diff --git a/files/model_micro.jit b/files/model_micro.jit
new file mode 100644
index 0000000..ec76fca
Binary files /dev/null and b/files/model_micro.jit differ
diff --git a/files/model_micro.onnx b/files/model_micro.onnx
new file mode 100644
index 0000000..1f36b09
Binary files /dev/null and b/files/model_micro.onnx differ
diff --git a/hubconf.py b/hubconf.py
index 6b8f056..5a515ca 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -29,6 +29,23 @@ def silero_vad(**kwargs):
     return model, utils
 
 
+def silero_vad_micro(**kwargs):
+    """Silero Voice Activity Detector
+    Returns a model with a set of utils
+    Please see https://github.com/snakers4/silero-vad for usage examples
+    """
+    hub_dir = torch.hub.get_dir()
+    model = init_jit_model(model_path=f'{hub_dir}/snakers4_silero-vad_master/files/model_micro.jit')
+    utils = (get_speech_ts,
+             save_audio,
+             read_audio,
+             state_generator,
+             single_audio_stream,
+             collect_chunks)
+
+    return model, utils
+
+
 def silero_number_detector(**kwargs):
     """Silero Number Detector
     Returns a model with a set of utils
diff --git a/utils_vad.py b/utils_vad.py
index 258001b..30f2408 100644
--- a/utils_vad.py
+++ b/utils_vad.py
@@ -4,6 +4,7 @@ from typing import List
 from itertools import repeat
 from collections import deque
 import torch.nn.functional as F
+import time
 
 
 torchaudio.set_audio_backend("soundfile")  # switch backend
@@ -60,7 +61,7 @@ def get_speech_ts(wav: torch.Tensor,
                   batch_size: int = 200,
                   num_samples_per_window: int = 4000,
                   min_speech_samples: int = 10000, #samples
-                  min_silence_samples: int = 8000,
+                  min_silence_samples: int = 500,
                   run_function=validate,
                   visualize_probs=False):
 
@@ -308,6 +309,7 @@ def single_audio_stream(model,
     wav = read_audio(audio)
     wav_chunks = iter([wav[i:i+num_samples] for i in range(0, len(wav), num_samples)])
     for chunk in wav_chunks:
+        time.sleep(0.1)
         batch = VADiter.prepare_batch(chunk)
 
         outs = run_function(model, batch)