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"""Conformer common arguments.""" |
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def add_arguments_rnn_encoder_common(group): |
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"""Define common arguments for RNN encoder.""" |
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group.add_argument( |
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"--etype", |
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default="blstmp", |
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type=str, |
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choices=[ |
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"lstm", |
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"blstm", |
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"lstmp", |
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"blstmp", |
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"vgglstmp", |
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"vggblstmp", |
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"vgglstm", |
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"vggblstm", |
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"gru", |
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"bgru", |
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"grup", |
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"bgrup", |
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"vgggrup", |
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"vggbgrup", |
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"vgggru", |
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"vggbgru", |
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], |
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help="Type of encoder network architecture", |
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) |
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group.add_argument( |
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"--elayers", |
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default=4, |
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type=int, |
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help="Number of encoder layers", |
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) |
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group.add_argument( |
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"--eunits", |
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"-u", |
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default=300, |
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type=int, |
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help="Number of encoder hidden units", |
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) |
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group.add_argument( |
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"--eprojs", default=320, type=int, help="Number of encoder projection units" |
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) |
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group.add_argument( |
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"--subsample", |
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default="1", |
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type=str, |
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help="Subsample input frames x_y_z means " |
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"subsample every x frame at 1st layer, " |
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"every y frame at 2nd layer etc.", |
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) |
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return group |
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def add_arguments_rnn_decoder_common(group): |
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"""Define common arguments for RNN decoder.""" |
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group.add_argument( |
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"--dtype", |
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default="lstm", |
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type=str, |
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choices=["lstm", "gru"], |
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help="Type of decoder network architecture", |
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) |
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group.add_argument( |
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"--dlayers", default=1, type=int, help="Number of decoder layers" |
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) |
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group.add_argument( |
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"--dunits", default=320, type=int, help="Number of decoder hidden units" |
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) |
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group.add_argument( |
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"--dropout-rate-decoder", |
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default=0.0, |
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type=float, |
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help="Dropout rate for the decoder", |
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) |
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group.add_argument( |
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"--sampling-probability", |
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default=0.0, |
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type=float, |
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help="Ratio of predicted labels fed back to decoder", |
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) |
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group.add_argument( |
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"--lsm-type", |
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const="", |
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default="", |
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type=str, |
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nargs="?", |
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choices=["", "unigram"], |
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help="Apply label smoothing with a specified distribution type", |
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) |
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return group |
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def add_arguments_rnn_attention_common(group): |
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"""Define common arguments for RNN attention.""" |
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group.add_argument( |
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"--atype", |
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default="dot", |
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type=str, |
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choices=[ |
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"noatt", |
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"dot", |
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"add", |
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"location", |
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"coverage", |
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"coverage_location", |
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"location2d", |
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"location_recurrent", |
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"multi_head_dot", |
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"multi_head_add", |
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"multi_head_loc", |
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"multi_head_multi_res_loc", |
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], |
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help="Type of attention architecture", |
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) |
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group.add_argument( |
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"--adim", |
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default=320, |
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type=int, |
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help="Number of attention transformation dimensions", |
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) |
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group.add_argument( |
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"--awin", default=5, type=int, help="Window size for location2d attention" |
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) |
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group.add_argument( |
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"--aheads", |
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default=4, |
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type=int, |
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help="Number of heads for multi head attention", |
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) |
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group.add_argument( |
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"--aconv-chans", |
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default=-1, |
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type=int, |
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help="Number of attention convolution channels \ |
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(negative value indicates no location-aware attention)", |
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) |
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group.add_argument( |
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"--aconv-filts", |
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default=100, |
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type=int, |
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help="Number of attention convolution filters \ |
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(negative value indicates no location-aware attention)", |
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) |
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group.add_argument( |
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"--dropout-rate", |
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default=0.0, |
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type=float, |
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help="Dropout rate for the encoder", |
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) |
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return group |
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