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https://github.com/modelscope/FunASR
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288 lines
9.2 KiB
Python
288 lines
9.2 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import argparse
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import logging
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import os
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import sys
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from typing import Optional
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from typing import Union
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import numpy as np
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import torch
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from funasr.bin.tp_infer import Speech2Timestamp
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from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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from funasr.datasets.preprocessor import LMPreprocessor
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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def inference_tp(
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batch_size: int,
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ngpu: int,
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log_level: Union[int, str],
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# data_path_and_name_and_type,
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timestamp_infer_config: Optional[str],
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timestamp_model_file: Optional[str],
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timestamp_cmvn_file: Optional[str] = None,
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# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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key_file: Optional[str] = None,
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allow_variable_data_keys: bool = False,
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output_dir: Optional[str] = None,
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dtype: str = "float32",
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seed: int = 0,
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num_workers: int = 1,
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split_with_space: bool = True,
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seg_dict_file: Optional[str] = None,
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**kwargs,
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):
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ncpu = kwargs.get("ncpu", 1)
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torch.set_num_threads(ncpu)
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if batch_size > 1:
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raise NotImplementedError("batch decoding is not implemented")
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if ngpu > 1:
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raise NotImplementedError("only single GPU decoding is supported")
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build speech2vadsegment
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speechtext2timestamp_kwargs = dict(
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timestamp_infer_config=timestamp_infer_config,
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timestamp_model_file=timestamp_model_file,
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timestamp_cmvn_file=timestamp_cmvn_file,
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device=device,
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dtype=dtype,
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)
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logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
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speechtext2timestamp = Speech2Timestamp(**speechtext2timestamp_kwargs)
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preprocessor = LMPreprocessor(
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train=False,
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token_type=speechtext2timestamp.tp_train_args.token_type,
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token_list=speechtext2timestamp.tp_train_args.token_list,
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bpemodel=None,
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text_cleaner=None,
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g2p_type=None,
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text_name="text",
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non_linguistic_symbols=speechtext2timestamp.tp_train_args.non_linguistic_symbols,
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split_with_space=split_with_space,
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seg_dict_file=seg_dict_file,
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)
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if output_dir is not None:
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writer = DatadirWriter(output_dir)
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tp_writer = writer[f"timestamp_prediction"]
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# ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
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else:
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tp_writer = None
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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output_dir_v2: Optional[str] = None,
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fs: dict = None,
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param_dict: dict = None,
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**kwargs
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):
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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writer = None
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if output_path is not None:
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writer = DatadirWriter(output_path)
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tp_writer = writer[f"timestamp_prediction"]
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else:
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tp_writer = None
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# 3. Build data-iterator
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if data_path_and_name_and_type is None and raw_inputs is not None:
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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loader = build_streaming_iterator(
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task_name="asr",
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preprocess_args=speechtext2timestamp.tp_train_args,
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data_path_and_name_and_type=data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=preprocessor,
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)
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tp_result_list = []
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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logging.info("timestamp predicting, utt_id: {}".format(keys))
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_batch = {'speech': batch['speech'],
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'speech_lengths': batch['speech_lengths'],
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'text_lengths': batch['text_lengths']}
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us_alphas, us_cif_peak = speechtext2timestamp(**_batch)
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for batch_id in range(_bs):
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key = keys[batch_id]
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token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
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ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token,
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force_time_shift=-3.0)
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logging.warning(ts_str)
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item = {'key': key, 'value': ts_str, 'timestamp': ts_list}
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if tp_writer is not None:
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tp_writer["tp_sync"][key + '#'] = ts_str
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tp_writer["tp_time"][key + '#'] = str(ts_list)
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tp_result_list.append(item)
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return tp_result_list
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "tp_norm":
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return inference_tp(**kwargs)
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else:
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Timestamp Prediction Inference",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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# Note(kamo): Use '_' instead of '-' as separator.
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# '-' is confusing if written in yaml.
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=False)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument(
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"--njob",
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type=int,
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default=1,
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help="The number of jobs for each gpu",
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)
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parser.add_argument(
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"--gpuid_list",
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type=str,
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default="",
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help="The visible gpus",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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required=True,
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action="append",
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)
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group.add_argument("--key_file", type=str_or_none)
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group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument(
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"--timestamp_infer_config",
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type=str,
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help="VAD infer configuration",
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)
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group.add_argument(
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"--timestamp_model_file",
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type=str,
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help="VAD model parameter file",
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)
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group.add_argument(
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"--timestamp_cmvn_file",
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type=str,
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help="Global CMVN file",
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)
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group = parser.add_argument_group("The inference configuration related")
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group.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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return parser
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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parser.add_argument(
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"--mode",
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type=str,
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default="tp_norm",
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help="The decoding mode",
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)
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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kwargs.pop("config", None)
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# set logging messages
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logging.basicConfig(
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level=args.log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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logging.info("Decoding args: {}".format(kwargs))
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# gpu setting
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if args.ngpu > 0:
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jobid = int(args.output_dir.split(".")[-1])
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gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
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inference_pipeline = inference_launch(**kwargs)
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return inference_pipeline(kwargs["data_path_and_name_and_type"])
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if __name__ == "__main__":
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main()
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