def get_args():
    ' Get args from stdin.\n\n    The common options are defined in the object\n    libs.nnet3.train.common.CommonParser.parser.\n    See steps/libs/nnet3/train/common.py\n    '
    parser = argparse.ArgumentParser(description='Trains a feed forward raw DNN (without transition model)\n        using frame-level objectives like cross-entropy and mean-squared-error.\n        DNNs include simple DNNs, TDNNs and CNNs.', formatter_class=argparse.ArgumentDefaultsHelpFormatter, conflict_handler='resolve', parents=[common_train_lib.CommonParser(include_chunk_context=False).parser])
    parser.add_argument('--egs.frames-per-eg', type=int, dest='frames_per_eg', default=8, help='Number of output labels per example')
    parser.add_argument('--image.augmentation-opts', type=str, dest='image_augmentation_opts', default=None, help='Image augmentation options')
    parser.add_argument('--trainer.input-model', type=str, dest='input_model', default=None, action=common_lib.NullstrToNoneAction, help='If specified, this model is used as initial\n                        raw model (0.raw in the script) instead of initializing\n                        the model from xconfig. Configs dir is not expected to\n                        exist and left/right context is computed from this\n                        model.')
    parser.add_argument('--trainer.prior-subset-size', type=int, dest='prior_subset_size', default=20000, help='Number of samples for computing priors')
    parser.add_argument('--trainer.num-jobs-compute-prior', type=int, dest='num_jobs_compute_prior', default=10, help='The prior computation jobs are single threaded and run on the CPU')
    parser.add_argument('--trainer.optimization.minibatch-size', type=str, dest='minibatch_size', default='512', help='Size of the minibatch used in SGD training\n                        (argument to nnet3-merge-egs); may be a more general\n                        rule as accepted by the --minibatch-size option of\n                        nnet3-merge-egs; run that program without args to see\n                        the format.')
    parser.add_argument('--compute-average-posteriors', type=str, action=common_lib.StrToBoolAction, choices=['true', 'false'], default=False, help='If true, then the average output of the\n                        network is computed and dumped as post.final.vec')
    parser.add_argument('--nj', type=int, default=4, help='Number of parallel jobs')
    parser.add_argument('--use-dense-targets', type=str, action=common_lib.StrToBoolAction, default=True, choices=['true', 'false'], help='Train neural network using dense targets')
    parser.add_argument('--feat-dir', type=str, required=False, help='Directory with features used for training the neural network.')
    parser.add_argument('--targets-scp', type=str, required=False, help='Targets for training neural network.\n                        This is a kaldi-format SCP file of target matrices.\n                        <utterance-id> <extended-filename-of-target-matrix>.\n                        The target matrix\'s column dim must match \n                        the neural network output dim, and the\n                        row dim must match the number of output frames \n                        i.e. after subsampling if "--frame-subsampling-factor" \n                        option is passed to --egs.opts.')
    parser.add_argument('--vad-egs', type=str, action=common_lib.StrToBoolAction, default=False, choices=['true', 'false'], help='Get nnet3 egs with vad applied on features.')
    parser.add_argument('--dir', type=str, required=True, help='Directory to store the models and all other files.')
    print(' '.join(sys.argv))
    print(sys.argv)
    args = parser.parse_args()
    [args, run_opts] = process_args(args)
    return [args, run_opts]
Get args from stdin.

The common options are defined in the object
libs.nnet3.train.common.CommonParser.parser.
See steps/libs/nnet3/train/common.py
def get_args():
    ' Get args from stdin.\n\n    The common options are defined in the object\n    libs.nnet3.train.common.CommonParser.parser.\n    See steps/libs/nnet3/train/common.py\n    '
    parser = argparse.ArgumentParser(description='Trains a feed forward raw DNN (without transition model)\n        using frame-level objectives like cross-entropy and mean-squared-error.\n        DNNs include simple DNNs, TDNNs and CNNs.', formatter_class=argparse.ArgumentDefaultsHelpFormatter, conflict_handler='resolve', parents=[common_train_lib.CommonParser(include_chunk_context=False).parser])
    parser.add_argument('--egs.frames-per-eg', type=int, dest='frames_per_eg', default=8, help='Number of output labels per example')
    parser.add_argument('--image.augmentation-opts', type=str, dest='image_augmentation_opts', default=None, help='Image augmentation options')
    parser.add_argument('--trainer.input-model', type=str, dest='input_model', default=None, action=common_lib.NullstrToNoneAction, help='If specified, this model is used as initial\n                        raw model (0.raw in the script) instead of initializing\n                        the model from xconfig. Configs dir is not expected to\n                        exist and left/right context is computed from this\n                        model.')
    parser.add_argument('--trainer.prior-subset-size', type=int, dest='prior_subset_size', default=20000, help='Number of samples for computing priors')
    parser.add_argument('--trainer.num-jobs-compute-prior', type=int, dest='num_jobs_compute_prior', default=10, help='The prior computation jobs are single threaded and run on the CPU')
    parser.add_argument('--trainer.optimization.minibatch-size', type=str, dest='minibatch_size', default='512', help='Size of the minibatch used in SGD training\n                        (argument to nnet3-merge-egs); may be a more general\n                        rule as accepted by the --minibatch-size option of\n                        nnet3-merge-egs; run that program without args to see\n                        the format.')
    parser.add_argument('--compute-average-posteriors', type=str, action=common_lib.StrToBoolAction, choices=['true', 'false'], default=False, help='If true, then the average output of the\n                        network is computed and dumped as post.final.vec')
    parser.add_argument('--nj', type=int, default=4, help='Number of parallel jobs')
    parser.add_argument('--use-dense-targets', type=str, action=common_lib.StrToBoolAction, default=True, choices=['true', 'false'], help='Train neural network using dense targets')
    parser.add_argument('--feat-dir', type=str, required=False, help='Directory with features used for training the neural network.')
    parser.add_argument('--targets-scp', type=str, required=False, help='Targets for training neural network.\n                        This is a kaldi-format SCP file of target matrices.\n                        <utterance-id> <extended-filename-of-target-matrix>.\n                        The target matrix\'s column dim must match \n                        the neural network output dim, and the\n                        row dim must match the number of output frames \n                        i.e. after subsampling if "--frame-subsampling-factor" \n                        option is passed to --egs.opts.')
    parser.add_argument('--vad-egs', type=str, action=common_lib.StrToBoolAction, default=False, choices=['true', 'false'], help='Get nnet3 egs with vad applied on features.')
    parser.add_argument('--dir', type=str, required=True, help='Directory to store the models and all other files.')
    print(' '.join(sys.argv))
    print(sys.argv)
    args = parser.parse_args()
    [args, run_opts] = process_args(args)
    return [args, run_opts]