Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

游雁

2023-08-30 c2e4e3c2e9be855277d9f4fa9cd0544892ff829a

 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()


@@ -24,15 +24,32 @@
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
                 quantize: bool = False,
                 intra_op_num_threads: int = 4
                 intra_op_num_threads: int = 4,
                 cache_dir: str = None,
                 ):

    
        if not Path(model_dir).exists():
            raise FileNotFoundError(f'{model_dir} does not exist.')

            from modelscope.hub.snapshot_download import snapshot_download
            try:
                model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
            except:
                raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
                    model_dir)
    
        model_file = os.path.join(model_dir, 'model.onnx')
        if quantize:
            model_file = os.path.join(model_dir, 'model_quant.onnx')
        if not os.path.exists(model_file):
            print(".onnx is not exist, begin to export onnx")
            from funasr.export.export_model import ModelExport
            export_model = ModelExport(
                cache_dir=cache_dir,
                onnx=True,
                device="cpu",
                quant=quantize,
            )
            export_model.export(model_dir)
            
        config_file = os.path.join(model_dir, 'punc.yaml')
        config = read_yaml(config_file)

@@ -48,9 +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)
@@ -135,9 +161,10 @@
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
                 quantize: bool = False,
                 intra_op_num_threads: int = 4
                 intra_op_num_threads: int = 4,
                 cache_dir: str = None
                 ):
        super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads)
        super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads, cache_dir=cache_dir)

    def __call__(self, text: str, param_dict: map, split_size=20):
        cache_key = "cache"
@@ -168,11 +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"])