leave the old code in comments for reference

egs/librispeech/ASR/conformer_ctc3/train.py

@@ -954,6 +954,15 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts
+        # strictly speaking, shuffled training cuts should be used instead
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
+
+        # train_cuts = librispeech.train_clean_100_cuts()
+        # train_cuts += librispeech.train_clean_360_cuts()
+        # train_cuts += librispeech.train_other_500_cuts()
     else:
         train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/conformer_mmi/train.py

@@ -774,6 +774,15 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts,
+        # strictly speaking, shuffled training cuts should be used instead,
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
+
+        # train_cuts = librispeech.train_clean_100_cuts()
+        # train_cuts += librispeech.train_clean_360_cuts()
+        # train_cuts += librispeech.train_other_500_cuts()
     else:
         train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/lstm_transducer_stateless3/train.py

@@ -991,6 +991,15 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts,
+        # strictly speaking, shuffled training cuts should be used instead,
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
+
+        # train_cuts = librispeech.train_clean_100_cuts()
+        # train_cuts += librispeech.train_clean_360_cuts()
+        # train_cuts += librispeech.train_other_500_cuts()
     else:
         train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/pruned2_knowledge/train.py

@@ -819,6 +819,15 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts,
+        # strictly speaking, shuffled training cuts should be used instead,
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
+
+        # train_cuts = librispeech.train_clean_100_cuts()
+        # train_cuts += librispeech.train_clean_360_cuts()
+        # train_cuts += librispeech.train_other_500_cuts()
     else:
         train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -1047,6 +1047,15 @@ def run(rank, world_size, args):
     else:
         if params.full_libri:
             train_cuts = librispeech.train_all_shuf_cuts()
+
+            # previously we used the following code to load all training cuts,
+            # strictly speaking, shuffled training cuts should be used instead,
+            # but we leave the code here to demonstrate that there is an option
+            # like this to combine multiple cutsets
+
+            # train_cuts = librispeech.train_clean_100_cuts()
+            # train_cuts += librispeech.train_clean_360_cuts()
+            # train_cuts += librispeech.train_other_500_cuts()
         else:
             train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py

@@ -1152,6 +1152,11 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts,
+        # strictly speaking, shuffled training cuts should be used instead,
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
     else:
         train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/zipformer/train.py

@@ -1176,6 +1176,15 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts,
+        # strictly speaking, shuffled training cuts should be used instead,
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
+
+        # train_cuts = librispeech.train_clean_100_cuts()
+        # train_cuts += librispeech.train_clean_360_cuts()
+        # train_cuts += librispeech.train_other_500_cuts()
     else:
         train_cuts = librispeech.train_clean_100_cuts()
 

egs/librispeech/ASR/zipformer_mmi/train.py

@@ -992,6 +992,11 @@ def run(rank, world_size, args):
 
     if params.full_libri:
         train_cuts = librispeech.train_all_shuf_cuts()
+
+        # previously we used the following code to load all training cuts,
+        # strictly speaking, shuffled training cuts should be used instead,
+        # but we leave the code here to demonstrate that there is an option
+        # like this to combine multiple cutsets
     else:
         train_cuts = librispeech.train_clean_100_cuts()