雾聪
2023-08-10 ffb05b9ae7eccc47416e9e7fae9dea54d400a245
funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py
@@ -10,7 +10,7 @@
from .utils.utils import (ONNXRuntimeError,
                          OrtInferSession, get_logger,
                          read_yaml)
from .utils.utils import (TokenIDConverter, split_to_mini_sentence,code_mix_split_words)
from .utils.utils import (TokenIDConverter, split_to_mini_sentence,code_mix_split_words,code_mix_split_words_jieba)
logging = get_logger()
@@ -65,9 +65,18 @@
                self.punc_list[i] = "?"
            elif self.punc_list[i] == "。":
                self.period = i
        if "seg_jieba" in config:
            self.seg_jieba = True
            self.jieba_usr_dict_path = os.path.join(model_dir, 'jieba_usr_dict')
            self.code_mix_split_words_jieba = code_mix_split_words_jieba(self.jieba_usr_dict_path)
        else:
            self.seg_jieba = False
    def __call__(self, text: Union[list, str], split_size=20):
        split_text = code_mix_split_words(text)
        if self.seg_jieba:
            split_text = self.code_mix_split_words_jieba(text)
        else:
            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)
@@ -186,11 +195,12 @@
            mini_sentence = cache_sent + mini_sentence
            mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0,dtype='int32')
            text_length = len(mini_sentence_id)
            vad_mask = self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32)
            data = {
                "input": mini_sentence_id[None,:],
                "text_lengths": np.array([text_length], dtype='int32'),
                "vad_mask": self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32),
                "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
                "vad_mask": vad_mask,
                "sub_masks": vad_mask
            }
            try:
                outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"])