| | |
|---|
| | | def seg_tokenize(txt, seg_dict): |
|---|
| | | out_txt = "" |
|---|
| | | for word in txt: |
|---|
| | | word = word.lower() |
|---|
| | | if word in seg_dict: |
|---|
| | | out_txt += seg_dict[word] + " " |
|---|
| | | else: |
|---|
| | |
|---|
| | | 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) |
|---|
| | |
|---|
| | | ) -> 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) |
|---|
| | |
|---|
| | | 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 |
|---|
| | |
|---|
| | | 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 |
|---|
