From a8fa75b81f2d5b12cd4dc7eb2bb7d989078bc840 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 06 二月 2023 10:55:22 +0800
Subject: [PATCH] readme
---
funasr/bin/asr_inference_uniasr.py | 144 ------------------------------------------------
1 files changed, 0 insertions(+), 144 deletions(-)
diff --git a/funasr/bin/asr_inference_uniasr.py b/funasr/bin/asr_inference_uniasr.py
index 8a99058..cfec9a0 100644
--- a/funasr/bin/asr_inference_uniasr.py
+++ b/funasr/bin/asr_inference_uniasr.py
@@ -272,150 +272,6 @@
return results
-# def inference(
-# maxlenratio: float,
-# minlenratio: float,
-# batch_size: int,
-# beam_size: int,
-# ngpu: int,
-# ctc_weight: float,
-# lm_weight: float,
-# penalty: float,
-# log_level: Union[int, str],
-# data_path_and_name_and_type,
-# asr_train_config: Optional[str],
-# asr_model_file: Optional[str],
-# ngram_file: Optional[str] = None,
-# cmvn_file: Optional[str] = None,
-# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
-# lm_train_config: Optional[str] = None,
-# lm_file: Optional[str] = None,
-# token_type: Optional[str] = None,
-# key_file: Optional[str] = None,
-# word_lm_train_config: Optional[str] = None,
-# bpemodel: Optional[str] = None,
-# allow_variable_data_keys: bool = False,
-# streaming: bool = False,
-# output_dir: Optional[str] = None,
-# dtype: str = "float32",
-# seed: int = 0,
-# ngram_weight: float = 0.9,
-# nbest: int = 1,
-# num_workers: int = 1,
-# token_num_relax: int = 1,
-# decoding_ind: int = 0,
-# decoding_mode: str = "model1",
-# **kwargs,
-# ):
-# assert check_argument_types()
-# if batch_size > 1:
-# raise NotImplementedError("batch decoding is not implemented")
-# if word_lm_train_config is not None:
-# raise NotImplementedError("Word LM is not implemented")
-# if ngpu > 1:
-# raise NotImplementedError("only single GPU decoding is supported")
-#
-# logging.basicConfig(
-# level=log_level,
-# format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
-# )
-#
-# if ngpu >= 1 and torch.cuda.is_available():
-# device = "cuda"
-# else:
-# device = "cpu"
-#
-# # 1. Set random-seed
-# set_all_random_seed(seed)
-#
-# # 2. Build speech2text
-# speech2text_kwargs = dict(
-# asr_train_config=asr_train_config,
-# asr_model_file=asr_model_file,
-# cmvn_file=cmvn_file,
-# lm_train_config=lm_train_config,
-# lm_file=lm_file,
-# ngram_file=ngram_file,
-# token_type=token_type,
-# bpemodel=bpemodel,
-# device=device,
-# maxlenratio=maxlenratio,
-# minlenratio=minlenratio,
-# dtype=dtype,
-# beam_size=beam_size,
-# ctc_weight=ctc_weight,
-# lm_weight=lm_weight,
-# ngram_weight=ngram_weight,
-# penalty=penalty,
-# nbest=nbest,
-# streaming=streaming,
-# token_num_relax=token_num_relax,
-# decoding_ind=decoding_ind,
-# decoding_mode=decoding_mode,
-# )
-# speech2text = Speech2Text(**speech2text_kwargs)
-#
-# # 3. Build data-iterator
-# loader = ASRTask.build_streaming_iterator(
-# data_path_and_name_and_type,
-# dtype=dtype,
-# batch_size=batch_size,
-# key_file=key_file,
-# num_workers=num_workers,
-# preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-# collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-# allow_variable_data_keys=allow_variable_data_keys,
-# inference=True,
-# )
-#
-# finish_count = 0
-# file_count = 1
-# # 7 .Start for-loop
-# # FIXME(kamo): The output format should be discussed about
-# asr_result_list = []
-# if output_dir is not None:
-# writer = DatadirWriter(output_dir)
-# else:
-# writer = None
-#
-# for keys, batch in loader:
-# assert isinstance(batch, dict), type(batch)
-# assert all(isinstance(s, str) for s in keys), keys
-# _bs = len(next(iter(batch.values())))
-# assert len(keys) == _bs, f"{len(keys)} != {_bs}"
-# #batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
-#
-# # N-best list of (text, token, token_int, hyp_object)
-# try:
-# results = speech2text(**batch)
-# except TooShortUttError as e:
-# logging.warning(f"Utterance {keys} {e}")
-# hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
-# results = [[" ", ["sil"], [2], hyp]] * nbest
-#
-# # Only supporting batch_size==1
-# key = keys[0]
-# logging.info(f"Utterance: {key}")
-# for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
-# # Create a directory: outdir/{n}best_recog
-# if writer is not None:
-# ibest_writer = writer[f"{n}best_recog"]
-#
-# # Write the result to each file
-# ibest_writer["token"][key] = " ".join(token)
-# ibest_writer["token_int"][key] = " ".join(map(str, token_int))
-# ibest_writer["score"][key] = str(hyp.score)
-#
-# if text is not None:
-# text_postprocessed = postprocess_utils.sentence_postprocess(token)
-# item = {'key': key, 'value': text_postprocessed}
-# asr_result_list.append(item)
-# finish_count += 1
-# asr_utils.print_progress(finish_count / file_count)
-# if writer is not None:
-# ibest_writer["text"][key] = text
-# return asr_result_list
-
def inference(
maxlenratio: float,
minlenratio: float,
--
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