles: float
+):
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+    progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
+    return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
+
+
+def get_cosine_schedule_with_warmup(
+    optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
+):
+    """
+    Create a schedule with a learning rate that decreases following the values of the cosine function between the
+    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
+    initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        num_training_steps (`int`):
+            The total number of training steps.
+        num_cycles (`float`, *optional*, defaults to 0.5):
+            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
+            following a half-cosine).
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    lr_lambda = partial(
+        _get_cosine_schedule_with_warmup_lr_lambda,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+        num_cycles=num_cycles,
+    )
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def _get_cosine_with_hard_restarts_schedule_with_warmup_lr_lambda(
+    current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_cycles: int
+):
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+    progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
+    if progress >= 1.0:
+        return 0.0
+    return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
+
+
+def get_cosine_with_hard_restarts_schedule_with_warmup(
+    optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
+):
+    """
+    Create a schedule with a learning rate that decreases following the values of the cosine function between the
+    initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
+    linearly between 0 and the initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        num_training_steps (`int`):
+            The total number of training steps.
+        num_cycles (`int`, *optional*, defaults to 1):
+            The number of hard restarts to use.
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    lr_lambda = partial(
+        _get_cosine_with_hard_restarts_schedule_with_warmup_lr_lambda,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+        num_cycles=num_cycles,
+    )
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def _get_polynomial_decay_schedule_with_warmup_lr_lambda(
+    current_step: int,
+    *,
+    num_warmup_steps: int,
+    num_training_steps: int,
+    lr_end: float,
+    power: float,
+    lr_init: int,
+):
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+    elif current_step > num_training_steps:
+        return lr_end / lr_init  # as LambdaLR multiplies by lr_init
+    else:
+        lr_range = lr_init - lr_end
+        decay_steps = num_training_steps - num_warmup_steps
+        pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
+        decay = lr_range * pct_remaining**power + lr_end
+        return decay / lr_init  # as LambdaLR multiplies by lr_init
+
+
+def get_polynomial_decay_schedule_with_warmup(
+    optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
+):
+    """
+    Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
+    optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
+    initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        num_training_steps (`int`):
+            The total number of training steps.
+        lr_end (`float`, *optional*, defaults to 1e-7):
+            The end LR.
+        power (`float`, *optional*, defaults to 1.0):
+            Power factor.
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
+    implementation at
+    https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+
+    """
+
+    lr_init = optimizer.defaults["lr"]
+    if not (lr_init > lr_end):
+        raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
+
+    lr_lambda = partial(
+        _get_polynomial_decay_schedule_with_warmup_lr_lambda,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+        lr_end=lr_end,
+        power=power,
+        lr_init=lr_init,
+    )
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def _get_inverse_sqrt_schedule_lr_lambda(current_step: int, *, num_warmup_steps: int, timescale: int = None):
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+    shift = timescale - num_warmup_steps
+    decay = 1.0 / math.sqrt((current_step + shift) / timescale)
+    return decay
+
+
+def get_inverse_sqrt_schedule(
+    optimizer: Optimizer, num_warmup_steps: int, timescale: int = None, last_epoch: int = -1
+):
+    """
+    Create a schedule with an inverse square-root learning rate, from the initial lr set in the optimizer, after a
+    warmup period which increases lr linearly from 0 to the initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        timescale (`int`, *optional*, defaults to `num_warmup_steps`):
+            Time scale.
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+    # Note: this implementation is adapted from
+    # https://github.com/google-research/big_vision/blob/f071ce68852d56099437004fd70057597a95f6ef/big_vision/utils.py#L930
+
+    if timescale is None:
+        timescale = num_warmup_steps
+
+    lr_lambda = partial(_get_inverse_sqrt_schedule_lr_lambda, num_warmup_steps=num_warmup_steps, timescale=timescale)
+    return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
\ No newline at end of file
--- a/vocoder.pt
+++ b/vocoder.pt
--- a/vocos_pytorch/README.md
+++ b/vocos_pytorch/README.md
+<div align="center">
+
+# Vocos for StableTTS
+
+Modified from the official implementation of [Vocos](https://github.com/gemelo-ai/vocos/tree/main). 
+
+</div>
+
+## Introduction
+
+Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.
+
+
+## Inference
+
+For detailed inference instructions, please refer to `inference.ipynb`
+
+## Training
+
+Setting up and training your model with Vocos is straightforward. Follow these steps to get started:
+
+### Preparing Your Data
+
+1. **Configure Data Settings**: Update the `DataConfig` in `preprocess.py`. Specifically, adjust the audio_dir to point to your collection of audio files.
+
+2. **Run Preprocessing**: Run `preprocess.py`. This script will search (glob) for all audio files in the specified directory, resample them to the target sample_rate (modifiable in config.py), and generate a file list for training.
+
+### Start training
+
+1. **Adjust Training Configuration**: Edit `TrainConfig` in `config.py` to specify the file list path and tweak training hyperparameters to your needs.
+
+2. **Start the Training Process**: Launch `train.py` to begin training your model.
+
+### Experiment with Configurations
+
+Feel free to explore and modify settings in `config.py` to modify the hyperparameters of vocos!
+
+
+## References
+
+[Vocos](https://github.com/gemelo-ai/vocos/tree/main)
\ No newline at end of file