From 8dab6d184a034ca86eafa644ea0d2100aadfe27d Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 09 五月 2023 10:58:33 +0800
Subject: [PATCH] Merge pull request #473 from alibaba-damo-academy/dev_smohan

---
 funasr/datasets/preprocessor.py |   18 +++++++++++++-----
 1 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/funasr/datasets/preprocessor.py b/funasr/datasets/preprocessor.py
index afeff4e..758c750 100644
--- a/funasr/datasets/preprocessor.py
+++ b/funasr/datasets/preprocessor.py
@@ -44,14 +44,22 @@
         i += len(longest_word)
     return word_list
 
-
 def seg_tokenize(txt, seg_dict):
+    pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
     out_txt = ""
     for word in txt:
+        word = word.lower()
         if word in seg_dict:
             out_txt += seg_dict[word] + " "
         else:
-            out_txt += "<unk>" + " "
+            if pattern.match(word):
+                for char in word:
+                    if char in seg_dict:
+                        out_txt += seg_dict[char] + " "
+                    else:
+                        out_txt += "<unk>" + " "
+            else:
+                out_txt += "<unk>" + " "
     return out_txt.strip().split()
 
 def seg_tokenize_wo_pattern(txt, seg_dict):
@@ -359,7 +367,6 @@
             if self.split_with_space:
                 tokens = text.strip().split(" ")
                 if self.seg_dict is not None:
-                    tokens = forward_segment("".join(tokens), self.seg_dict)
                     tokens = seg_tokenize(tokens, self.seg_dict)
             else:
                 tokens = self.tokenizer.text2tokens(text)
@@ -786,6 +793,7 @@
     ) -> Dict[str, np.ndarray]:
         for i in range(self.num_tokenizer):
             text_name = self.text_name[i]
+            #import pdb; pdb.set_trace()
             if text_name in data and self.tokenizer[i] is not None:
                 text = data[text_name]
                 text = self.text_cleaner(text)
@@ -800,7 +808,7 @@
                     data[self.vad_name] = np.array([vad], dtype=np.int64)
                 text_ints = self.token_id_converter[i].tokens2ids(tokens)
                 data[text_name] = np.array(text_ints, dtype=np.int64)
-
+        return data
 
 def split_to_mini_sentence(words: list, word_limit: int = 20):
     assert word_limit > 1
@@ -813,4 +821,4 @@
         sentences.append(words[i * word_limit:(i + 1) * word_limit])
     if length % word_limit > 0:
         sentences.append(words[sentence_len * word_limit:])
-    return sentences
\ No newline at end of file
+    return sentences

--
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