#!/usr/bin/env python
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import re
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import numpy as np
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from funasr.datasets.large_datasets.utils.hotword_utils import sample_hotword
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def forward_segment(text, seg_dict):
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word_list = []
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i = 0
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while i < len(text):
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longest_word = text[i]
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for j in range(i + 1, len(text) + 1):
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word = text[i:j]
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if word in seg_dict:
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if len(word) > len(longest_word):
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longest_word = word
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word_list.append(longest_word)
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i += len(longest_word)
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return word_list
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def seg_tokenize(txt, seg_dict):
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pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
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out_txt = ""
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for word in txt:
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word = word.lower()
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if word in seg_dict:
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out_txt += seg_dict[word] + " "
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else:
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if pattern.match(word):
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for char in word:
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if char in seg_dict:
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out_txt += seg_dict[char] + " "
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else:
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out_txt += "<unk>" + " "
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else:
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out_txt += "<unk>" + " "
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return out_txt.strip().split()
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def tokenize(data,
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vocab=None,
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seg_dict=None,
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punc_dict=None,
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bpe_tokenizer=None,
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hw_config=None):
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assert "text" in data
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assert isinstance(vocab, dict)
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text = data["text"]
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token = []
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vad = -2
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if bpe_tokenizer is not None:
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text = bpe_tokenizer.text2tokens(" ".join(text))
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if seg_dict is not None:
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assert isinstance(seg_dict, dict)
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text = seg_tokenize(text, seg_dict)
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length = len(text)
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if 'hw_tag' in data:
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pre_index = None
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if hw_config['pre_hwlist'] is not None and hw_config['pre_prob'] > 0:
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# enable preset hotword detect in sampling
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for hw in hw_config['pre_hwlist']:
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hw = " ".join(seg_tokenize(hw, seg_dict))
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_find = " ".join(text).find(hw)
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if _find != -1:
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# _find = text[:_find].count(" ") # bpe sometimes
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pre_index = [_find, _find + max(hw.count(" "), 1)]
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break
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hotword_indxs = sample_hotword(length, **hw_config, pre_index=pre_index)
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data['hotword_indxs'] = hotword_indxs
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del data['hw_tag']
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for i in range(length):
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x = text[i]
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if i == length-1 and "punc" in data and x.startswith("vad:"):
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vad = x[4:]
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if len(vad) == 0:
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vad = -1
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else:
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vad = int(vad)
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elif x in vocab:
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token.append(vocab[x])
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else:
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token.append(vocab['<unk>'])
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if "punc" in data and punc_dict is not None:
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punc_token = []
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for punc in data["punc"]:
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if punc in punc_dict:
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punc_token.append(punc_dict[punc])
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else:
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punc_token.append(punc_dict["_"])
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data["punc"] = np.array(punc_token)
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data["text"] = np.array(token)
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if vad is not -2:
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data["vad_indexes"]=np.array([vad], dtype=np.int64)
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return data
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