# network architecture
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# encoder related
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encoder: data2vec_encoder
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encoder_conf:
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extractor_mode: layer_norm
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encoder_layerdrop: 0.1
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dropout_input: 0.0
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dropout_features: 0.0
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feature_grad_mult: 0.0
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encoder_embed_dim: 768
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mask_prob: 0.65
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mask_length: 10
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loss_beta: 0
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loss_scale: null
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instance_norm_target_layer: true
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average_top_k_layers: 8
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pos_conv_depth: 5
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conv_pos: 95
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ema_decay: 0.999
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ema_end_decay: 0.9999
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ema_anneal_end_step: 30000
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ema_transformer_only: true
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ema_layers_only: true
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require_same_masks: true
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mask_dropout: 0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 1.0
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# for logger
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log_interval: 50
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# minibatch related
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batch_type: length
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batch_bins: 16000
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num_workers: 16
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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: none
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max_epoch: 50
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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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- cer_ctc
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- min
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keep_nbest_models: 10
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unused_parameters: true
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normalize: None
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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: 0.00005
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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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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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- 30
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num_freq_mask: 2
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apply_time_mask: true
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time_mask_width_range:
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- 0
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- 40
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num_time_mask: 2
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