# -*- encoding: utf-8 -*-
|
|
import os.path
|
from pathlib import Path
|
from typing import List, Union, Tuple
|
import numpy as np
|
|
from .utils.utils import (ONNXRuntimeError,
|
OrtInferSession, get_logger,
|
read_yaml)
|
from .utils.utils import (TokenIDConverter, split_to_mini_sentence,code_mix_split_words)
|
logging = get_logger()
|
|
|
class CT-Transformer():
|
def __init__(self, model_dir: Union[str, Path] = None,
|
batch_size: int = 1,
|
device_id: Union[str, int] = "-1",
|
quantize: bool = False,
|
intra_op_num_threads: int = 4
|
):
|
|
if not Path(model_dir).exists():
|
raise FileNotFoundError(f'{model_dir} does not exist.')
|
|
model_file = os.path.join(model_dir, 'model.onnx')
|
if quantize:
|
model_file = os.path.join(model_dir, 'model_quant.onnx')
|
config_file = os.path.join(model_dir, 'punc.yaml')
|
config = read_yaml(config_file)
|
|
self.converter = TokenIDConverter(config['token_list'])
|
self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
|
self.batch_size = 1
|
self.punc_list = config['punc_list']
|
self.period = 0
|
for i in range(len(self.punc_list)):
|
if self.punc_list[i] == ",":
|
self.punc_list[i] = "，"
|
elif self.punc_list[i] == "?":
|
self.punc_list[i] = "？"
|
elif self.punc_list[i] == "。":
|
self.period = i
|
|
def __call__(self, text: Union[list, str], split_size=20):
|
split_text = code_mix_split_words(text)
|
split_text_id = self.converter.tokens2ids(split_text)
|
mini_sentences = split_to_mini_sentence(split_text, split_size)
|
mini_sentences_id = split_to_mini_sentence(split_text_id, split_size)
|
assert len(mini_sentences) == len(mini_sentences_id)
|
cache_sent = []
|
cache_sent_id = []
|
new_mini_sentence = ""
|
new_mini_sentence_punc = []
|
cache_pop_trigger_limit = 200
|
for mini_sentence_i in range(len(mini_sentences)):
|
mini_sentence = mini_sentences[mini_sentence_i]
|
mini_sentence_id = mini_sentences_id[mini_sentence_i]
|
mini_sentence = cache_sent + mini_sentence
|
mini_sentence_id = np.array(cache_sent_id + mini_sentence_id, dtype='int64')
|
data = {
|
"text": mini_sentence_id[None,:],
|
"text_lengths": np.array([len(mini_sentence_id)], dtype='int32'),
|
}
|
try:
|
outputs = self.infer(data['text'], data['text_lengths'])
|
y = outputs[0]
|
punctuations = np.argmax(y,axis=-1)[0]
|
assert punctuations.size == len(mini_sentence)
|
except ONNXRuntimeError:
|
logging.warning("error")
|
|
# Search for the last Period/QuestionMark as cache
|
if mini_sentence_i < len(mini_sentences) - 1:
|
sentenceEnd = -1
|
last_comma_index = -1
|
for i in range(len(punctuations) - 2, 1, -1):
|
if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
|
sentenceEnd = i
|
break
|
if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
|
last_comma_index = i
|
|
if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
|
# The sentence it too long, cut off at a comma.
|
sentenceEnd = last_comma_index
|
punctuations[sentenceEnd] = self.period
|
cache_sent = mini_sentence[sentenceEnd + 1:]
|
cache_sent_id = mini_sentence_id[sentenceEnd + 1:].tolist()
|
mini_sentence = mini_sentence[0:sentenceEnd + 1]
|
punctuations = punctuations[0:sentenceEnd + 1]
|
|
new_mini_sentence_punc += [int(x) for x in punctuations]
|
words_with_punc = []
|
for i in range(len(mini_sentence)):
|
if i > 0:
|
if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
|
mini_sentence[i] = " " + mini_sentence[i]
|
words_with_punc.append(mini_sentence[i])
|
if self.punc_list[punctuations[i]] != "_":
|
words_with_punc.append(self.punc_list[punctuations[i]])
|
new_mini_sentence += "".join(words_with_punc)
|
# Add Period for the end of the sentence
|
new_mini_sentence_out = new_mini_sentence
|
new_mini_sentence_punc_out = new_mini_sentence_punc
|
if mini_sentence_i == len(mini_sentences) - 1:
|
if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、":
|
new_mini_sentence_out = new_mini_sentence[:-1] + "。"
|
new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
|
elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？":
|
new_mini_sentence_out = new_mini_sentence + "。"
|
new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
|
return new_mini_sentence_out, new_mini_sentence_punc_out
|
|
def infer(self, feats: np.ndarray,
|
feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
|
outputs = self.ort_infer([feats, feats_len])
|
return outputs
|
