From 8706e767affc6bdc8cb7a67ca3a20a62779ff048 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期三, 17 五月 2023 15:45:46 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
funasr/datasets/preprocessor.py | 28 +++++++++++++++++++++++++---
1 files changed, 25 insertions(+), 3 deletions(-)
diff --git a/funasr/datasets/preprocessor.py b/funasr/datasets/preprocessor.py
index 98cca1d..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)
@@ -800,3 +808,17 @@
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
+ if len(words) <= word_limit:
+ return [words]
+ sentences = []
+ length = len(words)
+ sentence_len = length // word_limit
+ for i in range(sentence_len):
+ sentences.append(words[i * word_limit:(i + 1) * word_limit])
+ if length % word_limit > 0:
+ sentences.append(words[sentence_len * word_limit:])
+ return sentences
--
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