Merge branch 'main' into dev_smohan

yhliang

2023-05-11 1d1ef01b4e23630a99a3be7e9d1dce9550a793e9

 funasr/bin/punctuation_infer_vadrealtime.py

@@ -23,7 +23,7 @@
from funasr.utils import config_argparse
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.punctuation.text_preprocessor import split_to_mini_sentence
from funasr.datasets.preprocessor import split_to_mini_sentence


class Text2Punc:
@@ -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"
@@ -226,7 +224,7 @@
    ):
        results = []
        split_size = 10

        cache_in = param_dict["cache"]
        if raw_inputs != None:
            line = raw_inputs.strip()
            key = "demo"
@@ -234,34 +232,12 @@
                item = {'key': key, 'value': ""}
                results.append(item)
                return results
            result, _, cache = text2punc(line, cache)
            item = {'key': key, 'value': result, 'cache': cache}
            result, _, cache = text2punc(line, cache_in)
            param_dict["cache"] = cache
            item = {'key': key, 'value': result}
            results.append(item)
            return results

        for inference_text, _, _ in data_path_and_name_and_type:
            with open(inference_text, "r", encoding="utf-8") as fin:
                for line in fin:
                    line = line.strip()
                    segs = line.split("\t")
                    if len(segs) != 2:
                        continue
                    key = segs[0]
                    if len(segs[1]) == 0:
                        continue
                    result, _ = text2punc(segs[1])
                    item = {'key': key, 'value': result}
                    results.append(item)
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        if output_path != None:
            output_file_name = "infer.out"
            Path(output_path).mkdir(parents=True, exist_ok=True)
            output_file_path = (Path(output_path) / output_file_name).absolute()
            with open(output_file_path, "w", encoding="utf-8") as fout:
                for item_i in results:
                    key_out = item_i["key"]
                    value_out = item_i["value"]
                    fout.write(f"{key_out}\t{value_out}\n")
        return results

    return _forward