diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/decode_stream.py b/egs/librispeech/ASR/lstm_transducer_stateless/decode_stream.py
index ba5e80555..7df96a9af 100644
--- a/egs/librispeech/ASR/lstm_transducer_stateless/decode_stream.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless/decode_stream.py
@@ -71,9 +71,9 @@ class DecodeStream(object):
         # encoder.streaming_forward
         self.done_frames: int = 0
 
-        self.pad_length = (
-            params.right_context + 2
-        ) * params.subsampling_factor + 3
+        # add 2 here since we will drop the first and last frames after
+        # the convolutional subsampling module
+        self.pad_length = 2 * params.subsampling_factor + 3
 
         if params.decoding_method == "greedy_search":
             self.hyp = [params.blank_id] * params.context_size
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py b/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py
index b3e1f04c3..6ba72ca2a 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py
@@ -17,15 +17,13 @@
 
 """
 Usage:
-./pruned_transducer_stateless2/streaming_decode.py \
-        --epoch 28 \
-        --avg 15 \
-        --left-context 32 \
-        --decode-chunk-size 8 \
-        --right-context 0 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
-        --decoding_method greedy_search \
-        --num-decode-streams 1000
+./lstm_transducer_stateless/streaming_decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./lstm_transducer_stateless/exp \
+    --decoding_method greedy_search \
+    --decode-chunk-size 1 \
+    --num-decode-streams 1000
 """
 
 import argparse
@@ -44,7 +42,7 @@ from decode_stream import DecodeStream
 from kaldifeat import Fbank, FbankOptions
 from lhotse import CutSet
 from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
+from train import get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,
@@ -155,24 +153,10 @@ def get_parser():
     parser.add_argument(
         "--decode-chunk-size",
         type=int,
-        default=16,
+        default=1,
         help="The chunk size for decoding (in frames after subsampling)",
     )
 
-    parser.add_argument(
-        "--left-context",
-        type=int,
-        default=64,
-        help="left context can be seen during decoding (in frames after subsampling)",
-    )
-
-    parser.add_argument(
-        "--right-context",
-        type=int,
-        default=0,
-        help="right context can be seen during decoding (in frames after subsampling)",
-    )
-
     parser.add_argument(
         "--num-decode-streams",
         type=int,
@@ -180,8 +164,6 @@ def get_parser():
         help="The number of streams that can be decoded parallel.",
     )
 
-    add_model_arguments(parser)
-
     return parser
 
 
@@ -334,7 +316,7 @@ def decode_one_chunk(
     # we plus 2 here because we will cut off one frame on each size of
     # encoder_embed output as they see invalid paddings. so we need extra 2
     # frames.
-    tail_length = 7 + (2 + params.right_context) * params.subsampling_factor
+    tail_length = 7 + 2 * params.subsampling_factor
     if features.size(1) < tail_length:
         feature_lens += tail_length - features.size(1)
         features = torch.cat(
@@ -357,13 +339,10 @@ def decode_one_chunk(
     ]
     processed_lens = torch.tensor(processed_lens, device=device)
 
-    encoder_out, encoder_out_lens, states = model.encoder.streaming_forward(
+    encoder_out, encoder_out_lens, states = model.encoder.infer(
         x=features,
         x_lens=feature_lens,
         states=states,
-        left_context=params.left_context,
-        right_context=params.right_context,
-        processed_lens=processed_lens,
     )
 
     encoder_out = model.joiner.encoder_proj(encoder_out)
@@ -442,8 +421,8 @@ def decode_dataset(
     decode_results = []
     # Contain decode streams currently running.
     decode_streams = []
-    initial_states = model.encoder.get_init_state(
-        params.left_context, device=device
+    initial_states = model.encoder.get_init_states(
+        device=device
     )
     for num, cut in enumerate(cuts):
         # each utterance has a DecodeStream.
@@ -584,8 +563,6 @@ def main():
 
     # for streaming
     params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
-    params.suffix += f"-left-context-{params.left_context}"
-    params.suffix += f"-right-context-{params.right_context}"
 
     # for fast_beam_search
     if params.decoding_method == "fast_beam_search":
@@ -609,8 +586,6 @@ def main():
     params.blank_id = sp.piece_to_id("<blk>")
     params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
-    # Decoding in streaming requires causal convolution
-    params.causal_convolution = True
 
     logging.info(params)