diff --git a/files/model.jit b/files/model.jit
index 0bec210..4a8b0ed 100644
Binary files a/files/model.jit and b/files/model.jit differ
diff --git a/files/model.onnx b/files/model.onnx
index e4596f1..2ec5c38 100644
Binary files a/files/model.onnx and b/files/model.onnx differ
diff --git a/silero-vad.ipynb b/silero-vad.ipynb
index 34dabb6..818b8e6 100644
--- a/silero-vad.ipynb
+++ b/silero-vad.ipynb
@@ -233,7 +233,7 @@
     "        ort_inputs = {'input': inputs.cpu().numpy()}\n",
     "        outs = model.run(None, ort_inputs)\n",
     "        outs = [torch.Tensor(x) for x in outs]\n",
-    "    return outs"
+    "    return outs[0]"
    ]
   },
   {
@@ -405,5 +405,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/utils.py b/utils.py
index 00d9cc6..98a8f29 100644
--- a/utils.py
+++ b/utils.py
@@ -52,7 +52,7 @@ def init_jit_model(model_path: str,
 def get_speech_ts(wav: torch.Tensor,
                   model,
                   trig_sum: float = 0.25,
-                  neg_trig_sum: float = 0.02,
+                  neg_trig_sum: float = 0.1,
                   num_steps: int = 8,
                   batch_size: int = 200,
                   run_function=validate):
@@ -70,13 +70,13 @@ def get_speech_ts(wav: torch.Tensor,
         to_concat.append(chunk.unsqueeze(0))
         if len(to_concat) >= batch_size:
             chunks = torch.Tensor(torch.cat(to_concat, dim=0))
-            out = run_function(model, chunks)[-2]
+            out = run_function(model, chunks)
             outs.append(out)
             to_concat = []
 
     if to_concat:
         chunks = torch.Tensor(torch.cat(to_concat, dim=0))
-        out = run_function(model, chunks)[-2]
+        out = run_function(model, chunks)
         outs.append(out)
 
     outs = torch.cat(outs, dim=0)
@@ -107,7 +107,7 @@ def get_speech_ts(wav: torch.Tensor,
 class VADiterator:
     def __init__(self,
                  trig_sum: float = 0.26,
-                 neg_trig_sum: float = 0.02,
+                 neg_trig_sum: float = 0.1,
                  num_steps: int = 8):
         self.num_samples = 4000
         self.num_steps = num_steps
@@ -168,7 +168,7 @@ def state_generator(model,
                     audios: List[str],
                     onnx: bool = False,
                     trig_sum: float = 0.26,
-                    neg_trig_sum: float = 0.02,
+                    neg_trig_sum: float = 0.1,
                     num_steps: int = 8,
                     audios_in_stream: int = 2,
                     run_function=validate):
@@ -178,7 +178,7 @@ def state_generator(model,
         batch = torch.cat(for_batch)
 
         outs = run_function(model, batch)
-        vad_outs = torch.split(outs[-2], num_steps)
+        vad_outs = torch.split(outs, num_steps)
 
         states = []
         for x, y in zip(VADiters, vad_outs):
@@ -227,7 +227,7 @@ def single_audio_stream(model,
                         audio: str,
                         onnx: bool = False,
                         trig_sum: float = 0.26,
-                        neg_trig_sum: float = 0.02,
+                        neg_trig_sum: float = 0.1,
                         num_steps: int = 8,
                         run_function=validate):
     num_samples = 4000
@@ -238,7 +238,7 @@ def single_audio_stream(model,
         batch = VADiter.prepare_batch(chunk)
 
         outs = run_function(model, batch)
-        vad_outs = outs[-2]  # this is very misleading
+        vad_outs = outs  # this is very misleading
 
         states = []
         state = VADiter.state(vad_outs)