# network architecture
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# encoder related
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encoder: branchformer
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encoder_conf:
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output_size: 256
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use_attn: true
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attention_heads: 4
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attention_layer_type: rel_selfattn
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pos_enc_layer_type: rel_pos
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rel_pos_type: latest
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use_cgmlp: true
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cgmlp_linear_units: 2048
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cgmlp_conv_kernel: 31
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use_linear_after_conv: false
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gate_activation: identity
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merge_method: concat
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cgmlp_weight: 0.5 # used only if merge_method is "fixed_ave"
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attn_branch_drop_rate: 0.0 # used only if merge_method is "learned_ave"
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num_blocks: 24
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.1
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input_layer: conv2d
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stochastic_depth_rate: 0.0
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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.
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src_attention_dropout_rate: 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: numel
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batch_bins: 25000000
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# optimization related
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accum_grad: 1
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grad_clip: 5
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max_epoch: 60
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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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optim: adam
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optim_conf:
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lr: 0.001
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weight_decay: 0.000001
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 35000
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num_workers: 4 # num of workers of data loader
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use_amp: true # automatic mixed precision
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unused_parameters: false # set as true if some params are unused in DDP
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specaug: specaug
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specaug_conf:
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apply_time_warp: true
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time_warp_window: 5
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time_warp_mode: bicubic
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apply_freq_mask: true
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freq_mask_width_range:
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- 0
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- 27
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num_freq_mask: 2
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apply_time_mask: true
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time_mask_width_ratio_range:
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- 0.
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- 0.05
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num_time_mask: 10
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