diff --git a/egs/librispeech/ASR/transducer_lstm/decode.py b/egs/librispeech/ASR/transducer_lstm/decode.py
index 03d1b840b..8db103672 100755
--- a/egs/librispeech/ASR/transducer_lstm/decode.py
+++ b/egs/librispeech/ASR/transducer_lstm/decode.py
@@ -24,15 +24,24 @@ Usage:
         --exp-dir ./transducer_lstm/exp \
         --max-duration 100 \
         --decoding-method greedy_search
-(2) beam search
 
+(2) beam search
 ./transducer_lstm/decode.py \
         --epoch 14 \
         --avg 7 \
         --exp-dir ./transducer_lstm/exp \
         --max-duration 100 \
         --decoding-method beam_search \
-        --beam-size 8
+        --beam-size 4
+
+(3) modified beam search
+./transducer_lstm/decode.py \
+        --epoch 14 \
+        --avg 7 \
+        --exp-dir ./transducer_lstm/exp \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
 """
 
 
@@ -71,14 +80,14 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=77,
+        default=29,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
-        default=55,
+        default=13,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch'. ",
@@ -112,8 +121,9 @@ def get_parser():
     parser.add_argument(
         "--beam-size",
         type=int,
-        default=5,
-        help="Used only when --decoding-method is beam_search",
+        default=4,
+        help="""Used only when --decoding-method is
+        beam_search or modified_beam_search""",
     )
 
     parser.add_argument(
@@ -123,7 +133,6 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
-
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
@@ -348,12 +357,19 @@ def main():
     params = get_params()
     params.update(vars(args))
 
-    assert params.decoding_method in ("greedy_search", "beam_search")
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "modified_beam_search",
+    )
     params.res_dir = params.exp_dir / params.decoding_method
 
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
-    if params.decoding_method == "beam_search":
+    if "beam_search" in params.decoding_method:
         params.suffix += f"-beam-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
 
     setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
     logging.info("Decoding started")
@@ -423,8 +439,5 @@ def main():
     logging.info("Done!")
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
 if __name__ == "__main__":
     main()
diff --git a/egs/librispeech/ASR/transducer_lstm/joiner.py b/egs/librispeech/ASR/transducer_lstm/joiner.py
index 0422f8a6f..8c3710011 100644
--- a/egs/librispeech/ASR/transducer_lstm/joiner.py
+++ b/egs/librispeech/ASR/transducer_lstm/joiner.py
@@ -26,7 +26,7 @@ class Joiner(nn.Module):
         self.output_linear = nn.Linear(input_dim, output_dim)
 
     def forward(
-        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
+        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor, *unused
     ) -> torch.Tensor:
         """
         Args:
@@ -51,5 +51,7 @@ class Joiner(nn.Module):
         logit = F.relu(logit)
 
         output = self.output_linear(logit)
+        if not self.training:
+            output = output.squeeze(2).squeeze(1)
 
         return output
diff --git a/egs/librispeech/ASR/transducer_lstm/train.py b/egs/librispeech/ASR/transducer_lstm/train.py
index 7f4dc32cf..98859a58f 100755
--- a/egs/librispeech/ASR/transducer_lstm/train.py
+++ b/egs/librispeech/ASR/transducer_lstm/train.py
@@ -634,13 +634,23 @@ def run(rank, world_size, args):
 
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
-    num_left = len(train_cuts)
-    num_removed = num_in_total - num_left
-    removed_percent = num_removed / num_in_total * 100
+    try:
+        num_left = len(train_cuts)
+        num_removed = num_in_total - num_left
+        removed_percent = num_removed / num_in_total * 100
 
-    logging.info(f"Before removing short and long utterances: {num_in_total}")
-    logging.info(f"After removing short and long utterances: {num_left}")
-    logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)")
+        logging.info(
+            f"Before removing short and long utterances: {num_in_total}"
+        )
+        logging.info(f"After removing short and long utterances: {num_left}")
+        logging.info(
+            f"Removed {num_removed} utterances ({removed_percent:.5f}%)"
+        )
+    except TypeError as e:
+        # You can ignore this error as previous versions of Lhotse work fine
+        # for the above code. In recent versions of Lhotse, it uses
+        # lazy filter, producing cutsets that don't have the __len__  method
+        logging.info(str(e))
 
     train_dl = librispeech.train_dataloaders(train_cuts)