From 4ace5a95b052d338947fc88809a440ccd55cf6b4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 十一月 2023 16:39:52 +0800
Subject: [PATCH] funasr pages
---
funasr/datasets/large_datasets/utils/tokenize.py | 84 ++++++++++++++++++++++++++++++++++++++++-
1 files changed, 81 insertions(+), 3 deletions(-)
diff --git a/funasr/datasets/large_datasets/utils/tokenize.py b/funasr/datasets/large_datasets/utils/tokenize.py
index 937e144..34a97c1 100644
--- a/funasr/datasets/large_datasets/utils/tokenize.py
+++ b/funasr/datasets/large_datasets/utils/tokenize.py
@@ -1,17 +1,95 @@
#!/usr/bin/env python
+import re
import numpy as np
+from funasr.datasets.large_datasets.utils.hotword_utils import sample_hotword
+
+def forward_segment(text, seg_dict):
+ word_list = []
+ i = 0
+ while i < len(text):
+ longest_word = text[i]
+ for j in range(i + 1, len(text) + 1):
+ word = text[i:j]
+ if word in seg_dict:
+ if len(word) > len(longest_word):
+ longest_word = word
+ word_list.append(longest_word)
+ 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:
+ 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 tokenize(data,
- vocab=None):
+ vocab=None,
+ seg_dict=None,
+ punc_dict=None,
+ bpe_tokenizer=None,
+ hw_config=None):
assert "text" in data
assert isinstance(vocab, dict)
text = data["text"]
token = []
- for x in text:
- if x in vocab:
+ vad = -2
+ if bpe_tokenizer is not None:
+ text = bpe_tokenizer.text2tokens(" ".join(text))
+ if seg_dict is not None:
+ assert isinstance(seg_dict, dict)
+ text = seg_tokenize(text, seg_dict)
+
+ length = len(text)
+ if 'hw_tag' in data:
+ pre_index = None
+ if hw_config['pre_hwlist'] is not None and hw_config['pre_prob'] > 0:
+ # enable preset hotword detect in sampling
+ for hw in hw_config['pre_hwlist']:
+ hw = " ".join(seg_tokenize(hw, seg_dict))
+ _find = " ".join(text).find(hw)
+ if _find != -1:
+ # _find = text[:_find].count(" ") # bpe sometimes
+ pre_index = [_find, _find + max(hw.count(" "), 1)]
+ break
+ hotword_indxs = sample_hotword(length, **hw_config, pre_index=pre_index)
+ data['hotword_indxs'] = hotword_indxs
+ del data['hw_tag']
+ for i in range(length):
+ x = text[i]
+ if i == length-1 and "punc" in data and x.startswith("vad:"):
+ vad = x[4:]
+ if len(vad) == 0:
+ vad = -1
+ else:
+ vad = int(vad)
+ elif x in vocab:
token.append(vocab[x])
else:
token.append(vocab['<unk>'])
+ if "punc" in data and punc_dict is not None:
+ punc_token = []
+ for punc in data["punc"]:
+ if punc in punc_dict:
+ punc_token.append(punc_dict[punc])
+ else:
+ punc_token.append(punc_dict["_"])
+ data["punc"] = np.array(punc_token)
+
data["text"] = np.array(token)
+ if vad is not -2:
+ data["vad_indexes"]=np.array([vad], dtype=np.int64)
return data
--
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