diff --git a/.github/scripts/librispeech/ASR/run.sh b/.github/scripts/librispeech/ASR/run.sh
index 293ed66e5..b4450afea 100755
--- a/.github/scripts/librispeech/ASR/run.sh
+++ b/.github/scripts/librispeech/ASR/run.sh
@@ -64,6 +64,46 @@ function run_diagnostics() {
     --print-diagnostics 1
 }
 
+function test_streaming_zipformer_ctc_hlg() {
+  repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-streaming-zipformer-small-2024-03-18
+
+  log "Downloading pre-trained model from $repo_url"
+  git lfs install
+  git clone $repo_url
+  repo=$(basename $repo_url)
+
+  rm $repo/exp-ctc-rnnt-small/*.onnx
+  ls -lh $repo/exp-ctc-rnnt-small
+
+  # export models to onnx
+  ./zipformer/export-onnx-streaming-ctc.py \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
+    --epoch 30 \
+    --avg 3 \
+    --exp-dir $repo/exp-ctc-rnnt-small \
+    --causal 1 \
+    --use-ctc 1 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    \
+    --num-encoder-layers 2,2,2,2,2,2 \
+    --feedforward-dim 512,768,768,768,768,768 \
+    --encoder-dim 192,256,256,256,256,256 \
+    --encoder-unmasked-dim 192,192,192,192,192,192
+
+  ls -lh $repo/exp-ctc-rnnt-small
+
+  for wav in 0.wav 1.wav 8k.wav; do
+    python3 ./zipformer/onnx_pretrained_ctc_HLG_streaming.py \
+      --nn-model $repo/exp-ctc-rnnt-small/ctc-epoch-30-avg-3-chunk-16-left-128.int8.onnx \
+      --words $repo/data/lang_bpe_500/words.txt \
+      --HLG $repo/data/lang_bpe_500/HLG.fst \
+      $repo/test_wavs/$wav
+  done
+
+  rm -rf $repo
+}
+
 function test_pruned_transducer_stateless_2022_03_12() {
   repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
 
@@ -1577,6 +1617,7 @@ function test_transducer_bpe_500_2021_12_23() {
 
 prepare_data
 run_diagnostics
+test_streaming_zipformer_ctc_hlg
 test_pruned_transducer_stateless_2022_03_12
 test_pruned_transducer_stateless2_2022_04_29
 test_pruned_transducer_stateless3_2022_04_29
diff --git a/egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_HLG_streaming.py b/egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_HLG_streaming.py
index f15439dfc..04b53701e 100755
--- a/egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_HLG_streaming.py
+++ b/egs/librispeech/ASR/zipformer/onnx_pretrained_ctc_HLG_streaming.py
@@ -27,10 +27,10 @@ popd
 2. Export the model to ONNX
 
 ./zipformer/export-onnx-streaming-ctc.py \
-  --tokens ./data/lang_bpe_500/tokens.txt \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 3 \
-  --exp-dir zipformer/exp-ctc-rnnt-small \
+  --exp-dir $repo/exp-ctc-rnnt-small \
   --causal 1 \
   --use-ctc 1 \
   --chunk-size 16 \
@@ -107,8 +107,7 @@ def get_parser():
         type=str,
         help="The input sound file to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
+        "For example, wav and flac are supported. ",
     )
 
     return parser
@@ -311,9 +310,13 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        if sample_rate != expected_sample_rate:
+            logging.info(f"Resample {sample_rate} to {expected_sample_rate}")
+            wave = torchaudio.functional.resample(
+                wave,
+                orig_freq=sample_rate,
+                new_freq=expected_sample_rate,
+            )
         # We use only the first channel
         ans.append(wave[0].contiguous())
     return ans