add neg_threshold parameter explicitly

src/silero_vad/utils_vad.py

@@ -53,10 +53,10 @@ class OnnxWrapper():
 
         x, sr = self._validate_input(x, sr)
         num_samples = 512 if sr == 16000 else 256
-        
+
         if x.shape[-1] != num_samples:
             raise ValueError(f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")
-        
+
         batch_size = x.shape[0]
         context_size = 64 if sr == 16000 else 32
 
@@ -133,7 +133,7 @@ class Validator():
 def read_audio(path: str,
                sampling_rate: int = 16000):
     list_backends = torchaudio.list_audio_backends()
-    
+
     assert len(list_backends) > 0, 'The list of available backends is empty, please install backend manually. \
                                     \n Recommendations: \n \tSox (UNIX OS) \n \tSoundfile (Windows OS, UNIX OS) \n \tffmpeg (Windows OS, UNIX OS)'
 
@@ -195,6 +195,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                           return_seconds: bool = False,
                           visualize_probs: bool = False,
                           progress_tracking_callback: Callable[[float], None] = None,
+                          neg_threshold: float = None,
                           window_size_samples: int = 512,):
 
     """
@@ -237,6 +238,9 @@ def get_speech_timestamps(audio: torch.Tensor,
     progress_tracking_callback: Callable[[float], None] (default - None)
         callback function taking progress in percents as an argument
 
+    neg_threshold: float (default = threshold - 0.15)
+        Negative threshold (noise or exit threshold). If model's current state is SPEECH, values BELOW this value are considered as NON-SPEECH.
+
     window_size_samples: int (default - 512 samples)
         !!! DEPRECATED, DOES NOTHING !!!
 
@@ -298,15 +302,17 @@ def get_speech_timestamps(audio: torch.Tensor,
     triggered = False
     speeches = []
     current_speech = {}
-    neg_threshold = threshold - 0.15
+
-    temp_end = 0 # to save potential segment end (and tolerate some silence)
+    if neg_threshold is None:
-    prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached
+        neg_threshold = threshold - 0.15
+    temp_end = 0  # to save potential segment end (and tolerate some silence)
+    prev_end = next_start = 0  # to save potential segment limits in case of maximum segment size reached
 
     for i, speech_prob in enumerate(speech_probs):
         if (speech_prob >= threshold) and temp_end:
             temp_end = 0
             if next_start < prev_end:
-               next_start = window_size_samples * i
+                next_start = window_size_samples * i
 
         if (speech_prob >= threshold) and not triggered:
             triggered = True
@@ -318,7 +324,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                 current_speech['end'] = prev_end
                 speeches.append(current_speech)
                 current_speech = {}
-                if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres)
+                if next_start < prev_end:  # previously reached silence (< neg_thres) and is still not speech (< thres)
                     triggered = False
                 else:
                     current_speech['start'] = next_start
@@ -334,7 +340,7 @@ def get_speech_timestamps(audio: torch.Tensor,
         if (speech_prob < neg_threshold) and triggered:
             if not temp_end:
                 temp_end = window_size_samples * i
-            if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech : # condition to avoid cutting in very short silence
+            if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech:  # condition to avoid cutting in very short silence
                 prev_end = temp_end
             if (window_size_samples * i) - temp_end < min_silence_samples:
                 continue