jmwang66
2023-05-09 8dab6d184a034ca86eafa644ea0d2100aadfe27d
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
    return sentences