diff --git a/vggish.py b/vggish.py
index 59fe732..86d327b 100644
--- a/vggish.py
+++ b/vggish.py
@@ -19,6 +19,7 @@ import os
 import sys
 import numpy
 from pathlib import Path
+from typing import Union
 
 import torch
 
@@ -37,6 +38,7 @@ log = logging.getLogger()
 class Vggish(NNOperator):
     """
     """
+
     def __init__(self, weights_path: str = None, framework: str = 'pytorch') -> None:
         super().__init__(framework=framework)
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -49,19 +51,26 @@ class Vggish(NNOperator):
         self.model.eval()
         self.model.to(self.device)
 
-    def __call__(self, audio: str) -> numpy.ndarray:
-        audio_tensors = self.preprocess(audio).to(self.device)
+    def __call__(self, audio: Union[str, numpy.ndarray], sr: int = None) -> numpy.ndarray:
+        audio_tensors = self.preprocess(audio, sr).to(self.device)
         features = self.model(audio_tensors)
         outs = features.to("cpu")
         return outs.detach().numpy()
 
-    def preprocess(self, audio_path: str):
-        audio_tensors = vggish_input.wavfile_to_examples(audio_path)
+    def preprocess(self, audio: Union[str, numpy.ndarray], sr: int = None):
+        if isinstance(audio, str):
+            audio_tensors = vggish_input.wavfile_to_examples(audio)
+        elif isinstance(audio, numpy.ndarray):
+            try:
+                audio_tensors = vggish_input.waveform_to_examples(audio, sr, return_tensor=True)
+            except Exception as e:
+                log.error("Fail to load audio data.")
+                raise e
         return audio_tensors
 
 
 # if __name__ == '__main__':
 #     encoder = Vggish()
-#     audio_path = '/path/to/audio/wav'
+#     audio_path = '/path/to/audio'
 #     vec = encoder(audio_path)
-#     print(vec.shape)
+#     print(vec)
diff --git a/vggish_input.py b/vggish_input.py
index 49368d4..56e6176 100644
--- a/vggish_input.py
+++ b/vggish_input.py
@@ -93,7 +93,5 @@ def wavfile_to_examples(wav_file, return_tensor=True):
     See waveform_to_examples.
   """
     data, sr = torchaudio.load(wav_file)
-    wav_data = data.short().detach().numpy().transpose()
-    assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
-    samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
-    return waveform_to_examples(samples, sr, return_tensor)
+    wav_data = data.detach().numpy().transpose()
+    return waveform_to_examples(wav_data, sr, return_tensor)