#!/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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import librosa
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from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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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.types import str2triple_str
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from funasr.bin.ss_infer import SpeechSeparator
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def inference_ss(
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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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ss_infer_config: Optional[str],
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ss_model_file: Optional[str],
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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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num_spks: int = 2,
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sample_rate: int = 8000,
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param_dict: dict = 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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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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batch_size = 1
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build speech separator
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speech_separator_kwargs = dict(
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ss_infer_config=ss_infer_config,
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ss_model_file=ss_model_file,
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device=device,
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dtype=dtype,
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)
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logging.info("speech_separator_kwargs: {}".format(speech_separator_kwargs))
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speech_separator = SpeechSeparator(**speech_separator_kwargs)
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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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):
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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="ss",
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preprocess_args=None,
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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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fs=fs,
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batch_size=batch_size,
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num_workers=num_workers,
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)
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# 4 .Start for-loop
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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if not os.path.exists(output_path):
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cmd = 'mkdir -p ' + output_path
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os.system(cmd)
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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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# do speech separation
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logging.info('decoding: {}'.format(keys[0]))
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ss_results = speech_separator(**batch)
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for spk in range(num_spks):
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# sf.write(os.path.join(output_path, keys[0] + '_s' + str(spk+1)+'.wav'), ss_results[spk], sample_rate)
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try:
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librosa.output.write_wav(os.path.join(output_path, keys[0] + '_s' + str(spk+1)+'.wav'), ss_results[spk], sample_rate)
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except:
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print("To write wav by librosa, you should install librosa<=0.8.0")
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raise
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torch.cuda.empty_cache()
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return ss_results
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "mossformer":
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return inference_ss(**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="Speech Separator Decoding",
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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=True)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=1,
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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="2",
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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 = parser.add_argument_group("The model configuration related")
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group.add_argument(
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"--ss_infer_config",
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type=str,
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help="SS infer configuration",
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)
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group.add_argument(
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"--ss_model_file",
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type=str,
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help="SS model parameter file",
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)
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group.add_argument(
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"--ss_train_config",
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type=str,
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help="SS training configuration",
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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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parser.add_argument(
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'--num-spks', dest='num_spks', type=int, default=2)
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parser.add_argument(
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'--one-time-decode-length', dest='one_time_decode_length', type=int,
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default=60, help='the max length (second) for one-time decoding')
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parser.add_argument(
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'--decode-window', dest='decode_window', type=int,
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default=1, help='segmental decoding window length (second)')
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parser.add_argument(
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'--sample-rate', dest='sample_rate', type=int, default='8000')
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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="mossformer",
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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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