Update pyproject.toml

Fixes and refines - use_max_poss_sil_at_max_speech arg

.github/workflows/test.yml

@@ -0,0 +1,39 @@
+name: Test Package
+
+on:
+  workflow_dispatch:       # запуск вручную
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.8","3.9","3.10","3.11","3.12","3.13"]
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build hatchling pytest soundfile
+
+    - name: Build package
+      run: python -m build --wheel --outdir dist
+
+    - name: Install package
+      run: |
+        import glob, subprocess, sys
+        whl = glob.glob("dist/*.whl")[0]
+        subprocess.check_call([sys.executable, "-m", "pip", "install", whl])
+      shell: python
+
+    - name: Run tests
+      run: pytest tests

CITATION.cff

@@ -0,0 +1,20 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+title: "Silero VAD"
+authors:
+  - family-names: "Silero Team"
+    email: "hello@silero.ai"
+type: software
+repository-code: "https://github.com/snakers4/silero-vad"
+license: MIT
+abstract: "Pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier"
+preferred-citation:
+  type: software
+  authors:
+    - family-names: "Silero Team"
+      email: "hello@silero.ai"
+  title: "Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier"
+  year: 2024
+  publisher: "GitHub"
+  journal: "GitHub repository"
+  howpublished: "https://github.com/snakers4/silero-vad"

README.md

@@ -1,6 +1,6 @@
-[![Mailing list : test](http://img.shields.io/badge/Email-gray.svg?style=for-the-badge&logo=gmail)](mailto:hello@silero.ai) [![Mailing list : test](http://img.shields.io/badge/Telegram-blue.svg?style=for-the-badge&logo=telegram)](https://t.me/silero_speech) [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-MIT-lightgrey.svg?style=for-the-badge)](https://github.com/snakers4/silero-vad/blob/master/LICENSE)
+[![Mailing list : test](http://img.shields.io/badge/Email-gray.svg?style=for-the-badge&logo=gmail)](mailto:hello@silero.ai) [![Mailing list : test](http://img.shields.io/badge/Telegram-blue.svg?style=for-the-badge&logo=telegram)](https://t.me/silero_speech) [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-MIT-lightgrey.svg?style=for-the-badge)](https://github.com/snakers4/silero-vad/blob/master/LICENSE) [![downloads](https://img.shields.io/pypi/dm/silero-vad?style=for-the-badge)](https://pypi.org/project/silero-vad/) 
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb) [![Test Package](https://github.com/snakers4/silero-vad/actions/workflows/test.yml/badge.svg)](https://github.com/snakers4/silero-vad/actions/workflows/test.yml) [![Pypi version](https://img.shields.io/pypi/v/silero-vad)](https://pypi.org/project/silero-vad/) [![Python version](https://img.shields.io/pypi/pyversions/silero-vad)](https://pypi.org/project/silero-vad)
 
 ![header](https://user-images.githubusercontent.com/12515440/89997349-b3523080-dc94-11ea-9906-ca2e8bc50535.png)
 
@@ -13,7 +13,7 @@
 <br/>
 
 <p align="center">
-  <img src="https://github.com/snakers4/silero-vad/assets/36505480/300bd062-4da5-4f19-9736-9c144a45d7a7" />
+  <img src="https://github.com/user-attachments/assets/f2940867-0a51-4bdb-8c14-1129d3c44e64" />
 </p>
 
 
@@ -22,6 +22,8 @@
 
 https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-9be7-004c891dd481.mp4
 
+Please note, that video loads only if you are logged in your GitHub account. 
+
 </details>
 
 <br/>
@@ -64,7 +66,11 @@ If you are planning to run the VAD using solely the `onnx-runtime`, it will run
 from silero_vad import load_silero_vad, read_audio, get_speech_timestamps
 model = load_silero_vad()
 wav = read_audio('path_to_audio_file')
-speech_timestamps = get_speech_timestamps(wav, model)
+speech_timestamps = get_speech_timestamps(
+  wav,
+  model,
+  return_seconds=True,  # Return speech timestamps in seconds (default is samples)
+)
 ```
 
 **Using torch.hub**:
@@ -76,7 +82,11 @@ model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_v
 (get_speech_timestamps, _, read_audio, _, _) = utils
 
 wav = read_audio('path_to_audio_file')
-speech_timestamps = get_speech_timestamps(wav, model)
+speech_timestamps = get_speech_timestamps(
+  wav,
+  model,
+  return_seconds=True,  # Return speech timestamps in seconds (default is samples)
+)
 ```
 
 <br/>
@@ -165,4 +175,4 @@ Please see our [wiki](https://github.com/snakers4/silero-models/wiki) for releva
 
 - Voice activity detection for the [browser](https://github.com/ricky0123/vad) using ONNX Runtime Web
 
-- [Rust](https://github.com/snakers4/silero-vad/tree/master/examples/rust-example), [Go](https://github.com/snakers4/silero-vad/tree/master/examples/go), [Java](https://github.com/snakers4/silero-vad/tree/master/examples/java-example) and [other](https://github.com/snakers4/silero-vad/tree/master/examples) examples
+- [Rust](https://github.com/snakers4/silero-vad/tree/master/examples/rust-example), [Go](https://github.com/snakers4/silero-vad/tree/master/examples/go), [Java](https://github.com/snakers4/silero-vad/tree/master/examples/java-example), [C++](https://github.com/snakers4/silero-vad/tree/master/examples/cpp), [C#](https://github.com/snakers4/silero-vad/tree/master/examples/csharp) and [other](https://github.com/snakers4/silero-vad/tree/master/examples) community examples

examples/colab_record_example.ipynb

@@ -17,6 +17,7 @@
    },
    "outputs": [],
    "source": [
+    "#!apt install ffmpeg\n",
     "!pip -q install pydub\n",
     "from google.colab import output\n",
     "from base64 import b64decode, b64encode\n",
@@ -37,13 +38,12 @@
     "                              model='silero_vad',\n",
     "                              force_reload=True)\n",
     "\n",
-    "def int2float(sound):\n",
+    "def int2float(audio):\n",
-    "    abs_max = np.abs(sound).max()\n",
+    "    samples = audio.get_array_of_samples()\n",
-    "    sound = sound.astype('float32')\n",
+    "    new_sound = audio._spawn(samples)\n",
-    "    if abs_max > 0:\n",
+    "    arr = np.array(samples).astype(np.float32)\n",
-    "        sound *= 1/32768\n",
+    "    arr = arr / np.abs(arr).max()\n",
-    "    sound = sound.squeeze()\n",
+    "    return arr\n",
-    "    return sound\n",
     "\n",
     "AUDIO_HTML = \"\"\"\n",
     "<script>\n",
@@ -68,10 +68,10 @@
     "    //bitsPerSecond: 8000, //chrome seems to ignore, always 48k\n",
     "    mimeType : 'audio/webm;codecs=opus'\n",
     "    //mimeType : 'audio/webm;codecs=pcm'\n",
-    "  };            \n",
+    "  };\n",
     "  //recorder = new MediaRecorder(stream, options);\n",
     "  recorder = new MediaRecorder(stream);\n",
-    "  recorder.ondataavailable = function(e) {            \n",
+    "  recorder.ondataavailable = function(e) {\n",
     "    var url = URL.createObjectURL(e.data);\n",
     "    // var preview = document.createElement('audio');\n",
     "    // preview.controls = true;\n",
@@ -79,7 +79,7 @@
     "    // document.body.appendChild(preview);\n",
     "\n",
     "    reader = new FileReader();\n",
-    "    reader.readAsDataURL(e.data); \n",
+    "    reader.readAsDataURL(e.data);\n",
     "    reader.onloadend = function() {\n",
     "      base64data = reader.result;\n",
     "      //console.log(\"Inside FileReader:\" + base64data);\n",
@@ -121,7 +121,7 @@
     "\n",
     "}\n",
     "});\n",
-    "      \n",
+    "\n",
     "</script>\n",
     "\"\"\"\n",
     "\n",
@@ -133,8 +133,8 @@
     "    audio.export('test.mp3', format='mp3')\n",
     "    audio = audio.set_channels(1)\n",
     "    audio = audio.set_frame_rate(16000)\n",
-    "    audio_float = int2float(np.array(audio.get_array_of_samples()))\n",
+    "    audio_float = int2float(audio)\n",
-    "    audio_tens = torch.tensor(audio_float )\n",
+    "    audio_tens = torch.tensor(audio_float)\n",
     "    return audio_tens\n",
     "\n",
     "def make_animation(probs, audio_duration, interval=40):\n",
@@ -154,19 +154,18 @@
     "    def animate(i):\n",
     "        x = i * interval / 1000 - 0.04\n",
     "        y = np.linspace(0, 1.02, 2)\n",
-    "        \n",
+    "\n",
     "        line.set_data(x, y)\n",
     "        line.set_color('#990000')\n",
     "        return line,\n",
+    "    anim = FuncAnimation(fig, animate, init_func=init, interval=interval, save_count=int(audio_duration / (interval / 1000)))\n",
     "\n",
-    "    anim = FuncAnimation(fig, animate, init_func=init, interval=interval, save_count=audio_duration / (interval / 1000))\n",
+    "    f = r\"animation.mp4\"\n",
-    "\n",
+    "    writervideo = FFMpegWriter(fps=1000/interval)\n",
-    "    f = r\"animation.mp4\" \n",
-    "    writervideo = FFMpegWriter(fps=1000/interval) \n",
     "    anim.save(f, writer=writervideo)\n",
     "    plt.close('all')\n",
     "\n",
-    "def combine_audio(vidname, audname, outname, fps=25): \n",
+    "def combine_audio(vidname, audname, outname, fps=25):\n",
     "    my_clip = mpe.VideoFileClip(vidname, verbose=False)\n",
     "    audio_background = mpe.AudioFileClip(audname)\n",
     "    final_clip = my_clip.set_audio(audio_background)\n",
@@ -174,15 +173,10 @@
     "\n",
     "def record_make_animation():\n",
     "  tensor = record()\n",
-    "\n",
     "  print('Calculating probabilities...')\n",
     "  speech_probs = []\n",
     "  window_size_samples = 512\n",
-    "  for i in range(0, len(tensor), window_size_samples):\n",
+    "  speech_probs = model.audio_forward(tensor, sr=16000)[0].tolist()\n",
-    "      if len(tensor[i: i+ window_size_samples]) < window_size_samples:\n",
-    "        break\n",
-    "      speech_prob = model(tensor[i: i+ window_size_samples], 16000).item()\n",
-    "      speech_probs.append(speech_prob)\n",
     "  model.reset_states()\n",
     "  print('Making animation...')\n",
     "  make_animation(speech_probs, len(tensor) / 16000)\n",
@@ -196,7 +190,9 @@
     "  <video width=800 controls>\n",
     "        <source src=\"%s\" type=\"video/mp4\">\n",
     "  </video>\n",
-    "  \"\"\" % data_url))"
+    "  \"\"\" % data_url))\n",
+    "\n",
+    "  return speech_probs"
    ]
   },
   {
@@ -216,7 +212,7 @@
    },
    "outputs": [],
    "source": [
-    "record_make_animation()"
+    "speech_probs = record_make_animation()"
    ]
   }
  ],

examples/cpp/silero-vad-onnx.cpp

@@ -1,211 +1,227 @@
+#ifndef _CRT_SECURE_NO_WARNINGS
+#define _CRT_SECURE_NO_WARNINGS
+#endif
+
 #include <iostream>
 #include <vector>
 #include <sstream>
 #include <cstring>
 #include <limits>
 #include <chrono>
+#include <iomanip>
 #include <memory>
 #include <string>
 #include <stdexcept>
-#include <iostream>
-#include <string>
-#include "onnxruntime_cxx_api.h"
-#include "wav.h"
 #include <cstdio>
 #include <cstdarg>
+#include <cmath>    // for std::rint
 #if __cplusplus < 201703L
 #include <memory>
 #endif
 
 //#define __DEBUG_SPEECH_PROB___
 
-class timestamp_t
+#include "onnxruntime_cxx_api.h"
-{
+#include "wav.h" // For reading WAV files
+
+// timestamp_t class: stores the start and end (in samples) of a speech segment.
+class timestamp_t {
 public:
     int start;
     int end;
 
-    // default + parameterized constructor
     timestamp_t(int start = -1, int end = -1)
-        : start(start), end(end)
+        : start(start), end(end) { }
-    {
-    };
 
-    // assignment operator modifies object, therefore non-const
+    timestamp_t& operator=(const timestamp_t& a) {
-    timestamp_t& operator=(const timestamp_t& a)
-    {
         start = a.start;
         end = a.end;
         return *this;
-    };
+    }
 
-    // equality comparison. doesn't modify object. therefore const.
+    bool operator==(const timestamp_t& a) const {
-    bool operator==(const timestamp_t& a) const
-    {
         return (start == a.start && end == a.end);
-    };
+    }
-    std::string c_str()
+
-    {
+    // Returns a formatted string of the timestamp.
-        //return std::format("timestamp {:08d}, {:08d}", start, end);
+    std::string c_str() const {
-        return format("{start:%08d,end:%08d}", start, end);
+        return format("{start:%08d, end:%08d}", start, end);
-    };
+    }
 private:
-
+    // Helper function for formatting.
-    std::string format(const char* fmt, ...)
+    std::string format(const char* fmt, ...) const {
-    {
         char buf[256];
-
         va_list args;
         va_start(args, fmt);
-        const auto r = std::vsnprintf(buf, sizeof buf, fmt, args);
+        const auto r = std::vsnprintf(buf, sizeof(buf), fmt, args);
         va_end(args);
-
         if (r < 0)
-            // conversion failed
             return {};
-
         const size_t len = r;
-        if (len < sizeof buf)
+        if (len < sizeof(buf))
-            // we fit in the buffer
+            return std::string(buf, len);
-            return { buf, len };
-
 #if __cplusplus >= 201703L
-        // C++17: Create a string and write to its underlying array
         std::string s(len, '\0');
         va_start(args, fmt);
         std::vsnprintf(s.data(), len + 1, fmt, args);
         va_end(args);
-
         return s;
 #else
-        // C++11 or C++14: We need to allocate scratch memory
         auto vbuf = std::unique_ptr<char[]>(new char[len + 1]);
         va_start(args, fmt);
         std::vsnprintf(vbuf.get(), len + 1, fmt, args);
         va_end(args);
-
+        return std::string(vbuf.get(), len);
-        return { vbuf.get(), len };
 #endif
-    };
+    }
 };
 
-
+// VadIterator class: uses ONNX Runtime to detect speech segments.
-class VadIterator
+class VadIterator {
-{
 private:
-    // OnnxRuntime resources
+    // ONNX Runtime resources
     Ort::Env env;
     Ort::SessionOptions session_options;
     std::shared_ptr<Ort::Session> session = nullptr;
     Ort::AllocatorWithDefaultOptions allocator;
     Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeCPU);
 
-private:
+    // ----- Context-related additions -----
-    void init_engine_threads(int inter_threads, int intra_threads)
+    const int context_samples = 64;  // For 16kHz, 64 samples are added as context.
-    {
+    std::vector<float> _context;     // Holds the last 64 samples from the previous chunk (initialized to zero).
-        // The method should be called in each thread/proc in multi-thread/proc work
+
+    // Original window size (e.g., 32ms corresponds to 512 samples)
+    int window_size_samples;
+    // Effective window size = window_size_samples + context_samples
+    int effective_window_size;
+
+    // Additional declaration: samples per millisecond
+    int sr_per_ms;
+
+    // ONNX Runtime input/output buffers
+    std::vector<Ort::Value> ort_inputs;
+    std::vector<const char*> input_node_names = { "input", "state", "sr" };
+    std::vector<float> input;
+    unsigned int size_state = 2 * 1 * 128;
+    std::vector<float> _state;
+    std::vector<int64_t> sr;
+    int64_t input_node_dims[2] = {};
+    const int64_t state_node_dims[3] = { 2, 1, 128 };
+    const int64_t sr_node_dims[1] = { 1 };
+    std::vector<Ort::Value> ort_outputs;
+    std::vector<const char*> output_node_names = { "output", "stateN" };
+
+    // Model configuration parameters
+    int sample_rate;
+    float threshold;
+    int min_silence_samples;
+    int min_silence_samples_at_max_speech;
+    int min_speech_samples;
+    float max_speech_samples;
+    int speech_pad_samples;
+    int audio_length_samples;
+
+    // State management
+    bool triggered = false;
+    unsigned int temp_end = 0;
+    unsigned int current_sample = 0;
+    int prev_end;
+    int next_start = 0;
+    std::vector<timestamp_t> speeches;
+    timestamp_t current_speech;
+
+    // Loads the ONNX model.
+    void init_onnx_model(const std::wstring& model_path) {
+        init_engine_threads(1, 1);
+        session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
+    }
+
+    // Initializes threading settings.
+    void init_engine_threads(int inter_threads, int intra_threads) {
         session_options.SetIntraOpNumThreads(intra_threads);
         session_options.SetInterOpNumThreads(inter_threads);
         session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
-    };
+    }
 
-    void init_onnx_model(const std::wstring& model_path)
+    // Resets internal state (_state, _context, etc.)
-    {
+    void reset_states() {
-        // Init threads = 1 for 
+        std::memset(_state.data(), 0, _state.size() * sizeof(float));
-        init_engine_threads(1, 1);
-        // Load model
-        session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
-    };
-
-    void reset_states()
-    {
-        // Call reset before each audio start
-        std::memset(_state.data(), 0.0f, _state.size() * sizeof(float));
         triggered = false;
         temp_end = 0;
         current_sample = 0;
-
         prev_end = next_start = 0;
-
         speeches.clear();
         current_speech = timestamp_t();
-    };
+        std::fill(_context.begin(), _context.end(), 0.0f);
+    }
 
-    void predict(const std::vector<float> &data)
+    // Inference: runs inference on one chunk of input data.
-    {
+    // data_chunk is expected to have window_size_samples samples.
-        // Infer
+    void predict(const std::vector<float>& data_chunk) {
-        // Create ort tensors
+        // Build new input: first context_samples from _context, followed by the current chunk (window_size_samples).
-        input.assign(data.begin(), data.end());
+        std::vector<float> new_data(effective_window_size, 0.0f);
+        std::copy(_context.begin(), _context.end(), new_data.begin());
+        std::copy(data_chunk.begin(), data_chunk.end(), new_data.begin() + context_samples);
+        input = new_data;
+
+        // Create input tensor (input_node_dims[1] is already set to effective_window_size).
         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);
-
-        // 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));
 
-        // Infer
+        // Run inference.
         ort_outputs = session->Run(
-            Ort::RunOptions{nullptr},
+            Ort::RunOptions{ nullptr },
             input_node_names.data(), ort_inputs.data(), ort_inputs.size(),
             output_node_names.data(), output_node_names.size());
 
-        // Output probability & update h,c recursively
         float speech_prob = ort_outputs[0].GetTensorMutableData<float>()[0];
-        float *stateN = ort_outputs[1].GetTensorMutableData<float>();
+        float* stateN = ort_outputs[1].GetTensorMutableData<float>();
         std::memcpy(_state.data(), stateN, size_state * sizeof(float));
+        current_sample += static_cast<unsigned int>(window_size_samples); // Advance by the original window size.
 
-        // Push forward sample index
+        // If speech is detected (probability >= threshold)
-        current_sample += window_size_samples;
+        if (speech_prob >= threshold) {
-
-        // Reset temp_end when > threshold 
-        if ((speech_prob >= threshold))
-        {
 #ifdef __DEBUG_SPEECH_PROB___
-            float speech = current_sample - window_size_samples; // minus window_size_samples to get precise start time point.
+            float speech = current_sample - window_size_samples;
-            printf("{    start: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample- window_size_samples);
+            printf("{ start: %.3f s (%.3f) %08d}\n", 1.0f * speech / sample_rate, speech_prob, current_sample - window_size_samples);
-#endif //__DEBUG_SPEECH_PROB___
+#endif
-            if (temp_end != 0)
+            if (temp_end != 0) {
-            {
                 temp_end = 0;
                 if (next_start < prev_end)
                     next_start = current_sample - window_size_samples;
             }
-            if (triggered == false)
+            if (!triggered) {
-            {
                 triggered = true;
-
                 current_speech.start = current_sample - window_size_samples;
             }
+            // Update context: copy the last context_samples from new_data.
+            std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
             return;
         }
 
-        if (
+        // If the speech segment becomes too long.
-            (triggered == true)
+        if (triggered && ((current_sample - current_speech.start) > max_speech_samples)) {
-            && ((current_sample - current_speech.start) > max_speech_samples)
-            ) {
             if (prev_end > 0) {
                 current_speech.end = prev_end;
                 speeches.push_back(current_speech);
                 current_speech = timestamp_t();
-                
-                // previously reached silence(< neg_thres) and is still not speech(< thres)
                 if (next_start < prev_end)
                     triggered = false;
-                else{
+                else
                     current_speech.start = next_start;
-                }
                 prev_end = 0;
                 next_start = 0;
                 temp_end = 0;
-
             }
-            else{ 
+            else {
                 current_speech.end = current_sample;
                 speeches.push_back(current_speech);
                 current_speech = timestamp_t();
@@ -214,53 +230,29 @@ private:
                 temp_end = 0;
                 triggered = false;
             }
+            std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
             return;
-
         }
-        if ((speech_prob >= (threshold - 0.15)) && (speech_prob < threshold))
+
-        {
+        if ((speech_prob >= (threshold - 0.15)) && (speech_prob < threshold)) {
+            // When the speech probability temporarily drops but is still in speech, update context without changing state.
+            std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
+            return;
+        }
+
+        if (speech_prob < (threshold - 0.15)) {
+#ifdef __DEBUG_SPEECH_PROB___
+            float speech = current_sample - window_size_samples - speech_pad_samples;
+            printf("{ end: %.3f s (%.3f) %08d}\n", 1.0f * speech / sample_rate, speech_prob, current_sample - window_size_samples);
+#endif
             if (triggered) {
-#ifdef __DEBUG_SPEECH_PROB___
-                float speech = current_sample - window_size_samples; // minus window_size_samples to get precise start time point.
-                printf("{ speeking: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample - window_size_samples);
-#endif //__DEBUG_SPEECH_PROB___
-            }
-            else {
-#ifdef __DEBUG_SPEECH_PROB___
-                float speech = current_sample - window_size_samples; // minus window_size_samples to get precise start time point.
-                printf("{  silence: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample - window_size_samples);
-#endif //__DEBUG_SPEECH_PROB___
-            }
-            return;
-        }
-
-
-        // 4) End 
-        if ((speech_prob < (threshold - 0.15)))
-        {
-#ifdef __DEBUG_SPEECH_PROB___
-            float speech = current_sample - window_size_samples - speech_pad_samples; // minus window_size_samples to get precise start time point.
-            printf("{      end: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample - window_size_samples);
-#endif //__DEBUG_SPEECH_PROB___
-            if (triggered == true)
-            {
                 if (temp_end == 0)
-                {
                     temp_end = current_sample;
-                }
                 if (current_sample - temp_end > min_silence_samples_at_max_speech)
                     prev_end = temp_end;
-                // a. silence < min_slience_samples, continue speaking 
+                if ((current_sample - temp_end) >= min_silence_samples) {
-                if ((current_sample - temp_end) < min_silence_samples)
-                {
-
-                }
-                // b. silence >= min_slience_samples, end speaking
-                else
-                {
                     current_speech.end = temp_end;
-                    if (current_speech.end - current_speech.start > min_speech_samples)
+                    if (current_speech.end - current_speech.start > min_speech_samples) {
-                    {
                         speeches.push_back(current_speech);
                         current_speech = timestamp_t();
                         prev_end = 0;
@@ -270,27 +262,23 @@ private:
                     }
                 }
             }
-            else {
+            std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
-                // may first windows see end state.
-            }
             return;
         }
-    };
+    }
+
 public:
-    void process(const std::vector<float>& input_wav)
+    // Process the entire audio input.
-    {
+    void process(const std::vector<float>& input_wav) {
         reset_states();
-
+        audio_length_samples = static_cast<int>(input_wav.size());
-        audio_length_samples = input_wav.size();
+        // Process audio in chunks of window_size_samples (e.g., 512 samples)
-
+        for (size_t j = 0; j < static_cast<size_t>(audio_length_samples); j += static_cast<size_t>(window_size_samples)) {
-        for (int j = 0; j < audio_length_samples; j += window_size_samples)
+            if (j + static_cast<size_t>(window_size_samples) > static_cast<size_t>(audio_length_samples))
-        {
-            if (j + window_size_samples > audio_length_samples)
                 break;
-            std::vector<float> r{ &input_wav[0] + j, &input_wav[0] + j + window_size_samples };
+            std::vector<float> chunk(&input_wav[j], &input_wav[j] + window_size_samples);
-            predict(r);
+            predict(chunk);
         }
-
         if (current_speech.start >= 0) {
             current_speech.end = audio_length_samples;
             speeches.push_back(current_speech);
@@ -300,179 +288,80 @@ public:
             temp_end = 0;
             triggered = false;
         }
-    };
-
-    void process(const std::vector<float>& input_wav, std::vector<float>& output_wav)
-    {
-        process(input_wav);
-        collect_chunks(input_wav, output_wav);
     }
 
-    void collect_chunks(const std::vector<float>& input_wav, std::vector<float>& output_wav)
+    // Returns the detected speech timestamps.
-    {
+    const std::vector<timestamp_t> get_speech_timestamps() const {
-        output_wav.clear();
-        for (int i = 0; i < speeches.size(); i++) {
-#ifdef __DEBUG_SPEECH_PROB___
-            std::cout << speeches[i].c_str() << std::endl;
-#endif //#ifdef __DEBUG_SPEECH_PROB___
-            std::vector<float> slice(&input_wav[speeches[i].start], &input_wav[speeches[i].end]);
-            output_wav.insert(output_wav.end(),slice.begin(),slice.end());
-        }
-    };
-
-    const std::vector<timestamp_t> get_speech_timestamps() const
-    {
         return speeches;
     }
 
-    void drop_chunks(const std::vector<float>& input_wav, std::vector<float>& output_wav)
+    // Public method to reset the internal state.
-    {
+    void reset() {
-        output_wav.clear();
+        reset_states();
-        int current_start = 0;
+    }
-        for (int i = 0; i < speeches.size(); i++) {
-
-            std::vector<float> slice(&input_wav[current_start],&input_wav[speeches[i].start]);
-            output_wav.insert(output_wav.end(), slice.begin(), slice.end());
-            current_start = speeches[i].end;
-        }
-
-        std::vector<float> slice(&input_wav[current_start], &input_wav[input_wav.size()]);
-        output_wav.insert(output_wav.end(), slice.begin(), slice.end());
-    };
-
-private:
-    // model config
-    int64_t window_size_samples;  // Assign when init, support 256 512 768 for 8k; 512 1024 1536 for 16k.
-    int sample_rate;  //Assign when init support 16000 or 8000      
-    int sr_per_ms;   // Assign when init, support 8 or 16
-    float threshold; 
-    int min_silence_samples; // sr_per_ms * #ms
-    int min_silence_samples_at_max_speech; // sr_per_ms * #98
-    int min_speech_samples; // sr_per_ms * #ms
-    float max_speech_samples;
-    int speech_pad_samples; // usually a 
-    int audio_length_samples;
-
-    // model states
-    bool triggered = false;
-    unsigned int temp_end = 0;
-    unsigned int current_sample = 0;    
-    // MAX 4294967295 samples / 8sample per ms / 1000 / 60 = 8947 minutes  
-    int prev_end;
-    int next_start = 0;
-
-    //Output timestamp
-    std::vector<timestamp_t> speeches;
-    timestamp_t current_speech;
-
-
-    // Onnx model
-    // Inputs
-    std::vector<Ort::Value> ort_inputs;
-    
-    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;
-
-    int64_t input_node_dims[2] = {};
-    const int64_t state_node_dims[3] = {2, 1, 128}; 
-    const int64_t sr_node_dims[1] = {1};
-
-    // Outputs
-    std::vector<Ort::Value> ort_outputs;
-    std::vector<const char *> output_node_names = {"output", "stateN"};
 
 public:
-    // Construction
+    // Constructor: sets model path, sample rate, window size (ms), and other parameters.
+    // The parameters are set to match the Python version.
     VadIterator(const std::wstring ModelPath,
         int Sample_rate = 16000, int windows_frame_size = 32,
-        float Threshold = 0.5, int min_silence_duration_ms = 0,
+        float Threshold = 0.5, int min_silence_duration_ms = 100,
-        int speech_pad_ms = 32, int min_speech_duration_ms = 32,
+        int speech_pad_ms = 30, int min_speech_duration_ms = 250,
         float max_speech_duration_s = std::numeric_limits<float>::infinity())
+        : sample_rate(Sample_rate), threshold(Threshold), speech_pad_samples(speech_pad_ms), prev_end(0)
     {
-        init_onnx_model(ModelPath);
+        sr_per_ms = sample_rate / 1000;  // e.g., 16000 / 1000 = 16
-        threshold = Threshold;
+        window_size_samples = windows_frame_size * sr_per_ms; // e.g., 32ms * 16 = 512 samples
-        sample_rate = Sample_rate;
+        effective_window_size = window_size_samples + context_samples; // e.g., 512 + 64 = 576 samples
-        sr_per_ms = sample_rate / 1000;
-
-        window_size_samples = windows_frame_size * sr_per_ms;
-
-        min_speech_samples = sr_per_ms * min_speech_duration_ms;
-        speech_pad_samples = sr_per_ms * speech_pad_ms;
-
-        max_speech_samples = (
-            sample_rate * max_speech_duration_s
-            - window_size_samples
-            - 2 * speech_pad_samples
-            );
-
-        min_silence_samples = sr_per_ms * min_silence_duration_ms;
-        min_silence_samples_at_max_speech = sr_per_ms * 98;
-
-        input.resize(window_size_samples);
         input_node_dims[0] = 1;
-        input_node_dims[1] = window_size_samples;
+        input_node_dims[1] = effective_window_size;
-
         _state.resize(size_state);
         sr.resize(1);
         sr[0] = sample_rate;
-    };
+        _context.assign(context_samples, 0.0f);
+        min_speech_samples = sr_per_ms * min_speech_duration_ms;
+        max_speech_samples = (sample_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples);
+        min_silence_samples = sr_per_ms * min_silence_duration_ms;
+        min_silence_samples_at_max_speech = sr_per_ms * 98;
+        init_onnx_model(ModelPath);
+    }
 };
 
-int main()
+int main() {
-{
+    // Read the WAV file (expects 16000 Hz, mono, PCM).
-    std::vector<timestamp_t> stamps;
+    wav::WavReader wav_reader("audio/recorder.wav"); // File located in the "audio" folder.
-
+    int numSamples = wav_reader.num_samples();
-    // Read wav
+    std::vector<float> input_wav(static_cast<size_t>(numSamples));
-    wav::WavReader wav_reader("recorder.wav"); //16000,1,32float
+    for (size_t i = 0; i < static_cast<size_t>(numSamples); i++) {
-    std::vector<float> input_wav(wav_reader.num_samples());
-    std::vector<float> output_wav;
-
-    for (int i = 0; i < wav_reader.num_samples(); i++)
-    {
         input_wav[i] = static_cast<float>(*(wav_reader.data() + i));
     }
 
+    // Set the ONNX model path (file located in the "model" folder).
+    std::wstring model_path = L"model/silero_vad.onnx";
 
+    // Initialize the VadIterator.
+    VadIterator vad(model_path);
 
-    // ===== Test configs =====
+    // Process the audio.
-    std::wstring path = L"silero_vad.onnx";
-    VadIterator vad(path);
-
-    // ==============================================
-    // ==== = Example 1 of full function  ===== 
-    // ==============================================
     vad.process(input_wav);
 
-    // 1.a get_speech_timestamps
+    // Retrieve the speech timestamps (in samples).
-    stamps = vad.get_speech_timestamps();
+    std::vector<timestamp_t> stamps = vad.get_speech_timestamps();
-    for (int i = 0; i < stamps.size(); i++) {
 
-        std::cout << stamps[i].c_str() << std::endl;
+    // Convert timestamps to seconds and round to one decimal place (for 16000 Hz).
+    const float sample_rate_float = 16000.0f;
+    for (size_t i = 0; i < stamps.size(); i++) {
+        float start_sec = std::rint((stamps[i].start / sample_rate_float) * 10.0f) / 10.0f;
+        float end_sec = std::rint((stamps[i].end / sample_rate_float) * 10.0f) / 10.0f;
+        std::cout << "Speech detected from "
+            << std::fixed << std::setprecision(1) << start_sec
+            << " s to "
+            << std::fixed << std::setprecision(1) << end_sec
+            << " s" << std::endl;
     }
 
-    // 1.b collect_chunks output wav
+    // Optionally, reset the internal state.
-    vad.collect_chunks(input_wav, output_wav);
+    vad.reset();
 
-    // 1.c drop_chunks output wav
+    return 0;
-    vad.drop_chunks(input_wav, output_wav);
-
-    // ==============================================
-    // ===== Example 2 of simple full function  =====
-    // ==============================================
-    vad.process(input_wav, output_wav);
-
-    stamps = vad.get_speech_timestamps();
-    for (int i = 0; i < stamps.size(); i++) {
-
-        std::cout << stamps[i].c_str() << std::endl;
-    }
-
-    // ==============================================
-    // ===== Example 3 of full function  =====
-    // ==============================================
-    for(int i = 0; i<2; i++)
-        vad.process(input_wav, output_wav);
 }

examples/cpp/wav.h

@@ -12,10 +12,10 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-
 #ifndef FRONTEND_WAV_H_
 #define FRONTEND_WAV_H_
 
+
 #include <assert.h>
 #include <stdint.h>
 #include <stdio.h>
@@ -24,6 +24,8 @@
 
 #include <string>
 
+#include <iostream>
+
 // #include "utils/log.h"
 
 namespace wav {
@@ -230,6 +232,6 @@ class WavWriter {
   int bits_per_sample_;
 };
 
-}  // namespace wenet
+}  // namespace wav
 
 #endif  // FRONTEND_WAV_H_

examples/cpp_libtorch/README.md

@@ -0,0 +1,45 @@
+# Silero-VAD V5 in C++ (based on LibTorch)
+
+This is the source code for Silero-VAD V5 in C++, utilizing LibTorch. The primary implementation is CPU-based, and you should compare its results with the Python version. Only results at 16kHz have been tested.
+
+Additionally, batch and CUDA inference options are available if you want to explore further. Note that when using batch inference, the speech probabilities may slightly differ from the standard version, likely due to differences in caching. Unlike individual input processing, batch inference may not use the cache from previous chunks. Despite this, batch inference offers significantly faster processing. For optimal performance, consider adjusting the threshold when using batch inference.
+
+## Requirements
+
+- GCC 11.4.0 (GCC >= 5.1)
+- LibTorch 1.13.0 (other versions are also acceptable)
+
+## Download LibTorch
+
+```bash
+-CPU Version
+wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-1.13.0%2Bcpu.zip
+unzip libtorch-shared-with-deps-1.13.0+cpu.zip'
+
+-CUDA Version
+wget https://download.pytorch.org/libtorch/cu116/libtorch-shared-with-deps-1.13.0%2Bcu116.zip
+unzip libtorch-shared-with-deps-1.13.0+cu116.zip
+```
+
+## Compilation
+
+```bash
+-CPU Version
+g++ main.cc silero_torch.cc -I ./libtorch/include/ -I ./libtorch/include/torch/csrc/api/include -L ./libtorch/lib/ -ltorch -ltorch_cpu -lc10 -Wl,-rpath,./libtorch/lib/ -o silero -std=c++14 -D_GLIBCXX_USE_CXX11_ABI=0
+
+-CUDA Version
+g++ main.cc silero_torch.cc -I ./libtorch/include/ -I ./libtorch/include/torch/csrc/api/include -L ./libtorch/lib/ -ltorch -ltorch_cuda -ltorch_cpu -lc10 -Wl,-rpath,./libtorch/lib/ -o silero -std=c++14 -D_GLIBCXX_USE_CXX11_ABI=0 -DUSE_GPU
+```
+
+
+## Optional Compilation Flags
+-DUSE_BATCH: Enable batch inference
+-DUSE_GPU: Use GPU for inference
+
+## Run the Program
+To run the program, use the following command:
+
+`./silero aepyx.wav 16000 0.5`
+
+The sample file aepyx.wav is part of the Voxconverse dataset.
+File details: aepyx.wav is a 16kHz, 16-bit audio file.

examples/cpp_libtorch/main.cc

@@ -0,0 +1,54 @@
+#include <iostream>
+#include "silero_torch.h"
+#include "wav.h"
+
+int main(int argc, char* argv[]) {
+
+	if(argc != 4){
+		std::cerr<<"Usage : "<<argv[0]<<" <wav.path> <SampleRate> <Threshold>"<<std::endl;
+		std::cerr<<"Usage : "<<argv[0]<<" sample.wav 16000 0.5"<<std::endl;
+		return 1;
+	}
+
+	std::string wav_path = argv[1];
+	float sample_rate = std::stof(argv[2]);
+	float threshold = std::stof(argv[3]);
+
+
+	//Load Model
+	std::string model_path = "../../src/silero_vad/data/silero_vad.jit";
+	silero::VadIterator vad(model_path);
+
+        vad.threshold=threshold;	//(Default:0.5)
+	vad.sample_rate=sample_rate;	//16000Hz,8000Hz. (Default:16000)
+        vad.print_as_samples=true;	//if true, it prints time-stamp with samples. otherwise, in seconds
+					//(Default:false)
+
+	vad.SetVariables();
+
+	// Read wav
+	wav::WavReader wav_reader(wav_path); 
+	std::vector<float> input_wav(wav_reader.num_samples());
+
+	for (int i = 0; i < wav_reader.num_samples(); i++)
+	{
+		input_wav[i] = static_cast<float>(*(wav_reader.data() + i));
+	}
+
+	vad.SpeechProbs(input_wav);
+
+	std::vector<silero::SpeechSegment> speeches = vad.GetSpeechTimestamps();
+	for(const auto& speech : speeches){
+		if(vad.print_as_samples){
+			std::cout<<"{'start': "<<static_cast<int>(speech.start)<<", 'end': "<<static_cast<int>(speech.end)<<"}"<<std::endl;
+		}
+		else{
+			std::cout<<"{'start': "<<speech.start<<", 'end': "<<speech.end<<"}"<<std::endl;
+		}
+	}	
+
+
+	return 0;
+	}
+
+

examples/cpp_libtorch/silero_torch.cc

@@ -0,0 +1,285 @@
+//Author      : Nathan Lee
+//Created On  : 2024-11-18
+//Description : silero 5.1 system for torch-script(c++).
+//Version     : 1.0
+
+
+#include "silero_torch.h"
+
+namespace silero {
+
+	VadIterator::VadIterator(const std::string &model_path, float threshold, int sample_rate, int window_size_ms, int speech_pad_ms, int min_silence_duration_ms, int min_speech_duration_ms, int max_duration_merge_ms, bool print_as_samples)
+		:sample_rate(sample_rate), threshold(threshold), window_size_ms(window_size_ms), speech_pad_ms(speech_pad_ms), min_silence_duration_ms(min_silence_duration_ms), min_speech_duration_ms(min_speech_duration_ms), max_duration_merge_ms(max_duration_merge_ms), print_as_samples(print_as_samples)
+	{
+		init_torch_model(model_path);
+		//init_engine(window_size_ms);
+	}
+	VadIterator::~VadIterator(){
+	}
+
+
+	void VadIterator::SpeechProbs(std::vector<float>& input_wav){
+		// Set the sample rate (must match the model's expected sample rate)
+		// Process the waveform in chunks of 512 samples
+		int num_samples = input_wav.size();
+		int num_chunks = num_samples / window_size_samples;
+		int remainder_samples = num_samples % window_size_samples;
+
+		total_sample_size += num_samples;
+
+		torch::Tensor output;
+		std::vector<torch::Tensor> chunks;
+
+		for (int i = 0; i < num_chunks; i++) {
+
+			float* chunk_start = input_wav.data() + i *window_size_samples;
+			torch::Tensor chunk = torch::from_blob(chunk_start, {1,window_size_samples}, torch::kFloat32);
+			//std::cout<<"chunk size : "<<chunk.sizes()<<std::endl;
+			chunks.push_back(chunk);
+
+
+			if(i==num_chunks-1 && remainder_samples>0){//마지막 chunk && 나머지가 존재
+				int remaining_samples = num_samples - num_chunks * window_size_samples;
+				//std::cout<<"Remainder size : "<<remaining_samples;
+				float* chunk_start_remainder = input_wav.data() + num_chunks *window_size_samples;
+
+				torch::Tensor remainder_chunk = torch::from_blob(chunk_start_remainder, {1,remaining_samples},
+						torch::kFloat32);
+				// Pad the remainder chunk to match window_size_samples
+				torch::Tensor padded_chunk = torch::cat({remainder_chunk, torch::zeros({1, window_size_samples
+							- remaining_samples}, torch::kFloat32)}, 1);
+				//std::cout<<", padded_chunk size : "<<padded_chunk.size(1)<<std::endl;
+
+				chunks.push_back(padded_chunk);
+			}
+		}
+
+		if (!chunks.empty()) {
+
+#ifdef USE_BATCH
+			torch::Tensor batched_chunks = torch::stack(chunks);  // Stack all chunks into a single tensor
+			//batched_chunks = batched_chunks.squeeze(1);
+			batched_chunks = torch::cat({batched_chunks.squeeze(1)});
+
+#ifdef USE_GPU
+			batched_chunks = batched_chunks.to(at::kCUDA);        // Move the entire batch to GPU once
+#endif
+			// Prepare input for model
+			std::vector<torch::jit::IValue> inputs;
+			inputs.push_back(batched_chunks);  // Batch of chunks
+			inputs.push_back(sample_rate);     // Assuming sample_rate is a valid input for the model
+
+			// Run inference on the batch
+			torch::NoGradGuard no_grad;
+			torch::Tensor output = model.forward(inputs).toTensor();
+#ifdef USE_GPU
+			output = output.to(at::kCPU);      // Move the output back to CPU once
+#endif
+			// Collect output probabilities
+			for (int i = 0; i < chunks.size(); i++) {
+				float output_f = output[i].item<float>();
+				outputs_prob.push_back(output_f);
+				//std::cout << "Chunk " << i << " prob: " << output_f<< "\n";
+			}
+#else
+
+			std::vector<torch::Tensor> outputs;
+			torch::Tensor batched_chunks = torch::stack(chunks);
+#ifdef USE_GPU
+			batched_chunks = batched_chunks.to(at::kCUDA);
+#endif
+			for (int i = 0; i < chunks.size(); i++) {
+				torch::NoGradGuard no_grad;
+				std::vector<torch::jit::IValue> inputs;
+				inputs.push_back(batched_chunks[i]);
+				inputs.push_back(sample_rate);
+
+				torch::Tensor output = model.forward(inputs).toTensor();
+				outputs.push_back(output);
+			}
+			torch::Tensor all_outputs = torch::stack(outputs);
+#ifdef USE_GPU
+			all_outputs = all_outputs.to(at::kCPU);
+#endif
+			for (int i = 0; i < chunks.size(); i++) {
+				float output_f = all_outputs[i].item<float>();
+				outputs_prob.push_back(output_f);
+			}
+
+
+
+#endif
+
+		}
+
+
+	}
+
+
+	std::vector<SpeechSegment> VadIterator::GetSpeechTimestamps() {
+		std::vector<SpeechSegment> speeches = DoVad();
+
+#ifdef USE_BATCH
+		//When you use BATCH inference. You would better use 'mergeSpeeches' function to arrage time stamp.
+		//It could be better get reasonable output because of distorted probs.
+		duration_merge_samples = sample_rate * max_duration_merge_ms / 1000;
+		std::vector<SpeechSegment> speeches_merge = mergeSpeeches(speeches, duration_merge_samples);
+		if(!print_as_samples){
+			for (auto& speech : speeches_merge) { //samples to second
+				speech.start /= sample_rate;
+				speech.end /= sample_rate;
+			}
+		}
+
+		return speeches_merge;
+#else
+
+		if(!print_as_samples){
+			for (auto& speech : speeches) { //samples to second
+				speech.start /= sample_rate;
+				speech.end /= sample_rate;
+			}
+		}
+
+		return speeches;
+
+#endif
+
+	}
+	void VadIterator::SetVariables(){
+		init_engine(window_size_ms);
+	}
+
+	void VadIterator::init_engine(int window_size_ms) {
+		min_silence_samples = sample_rate * min_silence_duration_ms / 1000;
+		speech_pad_samples = sample_rate * speech_pad_ms / 1000;
+		window_size_samples = sample_rate / 1000 * window_size_ms;
+		min_speech_samples = sample_rate * min_speech_duration_ms / 1000;
+	}
+
+	void VadIterator::init_torch_model(const std::string& model_path) {
+		at::set_num_threads(1);
+		model = torch::jit::load(model_path);
+
+#ifdef USE_GPU
+		if (!torch::cuda::is_available()) {
+			std::cout<<"CUDA is not available! Please check your GPU settings"<<std::endl;
+			throw std::runtime_error("CUDA is not available!");
+			model.to(at::Device(at::kCPU));    
+
+		} else {
+			std::cout<<"CUDA available! Running on '0'th GPU"<<std::endl;
+			model.to(at::Device(at::kCUDA, 0));        //select 0'th machine 
+		}
+#endif
+
+
+		model.eval();
+		torch::NoGradGuard no_grad;
+		std::cout << "Model loaded successfully"<<std::endl;
+	}
+
+	void VadIterator::reset_states() {
+		triggered = false;
+		current_sample = 0;
+		temp_end = 0;
+		outputs_prob.clear();
+		model.run_method("reset_states");
+		total_sample_size = 0;
+	}
+
+	std::vector<SpeechSegment> VadIterator::DoVad() {
+		std::vector<SpeechSegment> speeches;
+
+		for (size_t i = 0; i < outputs_prob.size(); ++i) {
+			float speech_prob = outputs_prob[i];
+			//std::cout << speech_prob << std::endl;
+			//std::cout << "Chunk " << i << " Prob: " << speech_prob << "\n";
+			//std::cout << speech_prob << " ";
+			current_sample += window_size_samples;
+
+			if (speech_prob >= threshold && temp_end != 0) {
+				temp_end = 0;
+			}
+
+			if (speech_prob >= threshold && !triggered) {
+				triggered = true;
+				SpeechSegment segment;
+				segment.start = std::max(static_cast<int>(0), current_sample - speech_pad_samples - window_size_samples);
+				speeches.push_back(segment);
+				continue;
+			}
+
+			if (speech_prob < threshold - 0.15f && triggered) {
+				if (temp_end == 0) {
+					temp_end = current_sample;
+				}
+
+				if (current_sample - temp_end < min_silence_samples) {
+					continue;
+				} else {
+					SpeechSegment& segment = speeches.back();
+					segment.end = temp_end + speech_pad_samples - window_size_samples;
+					temp_end = 0;
+					triggered = false;
+				}
+			}
+		}
+
+		if (triggered) { //만약 낮은 확률을 보이다가  마지막프레임 prbos만 딱 확률이 높게 나오면 위에서 triggerd = true 메핑과 동시에  segment start가 돼서 문제가 될것 같은데? start = end 같은값? 후처리가 있으니 문제가 없으려나?
+			std::cout<<"when last triggered is keep working until last Probs"<<std::endl;
+			SpeechSegment& segment = speeches.back();
+			segment.end = total_sample_size;  // 현재 샘플을 마지막 구간의 종료 시간으로 설정
+			triggered = false;  // VAD 상태 초기화
+		}
+
+		speeches.erase(
+                		std::remove_if(
+                        speeches.begin(),
+                        speeches.end(),
+                        [this](const SpeechSegment& speech) {
+                        return ((speech.end - this->speech_pad_samples) - (speech.start + this->speech_pad_samples) < min_speech_samples);
+			//min_speech_samples is 4000samples(0.25sec)
+			//여기서 포인트!! 계산 할때는 start,end sample에'speech_pad_samples' 사이즈를 추가한후 길이를 측정함. 
+                        }
+                ),
+                speeches.end()
+              );
+
+
+		//std::cout<<std::endl;
+		//std::cout<<"outputs_prob.size : "<<outputs_prob.size()<<std::endl;
+
+		reset_states();
+		return speeches;
+	}
+
+	std::vector<SpeechSegment> VadIterator::mergeSpeeches(const std::vector<SpeechSegment>& speeches, int duration_merge_samples) {
+		std::vector<SpeechSegment> mergedSpeeches;
+
+		if (speeches.empty()) {
+			return mergedSpeeches; // 빈 벡터 반환
+		}
+
+		// 첫 번째 구간으로 초기화
+		SpeechSegment currentSegment = speeches[0];
+
+		for (size_t i = 1; i < speeches.size(); ++i) {	//첫번째 start,end 정보 건너뛰기. 그래서 i=1부터
+			// 두 구간의 차이가 threshold(duration_merge_samples)보다 작은 경우, 합침
+			if (speeches[i].start - currentSegment.end < duration_merge_samples) {
+				// 현재 구간의 끝점을 업데이트
+				currentSegment.end = speeches[i].end;
+			} else {
+				// 차이가 threshold(duration_merge_samples) 이상이면 현재 구간을 저장하고 새로운 구간 시작
+				mergedSpeeches.push_back(currentSegment);
+				currentSegment = speeches[i];
+			}
+		}
+
+		// 마지막 구간 추가
+		mergedSpeeches.push_back(currentSegment);
+
+		return mergedSpeeches;
+	}
+
+	}

examples/cpp_libtorch/silero_torch.h

@@ -0,0 +1,75 @@
+//Author      : Nathan Lee
+//Created On  : 2024-11-18
+//Description : silero 5.1 system for torch-script(c++).
+//Version     : 1.0
+
+#ifndef SILERO_TORCH_H
+#define SILERO_TORCH_H
+
+#include <string>
+#include <memory>
+#include <stdexcept>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <fstream>
+#include <chrono>
+
+#include <torch/torch.h>
+#include <torch/script.h>
+
+
+namespace silero{
+
+	struct SpeechSegment{
+		int start;
+		int end;
+	};
+
+	class VadIterator{
+		public:
+
+			VadIterator(const std::string &model_path, float threshold = 0.5, int sample_rate = 16000, 
+				int window_size_ms = 32, int speech_pad_ms = 30, int min_silence_duration_ms = 100, 
+				int min_speech_duration_ms = 250, int max_duration_merge_ms = 300, bool print_as_samples = false);
+			~VadIterator(); 
+
+
+			void SpeechProbs(std::vector<float>& input_wav);
+			std::vector<silero::SpeechSegment> GetSpeechTimestamps();
+			void SetVariables();
+
+			float threshold;
+			int sample_rate;
+			int window_size_ms;
+			int min_speech_duration_ms;
+			int max_duration_merge_ms;
+			bool print_as_samples;
+
+		private:
+			torch::jit::script::Module model;
+			std::vector<float> outputs_prob;
+			int min_silence_samples;
+			int min_speech_samples;
+			int speech_pad_samples;
+			int window_size_samples;
+			int duration_merge_samples;
+			int current_sample = 0;
+
+			int total_sample_size=0;
+
+			int min_silence_duration_ms;
+			int speech_pad_ms;
+			bool triggered = false;
+			int temp_end = 0;
+
+			void init_engine(int window_size_ms);
+			void init_torch_model(const std::string& model_path);
+			void reset_states();
+			std::vector<SpeechSegment> DoVad();
+			std::vector<SpeechSegment> mergeSpeeches(const std::vector<SpeechSegment>& speeches, int duration_merge_samples);
+
+	};
+
+}
+#endif // SILERO_TORCH_H

examples/cpp_libtorch/wav.h

@@ -0,0 +1,235 @@
+// Copyright (c) 2016 Personal (Binbin Zhang)
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+
+#ifndef FRONTEND_WAV_H_
+#define FRONTEND_WAV_H_
+
+#include <assert.h>
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include <string>
+
+// #include "utils/log.h"
+
+namespace wav {
+
+struct WavHeader {
+  char riff[4];  // "riff"
+  unsigned int size;
+  char wav[4];  // "WAVE"
+  char fmt[4];  // "fmt "
+  unsigned int fmt_size;
+  uint16_t format;
+  uint16_t channels;
+  unsigned int sample_rate;
+  unsigned int bytes_per_second;
+  uint16_t block_size;
+  uint16_t bit;
+  char data[4];  // "data"
+  unsigned int data_size;
+};
+
+class WavReader {
+ public:
+  WavReader() : data_(nullptr) {}
+  explicit WavReader(const std::string& filename) { Open(filename); }
+
+  bool Open(const std::string& filename) {
+    FILE* fp = fopen(filename.c_str(), "rb"); //文件读取
+    if (NULL == fp) {
+      std::cout << "Error in read " << filename;
+      return false;
+    }
+
+    WavHeader header;
+    fread(&header, 1, sizeof(header), fp);
+    if (header.fmt_size < 16) {
+      printf("WaveData: expect PCM format data "
+              "to have fmt chunk of at least size 16.\n");
+      return false;
+    } else if (header.fmt_size > 16) {
+      int offset = 44 - 8 + header.fmt_size - 16;
+      fseek(fp, offset, SEEK_SET);
+      fread(header.data, 8, sizeof(char), fp);
+    }
+    // check "riff" "WAVE" "fmt " "data"
+
+    // Skip any sub-chunks between "fmt" and "data".  Usually there will
+    // be a single "fact" sub chunk, but on Windows there can also be a
+    // "list" sub chunk.
+    while (0 != strncmp(header.data, "data", 4)) {
+      // We will just ignore the data in these chunks.
+      fseek(fp, header.data_size, SEEK_CUR);
+      // read next sub chunk
+      fread(header.data, 8, sizeof(char), fp);
+    }
+
+    if (header.data_size == 0) {
+        int offset = ftell(fp);
+        fseek(fp, 0, SEEK_END);
+        header.data_size = ftell(fp) - offset;
+        fseek(fp, offset, SEEK_SET);
+    }
+
+    num_channel_ = header.channels;
+    sample_rate_ = header.sample_rate;
+    bits_per_sample_ = header.bit;
+    int num_data = header.data_size / (bits_per_sample_ / 8);
+    data_ = new float[num_data]; // Create 1-dim array
+    num_samples_ = num_data / num_channel_;
+
+    std::cout << "num_channel_    :" << num_channel_ << std::endl;
+    std::cout << "sample_rate_    :" << sample_rate_ << std::endl;
+    std::cout << "bits_per_sample_:" << bits_per_sample_ << std::endl;
+    std::cout << "num_samples     :" << num_data << std::endl;
+    std::cout << "num_data_size   :" << header.data_size << std::endl;
+
+    switch (bits_per_sample_) {
+        case 8: {
+            char sample;
+            for (int i = 0; i < num_data; ++i) {
+                fread(&sample, 1, sizeof(char), fp);
+                data_[i] = static_cast<float>(sample) / 32768;
+            }
+            break;
+        }
+        case 16: {
+            int16_t sample;
+            for (int i = 0; i < num_data; ++i) {
+                fread(&sample, 1, sizeof(int16_t), fp);
+                data_[i] = static_cast<float>(sample) / 32768;
+            }
+            break;
+        }
+        case 32:
+        {
+            if (header.format == 1) //S32
+            {
+                int sample;
+                for (int i = 0; i < num_data; ++i) {
+                    fread(&sample, 1, sizeof(int), fp);
+                    data_[i] = static_cast<float>(sample) / 32768;
+                }
+            }
+            else if (header.format == 3) // IEEE-float
+            {
+                float sample;
+                for (int i = 0; i < num_data; ++i) {
+                    fread(&sample, 1, sizeof(float), fp);
+                    data_[i] = static_cast<float>(sample);
+                }
+            }
+            else {
+                printf("unsupported quantization bits\n");
+            }
+            break;
+        }
+        default:
+            printf("unsupported quantization bits\n");
+            break;
+    }
+
+    fclose(fp);
+    return true;
+  }
+
+  int num_channel() const { return num_channel_; }
+  int sample_rate() const { return sample_rate_; }
+  int bits_per_sample() const { return bits_per_sample_; }
+  int num_samples() const { return num_samples_; }
+
+  ~WavReader() {
+    delete[] data_;
+  }
+
+  const float* data() const { return data_; }
+
+ private:
+  int num_channel_;
+  int sample_rate_;
+  int bits_per_sample_;
+  int num_samples_;  // sample points per channel
+  float* data_;
+};
+
+class WavWriter {
+ public:
+  WavWriter(const float* data, int num_samples, int num_channel,
+            int sample_rate, int bits_per_sample)
+      : data_(data),
+        num_samples_(num_samples),
+        num_channel_(num_channel),
+        sample_rate_(sample_rate),
+        bits_per_sample_(bits_per_sample) {}
+
+  void Write(const std::string& filename) {
+    FILE* fp = fopen(filename.c_str(), "w");
+    // init char 'riff' 'WAVE' 'fmt ' 'data'
+    WavHeader header;
+    char wav_header[44] = {0x52, 0x49, 0x46, 0x46, 0x00, 0x00, 0x00, 0x00, 0x57,
+                           0x41, 0x56, 0x45, 0x66, 0x6d, 0x74, 0x20, 0x10, 0x00,
+                           0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                           0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                           0x64, 0x61, 0x74, 0x61, 0x00, 0x00, 0x00, 0x00};
+    memcpy(&header, wav_header, sizeof(header));
+    header.channels = num_channel_;
+    header.bit = bits_per_sample_;
+    header.sample_rate = sample_rate_;
+    header.data_size = num_samples_ * num_channel_ * (bits_per_sample_ / 8);
+    header.size = sizeof(header) - 8 + header.data_size;
+    header.bytes_per_second =
+        sample_rate_ * num_channel_ * (bits_per_sample_ / 8);
+    header.block_size = num_channel_ * (bits_per_sample_ / 8);
+
+    fwrite(&header, 1, sizeof(header), fp);
+
+    for (int i = 0; i < num_samples_; ++i) {
+      for (int j = 0; j < num_channel_; ++j) {
+        switch (bits_per_sample_) {
+          case 8: {
+            char sample = static_cast<char>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+          case 16: {
+            int16_t sample = static_cast<int16_t>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+          case 32: {
+            int sample = static_cast<int>(data_[i * num_channel_ + j]);
+            fwrite(&sample, 1, sizeof(sample), fp);
+            break;
+          }
+        }
+      }
+    }
+    fclose(fp);
+  }
+
+ private:
+  const float* data_;
+  int num_samples_;  // total float points in data_
+  int num_channel_;
+  int sample_rate_;
+  int bits_per_sample_;
+};
+
+}  // namespace wenet
+
+#endif  // FRONTEND_WAV_H_

examples/haskell/README.md

@@ -0,0 +1,13 @@
+# Haskell example
+
+To run the example, make sure you put an ``example.wav`` in this directory, and then run the following:
+```bash
+stack run
+```
+
+The ``example.wav`` file must have the following requirements:
+- Must be 16khz sample rate.
+- Must be mono channel.
+- Must be 16-bit audio.
+
+This uses the [silero-vad](https://hackage.haskell.org/package/silero-vad) package, a haskell implementation based on the C# example.

examples/haskell/app/Main.hs

@@ -0,0 +1,22 @@
+module Main (main) where
+
+import qualified Data.Vector.Storable as Vector
+import Data.WAVE
+import Data.Function
+import Silero
+
+main :: IO ()
+main =
+  withModel $ \model -> do
+    wav <- getWAVEFile "example.wav"
+    let samples =
+          concat (waveSamples wav)
+            & Vector.fromList
+            & Vector.map (realToFrac . sampleToDouble)
+    let vad =
+          (defaultVad model)
+            { startThreshold = 0.5
+            , endThreshold = 0.35
+            }
+    segments <- detectSegments vad samples
+    print segments

examples/haskell/example.cabal

@@ -0,0 +1,23 @@
+cabal-version: 1.12
+
+-- This file has been generated from package.yaml by hpack version 0.37.0.
+--
+-- see: https://github.com/sol/hpack
+
+name:           example
+version:        0.1.0.0
+build-type:     Simple
+
+executable example-exe
+  main-is: Main.hs
+  other-modules:
+      Paths_example
+  hs-source-dirs:
+      app
+  ghc-options: -Wall -Wcompat -Widentities -Wincomplete-record-updates -Wincomplete-uni-patterns -Wmissing-export-lists -Wmissing-home-modules -Wpartial-fields -Wredundant-constraints -threaded -rtsopts -with-rtsopts=-N
+  build-depends:
+      WAVE
+    , base >=4.7 && <5
+    , silero-vad
+    , vector
+  default-language: Haskell2010

examples/haskell/package.yaml

@@ -0,0 +1,28 @@
+name: example
+version: 0.1.0.0
+
+dependencies:
+- base >= 4.7 && < 5
+- silero-vad
+- WAVE
+- vector
+
+ghc-options:
+- -Wall
+- -Wcompat
+- -Widentities
+- -Wincomplete-record-updates
+- -Wincomplete-uni-patterns
+- -Wmissing-export-lists
+- -Wmissing-home-modules
+- -Wpartial-fields
+- -Wredundant-constraints
+
+executables:
+  example-exe:
+    main: Main.hs
+    source-dirs: app
+    ghc-options:
+    - -threaded
+    - -rtsopts
+    - -with-rtsopts=-N

examples/haskell/stack.yaml

@@ -0,0 +1,11 @@
+snapshot:
+  url: https://raw.githubusercontent.com/commercialhaskell/stackage-snapshots/master/lts/20/26.yaml
+
+packages:
+- .
+
+extra-deps:
+  - silero-vad-0.1.0.4@sha256:2bff95be978a2782915b250edc795760d4cf76838e37bb7d4a965dc32566eb0f,5476
+  - WAVE-0.1.6@sha256:f744ff68f5e3a0d1f84fab373ea35970659085d213aef20860357512d0458c5c,1016
+  - derive-storable-0.3.1.0@sha256:bd1c51c155a00e2be18325d553d6764dd678904a85647d6ba952af998e70aa59,2313
+  - vector-0.13.2.0@sha256:98f5cb3080a3487527476e3c272dcadaba1376539f2aa0646f2f19b3af6b2f67,8481

examples/haskell/stack.yaml.lock

@@ -0,0 +1,41 @@
+# This file was autogenerated by Stack.
+# You should not edit this file by hand.
+# For more information, please see the documentation at:
+#   https://docs.haskellstack.org/en/stable/lock_files
+
+packages:
+- completed:
+    hackage: silero-vad-0.1.0.4@sha256:2bff95be978a2782915b250edc795760d4cf76838e37bb7d4a965dc32566eb0f,5476
+    pantry-tree:
+      sha256: a62e813f978d32c87769796fded981d25fcf2875bb2afdf60ed6279f931ccd7f
+      size: 1391
+  original:
+    hackage: silero-vad-0.1.0.4@sha256:2bff95be978a2782915b250edc795760d4cf76838e37bb7d4a965dc32566eb0f,5476
+- completed:
+    hackage: WAVE-0.1.6@sha256:f744ff68f5e3a0d1f84fab373ea35970659085d213aef20860357512d0458c5c,1016
+    pantry-tree:
+      sha256: ee5ccd70fa7fe6ffc360ebd762b2e3f44ae10406aa27f3842d55b8cbd1a19498
+      size: 405
+  original:
+    hackage: WAVE-0.1.6@sha256:f744ff68f5e3a0d1f84fab373ea35970659085d213aef20860357512d0458c5c,1016
+- completed:
+    hackage: derive-storable-0.3.1.0@sha256:bd1c51c155a00e2be18325d553d6764dd678904a85647d6ba952af998e70aa59,2313
+    pantry-tree:
+      sha256: 48e35a72d1bb593173890616c8d7efd636a650a306a50bb3e1513e679939d27e
+      size: 902
+  original:
+    hackage: derive-storable-0.3.1.0@sha256:bd1c51c155a00e2be18325d553d6764dd678904a85647d6ba952af998e70aa59,2313
+- completed:
+    hackage: vector-0.13.2.0@sha256:98f5cb3080a3487527476e3c272dcadaba1376539f2aa0646f2f19b3af6b2f67,8481
+    pantry-tree:
+      sha256: 2176fd677a02a4c47337f7dca5aeca2745dbb821a6ea5c7099b3a991ecd7f4f0
+      size: 4478
+  original:
+    hackage: vector-0.13.2.0@sha256:98f5cb3080a3487527476e3c272dcadaba1376539f2aa0646f2f19b3af6b2f67,8481
+snapshots:
+- completed:
+    sha256: 5a59b2a405b3aba3c00188453be172b85893cab8ebc352b1ef58b0eae5d248a2
+    size: 650475
+    url: https://raw.githubusercontent.com/commercialhaskell/stackage-snapshots/master/lts/20/26.yaml
+  original:
+    url: https://raw.githubusercontent.com/commercialhaskell/stackage-snapshots/master/lts/20/26.yaml

examples/java-example/pom.xml

@@ -1,30 +1,31 @@
 <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-  <modelVersion>4.0.0</modelVersion>
+    <modelVersion>4.0.0</modelVersion>
 
-  <groupId>org.example</groupId>
+    <groupId>org.example</groupId>
-  <artifactId>java-example</artifactId>
+    <artifactId>java-example</artifactId>
-  <version>1.0-SNAPSHOT</version>
+    <version>1.0-SNAPSHOT</version>
-  <packaging>jar</packaging>
+    <packaging>jar</packaging>
 
-  <name>sliero-vad-example</name>
+    <name>sliero-vad-example</name>
-  <url>http://maven.apache.org</url>
+    <url>http://maven.apache.org</url>
 
-  <properties>
+    <properties>
-    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-  </properties>
+    </properties>
 
-  <dependencies>
+    <dependencies>
-    <dependency>
+        <dependency>
-      <groupId>junit</groupId>
+            <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
+            <artifactId>junit</artifactId>
-      <version>3.8.1</version>
+            <version>3.8.1</version>
-      <scope>test</scope>
+            <scope>test</scope>
-    </dependency>
+        </dependency>
-    <dependency>
+        <!-- https://mvnrepository.com/artifact/com.microsoft.onnxruntime/onnxruntime -->
-      <groupId>com.microsoft.onnxruntime</groupId>
+        <dependency>
-      <artifactId>onnxruntime</artifactId>
+            <groupId>com.microsoft.onnxruntime</groupId>
-      <version>1.16.0-rc1</version>
+            <artifactId>onnxruntime</artifactId>
-    </dependency>
+            <version>1.23.1</version>
-  </dependencies>
+        </dependency>
+    </dependencies>
 </project>

examples/java-example/src/main/java/org/example/App.java

@@ -2,68 +2,263 @@ package org.example;
 
 import ai.onnxruntime.OrtException;
 import javax.sound.sampled.*;
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
 import java.util.Map;
 
+/**
+ * Silero VAD Java Example
+ * Voice Activity Detection using ONNX model
+ * 
+ * @author VvvvvGH
+ */
 public class App {
 
-    private static final String MODEL_PATH = "src/main/resources/silero_vad.onnx";
+    // ONNX model path - using the model file from the project
+    private static final String MODEL_PATH = "../../src/silero_vad/data/silero_vad.onnx";
+    // Test audio file path
+    private static final String AUDIO_FILE_PATH = "../../en_example.wav";
+    // Sampling rate
     private static final int SAMPLE_RATE = 16000;
-    private static final float START_THRESHOLD = 0.6f;
+    // Speech threshold (consistent with Python default)
-    private static final float END_THRESHOLD = 0.45f;
+    private static final float THRESHOLD = 0.5f;
-    private static final int MIN_SILENCE_DURATION_MS = 600;
+    // Negative threshold (used to determine speech end)
-    private static final int SPEECH_PAD_MS = 500;
+    private static final float NEG_THRESHOLD = 0.35f; // threshold - 0.15
-    private static final int WINDOW_SIZE_SAMPLES = 2048;
+    // Minimum speech duration (milliseconds)
+    private static final int MIN_SPEECH_DURATION_MS = 250;
+    // Minimum silence duration (milliseconds)
+    private static final int MIN_SILENCE_DURATION_MS = 100;
+    // Speech padding (milliseconds)
+    private static final int SPEECH_PAD_MS = 30;
+    // Window size (samples) - 512 samples for 16kHz
+    private static final int WINDOW_SIZE_SAMPLES = 512;
 
     public static void main(String[] args) {
-        // Initialize the Voice Activity Detector
+        System.out.println("=".repeat(60));
-        SlieroVadDetector vadDetector;
+        System.out.println("Silero VAD Java ONNX Example");
+        System.out.println("=".repeat(60));
+        
+        // Load ONNX model
+        SlieroVadOnnxModel model;
         try {
-            vadDetector = new SlieroVadDetector(MODEL_PATH, START_THRESHOLD, END_THRESHOLD, SAMPLE_RATE, MIN_SILENCE_DURATION_MS, SPEECH_PAD_MS);
+            System.out.println("Loading ONNX model: " + MODEL_PATH);
+            model = new SlieroVadOnnxModel(MODEL_PATH);
+            System.out.println("Model loaded successfully!");
         } catch (OrtException e) {
-            System.err.println("Error initializing the VAD detector: " + e.getMessage());
+            System.err.println("Failed to load model: " + e.getMessage());
+            e.printStackTrace();
             return;
         }
 
-        // Set audio format
+        // Read WAV file
-        AudioFormat format = new AudioFormat(SAMPLE_RATE, 16, 1, true, false);
+        float[] audioData;
-        DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);
-
-        // Get the target data line and open it with the specified format
-        TargetDataLine targetDataLine;
         try {
-            targetDataLine = (TargetDataLine) AudioSystem.getLine(info);
+            System.out.println("\nReading audio file: " + AUDIO_FILE_PATH);
-            targetDataLine.open(format);
+            audioData = readWavFileAsFloatArray(AUDIO_FILE_PATH);
-            targetDataLine.start();
+            System.out.println("Audio file read successfully, samples: " + audioData.length);
-        } catch (LineUnavailableException e) {
+            System.out.println("Audio duration: " + String.format("%.2f", (audioData.length / (float) SAMPLE_RATE)) + " seconds");
-            System.err.println("Error opening target data line: " + e.getMessage());
+        } catch (Exception e) {
+            System.err.println("Failed to read audio file: " + e.getMessage());
+            e.printStackTrace();
             return;
         }
 
-        // Main loop to continuously read data and apply Voice Activity Detection
+        // Get speech timestamps (batch mode, consistent with Python's get_speech_timestamps)
-        while (targetDataLine.isOpen()) {
+        System.out.println("\nDetecting speech segments...");
-            byte[] data = new byte[WINDOW_SIZE_SAMPLES];
+        List<Map<String, Integer>> speechTimestamps;
-
+        try {
-            int numBytesRead = targetDataLine.read(data, 0, data.length);
+            speechTimestamps = getSpeechTimestamps(
-            if (numBytesRead <= 0) {
+                audioData,
-                System.err.println("Error reading data from target data line.");
+                model,
-                continue;
+                THRESHOLD,
-            }
+                SAMPLE_RATE,
-
+                MIN_SPEECH_DURATION_MS,
-            // Apply the Voice Activity Detector to the data and get the result
+                MIN_SILENCE_DURATION_MS,
-            Map<String, Double> detectResult;
+                SPEECH_PAD_MS,
-            try {
+                NEG_THRESHOLD
-                detectResult = vadDetector.apply(data, true);
+            );
-            } catch (Exception e) {
+        } catch (OrtException e) {
-                System.err.println("Error applying VAD detector: " + e.getMessage());
+            System.err.println("Failed to detect speech timestamps: " + e.getMessage());
-                continue;
+            e.printStackTrace();
-            }
+            return;
-
-            if (!detectResult.isEmpty()) {
-                System.out.println(detectResult);
-            }
         }
 
-        // Close the target data line to release audio resources
+        // Output detection results
-        targetDataLine.close();
+        System.out.println("\nDetected speech timestamps (in samples):");
+        for (Map<String, Integer> timestamp : speechTimestamps) {
+            System.out.println(timestamp);
+        }
+
+        // Output summary
+        System.out.println("\n" + "=".repeat(60));
+        System.out.println("Detection completed!");
+        System.out.println("Total detected " + speechTimestamps.size() + " speech segments");
+        System.out.println("=".repeat(60));
+        
+        // Close model
+        try {
+            model.close();
+        } catch (OrtException e) {
+            System.err.println("Error closing model: " + e.getMessage());
+        }
     }
+
+    /**
+     * Get speech timestamps
+     * Implements the same logic as Python's get_speech_timestamps
+     * 
+     * @param audio Audio data (float array)
+     * @param model ONNX model
+     * @param threshold Speech threshold
+     * @param samplingRate Sampling rate
+     * @param minSpeechDurationMs Minimum speech duration (milliseconds)
+     * @param minSilenceDurationMs Minimum silence duration (milliseconds)
+     * @param speechPadMs Speech padding (milliseconds)
+     * @param negThreshold Negative threshold (used to determine speech end)
+     * @return List of speech timestamps
+     */
+    private static List<Map<String, Integer>> getSpeechTimestamps(
+            float[] audio,
+            SlieroVadOnnxModel model,
+            float threshold,
+            int samplingRate,
+            int minSpeechDurationMs,
+            int minSilenceDurationMs,
+            int speechPadMs,
+            float negThreshold) throws OrtException {
+        
+        // Reset model states
+        model.resetStates();
+        
+        // Calculate parameters
+        int minSpeechSamples = samplingRate * minSpeechDurationMs / 1000;
+        int speechPadSamples = samplingRate * speechPadMs / 1000;
+        int minSilenceSamples = samplingRate * minSilenceDurationMs / 1000;
+        int windowSizeSamples = samplingRate == 16000 ? 512 : 256;
+        int audioLengthSamples = audio.length;
+        
+        // Calculate speech probabilities for all audio chunks
+        List<Float> speechProbs = new ArrayList<>();
+        for (int currentStart = 0; currentStart < audioLengthSamples; currentStart += windowSizeSamples) {
+            float[] chunk = new float[windowSizeSamples];
+            int chunkLength = Math.min(windowSizeSamples, audioLengthSamples - currentStart);
+            System.arraycopy(audio, currentStart, chunk, 0, chunkLength);
+            
+            // Pad with zeros if chunk is shorter than window size
+            if (chunkLength < windowSizeSamples) {
+                for (int i = chunkLength; i < windowSizeSamples; i++) {
+                    chunk[i] = 0.0f;
+                }
+            }
+            
+            float speechProb = model.call(new float[][]{chunk}, samplingRate)[0];
+            speechProbs.add(speechProb);
+        }
+        
+        // Detect speech segments using the same algorithm as Python
+        boolean triggered = false;
+        List<Map<String, Integer>> speeches = new ArrayList<>();
+        Map<String, Integer> currentSpeech = null;
+        int tempEnd = 0;
+        
+        for (int i = 0; i < speechProbs.size(); i++) {
+            float speechProb = speechProbs.get(i);
+            
+            // Reset temporary end if speech probability exceeds threshold
+            if (speechProb >= threshold && tempEnd != 0) {
+                tempEnd = 0;
+            }
+            
+            // Detect speech start
+            if (speechProb >= threshold && !triggered) {
+                triggered = true;
+                currentSpeech = new HashMap<>();
+                currentSpeech.put("start", windowSizeSamples * i);
+                continue;
+            }
+            
+            // Detect speech end
+            if (speechProb < negThreshold && triggered) {
+                if (tempEnd == 0) {
+                    tempEnd = windowSizeSamples * i;
+                }
+                if (windowSizeSamples * i - tempEnd < minSilenceSamples) {
+                    continue;
+                } else {
+                    currentSpeech.put("end", tempEnd);
+                    if (currentSpeech.get("end") - currentSpeech.get("start") > minSpeechSamples) {
+                        speeches.add(currentSpeech);
+                    }
+                    currentSpeech = null;
+                    tempEnd = 0;
+                    triggered = false;
+                }
+            }
+        }
+        
+        // Handle the last speech segment
+        if (currentSpeech != null && 
+            (audioLengthSamples - currentSpeech.get("start")) > minSpeechSamples) {
+            currentSpeech.put("end", audioLengthSamples);
+            speeches.add(currentSpeech);
+        }
+        
+        // Add speech padding - same logic as Python
+        for (int i = 0; i < speeches.size(); i++) {
+            Map<String, Integer> speech = speeches.get(i);
+            if (i == 0) {
+                speech.put("start", Math.max(0, speech.get("start") - speechPadSamples));
+            }
+            if (i != speeches.size() - 1) {
+                int silenceDuration = speeches.get(i + 1).get("start") - speech.get("end");
+                if (silenceDuration < 2 * speechPadSamples) {
+                    speech.put("end", speech.get("end") + silenceDuration / 2);
+                    speeches.get(i + 1).put("start", 
+                        Math.max(0, speeches.get(i + 1).get("start") - silenceDuration / 2));
+                } else {
+                    speech.put("end", Math.min(audioLengthSamples, speech.get("end") + speechPadSamples));
+                    speeches.get(i + 1).put("start", 
+                        Math.max(0, speeches.get(i + 1).get("start") - speechPadSamples));
+                }
+            } else {
+                speech.put("end", Math.min(audioLengthSamples, speech.get("end") + speechPadSamples));
+            }
+        }
+        
+        return speeches;
+    }
+
+    /**
+     * Read WAV file and return as float array
+     * 
+     * @param filePath WAV file path
+     * @return Audio data as float array (normalized to -1.0 to 1.0)
+     */
+    private static float[] readWavFileAsFloatArray(String filePath) 
+            throws UnsupportedAudioFileException, IOException {
+        File audioFile = new File(filePath);
+        AudioInputStream audioStream = AudioSystem.getAudioInputStream(audioFile);
+        
+        // Get audio format information
+        AudioFormat format = audioStream.getFormat();
+        System.out.println("Audio format: " + format);
+        
+        // Read all audio data
+        byte[] audioBytes = audioStream.readAllBytes();
+        audioStream.close();
+        
+        // Convert to float array
+        float[] audioData = new float[audioBytes.length / 2];
+        for (int i = 0; i < audioData.length; i++) {
+            // 16-bit PCM: two bytes per sample (little-endian)
+            short sample = (short) ((audioBytes[i * 2] & 0xff) | (audioBytes[i * 2 + 1] << 8));
+            audioData[i] = sample / 32768.0f; // Normalize to -1.0 to 1.0
+        }
+        
+        return audioData;
+    }
+
 }

examples/java-example/src/main/java/org/example/SlieroVadDetector.java

@@ -8,25 +8,30 @@ import java.util.Collections;
 import java.util.HashMap;
 import java.util.Map;
 
-
+/**
+ * Silero VAD Detector
+ * Real-time voice activity detection
+ * 
+ * @author VvvvvGH
+ */
 public class SlieroVadDetector {
-    // OnnxModel model used for speech processing
+    // ONNX model for speech processing
     private final SlieroVadOnnxModel model;
-    // Threshold for speech start
+    // Speech start threshold
     private final float startThreshold;
-    // Threshold for speech end
+    // Speech end threshold
     private final float endThreshold;
     // Sampling rate
     private final int samplingRate;
-    // Minimum number of silence samples to determine the end threshold of speech
+    // Minimum silence samples to determine speech end
     private final float minSilenceSamples;
-    // Additional number of samples for speech start or end to calculate speech start or end time
+    // Speech padding samples for calculating speech boundaries
     private final float speechPadSamples;
-    // Whether in the triggered state (i.e. whether speech is being detected)
+    // Triggered state (whether speech is being detected)
     private boolean triggered;
-    // Temporarily stored number of speech end samples
+    // Temporary speech end sample position
     private int tempEnd;
-    // Number of samples currently being processed
+    // Current sample position
     private int currentSample;
 
 
@@ -36,23 +41,25 @@ public class SlieroVadDetector {
                              int samplingRate,
                              int minSilenceDurationMs,
                              int speechPadMs) throws OrtException {
-        // Check if the sampling rate is 8000 or 16000, if not, throw an exception
+        // Validate sampling rate
         if (samplingRate != 8000 && samplingRate != 16000) {
-            throw new IllegalArgumentException("does not support sampling rates other than [8000, 16000]");
+            throw new IllegalArgumentException("Does not support sampling rates other than [8000, 16000]");
         }
 
-        // Initialize the parameters
+        // Initialize parameters
         this.model = new SlieroVadOnnxModel(modelPath);
         this.startThreshold = startThreshold;
         this.endThreshold = endThreshold;
         this.samplingRate = samplingRate;
         this.minSilenceSamples = samplingRate * minSilenceDurationMs / 1000f;
         this.speechPadSamples = samplingRate * speechPadMs / 1000f;
-        // Reset the state
+        // Reset state
         reset();
     }
 
-    // Method to reset the state, including the model state, trigger state, temporary end time, and current sample count
+    /**
+     * Reset detector state
+     */
     public void reset() {
         model.resetStates();
         triggered = false;
@@ -60,21 +67,27 @@ public class SlieroVadDetector {
         currentSample = 0;
     }
 
-    // apply method for processing the audio array, returning possible speech start or end times
+    /**
+     * Process audio data and detect speech events
+     * 
+     * @param data Audio data as byte array
+     * @param returnSeconds Whether to return timestamps in seconds
+     * @return Speech event (start or end) or empty map if no event
+     */
     public Map<String, Double> apply(byte[] data, boolean returnSeconds) {
 
-        // Convert the byte array to a float array
+        // Convert byte array to float array
         float[] audioData = new float[data.length / 2];
         for (int i = 0; i < audioData.length; i++) {
             audioData[i] = ((data[i * 2] & 0xff) | (data[i * 2 + 1] << 8)) / 32767.0f;
         }
 
-        // Get the length of the audio array as the window size
+        // Get window size from audio data length
         int windowSizeSamples = audioData.length;
-        // Update the current sample count
+        // Update current sample position
         currentSample += windowSizeSamples;
 
-        // Call the model to get the prediction probability of speech
+        // Get speech probability from model
         float speechProb = 0;
         try {
             speechProb = model.call(new float[][]{audioData}, samplingRate)[0];
@@ -82,19 +95,18 @@ public class SlieroVadDetector {
             throw new RuntimeException(e);
         }
 
-        // If the speech probability is greater than the threshold and the temporary end time is not 0, reset the temporary end time
+        // Reset temporary end if speech probability exceeds threshold
-        // This indicates that the speech duration has exceeded expectations and needs to recalculate the end time
         if (speechProb >= startThreshold && tempEnd != 0) {
             tempEnd = 0;
         }
 
-        // If the speech probability is greater than the threshold and not in the triggered state, set to triggered state and calculate the speech start time
+        // Detect speech start
         if (speechProb >= startThreshold && !triggered) {
             triggered = true;
             int speechStart = (int) (currentSample - speechPadSamples);
             speechStart = Math.max(speechStart, 0);
             Map<String, Double> result = new HashMap<>();
-            // Decide whether to return the result in seconds or sample count based on the returnSeconds parameter
+            // Return in seconds or samples based on returnSeconds parameter
             if (returnSeconds) {
                 double speechStartSeconds = speechStart / (double) samplingRate;
                 double roundedSpeechStart = BigDecimal.valueOf(speechStartSeconds).setScale(1, RoundingMode.HALF_UP).doubleValue();
@@ -106,18 +118,17 @@ public class SlieroVadDetector {
             return result;
         }
 
-        // If the speech probability is less than a certain threshold and in the triggered state, calculate the speech end time
+        // Detect speech end
         if (speechProb < endThreshold && triggered) {
-            // Initialize or update the temporary end time
+            // Initialize or update temporary end position
             if (tempEnd == 0) {
                 tempEnd = currentSample;
             }
-            // If the number of silence samples between the current sample and the temporary end time is less than the minimum silence samples, return null
+            // Wait for minimum silence duration before confirming speech end
-            // This indicates that it is not yet possible to determine whether the speech has ended
             if (currentSample - tempEnd < minSilenceSamples) {
                 return Collections.emptyMap();
             } else {
-                // Calculate the speech end time, reset the trigger state and temporary end time
+                // Calculate speech end time and reset state
                 int speechEnd = (int) (tempEnd + speechPadSamples);
                 tempEnd = 0;
                 triggered = false;
@@ -134,7 +145,7 @@ public class SlieroVadDetector {
             }
         }
 
-        // If the above conditions are not met, return null by default
+        // No speech event detected
         return Collections.emptyMap();
     }
 

examples/java-example/src/main/java/org/example/SlieroVadOnnxModel.java

@@ -9,42 +9,58 @@ import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
+/**
+ * Silero VAD ONNX Model Wrapper
+ * 
+ * @author VvvvvGH
+ */
 public class SlieroVadOnnxModel {
-    // Define private variable OrtSession
+    // ONNX runtime session
     private final OrtSession session;
-    private float[][][] h;
+    // Model state - dimensions: [2, batch_size, 128]
-    private float[][][] c;
+    private float[][][] state;
-    // Define the last sample rate
+    // Context - stores the tail of the previous audio chunk
+    private float[][] context;
+    // Last sample rate
     private int lastSr = 0;
-    // Define the last batch size
+    // Last batch size
     private int lastBatchSize = 0;
-    // Define a list of supported sample rates
+    // Supported sample rates
     private static final List<Integer> SAMPLE_RATES = Arrays.asList(8000, 16000);
 
     // Constructor
     public SlieroVadOnnxModel(String modelPath) throws OrtException {
         // Get the ONNX runtime environment
         OrtEnvironment env = OrtEnvironment.getEnvironment();
-        // Create an ONNX session options object
+        // Create ONNX session options
         OrtSession.SessionOptions opts = new OrtSession.SessionOptions();
-        // Set the InterOp thread count to 1, InterOp threads are used for parallel processing of different computation graph operations
+        // Set InterOp thread count to 1 (for parallel processing of different graph operations)
         opts.setInterOpNumThreads(1);
-        // Set the IntraOp thread count to 1, IntraOp threads are used for parallel processing within a single operation
+        // Set IntraOp thread count to 1 (for parallel processing within a single operation)
         opts.setIntraOpNumThreads(1);
-        // Add a CPU device, setting to false disables CPU execution optimization
+        // Enable CPU execution optimization
         opts.addCPU(true);
-        // Create an ONNX session using the environment, model path, and options
+        // Create ONNX session with the environment, model path, and options
         session = env.createSession(modelPath, opts);
         // Reset states
         resetStates();
     }
 
     /**
-     * Reset states
+     * Reset states with default batch size
      */
     void resetStates() {
-        h = new float[2][1][64];
+        resetStates(1);
-        c = new float[2][1][64];
+    }
+    
+    /**
+     * Reset states with specific batch size
+     * 
+     * @param batchSize Batch size for state initialization
+     */
+    void resetStates(int batchSize) {
+        state = new float[2][batchSize][128];
+        context = new float[0][]; // Empty context
         lastSr = 0;
         lastBatchSize = 0;
     }
@@ -54,13 +70,12 @@ public class SlieroVadOnnxModel {
     }
 
     /**
-     * Define inner class ValidationResult
+     * Inner class for validation result
      */
     public static class ValidationResult {
         public final float[][] x;
         public final int sr;
 
-        // Constructor
         public ValidationResult(float[][] x, int sr) {
             this.x = x;
             this.sr = sr;
@@ -68,19 +83,23 @@ public class SlieroVadOnnxModel {
     }
 
     /**
-     * Function to validate input data
+     * Validate input data
+     * 
+     * @param x Audio data array
+     * @param sr Sample rate
+     * @return Validated input data and sample rate
      */
     private ValidationResult validateInput(float[][] x, int sr) {
-        // Process the input data with dimension 1
+        // Ensure input is at least 2D
         if (x.length == 1) {
             x = new float[][]{x[0]};
         }
-        // Throw an exception when the input data dimension is greater than 2
+        // Check if input dimension is valid
         if (x.length > 2) {
             throw new IllegalArgumentException("Incorrect audio data dimension: " + x[0].length);
         }
 
-        // Process the input data when the sample rate is not equal to 16000 and is a multiple of 16000
+        // Downsample if sample rate is a multiple of 16000
         if (sr != 16000 && (sr % 16000 == 0)) {
             int step = sr / 16000;
             float[][] reducedX = new float[x.length][];
@@ -100,22 +119,26 @@ public class SlieroVadOnnxModel {
             sr = 16000;
         }
 
-        // If the sample rate is not in the list of supported sample rates, throw an exception
+        // Validate sample rate
         if (!SAMPLE_RATES.contains(sr)) {
             throw new IllegalArgumentException("Only supports sample rates " + SAMPLE_RATES + " (or multiples of 16000)");
         }
 
-        // If the input audio block is too short, throw an exception
+        // Check if audio chunk is too short
         if (((float) sr) / x[0].length > 31.25) {
             throw new IllegalArgumentException("Input audio is too short");
         }
 
-        // Return the validated result
         return new ValidationResult(x, sr);
     }
 
     /**
-     * Method to call the ONNX model
+     * Call the ONNX model for inference
+     * 
+     * @param x Audio data array
+     * @param sr Sample rate
+     * @return Speech probability output
+     * @throws OrtException If ONNX runtime error occurs
      */
     public float[] call(float[][] x, int sr) throws OrtException {
         ValidationResult result = validateInput(x, sr);
@@ -123,38 +146,62 @@ public class SlieroVadOnnxModel {
         sr = result.sr;
 
         int batchSize = x.length;
+        int numSamples = sr == 16000 ? 512 : 256;
+        int contextSize = sr == 16000 ? 64 : 32;
 
-        if (lastBatchSize == 0 || lastSr != sr || lastBatchSize != batchSize) {
+        // Reset states only when sample rate or batch size changes
-            resetStates();
+        if (lastSr != 0 && lastSr != sr) {
+            resetStates(batchSize);
+        } else if (lastBatchSize != 0 && lastBatchSize != batchSize) {
+            resetStates(batchSize);
+        } else if (lastBatchSize == 0) {
+            // First call - state is already initialized, just set batch size
+            lastBatchSize = batchSize;
+        }
+
+        // Initialize context if needed
+        if (context.length == 0) {
+            context = new float[batchSize][contextSize];
+        }
+
+        // Concatenate context and input
+        float[][] xWithContext = new float[batchSize][contextSize + numSamples];
+        for (int i = 0; i < batchSize; i++) {
+            // Copy context
+            System.arraycopy(context[i], 0, xWithContext[i], 0, contextSize);
+            // Copy input
+            System.arraycopy(x[i], 0, xWithContext[i], contextSize, numSamples);
         }
 
         OrtEnvironment env = OrtEnvironment.getEnvironment();
 
         OnnxTensor inputTensor = null;
-        OnnxTensor hTensor = null;
+        OnnxTensor stateTensor = null;
-        OnnxTensor cTensor = null;
         OnnxTensor srTensor = null;
         OrtSession.Result ortOutputs = null;
 
         try {
             // Create input tensors
-            inputTensor = OnnxTensor.createTensor(env, x);
+            inputTensor = OnnxTensor.createTensor(env, xWithContext);
-            hTensor = OnnxTensor.createTensor(env, h);
+            stateTensor = OnnxTensor.createTensor(env, state);
-            cTensor = OnnxTensor.createTensor(env, c);
             srTensor = OnnxTensor.createTensor(env, new long[]{sr});
 
             Map<String, OnnxTensor> inputs = new HashMap<>();
             inputs.put("input", inputTensor);
             inputs.put("sr", srTensor);
-            inputs.put("h", hTensor);
+            inputs.put("state", stateTensor);
-            inputs.put("c", cTensor);
 
-            // Call the ONNX model for calculation
+            // Run ONNX model inference
             ortOutputs = session.run(inputs);
-            // Get the output results
+            // Get output results
             float[][] output = (float[][]) ortOutputs.get(0).getValue();
-            h = (float[][][]) ortOutputs.get(1).getValue();
+            state = (float[][][]) ortOutputs.get(1).getValue();
-            c = (float[][][]) ortOutputs.get(2).getValue();
+
+            // Update context - save the last contextSize samples from input
+            for (int i = 0; i < batchSize; i++) {
+                System.arraycopy(xWithContext[i], xWithContext[i].length - contextSize, 
+                               context[i], 0, contextSize);
+            }
 
             lastSr = sr;
             lastBatchSize = batchSize;
@@ -163,11 +210,8 @@ public class SlieroVadOnnxModel {
             if (inputTensor != null) {
                 inputTensor.close();
             }
-            if (hTensor != null) {
+            if (stateTensor != null) {
-                hTensor.close();
+                stateTensor.close();
-            }
-            if (cTensor != null) {
-                cTensor.close();
             }
             if (srTensor != null) {
                 srTensor.close();

examples/parallel_example.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -18,17 +17,19 @@
     "SAMPLING_RATE = 16000\n",
     "import torch\n",
     "from pprint import pprint\n",
+    "import time\n",
+    "import shutil\n",
     "\n",
     "torch.set_num_threads(1)\n",
     "NUM_PROCESS=4 # set to the number of CPU cores in the machine\n",
     "NUM_COPIES=8\n",
     "# download wav files, make multiple copies\n",
-    "for idx in range(NUM_COPIES):\n",
+    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', f\"en_example0.wav\")\n",
-    "    torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', f\"en_example{idx}.wav\")\n"
+    "for idx in range(NUM_COPIES-1):\n",
+    "    shutil.copy(f\"en_example0.wav\", f\"en_example{idx+1}.wav\")"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -54,7 +55,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -99,7 +99,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -127,7 +126,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "diarization",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -141,7 +140,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.14"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

examples/pyaudio-streaming/README.md

@@ -7,6 +7,8 @@ It has been designed as a low-level example for binary real-time streaming using
 Currently, the notebook consits of two examples:
  - One that records audio of a predefined length from the microphone, process it with Silero-VAD, and plots it afterwards.
  - The other one plots the speech probabilities in real-time (using jupyterplot) and records the audio until you press enter.
+ 
+ This example does not work in google colab! For local usage only.
 
 ## Example Video for the Real-Time Visualization
 

examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "62a0cccb",
+   "id": "76aa55ba",
    "metadata": {},
    "source": [
     "# Pyaudio Microphone Streaming Examples\n",
@@ -12,12 +12,14 @@
     "I created it as an example on how binary data from a stream could be feed into Silero VAD.\n",
     "\n",
     "\n",
-    "Has been tested on Ubuntu 21.04 (x86). After you installed the dependencies below, no additional setup is required."
+    "Has been tested on Ubuntu 21.04 (x86). After you installed the dependencies below, no additional setup is required.\n",
+    "\n",
+    "This notebook does not work in google colab! For local usage only."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "64cbe1eb",
+   "id": "4a4e15c2",
    "metadata": {},
    "source": [
     "## Dependencies\n",
@@ -26,22 +28,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
-   "id": "57bc2aac",
+   "id": "24205cce",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-09T08:47:34.056898Z",
+     "start_time": "2024-10-09T08:47:34.053418Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "#!pip install numpy==2.0.2\n",
+    "#!pip install numpy>=1.24.0\n",
-    "#!pip install torch==2.4.1\n",
+    "#!pip install torch>=1.12.0\n",
-    "#!pip install matplotlib==3.9.2\n",
+    "#!pip install matplotlib>=3.6.0\n",
-    "#!pip install torchaudio==2.4.1\n",
+    "#!pip install torchaudio>=0.12.0\n",
     "#!pip install soundfile==0.12.1\n",
-    "#!pip install pyaudio==0.2.11"
+    "#!apt install python3-pyaudio (linux) or pip install pyaudio (windows)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "110de761",
+   "id": "cd22818f",
    "metadata": {},
    "source": [
     "## Imports"
@@ -49,10 +56,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
-   "id": "5a647d8d",
+   "id": "994d7f3a",
-   "metadata": {},
+   "metadata": {
-   "outputs": [],
+    "ExecuteTime": {
+     "end_time": "2024-10-09T08:47:39.005032Z",
+     "start_time": "2024-10-09T08:47:36.489952Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pyaudio'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpylab\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyaudio\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyaudio'"
+     ]
+    }
+   ],
    "source": [
     "import io\n",
     "import numpy as np\n",
@@ -67,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "725d7066",
+   "id": "ac5c52f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1c0b2ea7",
+   "id": "ad5919dc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9112603",
+   "id": "784d1ab6",
    "metadata": {},
    "source": [
     "### Helper Methods"
@@ -101,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5abc6330",
+   "id": "af4bca64",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5124095e",
+   "id": "ca13e514",
    "metadata": {},
    "source": [
     "## Pyaudio Set-up"
@@ -133,7 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a845356e",
+   "id": "75f99022",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b910c99",
+   "id": "4da7d2ef",
    "metadata": {},
    "source": [
     "## Simple Example\n",
@@ -157,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9d3d2c10",
+   "id": "6fe77661",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3cb44a4a",
+   "id": "23f4da3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3dda982",
+   "id": "fd243e8f",
    "metadata": {},
    "source": [
     "## Real Time Visualization\n",
@@ -220,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "05ef4100",
+   "id": "d36980c2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +254,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d1d4cdd6",
+   "id": "5607b616",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1e398009",
+   "id": "dc4f0108",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.10.14"
   },
   "toc": {
    "base_numbering": 1,

hubconf.py

@@ -23,11 +23,14 @@ def versiontuple(v):
     return tuple(version_list)
 
 
-def silero_vad(onnx=False, force_onnx_cpu=False):
+def silero_vad(onnx=False, force_onnx_cpu=False, opset_version=16):
     """Silero Voice Activity Detector
     Returns a model with a set of utils
     Please see https://github.com/snakers4/silero-vad for usage examples
     """
+    available_ops = [15, 16]
+    if onnx and opset_version not in available_ops:
+        raise Exception(f'Available ONNX opset_version: {available_ops}')
 
     if not onnx:
         installed_version = torch.__version__
@@ -37,7 +40,11 @@ def silero_vad(onnx=False, force_onnx_cpu=False):
 
     model_dir = os.path.join(os.path.dirname(__file__), 'src', 'silero_vad', 'data')
     if onnx:
-        model = OnnxWrapper(os.path.join(model_dir, 'silero_vad.onnx'), force_onnx_cpu)
+        if opset_version == 16:
+            model_name = 'silero_vad.onnx'
+        else:
+            model_name = f'silero_vad_16k_op{opset_version}.onnx'
+        model = OnnxWrapper(os.path.join(model_dir, model_name), force_onnx_cpu)
     else:
         model = init_jit_model(os.path.join(model_dir, 'silero_vad.jit'))
     utils = (get_speech_timestamps,

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 [project]
 name = "silero-vad"
-version = "5.1"
+version = "6.1.0"
 authors = [
   {name="Silero Team", email="hello@silero.ai"},
 ]
@@ -21,10 +21,14 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
+    "Programming Language :: Python :: 3.15",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
+  "packaging",
   "torch>=1.12.0",
   "torchaudio>=0.12.0",
   "onnxruntime>=1.16.1",
@@ -32,4 +36,4 @@ dependencies = [
 
 [project.urls]
 Homepage = "https://github.com/snakers4/silero-vad"
-Issues = "https://github.com/snakers4/silero-vad/issues"
+Issues = "https://github.com/snakers4/silero-vad/issues"

src/silero_vad/__init__.py

@@ -9,4 +9,5 @@ from silero_vad.utils_vad import (get_speech_timestamps,
                                   save_audio,
                                   read_audio,
                                   VADIterator,
-                                  collect_chunks)
+                                  collect_chunks,
+                                  drop_chunks)

src/silero_vad/model.py

@@ -2,10 +2,21 @@ from .utils_vad import init_jit_model, OnnxWrapper
 import torch
 torch.set_num_threads(1)
 
-def load_silero_vad(onnx=False):
+
-    model_name = 'silero_vad.onnx' if onnx else 'silero_vad.jit'
+def load_silero_vad(onnx=False, opset_version=16):
+    available_ops = [15, 16]
+    if onnx and opset_version not in available_ops:
+        raise Exception(f'Available ONNX opset_version: {available_ops}')
+
+    if onnx:
+        if opset_version == 16:
+            model_name = 'silero_vad.onnx'
+        else:
+            model_name = f'silero_vad_16k_op{opset_version}.onnx'
+    else:
+        model_name = 'silero_vad.jit'
     package_path = "silero_vad.data"
-    
+
     try:
         import importlib_resources as impresources
         model_file_path = str(impresources.files(package_path).joinpath(model_name))
@@ -18,8 +29,8 @@ def load_silero_vad(onnx=False):
             model_file_path = str(impresources.files(package_path).joinpath(model_name))
 
     if onnx:
-        model = OnnxWrapper(model_file_path, force_onnx_cpu=True)
+        model = OnnxWrapper(str(model_file_path), force_onnx_cpu=True)
     else:
         model = init_jit_model(model_file_path)
-    
+
     return model

src/silero_vad/utils_vad.py

@@ -2,6 +2,7 @@ import torch
 import torchaudio
 from typing import Callable, List
 import warnings
+from packaging import version
 
 languages = ['ru', 'en', 'de', 'es']
 
@@ -23,7 +24,11 @@ class OnnxWrapper():
             self.session = onnxruntime.InferenceSession(path, sess_options=opts)
 
         self.reset_states()
-        self.sample_rates = [8000, 16000]
+        if '16k' in path:
+            warnings.warn('This model support only 16000 sampling rate!')
+            self.sample_rates = [16000]
+        else:
+            self.sample_rates = [8000, 16000]
 
     def _validate_input(self, x, sr: int):
         if x.dim() == 1:
@@ -130,40 +135,60 @@ class Validator():
         return outs
 
 
-def read_audio(path: str,
+def read_audio(path: str, sampling_rate: int = 16000) -> torch.Tensor:
-               sampling_rate: int = 16000):
+    ta_ver = version.parse(torchaudio.__version__)
-    list_backends = torchaudio.list_audio_backends()
+    if ta_ver < version.parse("2.9"):
+        try:
+            effects = [['channels', '1'],['rate', str(sampling_rate)]]
+            wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
+        except:
+            wav, sr = torchaudio.load(path)
+    else:
+        try:
+            wav, sr = torchaudio.load(path)
+        except:
+            try:
+                from torchcodec.decoders import AudioDecoder
+                samples = AudioDecoder(path).get_all_samples()
+                wav = samples.data
+                sr = samples.sample_rate
+            except ImportError:
+                raise RuntimeError(
+                    f"torchaudio version {torchaudio.__version__} requires torchcodec for audio I/O. "
+                    + "Install torchcodec or pin torchaudio < 2.9"
+                )
 
-    assert len(list_backends) > 0, 'The list of available backends is empty, please install backend manually. \
+    if wav.ndim > 1 and wav.size(0) > 1:
-                                    \n Recommendations: \n \tSox (UNIX OS) \n \tSoundfile (Windows OS, UNIX OS) \n \tffmpeg (Windows OS, UNIX OS)'
+        wav = wav.mean(dim=0, keepdim=True)
 
-    try:
+    if sr != sampling_rate:
-        effects = [
+        wav = torchaudio.transforms.Resample(sr, sampling_rate)(wav)
-            ['channels', '1'],
-            ['rate', str(sampling_rate)]
-        ]
 
-        wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
-    except:
-        wav, sr = torchaudio.load(path)
-
-        if wav.size(0) > 1:
-            wav = wav.mean(dim=0, keepdim=True)
-
-        if sr != sampling_rate:
-            transform = torchaudio.transforms.Resample(orig_freq=sr,
-                                                       new_freq=sampling_rate)
-            wav = transform(wav)
-            sr = sampling_rate
-
-    assert sr == sampling_rate
     return wav.squeeze(0)
 
 
-def save_audio(path: str,
+def save_audio(path: str, tensor: torch.Tensor, sampling_rate: int = 16000):
-               tensor: torch.Tensor,
+    tensor = tensor.detach().cpu()
-               sampling_rate: int = 16000):
+    if tensor.ndim == 1:
-    torchaudio.save(path, tensor.unsqueeze(0), sampling_rate, bits_per_sample=16)
+        tensor = tensor.unsqueeze(0)
+
+    ta_ver = version.parse(torchaudio.__version__)
+
+    try:
+        torchaudio.save(path, tensor, sampling_rate, bits_per_sample=16)
+    except Exception:
+        if ta_ver >= version.parse("2.9"):
+            try:
+                from torchcodec.encoders import AudioEncoder
+                encoder = AudioEncoder(tensor, sample_rate=16000)
+                encoder.to_file(path)
+            except ImportError:
+                raise RuntimeError(
+                    f"torchaudio version {torchaudio.__version__} requires torchcodec for saving. "
+                    + "Install torchcodec or pin torchaudio < 2.9"
+                )
+        else:
+            raise
 
 
 def init_jit_model(model_path: str,
@@ -193,10 +218,13 @@ 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,
-                          window_size_samples: int = 512,):
+                          window_size_samples: int = 512,
+                          min_silence_at_max_speech: float = 98,
+                          use_max_poss_sil_at_max_speech: bool = True):
 
     """
     This method is used for splitting long audios into speech chunks using silero VAD
@@ -220,7 +248,7 @@ def get_speech_timestamps(audio: torch.Tensor,
 
     max_speech_duration_s: int (default -  inf)
         Maximum duration of speech chunks in seconds
-        Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent agressive cutting.
+        Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent aggressive cutting.
         Otherwise, they will be split aggressively just before max_speech_duration_s.
 
     min_silence_duration_ms: int (default - 100 milliseconds)
@@ -232,6 +260,9 @@ 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
 
@@ -241,6 +272,12 @@ def get_speech_timestamps(audio: torch.Tensor,
     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.
 
+    min_silence_at_max_speech: float (default - 98ms)
+        Minimum silence duration in ms which is used to avoid abrupt cuts when max_speech_duration_s is reached
+
+    use_max_poss_sil_at_max_speech: bool (default - True)
+        Whether to use the maximum possible silence at max_speech_duration_s or not. If not, the last silence is used.
+
     window_size_samples: int (default - 512 samples)
         !!! DEPRECATED, DOES NOTHING !!!
 
@@ -249,7 +286,6 @@ def get_speech_timestamps(audio: torch.Tensor,
     speeches: list of dicts
         list containing ends and beginnings of speech chunks (samples or seconds based on return_seconds)
     """
-
     if not torch.is_tensor(audio):
         try:
             audio = torch.Tensor(audio)
@@ -280,7 +316,7 @@ def get_speech_timestamps(audio: torch.Tensor,
     speech_pad_samples = sampling_rate * speech_pad_ms / 1000
     max_speech_samples = sampling_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples
     min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
-    min_silence_samples_at_max_speech = sampling_rate * 98 / 1000
+    min_silence_samples_at_max_speech = sampling_rate * min_silence_at_max_speech / 1000
 
     audio_length_samples = len(audio)
 
@@ -291,7 +327,7 @@ 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
+        # calculate progress and send it to callback function
         progress = current_start_sample + window_size_samples
         if progress > audio_length_samples:
             progress = audio_length_samples
@@ -304,45 +340,76 @@ def get_speech_timestamps(audio: torch.Tensor,
     current_speech = {}
 
     if neg_threshold is None:
-        neg_threshold = threshold - 0.15
+        neg_threshold = max(threshold - 0.15, 0.01)
     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
+    possible_ends = []
 
     for i, speech_prob in enumerate(speech_probs):
+        cur_sample = window_size_samples * i
+
+        # If speech returns after a temp_end, record candidate silence if long enough and clear temp_end
         if (speech_prob >= threshold) and temp_end:
+            sil_dur = cur_sample - temp_end
+            if sil_dur > min_silence_samples_at_max_speech:
+                possible_ends.append((temp_end, sil_dur))
             temp_end = 0
             if next_start < prev_end:
-                next_start = window_size_samples * i
+                next_start = cur_sample
 
+        # Start of speech
         if (speech_prob >= threshold) and not triggered:
             triggered = True
-            current_speech['start'] = window_size_samples * i
+            current_speech['start'] = cur_sample
             continue
 
-        if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
+        # Max speech length reached: decide where to cut
-            if prev_end:
+        if triggered and (cur_sample - current_speech['start'] > max_speech_samples):
+            if use_max_poss_sil_at_max_speech and possible_ends:
+                prev_end, dur = max(possible_ends, key=lambda x: x[1])  # use the longest possible silence segment in the current speech chunk
                 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)
+                next_start = prev_end + dur
-                    triggered = False
-                else:
-                    current_speech['start'] = next_start
-                prev_end = next_start = temp_end = 0
-            else:
-                current_speech['end'] = window_size_samples * i
-                speeches.append(current_speech)
-                current_speech = {}
-                prev_end = next_start = temp_end = 0
-                triggered = False
-                continue
 
+                if next_start < prev_end + cur_sample:  # previously reached silence (< neg_thres) and is still not speech (< thres)
+                    current_speech['start'] = next_start
+                else:
+                    triggered = False
+                prev_end = next_start = temp_end = 0
+                possible_ends = []
+            else:
+                # Legacy max-speech cut (use_max_poss_sil_at_max_speech=False): prefer last valid silence (prev_end) if available
+                if prev_end:
+                    current_speech['end'] = prev_end
+                    speeches.append(current_speech)
+                    current_speech = {}
+                    if next_start < prev_end:
+                        triggered = False
+                    else:
+                        current_speech['start'] = next_start
+                    prev_end = next_start = temp_end = 0
+                    possible_ends = []
+                else:
+                    # No prev_end -> fallback to cutting at current sample
+                    current_speech['end'] = cur_sample
+                    speeches.append(current_speech)
+                    current_speech = {}
+                    prev_end = next_start = temp_end = 0
+                    triggered = False
+                    possible_ends = []
+                    continue
+
+        # Silence detection while in speech
         if (speech_prob < neg_threshold) and triggered:
             if not temp_end:
-                temp_end = window_size_samples * i
+                temp_end = cur_sample
-            if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech:  # condition to avoid cutting in very short silence
+            sil_dur_now = cur_sample - temp_end
+
+            if not use_max_poss_sil_at_max_speech and sil_dur_now > 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:
+
+            if sil_dur_now < min_silence_samples:
                 continue
             else:
                 current_speech['end'] = temp_end
@@ -351,6 +418,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                 current_speech = {}
                 prev_end = next_start = temp_end = 0
                 triggered = False
+                possible_ends = []
                 continue
 
     if current_speech and (audio_length_samples - current_speech['start']) > min_speech_samples:
@@ -372,9 +440,10 @@ def get_speech_timestamps(audio: torch.Tensor,
             speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))
 
     if return_seconds:
+        audio_length_seconds = audio_length_samples / sampling_rate
         for speech_dict in speeches:
-            speech_dict['start'] = round(speech_dict['start'] / sampling_rate, 1)
+            speech_dict['start'] = max(round(speech_dict['start'] / sampling_rate, time_resolution), 0)
-            speech_dict['end'] = round(speech_dict['end'] / sampling_rate, 1)
+            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
@@ -435,13 +504,16 @@ 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):
@@ -461,7 +533,7 @@ 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:
@@ -472,24 +544,112 @@ 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
 
 
 def collect_chunks(tss: List[dict],
-                   wav: torch.Tensor):
+                   wav: torch.Tensor,
-    chunks = []
+                   seconds: bool = False,
-    for i in tss:
+                   sampling_rate: int = None) -> torch.Tensor:
-        chunks.append(wav[i['start']: i['end']])
+    """Collect audio chunks from a longer audio clip
+
+    This method extracts audio chunks from an audio clip, using a list of
+    provided coordinates, and concatenates them together. Coordinates can be
+    passed either as sample numbers or in seconds, in which case the audio
+    sampling rate is also needed.
+
+    Parameters
+    ----------
+    tss: List[dict]
+        Coordinate list of the clips to collect from the audio.
+    wav: torch.Tensor, one dimensional
+        One dimensional float torch.Tensor, containing the audio to clip.
+    seconds: bool (default - False)
+        Whether input coordinates are passed as seconds or samples.
+    sampling_rate: int (default - None)
+        Input audio sampling rate. Required if seconds is True.
+
+    Returns
+    -------
+    torch.Tensor, one dimensional
+        One dimensional float torch.Tensor of the concatenated clipped audio
+        chunks.
+
+    Raises
+    ------
+    ValueError
+        Raised if sampling_rate is not provided when seconds is True.
+
+    """
+    if seconds and not sampling_rate:
+        raise ValueError('sampling_rate must be provided when seconds is True')
+
+    chunks = list()
+    _tss = _seconds_to_samples_tss(tss, sampling_rate) if seconds else tss
+
+    for i in _tss:
+        chunks.append(wav[i['start']:i['end']])
+
     return torch.cat(chunks)
 
 
 def drop_chunks(tss: List[dict],
-                wav: torch.Tensor):
+                wav: torch.Tensor,
-    chunks = []
+                seconds: bool = False,
+                sampling_rate: int = None) -> torch.Tensor:
+    """Drop audio chunks from a longer audio clip
+
+    This method extracts audio chunks from an audio clip, using a list of
+    provided coordinates, and drops them. Coordinates can be passed either as
+    sample numbers or in seconds, in which case the audio sampling rate is also
+    needed.
+
+    Parameters
+    ----------
+    tss: List[dict]
+        Coordinate list of the clips to drop from from the audio.
+    wav: torch.Tensor, one dimensional
+        One dimensional float torch.Tensor, containing the audio to clip.
+    seconds: bool (default - False)
+        Whether input coordinates are passed as seconds or samples.
+    sampling_rate: int (default - None)
+        Input audio sampling rate. Required if seconds is True.
+
+    Returns
+    -------
+    torch.Tensor, one dimensional
+        One dimensional float torch.Tensor of the input audio minus the dropped
+        chunks.
+
+    Raises
+    ------
+    ValueError
+        Raised if sampling_rate is not provided when seconds is True.
+
+    """
+    if seconds and not sampling_rate:
+        raise ValueError('sampling_rate must be provided when seconds is True')
+
+    chunks = list()
     cur_start = 0
-    for i in tss:
+
+    _tss = _seconds_to_samples_tss(tss, sampling_rate) if seconds else tss
+
+    for i in _tss:
         chunks.append((wav[cur_start: i['start']]))
         cur_start = i['end']
+
+    chunks.append(wav[cur_start:])
+
     return torch.cat(chunks)
+
+
+def _seconds_to_samples_tss(tss: List[dict], sampling_rate: int) -> List[dict]:
+    """Convert coordinates expressed in seconds to sample coordinates.
+    """
+    return [{
+        'start': round(crd['start']) * sampling_rate,
+        'end': round(crd['end']) * sampling_rate
+    } for crd in tss]

tests/test_basic.py

@@ -0,0 +1,22 @@
+from silero_vad import load_silero_vad, read_audio, get_speech_timestamps
+import torch
+torch.set_num_threads(1)
+
+def test_jit_model():
+    model = load_silero_vad(onnx=False)
+    for path in ["tests/data/test.wav", "tests/data/test.opus", "tests/data/test.mp3"]:
+        audio = read_audio(path, sampling_rate=16000)
+        speech_timestamps = get_speech_timestamps(audio, model, visualize_probs=False, return_seconds=True)
+        assert speech_timestamps is not None
+        out = model.audio_forward(audio, sr=16000)
+        assert out is not None
+
+def test_onnx_model():
+    model = load_silero_vad(onnx=True)
+    for path in ["tests/data/test.wav", "tests/data/test.opus", "tests/data/test.mp3"]:
+        audio = read_audio(path, sampling_rate=16000)
+        speech_timestamps = get_speech_timestamps(audio, model, visualize_probs=False, return_seconds=True)
+        assert speech_timestamps is not None
+
+        out = model.audio_forward(audio, sr=16000)
+        assert out is not None

tuning/utils.py

@@ -118,8 +118,6 @@ class SileroVadDataset(Dataset):
 
         assert len(gt) == len(wav) / self.num_samples
 
-        mask[gt == 0]
-
         return wav, gt, mask
 
     def get_ground_truth_annotated(self, annotation, audio_length_samples):
@@ -240,6 +238,7 @@ def train(config,
 
             loss = criterion(stacked, targets)
             loss = (loss * masks).mean()
+            optimizer.zero_grad()
             loss.backward()
             optimizer.step()
             losses.update(loss.item(), masks.numel())