Update configs

Signed-off-by: Igor Gitman <[email protected]>

examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_bpe.yaml

@@ -142,6 +142,11 @@ model:
     dropout_emb: 0.0 # The dropout used for embeddings
     dropout_att: 0.1 # The dropout for multi-headed attention modules
 
+    # set to non-zero to enable stochastic depth
+    stochastic_depth_drop_prob: 0.0
+    stochastic_depth_mode: linear  # linear or uniform
+    stochastic_depth_start_layer: 1
+
   decoder:
     _target_: nemo.collections.asr.modules.RNNTDecoder
     normalization_mode: null # Currently only null is supported for export.
@@ -203,6 +208,15 @@ model:
     decoding:
       strategy: "greedy"
 
+  # config for InterCTC loss: https://arxiv.org/abs/2102.03216
+  # specify loss weights and which layers to use for InterCTC
+  # e.g., to reproduce the paper results, set loss_weights: [0.3]
+  # and apply_at_layers: [8] (assuming 18 layers). Note that final
+  # layer loss coefficient is automatically adjusted (to 0.7 in above example)
+  interctc:
+    loss_weights: []
+    apply_at_layers: []
+
   loss:
     loss_name: "default"
 

examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_char.yaml

@@ -136,6 +136,11 @@ model:
     dropout_emb: 0.0 # The dropout used for embeddings
     dropout_att: 0.1 # The dropout for multi-headed attention modules
 
+    # set to non-zero to enable stochastic depth
+    stochastic_depth_drop_prob: 0.0
+    stochastic_depth_mode: linear  # linear or uniform
+    stochastic_depth_start_layer: 1
+
   decoder:
     _target_: nemo.collections.asr.modules.RNNTDecoder
     normalization_mode: null # Currently only null is supported for export.
@@ -197,6 +202,15 @@ model:
     decoding:
       strategy: "greedy"
 
+  # config for InterCTC loss: https://arxiv.org/abs/2102.03216
+  # specify loss weights and which layers to use for InterCTC
+  # e.g., to reproduce the paper results, set loss_weights: [0.3]
+  # and apply_at_layers: [8] (assuming 18 layers). Note that final
+  # layer loss coefficient is automatically adjusted (to 0.7 in above example)
+  interctc:
+    loss_weights: []
+    apply_at_layers: []
+
   loss:
     loss_name: "default"