+#######################

+# this is our workaround for Silero v5 requiring at least 512-sized audio chunks 

+# (see https://github.com/ufal/whisper_streaming/issues/116 )

+import numpy as np

+class FixedVADIterator(VADIterator):

+

+    def reset_states(self):

+        super().reset_states()

+        self.buffer = np.array([],dtype=np.float32)

+

+    def __call__(self, x, return_seconds=False):

+        self.buffer = np.append(self.buffer, x) 

+        if len(self.buffer) >= 512:

+            ret = super().__call__(self.buffer, return_seconds=return_seconds)

+            self.buffer = np.array([],dtype=np.float32)

+            return ret

+        return None

+

+if __name__ == "__main__":

+    # test/demonstrate the need for FixedVADIterator:

+

+    import torch

+    model, _ = torch.hub.load(

+        repo_or_dir='snakers4/silero-vad',

+        model='silero_vad'

+    )

+    vac = FixedVADIterator(model)

+#   vac = VADIterator(model)  # the second case crashes with this

+

+    # this works: for both

+    audio_buffer = np.array([0]*(512),dtype=np.float32)

+    vac(audio_buffer)

+

+    # this crashes on the non FixedVADIterator with 

+    # ops.prim.RaiseException("Input audio chunk is too short", "builtins.ValueError")

+    audio_buffer = np.array([0]*(512-1),dtype=np.float32)

+    vac(audio_buffer)

+            repo_or_dir='snakers4/silero-vad',