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/vad_inference.py | 84 ------------------------------------------
1 files changed, 0 insertions(+), 84 deletions(-)
diff --git a/funasr/bin/vad_inference.py b/funasr/bin/vad_inference.py
index eb51400..9f1d0f3 100644
--- a/funasr/bin/vad_inference.py
+++ b/funasr/bin/vad_inference.py
@@ -116,90 +116,6 @@
return segments
-#def inference(
-# batch_size: int,
-# ngpu: int,
-# log_level: Union[int, str],
-# data_path_and_name_and_type,
-# vad_infer_config: Optional[str],
-# vad_model_file: Optional[str],
-# vad_cmvn_file: Optional[str] = None,
-# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
-# key_file: Optional[str] = None,
-# allow_variable_data_keys: bool = False,
-# output_dir: Optional[str] = None,
-# dtype: str = "float32",
-# seed: int = 0,
-# num_workers: int = 1,
-# fs: Union[dict, int] = 16000,
-# **kwargs,
-#):
-# assert check_argument_types()
-# if batch_size > 1:
-# raise NotImplementedError("batch decoding 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 speech2vadsegment
-# speech2vadsegment_kwargs = dict(
-# vad_infer_config=vad_infer_config,
-# vad_model_file=vad_model_file,
-# vad_cmvn_file=vad_cmvn_file,
-# device=device,
-# dtype=dtype,
-# )
-# logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
-# speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
-# # 3. Build data-iterator
-# loader = VADTask.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=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
-# collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_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
-# if output_dir is not None:
-# writer = DatadirWriter(output_dir)
-# else:
-# writer = None
-#
-# vad_results = []
-# 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")}
-#
-# # do vad segment
-# results = speech2vadsegment(**batch)
-# for i, _ in enumerate(keys):
-# item = {'key': keys[i], 'value': results[i]}
-# vad_results.append(item)
-#
-# return vad_results
def inference(
--
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