How to use the nemo.utils.argparse.NemoArgParser function in NEMO

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github NVIDIA / NeMo / examples / tts / tacotron2.py View on Github external
def parse_args():
    parser = argparse.ArgumentParser(
        parents=[nm_argparse.NemoArgParser()],
        description='Tacotron2',
        conflict_handler='resolve')
    parser.set_defaults(
        checkpoint_dir=None,
        optimizer="adam",
        batch_size=48,
        eval_batch_size=32,
        lr=0.001,
        amp_opt_level="O0",
        create_tb_writer=True,
        lr_policy=None,
        weight_decay=1e-6
    )

    # Overwrite default args
    parser.add_argument("--max_steps", type=int, default=None, required=False,
github NVIDIA / NeMo / examples / asr / jasper_aishell.py View on Github external
def parse_args():
    parser = argparse.ArgumentParser(
        parents=[nm_argparse.NemoArgParser()],
        description='Jasper Aishell',
        conflict_handler='resolve')

    parser.set_defaults(
        model_config="./configs/jasper12x1SEP.yaml",
        work_dir="./tmp",
        checkpoint_dir="./tmp",
        optimizer="novograd",
        num_epochs=50,
        batch_size=32,
        eval_batch_size=16,
        lr=0.015,
        weight_decay=0.001,
        warmup_steps=8000,
        checkpoint_save_freq=1000,
        train_eval_freq=50,
github NVIDIA / NeMo / examples / asr / experimental / garnet_rnnlm.py View on Github external
def parse_args():
    parser = argparse.ArgumentParser(
        parents=[nm_argparse.NemoArgParser()],
        description='GarNet RnnLM',
        conflict_handler='resolve')
    parser.set_defaults(
        checkpoint_dir=None,
        optimizer="novograd",
        batch_size=32,
        eval_batch_size=32,
        num_epochs=25,
        weight_decay=1e-5,
        lr=0.02,
        amp_opt_level="O1",
        create_tb_writer=True
    )

    # Overwrite default args
    parser.add_argument("--num_epochs", type=int, default=None, required=True,
github NVIDIA / NeMo / examples / tts / waveglow.py View on Github external
def parse_args():
    parser = argparse.ArgumentParser(
        parents=[nm_argparse.NemoArgParser()],
        description='Waveglow',
        conflict_handler='resolve')
    parser.set_defaults(
        checkpoint_dir=None,
        optimizer="adam",
        batch_size=12,
        eval_batch_size=12,
        lr=0.0001,
        amp_opt_level="O1",
        create_tb_writer=True,
        lr_policy=None,
        weight_decay=1e-6
    )

    # Overwrite default args
    parser.add_argument("--max_steps", type=int, default=None, required=False,
github NVIDIA / NeMo / examples / asr / quartznet.py View on Github external
def parse_args():
    parser = argparse.ArgumentParser(
        parents=[nm_argparse.NemoArgParser()],
        description='QuartzNet',
        conflict_handler='resolve')
    parser.set_defaults(
        checkpoint_dir=None,
        optimizer="novograd",
        batch_size=32,
        eval_batch_size=64,
        lr=0.01,
        weight_decay=0.001,
        amp_opt_level="O0",
        create_tb_writer=True
    )

    # Overwrite default args
    parser.add_argument("--num_epochs", type=int, default=None, required=True,
                        help="number of epochs to train. You should specify"
github NVIDIA / NeMo / examples / asr / experimental / garnet.py View on Github external
def parse_args():
    parser = argparse.ArgumentParser(
        parents=[nm_argparse.NemoArgParser()],
        description='GarNet',
        conflict_handler='resolve')
    parser.set_defaults(
        checkpoint_dir=None,
        optimizer="novograd",
        batch_size=32,
        eval_batch_size=32,
        num_epochs=25,
        weight_decay=1e-5,
        lr=0.02,
        amp_opt_level="O1",
        create_tb_writer=True
    )

    # Overwrite default args
    parser.add_argument("--num_epochs", type=int, default=None, required=True,