Use shuffled LibriSpeech cuts instead

egs/librispeech/ASR/conformer_ctc3/train.py

@@ -952,10 +952,19 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+        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()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/conformer_mmi/train.py

@@ -771,10 +771,20 @@ def run(rank, world_size, args):
         valid_ali = None
 
     librispeech = LibriSpeechAsrDataModule(args)
-    train_cuts = librispeech.train_clean_100_cuts()
+
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+        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()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/lstm_transducer_stateless3/train.py

@@ -989,10 +989,19 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+        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()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/pruned2_knowledge/train.py

@@ -817,10 +817,19 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+        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()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -1038,13 +1038,26 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
+    assert not (
+        params.mini_libri and params.full_libri
+    ), f"Cannot set both mini-libri and full-libri flags to True, now mini-libri {params.mini_libri} and full-libri {params.full_libri}"
+
     if params.mini_libri:
         train_cuts = librispeech.train_clean_5_cuts()
     else:
-        train_cuts = librispeech.train_clean_100_cuts()
         if params.full_libri:
-            train_cuts += librispeech.train_clean_360_cuts()
-            train_cuts += librispeech.train_other_500_cuts()
+            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()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py

@@ -1150,10 +1150,15 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeech(manifest_dir=args.manifest_dir)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+        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()
 
     train_cuts = filter_short_and_long_utterances(train_cuts, sp)
 

egs/librispeech/ASR/zipformer/train.py

@@ -1174,10 +1174,19 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+        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()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/zipformer_mmi/train.py

@@ -990,11 +990,13 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    # train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        # train_cuts += librispeech.train_clean_360_cuts()
-        # train_cuts += librispeech.train_other_500_cuts()
         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()