diff --git a/examples/cpp/silero-vad-onnx.cpp b/examples/cpp/silero-vad-onnx.cpp
index eb92296..dd2bf4e 100644
--- a/examples/cpp/silero-vad-onnx.cpp
+++ b/examples/cpp/silero-vad-onnx.cpp
@@ -120,8 +120,7 @@ class VadIterator
     void reset_states()
     {
         // Call reset before each audio start
-        std::memset(_h.data(), 0.0f, _h.size() * sizeof(float));
-        std::memset(_c.data(), 0.0f, _c.size() * sizeof(float));
+        std::memset(_state.data(), 0.0f, _state.size() * sizeof(float));
         triggered = false;
         temp_end = 0;
         current_sample = 0;
@@ -139,19 +138,16 @@ class VadIterator
         input.assign(data.begin(), data.end());
         Ort::Value input_ort = Ort::Value::CreateTensor<float>(
             memory_info, input.data(), input.size(), input_node_dims, 2);
+        Ort::Value state_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _state.data(), _state.size(), state_node_dims, 3);
         Ort::Value sr_ort = Ort::Value::CreateTensor<int64_t>(
             memory_info, sr.data(), sr.size(), sr_node_dims, 1);
-        Ort::Value h_ort = Ort::Value::CreateTensor<float>(
-            memory_info, _h.data(), _h.size(), hc_node_dims, 3);
-        Ort::Value c_ort = Ort::Value::CreateTensor<float>(
-            memory_info, _c.data(), _c.size(), hc_node_dims, 3);
 
         // Clear and add inputs
         ort_inputs.clear();
         ort_inputs.emplace_back(std::move(input_ort));
+        ort_inputs.emplace_back(std::move(state_ort));
         ort_inputs.emplace_back(std::move(sr_ort));
-        ort_inputs.emplace_back(std::move(h_ort));
-        ort_inputs.emplace_back(std::move(c_ort));
 
         // Infer
         ort_outputs = session->Run(
@@ -161,10 +157,8 @@ class VadIterator
 
         // Output probability & update h,c recursively
         float speech_prob = ort_outputs[0].GetTensorMutableData<float>()[0];
-        float *hn = ort_outputs[1].GetTensorMutableData<float>();
-        std::memcpy(_h.data(), hn, size_hc * sizeof(float));
-        float *cn = ort_outputs[2].GetTensorMutableData<float>();
-        std::memcpy(_c.data(), cn, size_hc * sizeof(float));
+        float *stateN = ort_outputs[1].GetTensorMutableData<float>();
+        std::memcpy(_state.data(), stateN, size_state * sizeof(float));
 
         // Push forward sample index
         current_sample += window_size_samples;
@@ -376,27 +370,26 @@ class VadIterator
     // Inputs
     std::vector<Ort::Value> ort_inputs;
     
-    std::vector<const char *> input_node_names = {"input", "sr", "h", "c"};
+    std::vector<const char *> input_node_names = {"input", "state", "sr"};
     std::vector<float> input;
+    unsigned int size_state = 2 * 1 * 128; // It's FIXED.
+    std::vector<float> _state;
     std::vector<int64_t> sr;
-    unsigned int size_hc = 2 * 1 * 64; // It's FIXED.
-    std::vector<float> _h;
-    std::vector<float> _c;
 
-    int64_t input_node_dims[2] = {}; 
+    int64_t input_node_dims[2] = {};
+    const int64_t state_node_dims[3] = {2, 1, 128}; 
     const int64_t sr_node_dims[1] = {1};
-    const int64_t hc_node_dims[3] = {2, 1, 64};
 
     // Outputs
     std::vector<Ort::Value> ort_outputs;
-    std::vector<const char *> output_node_names = {"output", "hn", "cn"};
+    std::vector<const char *> output_node_names = {"output", "stateN"};
 
 public:
     // Construction
     VadIterator(const std::wstring ModelPath,
-        int Sample_rate = 16000, int windows_frame_size = 64,
+        int Sample_rate = 16000, int windows_frame_size = 32,
         float Threshold = 0.5, int min_silence_duration_ms = 0,
-        int speech_pad_ms = 64, int min_speech_duration_ms = 64,
+        int speech_pad_ms = 32, int min_speech_duration_ms = 32,
         float max_speech_duration_s = std::numeric_limits<float>::infinity())
     {
         init_onnx_model(ModelPath);
@@ -422,8 +415,7 @@ class VadIterator
         input_node_dims[0] = 1;
         input_node_dims[1] = window_size_samples;
 
-        _h.resize(size_hc);
-        _c.resize(size_hc);
+        _state.resize(size_state);
         sr.resize(1);
         sr[0] = sample_rate;
     };