diff --git a/scripts/whisper/test.py b/scripts/whisper/test.py
index 03e5e32c..d93da482 100755
--- a/scripts/whisper/test.py
+++ b/scripts/whisper/test.py
@@ -4,15 +4,14 @@
 Please first run ./export-onnx.py
 before you run this script
 """
+import argparse
 import base64
 from typing import Tuple
 
 import kaldi_native_fbank as knf
 import onnxruntime as ort
 import torch
-
-import whisper
-import argparse
+import torchaudio
 
 
 def get_args():
@@ -225,16 +224,24 @@ def load_tokens(filename):
     return tokens
 
 
-def main():
-    args = get_args()
-    encoder = args.encoder
-    decoder = args.decoder
-
-    audio = whisper.load_audio(args.sound_file)
+def compute_features(filename: str) -> torch.Tensor:
+    """
+    Args:
+      filename:
+        Path to an audio file.
+    Returns:
+      Return a 1-D float32 tensor of shape (1, 80, 3000) containing the features.
+    """
+    wave, sample_rate = torchaudio.load(filename)
+    audio = wave[0].contiguous()  # only use the first channel
+    if sample_rate != 16000:
+        audio = torchaudio.functional.resample(
+            audio, orig_freq=sample_rate, new_freq=16000
+        )
 
     features = []
     online_whisper_fbank = knf.OnlineWhisperFbank(knf.FrameExtractionOptions())
-    online_whisper_fbank.accept_waveform(16000, audio)
+    online_whisper_fbank.accept_waveform(16000, audio.numpy())
     online_whisper_fbank.input_finished()
     for i in range(online_whisper_fbank.num_frames_ready):
         f = online_whisper_fbank.get_frame(i)
@@ -250,7 +257,14 @@ def main():
     mel = torch.nn.functional.pad(mel, (0, 0, 0, target - mel.shape[0]), "constant", 0)
     mel = mel.t().unsqueeze(0)
 
-    model = OnnxModel(encoder, decoder)
+    return mel
+
+
+def main():
+    args = get_args()
+
+    mel = compute_features(args.sound_file)
+    model = OnnxModel(args.encoder, args.decoder)
 
     n_layer_cross_k, n_layer_cross_v = model.run_encoder(mel)