diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py
index 33478c630..a08ebad54 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py
@@ -21,20 +21,20 @@ Usage:
 
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
-./pruned_transducer_stateless2/train.py \
+./pruned_transducer_stateless4/train.py \
   --world-size 4 \
   --num-epochs 30 \
-  --start-epoch 0 \
+  --start-epoch 1 \
   --exp-dir pruned_transducer_stateless2/exp \
   --full-libri 1 \
   --max-duration 300
 
 # For mix precision training:
 
-./pruned_transducer_stateless2/train.py \
+./pruned_transducer_stateless4/train.py \
   --world-size 4 \
   --num-epochs 30 \
-  --start-epoch 0 \
+  --start-epoch 1 \
   --use-fp16 1 \
   --exp-dir pruned_transducer_stateless2/exp \
   --full-libri 1 \
@@ -123,7 +123,7 @@ def get_parser():
     parser.add_argument(
         "--start-epoch",
         type=int,
-        default=0,
+        default=1,
         help="""Resume training from from this epoch.
         If it is positive, it will load checkpoint from
         transducer_stateless2/exp/epoch-{start_epoch-1}.pt
@@ -418,7 +418,7 @@ def load_checkpoint_if_available(
 
     If params.start_batch is positive, it will load the checkpoint from
     `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
-    params.start_epoch is positive, it will load the checkpoint from
+    params.start_epoch is larger than 1, it will load the checkpoint from
     `params.start_epoch - 1`.
 
     Apart from loading state dict for `model` and `optimizer` it also updates
@@ -430,6 +430,8 @@ def load_checkpoint_if_available(
         The return value of :func:`get_params`.
       model:
         The training model.
+      model_avg:
+        The stored model averaged from the start of training.
       optimizer:
         The optimizer that we are using.
       scheduler:
@@ -439,7 +441,7 @@ def load_checkpoint_if_available(
     """
     if params.start_batch > 0:
         filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
-    elif params.start_epoch > 0:
+    elif params.start_epoch > 1:
         filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
     else:
         return None
@@ -849,7 +851,7 @@ def run(rank, world_size, args):
     logging.info(f"Number of model parameters: {num_param}")
 
     assert params.save_every_n >= params.average_period
-    model_avg: nn.Module = None
+    model_avg: Optional[nn.Module] = None
     if rank == 0:
         # model_avg is only used with rank 0
         model_avg = copy.deepcopy(model)
@@ -939,10 +941,10 @@ def run(rank, world_size, args):
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
 
-    for epoch in range(params.start_epoch, params.num_epochs):
-        scheduler.step_epoch(epoch)
-        fix_random_seed(params.seed + epoch)
-        train_dl.sampler.set_epoch(epoch)
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
+        scheduler.step_epoch(epoch - 1)
+        fix_random_seed(params.seed + epoch - 1)
+        train_dl.sampler.set_epoch(epoch - 1)
 
         if tb_writer is not None:
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
@@ -996,7 +998,7 @@ def scan_pessimistic_batches_for_oom(
     from lhotse.dataset import find_pessimistic_batches
 
     logging.info(
-        "Sanity check -- see if any of the batches in epoch 0 would cause OOM."
+        "Sanity check -- see if any of the batches in epoch 1 would cause OOM."
     )
     batches, crit_values = find_pessimistic_batches(train_dl.sampler)
     for criterion, cuts in batches.items():