diff --git a/egs/multi_ja_en/ASR/local/utils/asr_datamodule.py b/egs/multi_ja_en/ASR/local/utils/asr_datamodule.py
index 3995f21ee..581ef82a4 100644
--- a/egs/multi_ja_en/ASR/local/utils/asr_datamodule.py
+++ b/egs/multi_ja_en/ASR/local/utils/asr_datamodule.py
@@ -38,6 +38,12 @@ from torch.utils.data import DataLoader
 
 from icefall.utils import str2bool
 
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
 
 class MultiDatasetAsrDataModule:
     """
@@ -192,7 +198,23 @@ class MultiDatasetAsrDataModule:
 
         transforms = []
         input_transforms = []
+        if self.args.enable_musan:
+            logging.info("Enable MUSAN")
+            logging.info("About to get Musan cuts")
+            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+            transforms.append(CutMix(cuts=cuts_musan, p=0.5, snr=(10,20), preserve_id=True)
+        else:
+            logging.info("Disable MUSAN")
 
+        if self.args.concatenate_cuts:
+            logging.info(
+                f"Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap)
+            ] + transforms
+        
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
             logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
@@ -245,11 +267,13 @@ class MultiDatasetAsrDataModule:
 
         if self.args.bucketing_sampler:
             logging.info("Using DynamicBucketingSampler.")
-            train_sampler = DynamicBucketingSampler(
+            train_sampler = DynamicBucketingSampler( #added several new params here
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
+                buffer_size=self.args.num_buckers * 2000,
+                shuffle_buffer_size=self.args.num_buckers * 5000,
                 drop_last=self.args.drop_last,
             )
         else:
@@ -265,12 +289,16 @@ class MultiDatasetAsrDataModule:
             logging.info("Loading sampler state dict")
             train_sampler.load_state_dict(sampler_state_dict)
 
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
         train_dl = DataLoader(
             train,
             sampler=train_sampler,
             batch_size=None,
             num_workers=self.args.num_workers,
-            persistent_workers=False,
+            persistent_workers=True,
+            worker_init_fn=worker_init_fn, #changed bottom 2 params
         )
 
         return train_dl
@@ -332,24 +360,3 @@ class MultiDatasetAsrDataModule:
             num_workers=self.args.num_workers,
         )
         return test_dl
-
-    # @lru_cache()
-    # def train_cuts(self) -> CutSet:
-    #     logging.info("About to get train cuts")
-    #     return load_manifest_lazy(
-    #         self.args.manifest_dir / "reazonspeech_cuts_train.jsonl.gz"
-    #     )
-
-    # @lru_cache()
-    # def valid_cuts(self) -> CutSet:
-    #     logging.info("About to get dev cuts")
-    #     return load_manifest_lazy(
-    #         self.args.manifest_dir / "reazonspeech_cuts_dev.jsonl.gz"
-    #     )
-
-    # @lru_cache()
-    # def test_cuts(self) -> List[CutSet]:
-    #     logging.info("About to get test cuts")
-    #     return load_manifest_lazy(
-    #         self.args.manifest_dir / "reazonspeech_cuts_test.jsonl.gz"
-    #     )