diff --git a/utils_vad.py b/utils_vad.py
index 9e4b1bb..603f0ca 100644
--- a/utils_vad.py
+++ b/utils_vad.py
@@ -1,6 +1,6 @@
 import torch
 import torchaudio
-from typing import List
+from typing import Callable, List
 import torch.nn.functional as F
 import warnings
 
@@ -168,7 +168,8 @@ def get_speech_timestamps(audio: torch.Tensor,
                           window_size_samples: int = 512,
                           speech_pad_ms: int = 30,
                           return_seconds: bool = False,
-                          visualize_probs: bool = False):
+                          visualize_probs: bool = False,
+                          progress_tracking_callback: Callable[[float], None] = None):
 
     """
     This method is used for splitting long audios into speech chunks using silero VAD
@@ -212,6 +213,9 @@ def get_speech_timestamps(audio: torch.Tensor,
     visualize_probs: bool (default - False)
         whether draw prob hist or not
 
+    progress_tracking_callback: Callable[[float], None] (default - None)
+        callback function taking progress in percents as an argument
+
     Returns
     ----------
     speeches: list of dicts
@@ -259,6 +263,13 @@ def get_speech_timestamps(audio: torch.Tensor,
             chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk))))
         speech_prob = model(chunk, sampling_rate).item()
         speech_probs.append(speech_prob)
+        # caculate progress and seng it to callback function
+        progress = current_start_sample + window_size_samples
+        if progress > audio_length_samples:
+            progress = audio_length_samples
+        progress_percent = (progress / audio_length_samples) * 100
+        if progress_tracking_callback:
+            progress_tracking_callback(progress_percent)
 
     triggered = False
     speeches = []