diff --git a/src/silero_vad/utils_vad.py b/src/silero_vad/utils_vad.py
index 9a64717..3fd5635 100644
--- a/src/silero_vad/utils_vad.py
+++ b/src/silero_vad/utils_vad.py
@@ -197,6 +197,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                           min_silence_duration_ms: int = 100,
                           speech_pad_ms: int = 30,
                           return_seconds: bool = False,
+                          time_resolution: int = 1,
                           visualize_probs: bool = False,
                           progress_tracking_callback: Callable[[float], None] = None,
                           neg_threshold: float = None,
@@ -236,6 +237,9 @@ def get_speech_timestamps(audio: torch.Tensor,
     return_seconds: bool (default - False)
         whether return timestamps in seconds (default - samples)
 
+    time_resolution: bool (default - 1)
+        time resolution of speech coordinates when requested as seconds
+
     visualize_probs: bool (default - False)
         whether draw prob hist or not
 
@@ -378,8 +382,8 @@ def get_speech_timestamps(audio: torch.Tensor,
     if return_seconds:
         audio_length_seconds = audio_length_samples / sampling_rate
         for speech_dict in speeches:
-            speech_dict['start'] = max(round(speech_dict['start'] / sampling_rate, 1), 0)
-            speech_dict['end'] = min(round(speech_dict['end'] / sampling_rate, 1), audio_length_seconds)
+            speech_dict['start'] = max(round(speech_dict['start'] / sampling_rate, time_resolution), 0)
+            speech_dict['end'] = min(round(speech_dict['end'] / sampling_rate, time_resolution), audio_length_seconds)
     elif step > 1:
         for speech_dict in speeches:
             speech_dict['start'] *= step
@@ -440,13 +444,16 @@ class VADIterator:
         self.current_sample = 0
 
     @torch.no_grad()
-    def __call__(self, x, return_seconds=False):
+    def __call__(self, x, return_seconds=False, time_resolution: int = 1):
         """
         x: torch.Tensor
             audio chunk (see examples in repo)
 
         return_seconds: bool (default - False)
             whether return timestamps in seconds (default - samples)
+
+        time_resolution: int (default - 1)
+            time resolution of speech coordinates when requested as seconds
         """
 
         if not torch.is_tensor(x):
@@ -466,7 +473,7 @@ class VADIterator:
         if (speech_prob >= self.threshold) and not self.triggered:
             self.triggered = True
             speech_start = max(0, self.current_sample - self.speech_pad_samples - window_size_samples)
-            return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, 1)}
+            return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, time_resolution)}
 
         if (speech_prob < self.threshold - 0.15) and self.triggered:
             if not self.temp_end:
@@ -477,7 +484,7 @@ class VADIterator:
                 speech_end = self.temp_end + self.speech_pad_samples - window_size_samples
                 self.temp_end = 0
                 self.triggered = False
-                return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, 1)}
+                return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, time_resolution)}
 
         return None