94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
|
#!/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, seg_dict=None, punc_dict=None, bpe_tokenizer=None, hw_config=None):
|
||
|
|
assert "text" in data
|
||
|
|
assert isinstance(vocab, dict)
|
||
|
|
text = data["text"]
|
||
|
|
token = []
|
||
|
|
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
|