decoding key

游雁

2024-05-07 46e5beede954594983ccf01344793a76271f028f

decoding key

 funasr/auto/auto_model.py

@@ -107,6 +107,10 @@
class AutoModel:

    def __init__(self, **kwargs):

        log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
        logging.basicConfig(level=log_level)

        if not kwargs.get("disable_log", True):
            tables.print()


 funasr/bin/export.py

@@ -17,9 +17,6 @@
            return cfg_item

    kwargs = to_plain_list(cfg)
    log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())

    logging.basicConfig(level=log_level)

    if kwargs.get("debug", False):
        import pdb

 funasr/bin/inference.py

@@ -16,9 +16,6 @@
            return cfg_item

    kwargs = to_plain_list(cfg)
    log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())

    logging.basicConfig(level=log_level)

    if kwargs.get("debug", False):
        import pdb

 funasr/models/sense_voice/model.py

@@ -803,7 +803,10 @@
                tokenizer=tokenizer,
            )

            if len(kwargs.get("data_type", [])) > 1:
            if (
                isinstance(kwargs.get("data_type", None), (list, tuple))
                and len(kwargs.get("data_type", [])) > 1
            ):
                audio_sample_list, text_token_int_list = audio_sample_list
                text_token_int = text_token_int_list[0]
            else:
@@ -857,7 +860,7 @@
            ys_pad = torch.tensor(sos_int + text_token_int, dtype=torch.int64).to(kwargs["device"])[
                None, :
            ]
            ys_pad_lens = torch.tensor([len(text_token_int)], dtype=torch.int64).to(
            ys_pad_lens = torch.tensor([len(sos_int + text_token_int)], dtype=torch.int64).to(
                kwargs["device"]
            )[None, :]
            decoder_out = self.model.decoder(

 funasr/models/sense_voice/search.py

@@ -370,6 +370,8 @@
            # post process of one iteration
            running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
            # end detection
            # if len(ended_hyps) > 0:
            #     print(f"ended_hyps: {ended_hyps}")
            if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
                logging.info(f"end detected at {i}")
                break