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zhuyunfeng

2023-05-09 b15db52e4e67da8a133a67e8ffa415386de48b40

 funasr/bin/punctuation_infer_vadrealtime.py

@@ -61,7 +61,7 @@
            text_name="text",
            non_linguistic_symbols=train_args.non_linguistic_symbols,
        )
        print("start decoding!!!")
        

    @torch.no_grad()
    def __call__(self, text: Union[list, str], cache: list, split_size=20):
@@ -70,7 +70,7 @@
        else:
            precache = ""
            cache = []
        data = {"text": precache + text}
        data = {"text": precache + " " + text}
        result = self.preprocessor(data=data, uid="12938712838719")
        split_text = self.preprocessor.pop_split_text_data(result)
        mini_sentences = split_to_mini_sentence(split_text, split_size)
@@ -90,7 +90,7 @@
            data = {
                "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
                "text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
                "vad_indexes": torch.from_numpy(np.array([len(cache)-1], dtype='int32')),
                "vad_indexes": torch.from_numpy(np.array([len(cache)], dtype='int32')),
            }
            data = to_device(data, self.device)
            y, _ = self.wrapped_model(**data)
@@ -203,10 +203,8 @@
    **kwargs,
):
    assert check_argument_types()
    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )
    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"