# network architecture
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# encoder related
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encoder: transformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder architecture type
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normalize_before: true
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# minibatch related
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batch_type: length
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batch_bins: 32000
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num_workers: 8
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# optimization related
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accum_grad: 1
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grad_clip: 5
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patience: 3
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max_epoch: 20
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val_scheduler_criterion:
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- valid
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- acc
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best_model_criterion:
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- - valid
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- acc
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- max
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keep_nbest_models: 10
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# NoamLR is deprecated. Use WarmupLR.
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# The following is equivalent setting for NoamLR:
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#
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# optim: adam
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# optim_conf:
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# lr: 10.
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# scheduler: noamlr
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# scheduler_conf:
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# model_size: 256
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# warmup_steps: 25000
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#
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optim: adam
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optim_conf:
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lr: 0.002
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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log_interval: 50
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normalize: None
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