Update hubconf.py

Update README.md
2026-02-04 17:39:22 +08:00 · 2024-06-27 22:58:52 +03:00 · 2024-06-27 22:56:37 +03:00 · 2024-06-27 22:25:19 +03:00 · 2024-06-27 22:08:15 +03:00 · 2024-06-27 21:03:38 +03:00
29 changed files with 2896 additions and 448 deletions
--- a/README.md
+++ b/README.md
@@ -10,19 +10,18 @@

 **Silero VAD** - pre-trained enterprise-grade [Voice Activity Detector](https://en.wikipedia.org/wiki/Voice_activity_detection) (also see our [STT models](https://github.com/snakers4/silero-models)).

-This repository also includes Number Detector and Language classifier [models](https://github.com/snakers4/silero-vad/wiki/Other-Models)
-
 <br/>

 <p align="center">
-  <img src="https://user-images.githubusercontent.com/36505480/198026365-8da383e0-5398-4a12-b7f8-22c2c0059512.png" />
+  <img src="https://github.com/snakers4/silero-vad/assets/36505480/300bd062-4da5-4f19-9736-9c144a45d7a7" />
 </p>

+
 <details>
 <summary>Real Time Example</summary>
-  
+
 https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-9be7-004c891dd481.mp4
-  
+
 </details>

 <br/>
@@ -39,20 +38,16 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-

 - **Lightweight**

-  JIT model is around one megabyte in size.
+  JIT model is around two megabytes in size.

 - **General**

-  Silero VAD was trained on huge corpora that include over **100** languages and it performs well on audios from different domains with various background noise and quality levels.
+  Silero VAD was trained on huge corpora that include over **6000** languages and it performs well on audios from different domains with various background noise and quality levels.

 - **Flexible sampling rate**

  Silero VAD [supports](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison)  **8000 Hz** and **16000 Hz** [sampling rates](https://en.wikipedia.org/wiki/Sampling_(signal_processing)#Sampling_rate).

- **Flexible chunk size**
-
-  Model was trained on **30 ms**. Longer chunks are supported directly, others may work as well.
-
 - **Highly Portable**

  Silero VAD reaps benefits from the rich ecosystems built around **PyTorch** and **ONNX** running everywhere where these runtimes are available.
@@ -61,6 +56,21 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-

   Published under permissive license (MIT) Silero VAD has zero strings attached - no telemetry, no keys, no registration, no built-in expiration, no keys or vendor lock.

+<br/>
+<h2 align="center">Fast start</h2>
+<br/>
+
+```python3
+import torch
+torch.set_num_threads(1)
+
+model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
+(get_speech_timestamps, _, read_audio, _, _) = utils
+
+wav = read_audio('path_to_audio_file')
+speech_timestamps = get_speech_timestamps(wav, model)
+```
+
 <br/>
 <h2 align="center">Typical Use Cases</h2>
 <br/>
@@ -78,7 +88,6 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
 - [Examples and Dependencies](https://github.com/snakers4/silero-vad/wiki/Examples-and-Dependencies#dependencies)
 - [Quality Metrics](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics)
 - [Performance Metrics](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics)
- [Number Detector and Language classifier models](https://github.com/snakers4/silero-vad/wiki/Other-Models)
 - [Versions and Available Models](https://github.com/snakers4/silero-vad/wiki/Version-history-and-Available-Models)
 - [Further reading](https://github.com/snakers4/silero-models#further-reading)
 - [FAQ](https://github.com/snakers4/silero-vad/wiki/FAQ)
@@ -89,7 +98,7 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-

 Try our models, create an [issue](https://github.com/snakers4/silero-vad/issues/new), start a [discussion](https://github.com/snakers4/silero-vad/discussions/new), join our telegram [chat](https://t.me/silero_speech), [email](mailto:hello@silero.ai) us, read our [news](https://t.me/silero_news).

-Please see our [wiki](https://github.com/snakers4/silero-models/wiki) and [tiers](https://github.com/snakers4/silero-models/wiki/Licensing-and-Tiers) for relevant information and [email](mailto:hello@silero.ai) us directly.
+Please see our [wiki](https://github.com/snakers4/silero-models/wiki) for relevant information and [email](mailto:hello@silero.ai) us directly.

 **Citations**

@@ -107,7 +116,9 @@ Please see our [wiki](https://github.com/snakers4/silero-models/wiki) and [tiers
 ```

 <br/>
-<h2 align="center">VAD-based Community Apps</h2>
+<h2 align="center">Examples and VAD-based Community Apps</h2>
 <br/>

+- Example of VAD ONNX Runtime model usage in [C++](https://github.com/snakers4/silero-vad/tree/master/examples/cpp) 
+
 - Voice activity detection for the [browser](https://github.com/ricky0123/vad) using ONNX Runtime Web 
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -0,0 +1,84 @@
+# Датасет Silero-VAD
+
+> Датасет создан при поддержке Фонда содействия инновациям в рамках федерального проекта «Искусственный
+интеллект» национальной программы «Цифровая экономика Российской Федерации».
+
+По ссылкам ниже представлены `.feather` файлы, содержащие размеченные с помощью Silero VAD открытые наборы аудиоданных, а также короткое описание каждого набора данных с примерами загрузки. `.feather` файлы можно открыть с помощью библиотеки `pandas`:
+```python3
+import pandas as pd
+dataframe = pd.read_feather(PATH_TO_FEATHER_FILE)
+```
+
+Каждый `.feather` файл с разметкой содержит следующие колонки:
+- `speech_timings` - разметка данного аудио. Это список, содержащий словари вида `{'start': START_SECOND, 'end': END_SECOND}`, где `START_SECOND` и `END_SECOND` - время начала и конца речи в секундах. Количество данных словарей равно количеству речевых аудио отрывков, найденных в данном аудио;
+- `language` - ISO код языка данного аудио.
+
+Колонки, содержащие информацию о загрузке аудио файла различаются и описаны для каждого набора данных ниже.
+
+**Все данные размечены при временной дискретизации в ~30 миллисекунд (`num_samples` - 512)**
+
+| Название             | Число часов | Число языков | Ссылка | Лицензия | md5sum |
+|----------------------|-------------|-------------|--------|----------|----------|
+| **Bible.is**             | 53,138       | 1,596        |    [URL](https://live.bible.is/)    |    [Уникальная](https://live.bible.is/terms)      | ea404eeaf2cd283b8223f63002be11f9 |
+| **globalrecordings.net** | 9,743        | 6,171[^1]       |    [URL](https://globalrecordings.net/en)    |    CC BY-NC-SA 4.0      | 3c5c0f31b0abd9fe94ddbe8b1e2eb326 |
+| **VoxLingua107**         | 6,628        | 107         |    [URL](https://bark.phon.ioc.ee/voxlingua107/)    |     CC BY 4.0     | 5dfef33b4d091b6d399cfaf3d05f2140 |
+| **Common Voice**         | 30,329       | 120         |    [URL](https://commonvoice.mozilla.org/en/datasets)    |     CC0     | 5e30a85126adf74a5fd1496e6ac8695d |
+| **MLS**                  | 50,709       | 8           |    [URL](https://www.openslr.org/94/)    |    CC BY 4.0      | a339d0e94bdf41bba3c003756254ac4e |
+| **Итого**                  | **150,547**       | **6,171+**         |     |    |  |
+
+## Bible.is
+
+[Ссылка на `.feather` файл с разметкой](https://models.silero.ai/vad_datasets/BibleIs.feather)
+
+- Колонка `audio_link` содержит ссылки на конкретные аудио файлы.
+
+## globalrecordings.net
+
+[Ссылка на `.feather` файл с разметкой](https://models.silero.ai/vad_datasets/globalrecordings.feather)
+
+- Колонка `folder_link` содержит ссылки на скачивание `.zip` архива для конкретного языка. Внимание! Ссылки на архивы дублируются, т.к каждый архив может содержать множество аудио.
+- Колонка `audio_path` содержит пути до конкретного аудио после распаковки соответствующего архива из колонки `folder_link`
+
+``Количество уникальных ISO кодов данного датасета не совпадает с фактическим количеством представленных языков, т.к некоторые близкие языки могут кодироваться одним и тем же ISO кодом.``
+
+## VoxLingua107
+
+[Ссылка на `.feather` файл с разметкой](https://models.silero.ai/vad_datasets/VoxLingua107.feather)
+
+- Колонка `folder_link` содержит ссылки на скачивание `.zip` архива для конкретного языка. Внимание! Ссылки на архивы дублируются, т.к каждый архив может содержать множество аудио.
+- Колонка `audio_path` содержит пути до конкретного аудио после распаковки соответствующего архива из колонки `folder_link`
+
+## Common Voice
+
+[Ссылка на `.feather` файл с разметкой](https://models.silero.ai/vad_datasets/common_voice.feather)
+
+Этот датасет невозможно скачать по статичным ссылкам. Для загрузки необходимо перейти по [ссылке](https://commonvoice.mozilla.org/en/datasets) и, получив доступ в соответствующей форме, скачать архивы для каждого доступного языка. Внимание! Представленная разметка актуальна для версии исходного датасета `Common Voice Corpus 16.1`.
+
+- Колонка `audio_path` содержит уникальные названия `.mp3` файлов, полученных после скачивания соответствующего датасета.
+
+## MLS
+
+[Ссылка на `.feather` файл с разметкой](https://models.silero.ai/vad_datasets/MLS.feather)
+
+- Колонка `folder_link` содержит ссылки на скачивание `.zip` архива для конкретного языка. Внимание! Ссылки на архивы дублируются, т.к каждый архив может содержать множество аудио.
+- Колонка `audio_path` содержит пути до конкретного аудио после распаковки соответствующего архива из колонки `folder_link`
+
+## Лицензия
+
+Данный датасет распространяется под [лицензией](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) `CC BY-NC-SA 4.0`.
+
+## Цитирование
+
+```
+@misc{Silero VAD Dataset,
+  author = {Silero Team},
+  title = {Silero-VAD Dataset: a large public Internet-scale dataset for voice activity detection for 6000+ languages},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/snakers4/silero-vad/datasets/README.md}},
+  email = {hello@silero.ai}
+}
+```
+
+[^1]: ``Количество уникальных ISO кодов данного датасета не совпадает с фактическим количеством представленных языков, т.к некоторые близкие языки могут кодироваться одним и тем же ISO кодом.``
--- a/examples/colab_record_example.ipynb
+++ b/examples/colab_record_example.ipynb
@@ -41,7 +41,7 @@
    "    abs_max = np.abs(sound).max()\n",
    "    sound = sound.astype('float32')\n",
    "    if abs_max > 0:\n",
-    "        sound *= 1/abs_max\n",
+    "        sound *= 1/32768\n",
    "    sound = sound.squeeze()\n",
    "    return sound\n",
    "\n",
--- a/examples/cpp/README.md
+++ b/examples/cpp/README.md
@@ -0,0 +1,43 @@
+# Stream example in C++
+
+Here's a simple example of the vad model in c++ onnxruntime.
+
+
+
+## Requirements
+
+Code are tested in the environments bellow, feel free to try others.
+
+- WSL2 + Debian-bullseye (docker)  
+- gcc 12.2.0
+- onnxruntime-linux-x64-1.12.1
+
+
+
+## Usage
+
+1. Install gcc 12.2.0, or just pull the docker image with `docker pull gcc:12.2.0-bullseye`
+
+2. Install onnxruntime-linux-x64-1.12.1
+
+   - Download lib onnxruntime: 
+
+     `wget https://github.com/microsoft/onnxruntime/releases/download/v1.12.1/onnxruntime-linux-x64-1.12.1.tgz`
+
+   - Unzip. Assume the path is `/root/onnxruntime-linux-x64-1.12.1`
+
+3. Modify wav path & Test configs in main function
+
+   `wav::WavReader wav_reader("${path_to_your_wav_file}");`
+
+   test sample rate, frame per ms, threshold...
+
+4. Build with gcc and run
+
+   ```bash
+   # Build
+   g++ silero-vad-onnx.cpp -I /root/onnxruntime-linux-x64-1.12.1/include/ -L /root/onnxruntime-linux-x64-1.12.1/lib/ -lonnxruntime  -Wl,-rpath,/root/onnxruntime-linux-x64-1.12.1/lib/ -o test
+   
+   # Run
+   ./test
+   ```
--- a/examples/cpp/silero-vad-onnx.cpp
+++ b/examples/cpp/silero-vad-onnx.cpp
@@ -0,0 +1,486 @@
+#include <iostream>
+#include <vector>
+#include <sstream>
+#include <cstring>
+#include <limits>
+#include <chrono>
+#include <memory>
+#include <string>
+#include <stdexcept>
+#include <iostream>
+#include <string>
+#include "onnxruntime_cxx_api.h"
+#include "wav.h"
+#include <cstdio>
+#include <cstdarg>
+#if __cplusplus < 201703L
+#include <memory>
+#endif
+
+//#define __DEBUG_SPEECH_PROB___
+
+class timestamp_t
+{
+public:
+    int start;
+    int end;
+
+    // default + parameterized constructor
+    timestamp_t(int start = -1, int end = -1)
+        : start(start), end(end)
+    {
+    };
+
+    // assignment operator modifies object, therefore non-const
+    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
+    {
+        return (start == a.start && end == a.end);
+    };
+    std::string c_str()
+    {
+        //return std::format("timestamp {:08d}, {:08d}", start, end);
+        return format("{start:%08d,end:%08d}", start, end);
+    };
+private:
+
+    std::string format(const char* fmt, ...)
+    {
+        char buf[256];
+
+        va_list args;
+        va_start(args, fmt);
+        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)
+            // we fit in the buffer
+            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 { vbuf.get(), len };
+#endif
+    };
+};
+
+
+class VadIterator
+{
+private:
+    // OnnxRuntime 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:
+    void init_engine_threads(int inter_threads, int intra_threads)
+    {
+        // The method should be called in each thread/proc in multi-thread/proc work
+        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)
+    {
+        // Init threads = 1 for 
+        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(_h.data(), 0.0f, _h.size() * sizeof(float));
+        std::memset(_c.data(), 0.0f, _c.size() * sizeof(float));
+        triggered = false;
+        temp_end = 0;
+        current_sample = 0;
+
+        prev_end = next_start = 0;
+
+        speeches.clear();
+        current_speech = timestamp_t();
+    };
+
+    void predict(const std::vector<float> &data)
+    {
+        // Infer
+        // Create ort tensors
+        input.assign(data.begin(), data.end());
+        Ort::Value input_ort = Ort::Value::CreateTensor<float>(
+            memory_info, input.data(), input.size(), input_node_dims, 2);
+        Ort::Value sr_ort = Ort::Value::CreateTensor<int64_t>(
+            memory_info, sr.data(), sr.size(), sr_node_dims, 1);
+        Ort::Value h_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _h.data(), _h.size(), hc_node_dims, 3);
+        Ort::Value c_ort = Ort::Value::CreateTensor<float>(
+            memory_info, _c.data(), _c.size(), hc_node_dims, 3);
+
+        // Clear and add inputs
+        ort_inputs.clear();
+        ort_inputs.emplace_back(std::move(input_ort));
+        ort_inputs.emplace_back(std::move(sr_ort));
+        ort_inputs.emplace_back(std::move(h_ort));
+        ort_inputs.emplace_back(std::move(c_ort));
+
+        // Infer
+        ort_outputs = session->Run(
+            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 *hn = ort_outputs[1].GetTensorMutableData<float>();
+        std::memcpy(_h.data(), hn, size_hc * sizeof(float));
+        float *cn = ort_outputs[2].GetTensorMutableData<float>();
+        std::memcpy(_c.data(), cn, size_hc * sizeof(float));
+
+        // Push forward sample index
+        current_sample += window_size_samples;
+
+        // 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.
+            printf("{    start: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample- window_size_samples);
+#endif //__DEBUG_SPEECH_PROB___
+            if (temp_end != 0)
+            {
+                temp_end = 0;
+                if (next_start < prev_end)
+                    next_start = current_sample - window_size_samples;
+            }
+            if (triggered == false)
+            {
+                triggered = true;
+
+                current_speech.start = current_sample - window_size_samples;
+            }
+            return;
+        }
+
+        if (
+            (triggered == true)
+            && ((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{
+                    current_speech.start = next_start;
+                }
+                prev_end = 0;
+                next_start = 0;
+                temp_end = 0;
+
+            }
+            else{ 
+                current_speech.end = current_sample;
+                speeches.push_back(current_speech);
+                current_speech = timestamp_t();
+                prev_end = 0;
+                next_start = 0;
+                temp_end = 0;
+                triggered = false;
+            }
+            return;
+
+        }
+        if ((speech_prob >= (threshold - 0.15)) && (speech_prob < threshold))
+        {
+            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)
+                {
+
+                }
+                // b. silence >= min_slience_samples, end speaking
+                else
+                {
+                    current_speech.end = temp_end;
+                    if (current_speech.end - current_speech.start > min_speech_samples)
+                    {
+                        speeches.push_back(current_speech);
+                        current_speech = timestamp_t();
+                        prev_end = 0;
+                        next_start = 0;
+                        temp_end = 0;
+                        triggered = false;
+                    }
+                }
+            }
+            else {
+                // may first windows see end state.
+            }
+            return;
+        }
+    };
+public:
+    void process(const std::vector<float>& input_wav)
+    {
+        reset_states();
+
+        audio_length_samples = input_wav.size();
+
+        for (int j = 0; j < audio_length_samples; j += window_size_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 };
+            predict(r);
+        }
+
+        if (current_speech.start >= 0) {
+            current_speech.end = audio_length_samples;
+            speeches.push_back(current_speech);
+            current_speech = timestamp_t();
+            prev_end = 0;
+            next_start = 0;
+            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)
+    {
+        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)
+    {
+        output_wav.clear();
+        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", "sr", "h", "c"};
+    std::vector<float> input;
+    std::vector<int64_t> sr;
+    unsigned int size_hc = 2 * 1 * 64; // It's FIXED.
+    std::vector<float> _h;
+    std::vector<float> _c;
+
+    int64_t input_node_dims[2] = {}; 
+    const int64_t sr_node_dims[1] = {1};
+    const int64_t hc_node_dims[3] = {2, 1, 64};
+
+    // Outputs
+    std::vector<Ort::Value> ort_outputs;
+    std::vector<const char *> output_node_names = {"output", "hn", "cn"};
+
+public:
+    // Construction
+    VadIterator(const std::wstring ModelPath,
+        int Sample_rate = 16000, int windows_frame_size = 64,
+        float Threshold = 0.5, int min_silence_duration_ms = 0,
+        int speech_pad_ms = 64, int min_speech_duration_ms = 64,
+        float max_speech_duration_s = std::numeric_limits<float>::infinity())
+    {
+        init_onnx_model(ModelPath);
+        threshold = Threshold;
+        sample_rate = Sample_rate;
+        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;
+
+        _h.resize(size_hc);
+        _c.resize(size_hc);
+        sr.resize(1);
+        sr[0] = sample_rate;
+    };
+};
+
+int main()
+{
+    std::vector<timestamp_t> stamps;
+
+    // Read wav
+    wav::WavReader wav_reader("recorder.wav"); //16000,1,32float
+    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));
+    }
+
+
+
+    // ===== Test configs =====
+    std::wstring path = L"silero_vad.onnx";
+    VadIterator vad(path);
+
+    // ==============================================
+    // ==== = Example 1 of full function  ===== 
+    // ==============================================
+    vad.process(input_wav);
+
+    // 1.a get_speech_timestamps
+    stamps = vad.get_speech_timestamps();
+    for (int i = 0; i < stamps.size(); i++) {
+
+        std::cout << stamps[i].c_str() << std::endl;
+    }
+
+    // 1.b collect_chunks output wav
+    vad.collect_chunks(input_wav, output_wav);
+
+    // 1.c drop_chunks output wav
+    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);
+}
--- a/examples/cpp/wav.h
+++ b/examples/cpp/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_
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -0,0 +1,19 @@
+## Golang Example
+
+This is a sample program of how to run speech detection using `silero-vad` from Golang (CGO + ONNX Runtime).
+
+### Requirements
+
+- Golang >= v1.21
+- ONNX Runtime
+
+### Usage
+
+```sh
+go run ./cmd/main.go test.wav
+```
+
+> **_Note_**
+>
+> Make sure you have the ONNX Runtime library and C headers installed in your path.
+
--- a/examples/go/cmd/main.go
+++ b/examples/go/cmd/main.go
@@ -0,0 +1,60 @@
+package main
+
+import (
+	"log"
+	"os"
+
+	"github.com/streamer45/silero-vad-go/speech"
+
+	"github.com/go-audio/wav"
+)
+
+func main() {
+	sd, err := speech.NewDetector(speech.DetectorConfig{
+		ModelPath:            "../../files/silero_vad.onnx",
+		SampleRate:           16000,
+		WindowSize:           1536,
+		Threshold:            0.5,
+		MinSilenceDurationMs: 0,
+		SpeechPadMs:          0,
+	})
+	if err != nil {
+		log.Fatalf("failed to create speech detector: %s", err)
+	}
+
+	f, err := os.Open(os.Args[1])
+	if err != nil {
+		log.Fatalf("failed to open sample audio file: %s", err)
+	}
+	defer f.Close()
+
+	dec := wav.NewDecoder(f)
+
+	if ok := dec.IsValidFile(); !ok {
+		log.Fatalf("invalid WAV file")
+	}
+
+	buf, err := dec.FullPCMBuffer()
+	if err != nil {
+		log.Fatalf("failed to get PCM buffer")
+	}
+
+	pcmBuf := buf.AsFloat32Buffer()
+
+	segments, err := sd.Detect(pcmBuf.Data)
+	if err != nil {
+		log.Fatalf("Detect failed: %s", err)
+	}
+
+	for _, s := range segments {
+		log.Printf("speech starts at %0.2fs", s.SpeechStartAt)
+		if s.SpeechEndAt > 0 {
+			log.Printf("speech ends at %0.2fs", s.SpeechEndAt)
+		}
+	}
+
+	err = sd.Destroy()
+	if err != nil {
+		log.Fatalf("failed to destroy detector: %s", err)
+	}
+}
--- a/examples/go/go.mod
+++ b/examples/go/go.mod
@@ -0,0 +1,13 @@
+module silero
+
+go 1.21.4
+
+require (
+	github.com/go-audio/wav v1.1.0
+	github.com/streamer45/silero-vad-go v0.1.0
+)
+
+require (
+	github.com/go-audio/audio v1.0.0 // indirect
+	github.com/go-audio/riff v1.0.0 // indirect
+)
--- a/examples/go/go.sum
+++ b/examples/go/go.sum
@@ -0,0 +1,16 @@
+github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
+github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/go-audio/audio v1.0.0 h1:zS9vebldgbQqktK4H0lUqWrG8P0NxCJVqcj7ZpNnwd4=
+github.com/go-audio/audio v1.0.0/go.mod h1:6uAu0+H2lHkwdGsAY+j2wHPNPpPoeg5AaEFh9FlA+Zs=
+github.com/go-audio/riff v1.0.0 h1:d8iCGbDvox9BfLagY94fBynxSPHO80LmZCaOsmKxokA=
+github.com/go-audio/riff v1.0.0/go.mod h1:l3cQwc85y79NQFCRB7TiPoNiaijp6q8Z0Uv38rVG498=
+github.com/go-audio/wav v1.1.0 h1:jQgLtbqBzY7G+BM8fXF7AHUk1uHUviWS4X39d5rsL2g=
+github.com/go-audio/wav v1.1.0/go.mod h1:mpe9qfwbScEbkd8uybLuIpTgHyrISw/OTuvjUW2iGtE=
+github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
+github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
+github.com/streamer45/silero-vad-go v0.1.0 h1:0nGZ6VT3LKOkBG/w+4kljIB6brxtgQn6YuNjTVYjOQ4=
+github.com/streamer45/silero-vad-go v0.1.0/go.mod h1:B+2FXs/5fZ6pzl6unUZYhZqkYdOB+3saBVzjOzdZnUs=
+github.com/stretchr/testify v1.8.4 h1:CcVxjf3Q8PM0mHUKJCdn+eZZtm5yQwehR5yeSVQQcUk=
+github.com/stretchr/testify v1.8.4/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
+gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
+gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
--- a/examples/java-example/pom.xml
+++ b/examples/java-example/pom.xml
@@ -0,0 +1,30 @@
+<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">
+  <modelVersion>4.0.0</modelVersion>
+
+  <groupId>org.example</groupId>
+  <artifactId>java-example</artifactId>
+  <version>1.0-SNAPSHOT</version>
+  <packaging>jar</packaging>
+
+  <name>sliero-vad-example</name>
+  <url>http://maven.apache.org</url>
+
+  <properties>
+    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <version>3.8.1</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.microsoft.onnxruntime</groupId>
+      <artifactId>onnxruntime</artifactId>
+      <version>1.16.0-rc1</version>
+    </dependency>
+  </dependencies>
+</project>
--- a/examples/java-example/src/main/java/org/example/App.java
+++ b/examples/java-example/src/main/java/org/example/App.java
@@ -0,0 +1,69 @@
+package org.example;
+
+import ai.onnxruntime.OrtException;
+import javax.sound.sampled.*;
+import java.util.Map;
+
+public class App {
+
+    private static final String MODEL_PATH = "src/main/resources/silero_vad.onnx";
+    private static final int SAMPLE_RATE = 16000;
+    private static final float START_THRESHOLD = 0.6f;
+    private static final float END_THRESHOLD = 0.45f;
+    private static final int MIN_SILENCE_DURATION_MS = 600;
+    private static final int SPEECH_PAD_MS = 500;
+    private static final int WINDOW_SIZE_SAMPLES = 2048;
+
+    public static void main(String[] args) {
+        // Initialize the Voice Activity Detector
+        SlieroVadDetector vadDetector;
+        try {
+            vadDetector = new SlieroVadDetector(MODEL_PATH, START_THRESHOLD, END_THRESHOLD, SAMPLE_RATE, MIN_SILENCE_DURATION_MS, SPEECH_PAD_MS);
+        } catch (OrtException e) {
+            System.err.println("Error initializing the VAD detector: " + e.getMessage());
+            return;
+        }
+
+        // Set audio format
+        AudioFormat format = new AudioFormat(SAMPLE_RATE, 16, 1, true, false);
+        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);
+            targetDataLine.open(format);
+            targetDataLine.start();
+        } catch (LineUnavailableException e) {
+            System.err.println("Error opening target data line: " + e.getMessage());
+            return;
+        }
+
+        // Main loop to continuously read data and apply Voice Activity Detection
+        while (targetDataLine.isOpen()) {
+            byte[] data = new byte[WINDOW_SIZE_SAMPLES];
+
+            int numBytesRead = targetDataLine.read(data, 0, data.length);
+            if (numBytesRead <= 0) {
+                System.err.println("Error reading data from target data line.");
+                continue;
+            }
+
+            // Apply the Voice Activity Detector to the data and get the result
+            Map<String, Double> detectResult;
+            try {
+                detectResult = vadDetector.apply(data, true);
+            } catch (Exception e) {
+                System.err.println("Error applying VAD detector: " + e.getMessage());
+                continue;
+            }
+
+            if (!detectResult.isEmpty()) {
+                System.out.println(detectResult);
+            }
+        }
+
+        // Close the target data line to release audio resources
+        targetDataLine.close();
+    }
+}
--- a/examples/java-example/src/main/java/org/example/SlieroVadDetector.java
+++ b/examples/java-example/src/main/java/org/example/SlieroVadDetector.java
@@ -0,0 +1,145 @@
+package org.example;
+
+import ai.onnxruntime.OrtException;
+
+import java.math.BigDecimal;
+import java.math.RoundingMode;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Map;
+
+
+public class SlieroVadDetector {
+    // OnnxModel model used for speech processing
+    private final SlieroVadOnnxModel model;
+    // Threshold for speech start
+    private final float startThreshold;
+    // Threshold for speech end
+    private final float endThreshold;
+    // Sampling rate
+    private final int samplingRate;
+    // Minimum number of silence samples to determine the end threshold of speech
+    private final float minSilenceSamples;
+    // Additional number of samples for speech start or end to calculate speech start or end time
+    private final float speechPadSamples;
+    // Whether in the triggered state (i.e. whether speech is being detected)
+    private boolean triggered;
+    // Temporarily stored number of speech end samples
+    private int tempEnd;
+    // Number of samples currently being processed
+    private int currentSample;
+
+
+    public SlieroVadDetector(String modelPath,
+                             float startThreshold,
+                             float endThreshold,
+                             int samplingRate,
+                             int minSilenceDurationMs,
+                             int speechPadMs) throws OrtException {
+        // Check if the sampling rate is 8000 or 16000, if not, throw an exception
+        if (samplingRate != 8000 && samplingRate != 16000) {
+            throw new IllegalArgumentException("does not support sampling rates other than [8000, 16000]");
+        }
+
+        // Initialize the 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();
+    }
+
+    // Method to reset the state, including the model state, trigger state, temporary end time, and current sample count
+    public void reset() {
+        model.resetStates();
+        triggered = false;
+        tempEnd = 0;
+        currentSample = 0;
+    }
+
+    // apply method for processing the audio array, returning possible speech start or end times
+    public Map<String, Double> apply(byte[] data, boolean returnSeconds) {
+
+        // Convert the byte array to a 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
+        int windowSizeSamples = audioData.length;
+        // Update the current sample count
+        currentSample += windowSizeSamples;
+
+        // Call the model to get the prediction probability of speech
+        float speechProb = 0;
+        try {
+            speechProb = model.call(new float[][]{audioData}, samplingRate)[0];
+        } catch (OrtException e) {
+            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
+        // 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
+        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
+            if (returnSeconds) {
+                double speechStartSeconds = speechStart / (double) samplingRate;
+                double roundedSpeechStart = BigDecimal.valueOf(speechStartSeconds).setScale(1, RoundingMode.HALF_UP).doubleValue();
+                result.put("start", roundedSpeechStart);
+            } else {
+                result.put("start", (double) speechStart);
+            }
+
+            return result;
+        }
+
+        // If the speech probability is less than a certain threshold and in the triggered state, calculate the speech end time
+        if (speechProb < endThreshold && triggered) {
+            // Initialize or update the temporary end time
+            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
+            // 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
+                int speechEnd = (int) (tempEnd + speechPadSamples);
+                tempEnd = 0;
+                triggered = false;
+                Map<String, Double> result = new HashMap<>();
+
+                if (returnSeconds) {
+                    double speechEndSeconds = speechEnd / (double) samplingRate;
+                    double roundedSpeechEnd = BigDecimal.valueOf(speechEndSeconds).setScale(1, RoundingMode.HALF_UP).doubleValue();
+                    result.put("end", roundedSpeechEnd);
+                } else {
+                    result.put("end", (double) speechEnd);
+                }
+                return result;
+            }
+        }
+
+        // If the above conditions are not met, return null by default
+        return Collections.emptyMap();
+    }
+
+    public void close() throws OrtException {
+        reset();
+        model.close();
+    }
+}
--- a/examples/java-example/src/main/java/org/example/SlieroVadOnnxModel.java
+++ b/examples/java-example/src/main/java/org/example/SlieroVadOnnxModel.java
@@ -0,0 +1,180 @@
+package org.example;
+
+import ai.onnxruntime.OnnxTensor;
+import ai.onnxruntime.OrtEnvironment;
+import ai.onnxruntime.OrtException;
+import ai.onnxruntime.OrtSession;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class SlieroVadOnnxModel {
+    // Define private variable OrtSession
+    private final OrtSession session;
+    private float[][][] h;
+    private float[][][] c;
+    // Define the last sample rate
+    private int lastSr = 0;
+    // Define the last batch size
+    private int lastBatchSize = 0;
+    // Define a list of 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
+        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
+        opts.setInterOpNumThreads(1);
+        // Set the IntraOp thread count to 1, IntraOp threads are used for parallel processing within a single operation
+        opts.setIntraOpNumThreads(1);
+        // Add a CPU device, setting to false disables CPU execution optimization
+        opts.addCPU(true);
+        // Create an ONNX session using the environment, model path, and options
+        session = env.createSession(modelPath, opts);
+        // Reset states
+        resetStates();
+    }
+
+    /**
+     * Reset states
+     */
+    void resetStates() {
+        h = new float[2][1][64];
+        c = new float[2][1][64];
+        lastSr = 0;
+        lastBatchSize = 0;
+    }
+
+    public void close() throws OrtException {
+        session.close();
+    }
+
+    /**
+     * Define inner class ValidationResult
+     */
+    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;
+        }
+    }
+
+    /**
+     * Function to validate input data
+     */
+    private ValidationResult validateInput(float[][] x, int sr) {
+        // Process the input data with dimension 1
+        if (x.length == 1) {
+            x = new float[][]{x[0]};
+        }
+        // Throw an exception when the input data dimension is greater than 2
+        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
+        if (sr != 16000 && (sr % 16000 == 0)) {
+            int step = sr / 16000;
+            float[][] reducedX = new float[x.length][];
+
+            for (int i = 0; i < x.length; i++) {
+                float[] current = x[i];
+                float[] newArr = new float[(current.length + step - 1) / step];
+
+                for (int j = 0, index = 0; j < current.length; j += step, index++) {
+                    newArr[index] = current[j];
+                }
+
+                reducedX[i] = newArr;
+            }
+
+            x = reducedX;
+            sr = 16000;
+        }
+
+        // If the sample rate is not in the list of supported sample rates, throw an exception
+        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
+        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
+     */
+    public float[] call(float[][] x, int sr) throws OrtException {
+        ValidationResult result = validateInput(x, sr);
+        x = result.x;
+        sr = result.sr;
+
+        int batchSize = x.length;
+
+        if (lastBatchSize == 0 || lastSr != sr || lastBatchSize != batchSize) {
+            resetStates();
+        }
+
+        OrtEnvironment env = OrtEnvironment.getEnvironment();
+
+        OnnxTensor inputTensor = null;
+        OnnxTensor hTensor = null;
+        OnnxTensor cTensor = null;
+        OnnxTensor srTensor = null;
+        OrtSession.Result ortOutputs = null;
+
+        try {
+            // Create input tensors
+            inputTensor = OnnxTensor.createTensor(env, x);
+            hTensor = OnnxTensor.createTensor(env, h);
+            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("c", cTensor);
+
+            // Call the ONNX model for calculation
+            ortOutputs = session.run(inputs);
+            // Get the output results
+            float[][] output = (float[][]) ortOutputs.get(0).getValue();
+            h = (float[][][]) ortOutputs.get(1).getValue();
+            c = (float[][][]) ortOutputs.get(2).getValue();
+
+            lastSr = sr;
+            lastBatchSize = batchSize;
+            return output[0];
+        } finally {
+            if (inputTensor != null) {
+                inputTensor.close();
+            }
+            if (hTensor != null) {
+                hTensor.close();
+            }
+            if (cTensor != null) {
+                cTensor.close();
+            }
+            if (srTensor != null) {
+                srTensor.close();
+            }
+            if (ortOutputs != null) {
+                ortOutputs.close();
+            }
+        }
+    }
+}
--- a/examples/parallel_example.ipynb
+++ b/examples/parallel_example.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install -q torchaudio\n",
+    "SAMPLING_RATE = 16000\n",
+    "import torch\n",
+    "from pprint import pprint\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_example{idx}.wav\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load VAD model from torch hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
+    "                                model='silero_vad',\n",
+    "                                force_reload=True,\n",
+    "                                onnx=False)\n",
+    "\n",
+    "(get_speech_timestamps,\n",
+    "save_audio,\n",
+    "read_audio,\n",
+    "VADIterator,\n",
+    "collect_chunks) = utils"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define a vad process function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import multiprocessing\n",
+    "\n",
+    "vad_models = dict()\n",
+    "\n",
+    "def init_model(model):\n",
+    "  pid = multiprocessing.current_process().pid\n",
+    "  model, _ = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
+    "                                model='silero_vad',\n",
+    "                                force_reload=False,\n",
+    "                                onnx=False)\n",
+    "  vad_models[pid] = model\n",
+    "\n",
+    "def vad_process(audio_file: str):\n",
+    "    \n",
+    "    pid = multiprocessing.current_process().pid\n",
+    "    \n",
+    "    with torch.no_grad():\n",
+    "        wav = read_audio(audio_file, sampling_rate=SAMPLING_RATE)\n",
+    "        return get_speech_timestamps(\n",
+    "            wav,\n",
+    "            vad_models[pid],\n",
+    "            0.46,  # speech prob threshold\n",
+    "            16000,  # sample rate\n",
+    "            300,  # min speech duration in ms\n",
+    "            20,  # max speech duration in seconds\n",
+    "            600,  # min silence duration\n",
+    "            512,  # window size\n",
+    "            200,  # spech pad ms\n",
+    "        )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallelization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
+    "\n",
+    "futures = []\n",
+    "\n",
+    "with ProcessPoolExecutor(max_workers=NUM_PROCESS, initializer=init_model, initargs=(model,)) as ex:\n",
+    "    for i in range(NUM_COPIES):\n",
+    "        futures.append(ex.submit(vad_process, f\"en_example{idx}.wav\"))\n",
+    "\n",
+    "for finished in as_completed(futures):\n",
+    "    pprint(finished.result())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "diarization",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb
+++ b/examples/pyaudio-streaming/pyaudio-streaming-examples.ipynb
@@ -118,7 +118,7 @@
    "    abs_max = np.abs(sound).max()\n",
    "    sound = sound.astype('float32')\n",
    "    if abs_max > 0:\n",
-    "        sound *= 1/abs_max\n",
+    "        sound *= 1/32768\n",
    "    sound = sound.squeeze()  # depends on the use case\n",
    "    return sound"
   ]
--- a/examples/rust-example/.gitignore
+++ b/examples/rust-example/.gitignore
@@ -0,0 +1,2 @@
+target/
+recorder.wav
--- a/examples/rust-example/Cargo.lock
+++ b/examples/rust-example/Cargo.lock
@@ -0,0 +1,781 @@
+# This file is automatically @generated by Cargo.
+# It is not intended for manual editing.
+version = 3
+
+[[package]]
+name = "adler"
+version = "1.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f26201604c87b1e01bd3d98f8d5d9a8fcbb815e8cedb41ffccbeb4bf593a35fe"
+
+[[package]]
+name = "autocfg"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
+
+[[package]]
+name = "base64"
+version = "0.22.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
+
+[[package]]
+name = "bitflags"
+version = "1.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bef38d45163c2f1dde094a7dfd33ccf595c92905c8f8f4fdc18d06fb1037718a"
+
+[[package]]
+name = "bitflags"
+version = "2.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cf4b9d6a944f767f8e5e0db018570623c85f3d925ac718db4e06d0187adb21c1"
+
+[[package]]
+name = "block-buffer"
+version = "0.10.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71"
+dependencies = [
+ "generic-array",
+]
+
+[[package]]
+name = "bumpalo"
+version = "3.16.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c"
+
+[[package]]
+name = "cc"
+version = "1.0.98"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "41c270e7540d725e65ac7f1b212ac8ce349719624d7bcff99f8e2e488e8cf03f"
+
+[[package]]
+name = "cfg-if"
+version = "1.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
+
+[[package]]
+name = "cpufeatures"
+version = "0.2.12"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "53fe5e26ff1b7aef8bca9c6080520cfb8d9333c7568e1829cef191a9723e5504"
+dependencies = [
+ "libc",
+]
+
+[[package]]
+name = "crc32fast"
+version = "1.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a97769d94ddab943e4510d138150169a2758b5ef3eb191a9ee688de3e23ef7b3"
+dependencies = [
+ "cfg-if",
+]
+
+[[package]]
+name = "crunchy"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7"
+
+[[package]]
+name = "crypto-common"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1bfb12502f3fc46cca1bb51ac28df9d618d813cdc3d2f25b9fe775a34af26bb3"
+dependencies = [
+ "generic-array",
+ "typenum",
+]
+
+[[package]]
+name = "digest"
+version = "0.10.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292"
+dependencies = [
+ "block-buffer",
+ "crypto-common",
+]
+
+[[package]]
+name = "errno"
+version = "0.3.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "534c5cf6194dfab3db3242765c03bbe257cf92f22b38f6bc0c58d59108a820ba"
+dependencies = [
+ "libc",
+ "windows-sys",
+]
+
+[[package]]
+name = "filetime"
+version = "0.2.23"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1ee447700ac8aa0b2f2bd7bc4462ad686ba06baa6727ac149a2d6277f0d240fd"
+dependencies = [
+ "cfg-if",
+ "libc",
+ "redox_syscall",
+ "windows-sys",
+]
+
+[[package]]
+name = "flate2"
+version = "1.0.30"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5f54427cfd1c7829e2a139fcefea601bf088ebca651d2bf53ebc600eac295dae"
+dependencies = [
+ "crc32fast",
+ "miniz_oxide",
+]
+
+[[package]]
+name = "form_urlencoded"
+version = "1.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e13624c2627564efccf4934284bdd98cbaa14e79b0b5a141218e507b3a823456"
+dependencies = [
+ "percent-encoding",
+]
+
+[[package]]
+name = "generic-array"
+version = "0.14.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a"
+dependencies = [
+ "typenum",
+ "version_check",
+]
+
+[[package]]
+name = "getrandom"
+version = "0.2.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c4567c8db10ae91089c99af84c68c38da3ec2f087c3f82960bcdbf3656b6f4d7"
+dependencies = [
+ "cfg-if",
+ "libc",
+ "wasi",
+]
+
+[[package]]
+name = "half"
+version = "2.4.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6dd08c532ae367adf81c312a4580bc67f1d0fe8bc9c460520283f4c0ff277888"
+dependencies = [
+ "cfg-if",
+ "crunchy",
+]
+
+[[package]]
+name = "hound"
+version = "3.5.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "62adaabb884c94955b19907d60019f4e145d091c75345379e70d1ee696f7854f"
+
+[[package]]
+name = "idna"
+version = "0.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "634d9b1461af396cad843f47fdba5597a4f9e6ddd4bfb6ff5d85028c25cb12f6"
+dependencies = [
+ "unicode-bidi",
+ "unicode-normalization",
+]
+
+[[package]]
+name = "js-sys"
+version = "0.3.69"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "29c15563dc2726973df627357ce0c9ddddbea194836909d655df6a75d2cf296d"
+dependencies = [
+ "wasm-bindgen",
+]
+
+[[package]]
+name = "libc"
+version = "0.2.155"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
+
+[[package]]
+name = "libloading"
+version = "0.8.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c2a198fb6b0eada2a8df47933734e6d35d350665a33a3593d7164fa52c75c19"
+dependencies = [
+ "cfg-if",
+ "windows-targets",
+]
+
+[[package]]
+name = "linux-raw-sys"
+version = "0.4.14"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"
+
+[[package]]
+name = "log"
+version = "0.4.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c"
+
+[[package]]
+name = "matrixmultiply"
+version = "0.3.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7574c1cf36da4798ab73da5b215bbf444f50718207754cb522201d78d1cd0ff2"
+dependencies = [
+ "autocfg",
+ "rawpointer",
+]
+
+[[package]]
+name = "miniz_oxide"
+version = "0.7.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87dfd01fe195c66b572b37921ad8803d010623c0aca821bea2302239d155cdae"
+dependencies = [
+ "adler",
+]
+
+[[package]]
+name = "ndarray"
+version = "0.15.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
+dependencies = [
+ "matrixmultiply",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+ "rawpointer",
+]
+
+[[package]]
+name = "num-complex"
+version = "0.4.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "num-integer"
+version = "0.1.46"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "num-traits"
+version = "0.2.19"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
+dependencies = [
+ "autocfg",
+]
+
+[[package]]
+name = "once_cell"
+version = "1.19.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
+
+[[package]]
+name = "ort"
+version = "2.0.0-rc.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0bc80894094c6a875bfac64415ed456fa661081a278a035e22be661305c87e14"
+dependencies = [
+ "half",
+ "js-sys",
+ "libloading",
+ "ndarray",
+ "ort-sys",
+ "thiserror",
+ "tracing",
+ "web-sys",
+]
+
+[[package]]
+name = "ort-sys"
+version = "2.0.0-rc.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b3d9c1373fc813d3f024d394f621f4c6dde0734c79b1c17113c3bb5bf0084bbe"
+dependencies = [
+ "flate2",
+ "sha2",
+ "tar",
+ "ureq",
+]
+
+[[package]]
+name = "percent-encoding"
+version = "2.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e3148f5046208a5d56bcfc03053e3ca6334e51da8dfb19b6cdc8b306fae3283e"
+
+[[package]]
+name = "pin-project-lite"
+version = "0.2.14"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bda66fc9667c18cb2758a2ac84d1167245054bcf85d5d1aaa6923f45801bdd02"
+
+[[package]]
+name = "proc-macro2"
+version = "1.0.84"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ec96c6a92621310b51366f1e28d05ef11489516e93be030060e5fc12024a49d6"
+dependencies = [
+ "unicode-ident",
+]
+
+[[package]]
+name = "quote"
+version = "1.0.36"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0fa76aaf39101c457836aec0ce2316dbdc3ab723cdda1c6bd4e6ad4208acaca7"
+dependencies = [
+ "proc-macro2",
+]
+
+[[package]]
+name = "rawpointer"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3"
+
+[[package]]
+name = "redox_syscall"
+version = "0.4.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4722d768eff46b75989dd134e5c353f0d6296e5aaa3132e776cbdb56be7731aa"
+dependencies = [
+ "bitflags 1.3.2",
+]
+
+[[package]]
+name = "ring"
+version = "0.17.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c17fa4cb658e3583423e915b9f3acc01cceaee1860e33d59ebae66adc3a2dc0d"
+dependencies = [
+ "cc",
+ "cfg-if",
+ "getrandom",
+ "libc",
+ "spin",
+ "untrusted",
+ "windows-sys",
+]
+
+[[package]]
+name = "rust-example"
+version = "0.1.0"
+dependencies = [
+ "hound",
+ "ndarray",
+ "ort",
+]
+
+[[package]]
+name = "rustix"
+version = "0.38.34"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f"
+dependencies = [
+ "bitflags 2.5.0",
+ "errno",
+ "libc",
+ "linux-raw-sys",
+ "windows-sys",
+]
+
+[[package]]
+name = "rustls"
+version = "0.22.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bf4ef73721ac7bcd79b2b315da7779d8fc09718c6b3d2d1b2d94850eb8c18432"
+dependencies = [
+ "log",
+ "ring",
+ "rustls-pki-types",
+ "rustls-webpki",
+ "subtle",
+ "zeroize",
+]
+
+[[package]]
+name = "rustls-pki-types"
+version = "1.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "976295e77ce332211c0d24d92c0e83e50f5c5f046d11082cea19f3df13a3562d"
+
+[[package]]
+name = "rustls-webpki"
+version = "0.102.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ff448f7e92e913c4b7d4c6d8e4540a1724b319b4152b8aef6d4cf8339712b33e"
+dependencies = [
+ "ring",
+ "rustls-pki-types",
+ "untrusted",
+]
+
+[[package]]
+name = "sha2"
+version = "0.10.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "793db75ad2bcafc3ffa7c68b215fee268f537982cd901d132f89c6343f3a3dc8"
+dependencies = [
+ "cfg-if",
+ "cpufeatures",
+ "digest",
+]
+
+[[package]]
+name = "spin"
+version = "0.9.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67"
+
+[[package]]
+name = "subtle"
+version = "2.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "81cdd64d312baedb58e21336b31bc043b77e01cc99033ce76ef539f78e965ebc"
+
+[[package]]
+name = "syn"
+version = "2.0.66"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c42f3f41a2de00b01c0aaad383c5a45241efc8b2d1eda5661812fda5f3cdcff5"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "unicode-ident",
+]
+
+[[package]]
+name = "tar"
+version = "0.4.40"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b16afcea1f22891c49a00c751c7b63b2233284064f11a200fc624137c51e2ddb"
+dependencies = [
+ "filetime",
+ "libc",
+ "xattr",
+]
+
+[[package]]
+name = "thiserror"
+version = "1.0.61"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
+dependencies = [
+ "thiserror-impl",
+]
+
+[[package]]
+name = "thiserror-impl"
+version = "1.0.61"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "tinyvec"
+version = "1.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87cc5ceb3875bb20c2890005a4e226a4651264a5c75edb2421b52861a0a0cb50"
+dependencies = [
+ "tinyvec_macros",
+]
+
+[[package]]
+name = "tinyvec_macros"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
+
+[[package]]
+name = "tracing"
+version = "0.1.40"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c3523ab5a71916ccf420eebdf5521fcef02141234bbc0b8a49f2fdc4544364ef"
+dependencies = [
+ "pin-project-lite",
+ "tracing-attributes",
+ "tracing-core",
+]
+
+[[package]]
+name = "tracing-attributes"
+version = "0.1.27"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "tracing-core"
+version = "0.1.32"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c06d3da6113f116aaee68e4d601191614c9053067f9ab7f6edbcb161237daa54"
+dependencies = [
+ "once_cell",
+]
+
+[[package]]
+name = "typenum"
+version = "1.17.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
+
+[[package]]
+name = "unicode-bidi"
+version = "0.3.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "08f95100a766bf4f8f28f90d77e0a5461bbdb219042e7679bebe79004fed8d75"
+
+[[package]]
+name = "unicode-ident"
+version = "1.0.12"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3354b9ac3fae1ff6755cb6db53683adb661634f67557942dea4facebec0fee4b"
+
+[[package]]
+name = "unicode-normalization"
+version = "0.1.23"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a56d1686db2308d901306f92a263857ef59ea39678a5458e7cb17f01415101f5"
+dependencies = [
+ "tinyvec",
+]
+
+[[package]]
+name = "untrusted"
+version = "0.9.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1"
+
+[[package]]
+name = "ureq"
+version = "2.9.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d11a831e3c0b56e438a28308e7c810799e3c118417f342d30ecec080105395cd"
+dependencies = [
+ "base64",
+ "log",
+ "once_cell",
+ "rustls",
+ "rustls-pki-types",
+ "rustls-webpki",
+ "url",
+ "webpki-roots",
+]
+
+[[package]]
+name = "url"
+version = "2.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "31e6302e3bb753d46e83516cae55ae196fc0c309407cf11ab35cc51a4c2a4633"
+dependencies = [
+ "form_urlencoded",
+ "idna",
+ "percent-encoding",
+]
+
+[[package]]
+name = "version_check"
+version = "0.9.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "49874b5167b65d7193b8aba1567f5c7d93d001cafc34600cee003eda787e483f"
+
+[[package]]
+name = "wasi"
+version = "0.11.0+wasi-snapshot-preview1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"
+
+[[package]]
+name = "wasm-bindgen"
+version = "0.2.92"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4be2531df63900aeb2bca0daaaddec08491ee64ceecbee5076636a3b026795a8"
+dependencies = [
+ "cfg-if",
+ "wasm-bindgen-macro",
+]
+
+[[package]]
+name = "wasm-bindgen-backend"
+version = "0.2.92"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "614d787b966d3989fa7bb98a654e369c762374fd3213d212cfc0251257e747da"
+dependencies = [
+ "bumpalo",
+ "log",
+ "once_cell",
+ "proc-macro2",
+ "quote",
+ "syn",
+ "wasm-bindgen-shared",
+]
+
+[[package]]
+name = "wasm-bindgen-macro"
+version = "0.2.92"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a1f8823de937b71b9460c0c34e25f3da88250760bec0ebac694b49997550d726"
+dependencies = [
+ "quote",
+ "wasm-bindgen-macro-support",
+]
+
+[[package]]
+name = "wasm-bindgen-macro-support"
+version = "0.2.92"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+ "wasm-bindgen-backend",
+ "wasm-bindgen-shared",
+]
+
+[[package]]
+name = "wasm-bindgen-shared"
+version = "0.2.92"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "af190c94f2773fdb3729c55b007a722abb5384da03bc0986df4c289bf5567e96"
+
+[[package]]
+name = "web-sys"
+version = "0.3.69"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "77afa9a11836342370f4817622a2f0f418b134426d91a82dfb48f532d2ec13ef"
+dependencies = [
+ "js-sys",
+ "wasm-bindgen",
+]
+
+[[package]]
+name = "webpki-roots"
+version = "0.26.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b3de34ae270483955a94f4b21bdaaeb83d508bb84a01435f393818edb0012009"
+dependencies = [
+ "rustls-pki-types",
+]
+
+[[package]]
+name = "windows-sys"
+version = "0.52.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "282be5f36a8ce781fad8c8ae18fa3f9beff57ec1b52cb3de0789201425d9a33d"
+dependencies = [
+ "windows-targets",
+]
+
+[[package]]
+name = "windows-targets"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6f0713a46559409d202e70e28227288446bf7841d3211583a4b53e3f6d96e7eb"
+dependencies = [
+ "windows_aarch64_gnullvm",
+ "windows_aarch64_msvc",
+ "windows_i686_gnu",
+ "windows_i686_gnullvm",
+ "windows_i686_msvc",
+ "windows_x86_64_gnu",
+ "windows_x86_64_gnullvm",
+ "windows_x86_64_msvc",
+]
+
+[[package]]
+name = "windows_aarch64_gnullvm"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7088eed71e8b8dda258ecc8bac5fb1153c5cffaf2578fc8ff5d61e23578d3263"
+
+[[package]]
+name = "windows_aarch64_msvc"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9985fd1504e250c615ca5f281c3f7a6da76213ebd5ccc9561496568a2752afb6"
+
+[[package]]
+name = "windows_i686_gnu"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "88ba073cf16d5372720ec942a8ccbf61626074c6d4dd2e745299726ce8b89670"
+
+[[package]]
+name = "windows_i686_gnullvm"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87f4261229030a858f36b459e748ae97545d6f1ec60e5e0d6a3d32e0dc232ee9"
+
+[[package]]
+name = "windows_i686_msvc"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "db3c2bf3d13d5b658be73463284eaf12830ac9a26a90c717b7f771dfe97487bf"
+
+[[package]]
+name = "windows_x86_64_gnu"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4e4246f76bdeff09eb48875a0fd3e2af6aada79d409d33011886d3e1581517d9"
+
+[[package]]
+name = "windows_x86_64_gnullvm"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "852298e482cd67c356ddd9570386e2862b5673c85bd5f88df9ab6802b334c596"
+
+[[package]]
+name = "windows_x86_64_msvc"
+version = "0.52.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0"
+
+[[package]]
+name = "xattr"
+version = "1.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8da84f1a25939b27f6820d92aed108f83ff920fdf11a7b19366c27c4cda81d4f"
+dependencies = [
+ "libc",
+ "linux-raw-sys",
+ "rustix",
+]
+
+[[package]]
+name = "zeroize"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ced3678a2879b30306d323f4542626697a464a97c0a07c9aebf7ebca65cd4dde"
--- a/examples/rust-example/Cargo.toml
+++ b/examples/rust-example/Cargo.toml
@@ -0,0 +1,9 @@
+[package]
+name = "rust-example"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+ort = { version = "2.0.0-rc.2", features = ["load-dynamic", "ndarray"] }
+ndarray = "0.15"
+hound = "3"
--- a/examples/rust-example/README.md
+++ b/examples/rust-example/README.md
@@ -0,0 +1,19 @@
+# Stream example in Rust
+Made after [C++ stream example](https://github.com/snakers4/silero-vad/tree/master/examples/cpp)
+
+## Dependencies
+- To build Rust crate `ort` you need `cc` installed.
+
+## Usage
+Just
+```
+cargo run
+```
+If you run example outside of this repo adjust environment variable
+```
+SILERO_MODEL_PATH=/path/to/silero_vad.onnx cargo run 
+```
+If you need to test against other wav file, not `recorder.wav`, specify it as the first argument
+```
+cargo run -- /path/to/audio/file.wav
+```
--- a/examples/rust-example/src/main.rs
+++ b/examples/rust-example/src/main.rs
@@ -0,0 +1,36 @@
+mod silero;
+mod utils;
+mod vad_iter;
+
+fn main() {
+    let model_path = std::env::var("SILERO_MODEL_PATH")
+        .unwrap_or_else(|_| String::from("../../files/silero_vad.onnx"));
+    let audio_path = std::env::args()
+        .nth(1)
+        .unwrap_or_else(|| String::from("recorder.wav"));
+    let mut wav_reader = hound::WavReader::open(audio_path).unwrap();
+    let sample_rate = match wav_reader.spec().sample_rate {
+        8000 => utils::SampleRate::EightkHz,
+        16000 => utils::SampleRate::SixteenkHz,
+        _ => panic!("Unsupported sample rate. Expect 8 kHz or 16 kHz."),
+    };
+    if wav_reader.spec().sample_format != hound::SampleFormat::Int {
+        panic!("Unsupported sample format. Expect Int.");
+    }
+    let content = wav_reader
+        .samples()
+        .filter_map(|x| x.ok())
+        .collect::<Vec<i16>>();
+    assert!(!content.is_empty());
+    let silero = silero::Silero::new(sample_rate, model_path).unwrap();
+    let vad_params = utils::VadParams {
+        sample_rate: sample_rate.into(),
+        ..Default::default()
+    };
+    let mut vad_iterator = vad_iter::VadIter::new(silero, vad_params);
+    vad_iterator.process(&content).unwrap();
+    for timestamp in vad_iterator.speeches() {
+        println!("{}", timestamp);
+    }
+    println!("Finished.");
+}
--- a/examples/rust-example/src/silero.rs
+++ b/examples/rust-example/src/silero.rs
@@ -0,0 +1,59 @@
+use crate::utils;
+use ndarray::{Array, Array2, ArrayBase, ArrayD, Dim, IxDynImpl, OwnedRepr};
+use std::path::Path;
+
+#[derive(Debug)]
+pub struct Silero {
+    session: ort::Session,
+    sample_rate: ArrayBase<OwnedRepr<i64>, Dim<[usize; 1]>>,
+    h: ArrayBase<OwnedRepr<f32>, Dim<IxDynImpl>>,
+    c: ArrayBase<OwnedRepr<f32>, Dim<IxDynImpl>>,
+}
+
+impl Silero {
+    pub fn new(
+        sample_rate: utils::SampleRate,
+        model_path: impl AsRef<Path>,
+    ) -> Result<Self, ort::Error> {
+        let session = ort::Session::builder()?.commit_from_file(model_path)?;
+        let h = ArrayD::<f32>::zeros([2, 1, 64].as_slice());
+        let c = ArrayD::<f32>::zeros([2, 1, 64].as_slice());
+        let sample_rate = Array::from_shape_vec([1], vec![sample_rate.into()]).unwrap();
+        Ok(Self {
+            session,
+            sample_rate,
+            h,
+            c,
+        })
+    }
+
+    pub fn reset(&mut self) {
+        self.h = ArrayD::<f32>::zeros([2, 1, 64].as_slice());
+        self.c = ArrayD::<f32>::zeros([2, 1, 64].as_slice());
+    }
+
+    pub fn calc_level(&mut self, audio_frame: &[i16]) -> Result<f32, ort::Error> {
+        let data = audio_frame
+            .iter()
+            .map(|x| (*x as f32) / (i16::MAX as f32))
+            .collect::<Vec<_>>();
+        let frame = Array2::<f32>::from_shape_vec([1, data.len()], data).unwrap();
+        let inps = ort::inputs![
+            frame,
+            self.sample_rate.clone(),
+            std::mem::take(&mut self.h),
+            std::mem::take(&mut self.c)
+        ]?;
+        let res = self
+            .session
+            .run(ort::SessionInputs::ValueSlice::<4>(&inps))?;
+        self.h = res["hn"].try_extract_tensor().unwrap().to_owned();
+        self.c = res["cn"].try_extract_tensor().unwrap().to_owned();
+        Ok(*res["output"]
+            .try_extract_raw_tensor::<f32>()
+            .unwrap()
+            .1
+            .first()
+            .unwrap())
+    }
+}
--- a/examples/rust-example/src/utils.rs
+++ b/examples/rust-example/src/utils.rs
@@ -0,0 +1,60 @@
+#[derive(Debug, Clone, Copy)]
+pub enum SampleRate {
+    EightkHz,
+    SixteenkHz,
+}
+
+impl From<SampleRate> for i64 {
+    fn from(value: SampleRate) -> Self {
+        match value {
+            SampleRate::EightkHz => 8000,
+            SampleRate::SixteenkHz => 16000,
+        }
+    }
+}
+
+impl From<SampleRate> for usize {
+    fn from(value: SampleRate) -> Self {
+        match value {
+            SampleRate::EightkHz => 8000,
+            SampleRate::SixteenkHz => 16000,
+        }
+    }
+}
+
+#[derive(Debug)]
+pub struct VadParams {
+    pub frame_size: usize,
+    pub threshold: f32,
+    pub min_silence_duration_ms: usize,
+    pub speech_pad_ms: usize,
+    pub min_speech_duration_ms: usize,
+    pub max_speech_duration_s: f32,
+    pub sample_rate: usize,
+}
+
+impl Default for VadParams {
+    fn default() -> Self {
+        Self {
+            frame_size: 64,
+            threshold: 0.5,
+            min_silence_duration_ms: 0,
+            speech_pad_ms: 64,
+            min_speech_duration_ms: 64,
+            max_speech_duration_s: f32::INFINITY,
+            sample_rate: 16000,
+        }
+    }
+}
+
+#[derive(Debug, Default)]
+pub struct TimeStamp {
+    pub start: i64,
+    pub end: i64,
+}
+
+impl std::fmt::Display for TimeStamp {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "[start:{:08}, end:{:08}]", self.start, self.end)
+    }
+}
--- a/examples/rust-example/src/vad_iter.rs
+++ b/examples/rust-example/src/vad_iter.rs
@@ -0,0 +1,223 @@
+use crate::{silero, utils};
+
+const DEBUG_SPEECH_PROB: bool = true;
+#[derive(Debug)]
+pub struct VadIter {
+    silero: silero::Silero,
+    params: Params,
+    state: State,
+}
+
+impl VadIter {
+    pub fn new(silero: silero::Silero, params: utils::VadParams) -> Self {
+        Self {
+            silero,
+            params: Params::from(params),
+            state: State::new(),
+        }
+    }
+
+    pub fn process(&mut self, samples: &[i16]) -> Result<(), ort::Error> {
+        self.reset_states();
+        for audio_frame in samples.chunks_exact(self.params.frame_size_samples) {
+            let speech_prob = self.silero.calc_level(audio_frame)?;
+            self.state.update(&self.params, speech_prob);
+        }
+        self.state.check_for_last_speech(samples.len());
+        Ok(())
+    }
+
+    pub fn speeches(&self) -> &[utils::TimeStamp] {
+        &self.state.speeches
+    }
+}
+
+impl VadIter {
+    fn reset_states(&mut self) {
+        self.silero.reset();
+        self.state = State::new()
+    }
+}
+
+#[allow(unused)]
+#[derive(Debug)]
+struct Params {
+    frame_size: usize,
+    threshold: f32,
+    min_silence_duration_ms: usize,
+    speech_pad_ms: usize,
+    min_speech_duration_ms: usize,
+    max_speech_duration_s: f32,
+    sample_rate: usize,
+    sr_per_ms: usize,
+    frame_size_samples: usize,
+    min_speech_samples: usize,
+    speech_pad_samples: usize,
+    max_speech_samples: f32,
+    min_silence_samples: usize,
+    min_silence_samples_at_max_speech: usize,
+}
+
+impl From<utils::VadParams> for Params {
+    fn from(value: utils::VadParams) -> Self {
+        let frame_size = value.frame_size;
+        let threshold = value.threshold;
+        let min_silence_duration_ms = value.min_silence_duration_ms;
+        let speech_pad_ms = value.speech_pad_ms;
+        let min_speech_duration_ms = value.min_speech_duration_ms;
+        let max_speech_duration_s = value.max_speech_duration_s;
+        let sample_rate = value.sample_rate;
+        let sr_per_ms = sample_rate / 1000;
+        let frame_size_samples = frame_size * sr_per_ms;
+        let min_speech_samples = sr_per_ms * min_speech_duration_ms;
+        let speech_pad_samples = sr_per_ms * speech_pad_ms;
+        let max_speech_samples = sample_rate as f32 * max_speech_duration_s
+            - frame_size_samples as f32
+            - 2.0 * speech_pad_samples as f32;
+        let min_silence_samples = sr_per_ms * min_silence_duration_ms;
+        let min_silence_samples_at_max_speech = sr_per_ms * 98;
+        Self {
+            frame_size,
+            threshold,
+            min_silence_duration_ms,
+            speech_pad_ms,
+            min_speech_duration_ms,
+            max_speech_duration_s,
+            sample_rate,
+            sr_per_ms,
+            frame_size_samples,
+            min_speech_samples,
+            speech_pad_samples,
+            max_speech_samples,
+            min_silence_samples,
+            min_silence_samples_at_max_speech,
+        }
+    }
+}
+
+#[derive(Debug, Default)]
+struct State {
+    current_sample: usize,
+    temp_end: usize,
+    next_start: usize,
+    prev_end: usize,
+    triggered: bool,
+    current_speech: utils::TimeStamp,
+    speeches: Vec<utils::TimeStamp>,
+}
+
+impl State {
+    fn new() -> Self {
+        Default::default()
+    }
+
+    fn update(&mut self, params: &Params, speech_prob: f32) {
+        self.current_sample += params.frame_size_samples;
+        if speech_prob > params.threshold {
+            if self.temp_end != 0 {
+                self.temp_end = 0;
+                if self.next_start < self.prev_end {
+                    self.next_start = self
+                        .current_sample
+                        .saturating_sub(params.frame_size_samples)
+                }
+            }
+            if !self.triggered {
+                self.debug(speech_prob, params, "start");
+                self.triggered = true;
+                self.current_speech.start =
+                    self.current_sample as i64 - params.frame_size_samples as i64;
+            }
+            return;
+        }
+        if self.triggered
+            && (self.current_sample as i64 - self.current_speech.start) as f32
+                > params.max_speech_samples
+        {
+            if self.prev_end > 0 {
+                self.current_speech.end = self.prev_end as _;
+                self.take_speech();
+                if self.next_start < self.prev_end {
+                    self.triggered = false
+                } else {
+                    self.current_speech.start = self.next_start as _;
+                }
+                self.prev_end = 0;
+                self.next_start = 0;
+                self.temp_end = 0;
+            } else {
+                self.current_speech.end = self.current_sample as _;
+                self.take_speech();
+                self.prev_end = 0;
+                self.next_start = 0;
+                self.temp_end = 0;
+                self.triggered = false;
+            }
+            return;
+        }
+        if speech_prob >= (params.threshold - 0.15) && (speech_prob < params.threshold) {
+            if self.triggered {
+                self.debug(speech_prob, params, "speaking")
+            } else {
+                self.debug(speech_prob, params, "silence")
+            }
+        }
+        if self.triggered && speech_prob < (params.threshold - 0.15) {
+            self.debug(speech_prob, params, "end");
+            if self.temp_end == 0 {
+                self.temp_end = self.current_sample;
+            }
+            if self.current_sample.saturating_sub(self.temp_end)
+                > params.min_silence_samples_at_max_speech
+            {
+                self.prev_end = self.temp_end;
+            }
+            if self.current_sample.saturating_sub(self.temp_end) >= params.min_silence_samples {
+                self.current_speech.end = self.temp_end as _;
+                if self.current_speech.end - self.current_speech.start
+                    > params.min_speech_samples as _
+                {
+                    self.take_speech();
+                    self.prev_end = 0;
+                    self.next_start = 0;
+                    self.temp_end = 0;
+                    self.triggered = false;
+                }
+            }
+        }
+    }
+
+    fn take_speech(&mut self) {
+        self.speeches.push(std::mem::take(&mut self.current_speech)); // current speech becomes TimeStamp::default() due to take()
+    }
+
+    fn check_for_last_speech(&mut self, last_sample: usize) {
+        if self.current_speech.start > 0 {
+            self.current_speech.end = last_sample as _;
+            self.take_speech();
+            self.prev_end = 0;
+            self.next_start = 0;
+            self.temp_end = 0;
+            self.triggered = false;
+        }
+    }
+
+    fn debug(&self, speech_prob: f32, params: &Params, title: &str) {
+        if DEBUG_SPEECH_PROB {
+            let speech = self.current_sample as f32
+                - params.frame_size_samples as f32
+                - if title == "end" {
+                    params.speech_pad_samples
+                } else {
+                    0
+                } as f32; // minus window_size_samples to get precise start time point.
+            println!(
+                "[{:10}: {:.3} s ({:.3}) {:8}]",
+                title,
+                speech / params.sample_rate as f32,
+                speech_prob,
+                self.current_sample - params.frame_size_samples,
+            );
+        }
+    }
+}
--- a/files/silero_vad.jit
+++ b/files/silero_vad.jit
--- a/files/silero_vad.onnx
+++ b/files/silero_vad.onnx
--- a/hubconf.py
+++ b/hubconf.py
@@ -1,11 +1,9 @@
 dependencies = ['torch', 'torchaudio']
 import torch
 import json
+import os
 from utils_vad import (init_jit_model,
                       get_speech_timestamps,
-                       get_number_ts,
-                       get_language,
-                       get_language_and_group,
                       save_audio,
                       read_audio,
                       VADIterator,
@@ -16,7 +14,14 @@ from utils_vad import (init_jit_model,


 def versiontuple(v):
-    return tuple(map(int, (v.split('+')[0].split("."))))
+    splitted = v.split('+')[0].split(".")
+    version_list = []
+    for i in splitted:
+        try:
+            version_list.append(int(i))
+        except:
+            version_list.append(0)
+    return tuple(version_list)


 def silero_vad(onnx=False, force_onnx_cpu=False):
@@ -31,11 +36,11 @@ def silero_vad(onnx=False, force_onnx_cpu=False):
        if versiontuple(installed_version) < versiontuple(supported_version):
            raise Exception(f'Please install torch {supported_version} or greater ({installed_version} installed)')

-    hub_dir = torch.hub.get_dir()
+    model_dir = os.path.join(os.path.dirname(__file__), 'files')
    if onnx:
-        model = OnnxWrapper(f'{hub_dir}/snakers4_silero-vad_master/files/silero_vad.onnx', force_onnx_cpu)
+        model = OnnxWrapper(os.path.join(model_dir, 'silero_vad.onnx'), force_onnx_cpu)
    else:
-        model = init_jit_model(model_path=f'{hub_dir}/snakers4_silero-vad_master/files/silero_vad.jit')
+        model = init_jit_model(os.path.join(model_dir, 'silero_vad.jit'))
    utils = (get_speech_timestamps,
             save_audio,
             read_audio,
@@ -43,62 +48,3 @@ def silero_vad(onnx=False, force_onnx_cpu=False):
             collect_chunks)

    return model, utils
-
-
-def silero_number_detector(onnx=False, force_onnx_cpu=False):
-    """Silero Number Detector
-    Returns a model with a set of utils
-    Please see https://github.com/snakers4/silero-vad for usage examples
-    """
-    if onnx:
-        url = 'https://models.silero.ai/vad_models/number_detector.onnx'
-    else:
-        url = 'https://models.silero.ai/vad_models/number_detector.jit'
-    model = Validator(url, force_onnx_cpu)
-    utils = (get_number_ts,
-             save_audio,
-             read_audio,
-             collect_chunks,
-             drop_chunks)
-
-    return model, utils
-
-
-def silero_lang_detector(onnx=False, force_onnx_cpu=False):
-    """Silero Language Classifier
-    Returns a model with a set of utils
-    Please see https://github.com/snakers4/silero-vad for usage examples
-    """
-    if onnx:
-        url = 'https://models.silero.ai/vad_models/number_detector.onnx'
-    else:
-        url = 'https://models.silero.ai/vad_models/number_detector.jit'
-    model = Validator(url, force_onnx_cpu)
-    utils = (get_language,
-             read_audio)
-
-    return model, utils
-
-
-def silero_lang_detector_95(onnx=False, force_onnx_cpu=False):
-    """Silero Language Classifier (95 languages)
-    Returns a model with a set of utils
-    Please see https://github.com/snakers4/silero-vad for usage examples
-    """
-
-    hub_dir = torch.hub.get_dir()
-    if onnx:
-        url = 'https://models.silero.ai/vad_models/lang_classifier_95.onnx'
-    else:
-        url = 'https://models.silero.ai/vad_models/lang_classifier_95.jit'
-    model = Validator(url, force_onnx_cpu)
-
-    with open(f'{hub_dir}/snakers4_silero-vad_master/files/lang_dict_95.json', 'r') as f:
-        lang_dict = json.load(f)
-
-    with open(f'{hub_dir}/snakers4_silero-vad_master/files/lang_group_dict_95.json', 'r') as f:
-        lang_group_dict = json.load(f)
-
-    utils = (get_language_and_group, read_audio)
-
-    return model, lang_dict, lang_group_dict, utils
--- a/silero-vad.ipynb
+++ b/silero-vad.ipynb
@@ -1,14 +1,5 @@
 {
 "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FpMplOCA2Fwp"
-   },
-   "source": [
-    "#VAD"
-   ]
-  },
  {
   "cell_type": "markdown",
   "metadata": {
@@ -55,7 +46,7 @@
    "USE_ONNX = False # change this to True if you want to test onnx model\n",
    "if USE_ONNX:\n",
    "    !pip install -q onnxruntime\n",
-    "  \n",
+    "\n",
    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_vad',\n",
    "                              force_reload=True,\n",
@@ -74,16 +65,7 @@
    "id": "fXbbaUO3jsrw"
   },
   "source": [
-    "## Full Audio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RAfJPb_a-Auj"
-   },
-   "source": [
-    "**Speech timestapms from full audio**"
+    "## Speech timestapms from full audio"
   ]
  },
  {
@@ -110,10 +92,33 @@
   "source": [
    "# merge all speech chunks to one audio\n",
    "save_audio('only_speech.wav',\n",
-    "           collect_chunks(speech_timestamps, wav), sampling_rate=SAMPLING_RATE) \n",
+    "           collect_chunks(speech_timestamps, wav), sampling_rate=SAMPLING_RATE)\n",
    "Audio('only_speech.wav')"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zeO1xCqxUC6w"
+   },
+   "source": [
+    "## Entire audio inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "LjZBcsaTT7Mk"
+   },
+   "outputs": [],
+   "source": [
+    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
+    "# audio is being splitted into 31.25 ms long pieces\n",
+    "# so output length equals ceil(input_length * 31.25 / SAMPLING_RATE)\n",
+    "predicts = model.audio_forward(wav, sr=SAMPLING_RATE)"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {
@@ -133,10 +138,10 @@
   "source": [
    "## using VADIterator class\n",
    "\n",
-    "vad_iterator = VADIterator(model)\n",
+    "vad_iterator = VADIterator(model, sampling_rate=SAMPLING_RATE)\n",
    "wav = read_audio(f'en_example.wav', sampling_rate=SAMPLING_RATE)\n",
    "\n",
-    "window_size_samples = 1536 # number of samples in a single audio chunk\n",
+    "window_size_samples = 512 if SAMPLING_RATE == 16000 else 256\n",
    "for i in range(0, len(wav), window_size_samples):\n",
    "    chunk = wav[i: i+ window_size_samples]\n",
    "    if len(chunk) < window_size_samples:\n",
@@ -159,7 +164,7 @@
    "\n",
    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
    "speech_probs = []\n",
-    "window_size_samples = 1536\n",
+    "window_size_samples = 512 if SAMPLING_RATE == 16000 else 256\n",
    "for i in range(0, len(wav), window_size_samples):\n",
    "    chunk = wav[i: i+ window_size_samples]\n",
    "    if len(chunk) < window_size_samples:\n",
@@ -170,238 +175,6 @@
    "\n",
    "print(speech_probs[:10]) # first 10 chunks predicts"
   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "id": "36jY0niD2Fww"
-   },
-   "source": [
-    "# Number detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "scd1DlS42Fwx"
-   },
-   "source": [
-    "## Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "Kq5gQuYq2Fwx"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Install and Import Dependencies\n",
-    "\n",
-    "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio\n",
-    "\n",
-    "SAMPLING_RATE = 16000\n",
-    "\n",
-    "import torch\n",
-    "torch.set_num_threads(1)\n",
-    "\n",
-    "from IPython.display import Audio\n",
-    "from pprint import pprint\n",
-    "# download example\n",
-    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en_num.wav', 'en_number_example.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "dPwCFHmFycUF"
-   },
-   "outputs": [],
-   "source": [
-    "USE_ONNX = False # change this to True if you want to test onnx model\n",
-    "if USE_ONNX:\n",
-    "    !pip install -q onnxruntime\n",
-    "  \n",
-    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
-    "                              model='silero_number_detector',\n",
-    "                              force_reload=True,\n",
-    "                              onnx=USE_ONNX)\n",
-    "\n",
-    "(get_number_ts,\n",
-    " save_audio,\n",
-    " read_audio,\n",
-    " collect_chunks,\n",
-    " drop_chunks) = utils\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "qhPa30ij2Fwy"
-   },
-   "source": [
-    "## Full audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "EXpau6xq2Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "wav = read_audio('en_number_example.wav', sampling_rate=SAMPLING_RATE)\n",
-    "# get number timestamps from full audio file\n",
-    "number_timestamps = get_number_ts(wav, model)\n",
-    "pprint(number_timestamps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "u-KfXRhZ2Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "# convert ms in timestamps to samples\n",
-    "for timestamp in number_timestamps:\n",
-    "    timestamp['start'] = int(timestamp['start'] * SAMPLING_RATE / 1000)\n",
-    "    timestamp['end'] = int(timestamp['end'] * SAMPLING_RATE / 1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "iwYEC4aZ2Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "# merge all number chunks to one audio\n",
-    "save_audio('only_numbers.wav',\n",
-    "           collect_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
-    "Audio('only_numbers.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "fHaYejX12Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "# drop all number chunks from audio\n",
-    "save_audio('no_numbers.wav',\n",
-    "           drop_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
-    "Audio('no_numbers.wav')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "id": "PnKtJKbq2Fwz"
-   },
-   "source": [
-    "# Language detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "F5cAmMbP2Fwz"
-   },
-   "source": [
-    "## Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "Zu9D0t6n2Fwz"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Install and Import Dependencies\n",
-    "\n",
-    "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio\n",
-    "\n",
-    "SAMPLING_RATE = 16000\n",
-    "\n",
-    "import torch\n",
-    "torch.set_num_threads(1)\n",
-    "\n",
-    "from IPython.display import Audio\n",
-    "from pprint import pprint\n",
-    "# download example\n",
-    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JfRKDZiRztFe"
-   },
-   "outputs": [],
-   "source": [
-    "USE_ONNX = False # change this to True if you want to test onnx model\n",
-    "if USE_ONNX:\n",
-    "    !pip install -q onnxruntime\n",
-    "  \n",
-    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
-    "                              model='silero_lang_detector',\n",
-    "                              force_reload=True,\n",
-    "                              onnx=USE_ONNX)\n",
-    "\n",
-    "get_language, read_audio = utils"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "iC696eMX2Fwz"
-   },
-   "source": [
-    "## Full audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "c8UYnYBF2Fw0"
-   },
-   "outputs": [],
-   "source": [
-    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
-    "lang = get_language(wav, model)\n",
-    "print(lang)"
-   ]
  }
 ],
 "metadata": {
--- a/utils_vad.py
+++ b/utils_vad.py
@@ -1,7 +1,6 @@
 import torch
 import torchaudio
-from typing import List
-import torch.nn.functional as F
+from typing import Callable, List
 import warnings

 languages = ['ru', 'en', 'de', 'es']
@@ -13,12 +12,15 @@ class OnnxWrapper():
        import numpy as np
        global np
        import onnxruntime
+
+        opts = onnxruntime.SessionOptions()
+        opts.inter_op_num_threads = 1
+        opts.intra_op_num_threads = 1
+
        if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
-            self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'])
+            self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
        else:
-            self.session = onnxruntime.InferenceSession(path)
-        self.session.intra_op_num_threads = 1
-        self.session.inter_op_num_threads = 1
+            self.session = onnxruntime.InferenceSession(path, sess_options=opts)

        self.reset_states()
        self.sample_rates = [8000, 16000]
@@ -31,27 +33,32 @@ class OnnxWrapper():

        if sr != 16000 and (sr % 16000 == 0):
            step = sr // 16000
-            x = x[::step]
+            x = x[:,::step]
            sr = 16000

        if sr not in self.sample_rates:
            raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
-
        if sr / x.shape[1] > 31.25:
            raise ValueError("Input audio chunk is too short")

        return x, sr

    def reset_states(self, batch_size=1):
-        self._h = np.zeros((2, batch_size, 64)).astype('float32')
-        self._c = np.zeros((2, batch_size, 64)).astype('float32')
+        self._state = torch.zeros((2, batch_size, 128)).float()
+        self._context = torch.zeros(0)
        self._last_sr = 0
        self._last_batch_size = 0

    def __call__(self, x, sr: int):

        x, sr = self._validate_input(x, sr)
+        num_samples = 512 if sr == 16000 else 256
+        
+        if x.shape[-1] != num_samples:
+            raise ValueError(f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")
+        
        batch_size = x.shape[0]
+        context_size = 64 if sr == 16000 else 32

        if not self._last_batch_size:
            self.reset_states(batch_size)
@@ -60,28 +67,35 @@ class OnnxWrapper():
        if (self._last_batch_size) and (self._last_batch_size != batch_size):
            self.reset_states(batch_size)

+        if not len(self._context):
+            self._context = torch.zeros(batch_size, context_size)
+
+        x = torch.cat([self._context, x], dim=1)
        if sr in [8000, 16000]:
-            ort_inputs = {'input': x.numpy(), 'h': self._h, 'c': self._c, 'sr': np.array(sr)}
+            ort_inputs = {'input': x.numpy(), 'state': self._state.numpy(), 'sr': np.array(sr)}
            ort_outs = self.session.run(None, ort_inputs)
-            out, self._h, self._c = ort_outs
+            out, state = ort_outs
+            self._state = torch.from_numpy(state)
        else:
            raise ValueError()

+        self._context = x[..., -context_size:]
        self._last_sr = sr
        self._last_batch_size = batch_size

-        out = torch.tensor(out)
+        out = torch.from_numpy(out)
        return out

-    def audio_forward(self, x, sr: int, num_samples: int = 512):
+    def audio_forward(self, x, sr: int):
        outs = []
        x, sr = self._validate_input(x, sr)
+        self.reset_states()
+        num_samples = 512 if sr == 16000 else 256

        if x.shape[1] % num_samples:
            pad_num = num_samples - (x.shape[1] % num_samples)
            x = torch.nn.functional.pad(x, (0, pad_num), 'constant', value=0.0)

-        self.reset_states(x.shape[0])
        for i in range(0, x.shape[1], num_samples):
            wavs_batch = x[:, i:i+num_samples]
            out_chunk = self.__call__(wavs_batch, sr)
@@ -119,16 +133,27 @@ class Validator():
 def read_audio(path: str,
               sampling_rate: int = 16000):

-    wav, sr = torchaudio.load(path)
+    sox_backends = set(['sox', 'sox_io'])
+    audio_backends = torchaudio.list_audio_backends()

-    if wav.size(0) > 1:
-        wav = wav.mean(dim=0, keepdim=True)
+    if len(sox_backends.intersection(audio_backends)) > 0:
+        effects = [
+            ['channels', '1'],
+            ['rate', str(sampling_rate)]
+        ]

-    if sr != sampling_rate:
-        transform = torchaudio.transforms.Resample(orig_freq=sr,
-                                                   new_freq=sampling_rate)
-        wav = transform(wav)
-        sr = sampling_rate
+        wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
+    else:
+        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)
@@ -137,12 +162,11 @@ def read_audio(path: str,
 def save_audio(path: str,
               tensor: torch.Tensor,
               sampling_rate: int = 16000):
-    torchaudio.save(path, tensor.unsqueeze(0), sampling_rate)
+    torchaudio.save(path, tensor.unsqueeze(0), sampling_rate, bits_per_sample=16)


 def init_jit_model(model_path: str,
                   device=torch.device('cpu')):
-    torch.set_grad_enabled(False)
    model = torch.jit.load(model_path, map_location=device)
    model.eval()
    return model
@@ -158,16 +182,19 @@ def make_visualization(probs, step):
                 colormap='tab20')


+@torch.no_grad()
 def get_speech_timestamps(audio: torch.Tensor,
                          model,
                          threshold: float = 0.5,
                          sampling_rate: int = 16000,
                          min_speech_duration_ms: int = 250,
+                          max_speech_duration_s: float = float('inf'),
                          min_silence_duration_ms: int = 100,
-                          window_size_samples: int = 512,
                          speech_pad_ms: int = 30,
                          return_seconds: bool = False,
-                          visualize_probs: bool = False):
+                          visualize_probs: bool = False,
+                          progress_tracking_callback: Callable[[float], None] = None,
+                          window_size_samples: int = 512,):

    """
    This method is used for splitting long audios into speech chunks using silero VAD
@@ -177,26 +204,26 @@ def get_speech_timestamps(audio: torch.Tensor,
    audio: torch.Tensor, one dimensional
        One dimensional float torch.Tensor, other types are casted to torch if possible

-    model: preloaded .jit silero VAD model
+    model: preloaded .jit/.onnx silero VAD model

    threshold: float (default - 0.5)
        Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
        It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.

    sampling_rate: int (default - 16000)
-        Currently silero VAD models support 8000 and 16000 sample rates
+        Currently silero VAD models support 8000 and 16000 (or multiply of 16000) sample rates

    min_speech_duration_ms: int (default - 250 milliseconds)
        Final speech chunks shorter min_speech_duration_ms are thrown out

+    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.
+        Otherwise, they will be split aggressively just before max_speech_duration_s.
+
    min_silence_duration_ms: int (default - 100 milliseconds)
        In the end of each speech chunk wait for min_silence_duration_ms before separating it

-    window_size_samples: int (default - 1536 samples)
-        Audio chunks of window_size_samples size are fed to the silero VAD model.
-        WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate and 256, 512, 768 samples for 8000 sample rate.
-        Values other than these may affect model perfomance!!
-
    speech_pad_ms: int (default - 30 milliseconds)
        Final speech chunks are padded by speech_pad_ms each side

@@ -206,6 +233,12 @@ def get_speech_timestamps(audio: torch.Tensor,
    visualize_probs: bool (default - False)
        whether draw prob hist or not

+    progress_tracking_callback: Callable[[float], None] (default - None)
+        callback function taking progress in percents as an argument
+
+    window_size_samples: int (default - 512 samples)
+        !!! DEPRECATED, DOES NOTHING !!!
+
    Returns
    ----------
    speeches: list of dicts
@@ -232,15 +265,17 @@ def get_speech_timestamps(audio: torch.Tensor,
    else:
        step = 1

-    if sampling_rate == 8000 and window_size_samples > 768:
-        warnings.warn('window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 768 for 8000 sample rate!')
-    if window_size_samples not in [256, 512, 768, 1024, 1536]:
-        warnings.warn('Unusual window_size_samples! Supported window_size_samples:\n - [512, 1024, 1536] for 16000 sampling_rate\n - [256, 512, 768] for 8000 sampling_rate')
+    if sampling_rate not in [8000, 16000]:
+        raise ValueError("Currently silero VAD models support 8000 and 16000 (or multiply of 16000) sample rates")
+
+    window_size_samples = 512 if sampling_rate == 16000 else 256

    model.reset_states()
    min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
-    min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
    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

    audio_length_samples = len(audio)

@@ -251,33 +286,63 @@ 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
+        progress = current_start_sample + window_size_samples
+        if progress > audio_length_samples:
+            progress = audio_length_samples
+        progress_percent = (progress / audio_length_samples) * 100
+        if progress_tracking_callback:
+            progress_tracking_callback(progress_percent)

    triggered = False
    speeches = []
    current_speech = {}
    neg_threshold = threshold - 0.15
-    temp_end = 0
+    temp_end = 0 # to save potential segment end (and tolerate some silence)
+    prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached

    for i, speech_prob in enumerate(speech_probs):
        if (speech_prob >= threshold) and temp_end:
            temp_end = 0
+            if next_start < prev_end:
+               next_start = window_size_samples * i

        if (speech_prob >= threshold) and not triggered:
            triggered = True
            current_speech['start'] = window_size_samples * i
            continue

+        if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
+            if prev_end:
+                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)
+                    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 (speech_prob < neg_threshold) and triggered:
            if not temp_end:
                temp_end = window_size_samples * i
+            if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech : # condition to avoid cutting in very short silence
+                prev_end = temp_end
            if (window_size_samples * i) - temp_end < min_silence_samples:
                continue
            else:
                current_speech['end'] = temp_end
                if (current_speech['end'] - current_speech['start']) > min_speech_samples:
                    speeches.append(current_speech)
-                temp_end = 0
                current_speech = {}
+                prev_end = next_start = temp_end = 0
                triggered = False
                continue

@@ -314,72 +379,6 @@ def get_speech_timestamps(audio: torch.Tensor,
    return speeches


-def get_number_ts(wav: torch.Tensor,
-                  model,
-                  model_stride=8,
-                  hop_length=160,
-                  sample_rate=16000):
-    wav = torch.unsqueeze(wav, dim=0)
-    perframe_logits = model(wav)[0]
-    perframe_preds = torch.argmax(torch.softmax(perframe_logits, dim=1), dim=1).squeeze()   # (1, num_frames_strided)
-    extended_preds = []
-    for i in perframe_preds:
-        extended_preds.extend([i.item()] * model_stride)
-    # len(extended_preds) is *num_frames_real*; for each frame of audio we know if it has a number in it.
-    triggered = False
-    timings = []
-    cur_timing = {}
-    for i, pred in enumerate(extended_preds):
-        if pred == 1:
-            if not triggered:
-                cur_timing['start'] = int((i * hop_length) / (sample_rate / 1000))
-                triggered = True
-        elif pred == 0:
-            if triggered:
-                cur_timing['end'] = int((i * hop_length) / (sample_rate / 1000))
-                timings.append(cur_timing)
-                cur_timing = {}
-                triggered = False
-    if cur_timing:
-        cur_timing['end'] = int(len(wav) / (sample_rate / 1000))
-        timings.append(cur_timing)
-    return timings
-
-
-def get_language(wav: torch.Tensor,
-                 model):
-    wav = torch.unsqueeze(wav, dim=0)
-    lang_logits = model(wav)[2]
-    lang_pred = torch.argmax(torch.softmax(lang_logits, dim=1), dim=1).item()   # from 0 to len(languages) - 1
-    assert lang_pred < len(languages)
-    return languages[lang_pred]
-
-
-def get_language_and_group(wav: torch.Tensor,
-                           model,
-                           lang_dict: dict,
-                           lang_group_dict: dict,
-                           top_n=1):
-    wav = torch.unsqueeze(wav, dim=0)
-    lang_logits, lang_group_logits = model(wav)
-
-    softm = torch.softmax(lang_logits, dim=1).squeeze()
-    softm_group = torch.softmax(lang_group_logits, dim=1).squeeze()
-
-    srtd = torch.argsort(softm, descending=True)
-    srtd_group = torch.argsort(softm_group, descending=True)
-
-    outs = []
-    outs_group = []
-    for i in range(top_n):
-        prob = round(softm[srtd[i]].item(), 2)
-        prob_group = round(softm_group[srtd_group[i]].item(), 2)
-        outs.append((lang_dict[str(srtd[i].item())], prob))
-        outs_group.append((lang_group_dict[str(srtd_group[i].item())], prob_group))
-
-    return outs, outs_group
-
-
 class VADIterator:
    def __init__(self,
                 model,
@@ -394,7 +393,7 @@ class VADIterator:

        Parameters
        ----------
-        model: preloaded .jit silero VAD model
+        model: preloaded .jit/.onnx silero VAD model

        threshold: float (default - 0.5)
            Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
@@ -428,6 +427,7 @@ class VADIterator:
        self.temp_end = 0
        self.current_sample = 0

+    @torch.no_grad()
    def __call__(self, x, return_seconds=False):
        """
        x: torch.Tensor
@@ -453,7 +453,7 @@ class VADIterator:

        if (speech_prob >= self.threshold) and not self.triggered:
            self.triggered = True
-            speech_start = self.current_sample - self.speech_pad_samples
+            speech_start = 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)}

        if (speech_prob < self.threshold - 0.15) and self.triggered:
@@ -462,7 +462,7 @@ class VADIterator:
            if self.current_sample - self.temp_end < self.min_silence_samples:
                return None
            else:
-                speech_end = self.temp_end + self.speech_pad_samples
+                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)}
Author	SHA1	Message	Date
Alexander Veysov	5cd2ba54db	Update hubconf.py	2024-06-27 22:58:52 +03:00
Alexander Veysov	06b9e17c1e	Update hubconf.py	2024-06-27 22:56:37 +03:00
Alexander Veysov	aace7e25b1	Update README.md	2024-06-27 22:25:19 +03:00
Alexander Veysov	74c3f7f3fb	Remove old unused utils	2024-06-27 22:08:15 +03:00
Alexander Veysov	d77d0fd42c	Merge pull request #468 from snakers4/adamnsandle Adamnsandle	2024-06-27 21:03:38 +03:00
Dimitrii Voronin	49b421a9cd	Update README.md	2024-06-27 19:12:27 +03:00
adamnsandle	fd1f1a62b7	v5 initial push	2024-06-27 15:41:20 +00:00
adamnsandle	8145ed9a91	Merge branch 'master' of github.com:snakers4/silero-vad	2024-06-25 08:13:21 +00:00
Dimitrii Voronin	4392725328	Merge pull request #467 from gau-nernst/fix_grad Replace `torch.set_grad_enabled(False)` with `torch.no_grad()`	2024-06-21 16:31:27 +03:00
Thien Tran	8b0566682b	use torch.no_grad()	2024-06-21 21:10:48 +08:00
Alexander Veysov	82342b8a4c	Merge pull request #457 from akmitrich/rust-example Rust example	2024-05-30 13:25:18 +03:00
Alexander Kalashnikov	4b8ce743a8	Rust example	2024-05-30 12:08:47 +03:00
Alexander Veysov	b4b6f2ab3e	Update README.md	2024-04-02 07:18:09 +03:00
Alexander Veysov	5b02d84a4a	Update README.md	2024-03-26 22:28:38 +03:00
Alexander Veysov	60465a7e61	Update README.md	2024-03-26 22:21:43 +03:00
Dimitrii Voronin	fcef5b3955	Merge pull request #435 from snakers4/adamnsandle-patch-1 Update README.md	2024-03-26 21:37:22 +03:00
Dimitrii Voronin	156436762f	Update README.md	2024-03-26 21:37:06 +03:00
Dimitrii Voronin	a27b176e45	Update README.md	2024-03-26 21:24:06 +03:00
Alexander Veysov	8125483ef7	Merge pull request #433 from snakers4/snakers4-patch-3 Update README.md	2024-03-26 21:23:27 +03:00
Alexander Veysov	6969dcc2dc	Update README.md	2024-03-26 21:23:16 +03:00
Alexander Veysov	6c816a05f0	Merge pull request #432 from snakers4/snakers4-patch-2 Update README.md	2024-03-26 21:20:03 +03:00
Alexander Veysov	9dc344df7f	Update README.md	2024-03-26 21:19:40 +03:00
Dimitrii Voronin	41c5172dd9	Update README.md	2024-03-26 21:17:35 +03:00
Alexander Veysov	894ea259f9	Merge pull request #431 from snakers4/adamnsandle add open datasets' annotation	2024-03-26 21:13:58 +03:00
adamnsandle	f56f56ffaa	add open datasets' annotation	2024-03-26 18:06:11 +00:00
Alexander Veysov	6c8d844710	Merge pull request #424 from yairl/master Support both sox and sox_io backends for in-place audio resampling.	2024-02-16 14:08:40 +03:00
Yair Lifshitz	d8cc947c73	Support both sox and sox_io backends for in-place audio resampling.	2024-02-16 05:58:09 -05:00
Alexander Veysov	797a88a386	Update utils_vad.py	2024-02-16 10:55:47 +03:00
Alexander Veysov	48b7c742dd	Merge pull request #423 from snakers4/snakers4-patch-1 Update utils_vad.py	2024-02-16 10:53:21 +03:00
Alexander Veysov	c5ec6bae3d	Update utils_vad.py	2024-02-16 10:53:12 +03:00
Alexander Veysov	af152c18f6	Merge pull request #421 from yairl/master Perform in-place resampling during read_audio.	2024-02-16 09:49:28 +03:00
Yair Lifshitz	d391f4c302	Use SoX when possible for loading a file with in-place resampling, ffmpeg otherwise.	2024-02-15 12:25:22 -05:00
Yair Lifshitz	bf18ea6b56	Perform in-place resampling during read_audio.	2024-02-14 17:11:57 -05:00
Alexander Veysov	a65732a393	Merge pull request #417 from abinthomasonline/offset-bug-fix fix window size offset bug	2024-01-22 09:15:44 +03:00
Abin Thomas	aae1e4f40d	fix window size offset bug	2024-01-22 11:08:24 +05:30
Alexander Veysov	94504ece54	Merge pull request #407 from bygreencn/master Fix a bug at c sample code and some bugs at wav.h.	2023-12-17 20:26:40 +03:00
bygreencn	0b7da6e74b	Fix wav functions: 1. fix data_size is not correct and be 0. 2. detect data format of IEEE-float. 3. add PCMS8bit, PCMS16bit and PCMS32 convert to float 32bit at class WavReader.	2023-12-17 23:00:12 +08:00
bygreencn	efb5effc8f	Fix a bug in c code sample when only one timestamp and start from 0.	2023-12-17 23:00:12 +08:00
Alexander Veysov	03dc3fae5c	Merge pull request #406 from bygreencn/master Make the c code sample have the same function as the python code	2023-12-15 16:46:58 +03:00
bygreencn	4a6d1701a4	Make the c code sample have the same function as the python code	2023-12-15 21:32:22 +08:00
Alexander Veysov	5e7ee10ee0	Merge pull request #392 from xiaoqiang306/fix_example_cpp fix int16_t bytes normalized to float	2023-11-13 15:20:08 +03:00
jiqiang.fu	03fb810fab	fix int16_t bytes normalized to float	2023-11-13 17:14:34 +08:00
Alexander Veysov	e30a7e32a9	Merge pull request #391 from streamer45/golang-example Implement Golang example	2023-11-09 08:33:20 +03:00
streamer45	bbbc657dad	Golang example	2023-11-08 17:48:36 -06:00
Alexander Veysov	cb92cdd1e3	Merge pull request #386 from VvvvvGH/master add java onnx inference example	2023-10-18 09:29:04 +03:00
VvvvvGH	3780baf49f	add java onnx example	2023-10-18 13:57:18 +08:00
Alexander Veysov	563106ef8c	Merge pull request #350 from archive-r/archive-r-fix-typo Fix typo	2023-06-16 09:45:49 +03:00
kh	f795bc479b	Fix typo https://github.com/snakers4/silero-vad/discussions/319#discussion-5081706	2023-06-16 11:47:59 +09:00
Alexander Veysov	7e9680bc83	Merge pull request #342 from AlexRainHao/master fix #341 issue of cpp example coding mistake	2023-05-18 15:17:36 +03:00
AlexRainHao	3b4c02dfe3	fix #341 issue of cpp example coding mistake	2023-05-18 20:12:03 +08:00
Alexander Veysov	bc5a0a2dbf	Merge pull request #340 from chenqianhe/master fix speech and silence state transition	2023-05-12 12:03:50 +03:00
Qianhe Chen	b03fcb2ebe	fix speech and silence state transition	2023-05-12 16:59:51 +08:00
Dimitrii Voronin	026bc3d292	Merge pull request #329 from mhThomsen/master (Bug fix) Slices in correct dimension (audio dim), so batch size is not reduced	2023-04-28 15:00:12 +03:00
Dimitrii Voronin	e755baa3c2	Merge branch 'master' into master	2023-04-28 15:00:02 +03:00
Dimitrii Voronin	b88084c7ed	Merge pull request #332 from snakers4/adamnsandle fx https://github.com/snakers4/silero-vad/pull/329 bug	2023-04-28 14:54:44 +03:00
adamnsandle	a9d2b591de	fx https://github.com/snakers4/silero-vad/pull/329 bug	2023-04-28 11:48:01 +00:00
Dimitrii Voronin	c3c67cdcb8	Merge pull request #330 from snakers4/adamnsandle del deprecated models examples	2023-04-27 15:05:45 +03:00
adamnsandle	874c66ccbc	del deprecated models examples	2023-04-27 12:00:01 +00:00
Alexander Veysov	51fbbcb32e	Update README.md	2023-04-27 13:33:03 +03:00
Alexander Veysov	14a0715955	Deprecate lang detector and number detector models	2023-04-27 13:22:00 +03:00
Alexander Veysov	a0d26769e0	Update README.md	2023-04-27 13:19:39 +03:00
mhThomsen	e0c2015193	slices in correct dimension (audio dim), so batch size is not reduced	2023-04-27 07:37:54 +02:00
Alexander Veysov	5872cffd78	Update README.md	2023-03-29 21:56:23 +03:00
Dimitrii Voronin	86400b9a12	Merge pull request #313 from snakers4/adamnsandle change int2float	2023-03-28 17:31:27 +03:00
adamnsandle	6ef43d1c5d	change int2float	2023-03-28 14:30:35 +00:00
Alexander Veysov	540e092276	Merge pull request #308 from zzzacwork/example-parallelization add a simple parallelization example	2023-03-13 08:18:23 +03:00
Ziyuan Wang	55c41abf46	use a process specific copy of model	2023-03-10 16:09:07 +00:00
Ziyuan Wang	17903cb41d	add a initializer	2023-03-09 16:27:36 +00:00
Ziyuan Wang	c39dccc1fd	add a initializer	2023-03-09 16:27:18 +00:00
Ziyuan Wang	a6a067de44	add a initializer	2023-03-09 16:16:51 +00:00
Ziyuan Wang	9865b3cb93	fix typos	2023-03-09 04:26:15 +00:00
Ziyuan Wang	3d10c2d950	add parallel example	2023-03-09 04:17:34 +00:00
Dimitrii Voronin	4f57fae3fa	Merge pull request #289 from Tomiinek/patch-1 Fixing ONNX init	2023-02-28 15:43:18 +02:00
Tomáš Nekvinda	085d76f08e	Update utils_vad.py	2023-02-09 11:13:02 +01:00
Dimitrii Voronin	262bcb4b40	Merge pull request #285 from snakers4/adamnsandle fx versiontuple bug	2023-01-09 11:09:32 +02:00
adamnsandle	e84eca68d7	fx versiontuple bug	2023-01-09 09:07:59 +00:00
Dimitrii Voronin	e7c4539106	Merge pull request #280 from bclark-videra/test/hubconf-with-ref Use the path to hubconf.py to find models	2022-12-29 14:02:09 +02:00
Dimitrii Voronin	a480e85aec	Merge pull request #282 from saenyakorn/master Add `progress_tracking_callback` argument to `get_speech_timestamps` function	2022-12-29 13:31:55 +02:00
Saenyakorn Siangsanoh	c69cb6c9c0	fix progress logic	2022-12-28 14:40:55 +07:00
Saenyakorn Siangsanoh	11da69d88b	add progress_tracking callback to get_speech_timestamps	2022-12-28 14:18:42 +07:00
Byron Clark	df1d52042d	Use the path to hubconf.py to find models While `torch.hub.load` uses predictable names when downloading from github, the name referenced only worked when using the `master` branch of the repo. Using a ref like `snakers4/silero_vad:v4.0` results in a different directory name and the model files not being found. Since the model files are stored in a path relative to hubconf.py, use the path of hubconf.py to find the models instead of assuming the directory `torch.hub.load` will extract the files to.	2022-12-22 14:47:14 -07:00
Alexander Veysov	d5a944b9f1	Merge pull request #279 from pengzhendong/patch-1 fix sample rate of onnx input	2022-12-21 06:00:10 +03:00
彭震东	d90416e63e	fix sample rate of onnx input	2022-12-21 08:36:52 +08:00
Alexander Veysov	91f0aaecef	Merge pull request #277 from yuGAN6/yugan6 Yugan6	2022-12-11 16:27:48 +03:00
yuGAN6	015bfc8b21	Update README.md	2022-12-11 21:14:48 +08:00
yuGAN6	5d56b1ea40	Update README.md	2022-12-11 21:13:46 +08:00
yuGAN6	ff3c596cab	Update README.md	2022-12-11 21:09:57 +08:00
yuGAN6	63e1be5a22	Update README.md	2022-12-11 21:08:31 +08:00
yuGAN6	1d8f8f38db	Move to example	2022-12-11 21:05:11 +08:00
yuGAN6	7198087152	Move into examples	2022-12-11 13:06:21 +08:00
Alexander Veysov	ad57d17f5f	Update README.md	2022-12-11 05:57:19 +03:00
yuGAN6	04e87c208a	Move directory	2022-12-10 22:50:14 +08:00
yuGAN6	c583fd1e52	Add c++ onnxruntime example	2022-12-10 22:29:34 +08:00
Dimitrii Voronin	5814e548db	Merge pull request #270 from kafan1986/master Resolves Windows inference issue INVALID_ARGUMENT : Unexpected input data type.	2022-12-06 11:38:41 +02:00
Dimitrii Voronin	42565d5baa	Merge pull request #271 from b-med/max_speech_duration_v4 Max speech duration + bits per sample	2022-12-06 11:29:15 +02:00
Mohamed Bouaziz	ab7af9745b	delete commented lines	2022-11-17 17:52:34 +01:00
Mohamed Bouaziz	83e68c56ea	Merge branch 'master' of https://github.com/snakers4/silero-vad into max_speech_duration_v4	2022-11-17 17:32:22 +01:00
kafan1986	d3882c9ebf	Merge pull request #1 from kafan1986/kafan1986-patch-1 Solves data type mismatch issue on windows	2022-11-11 23:50:20 +05:30
kafan1986	25f04dda35	Solves data type mismatch issue on windows Solves windows 11 error regarding mismatched data type. Expected data type is int64 and actual data type is int32	2022-11-11 23:46:51 +05:30
Mohamed Bouaziz	94b4c21874	utils_vad save_audio force bits_per_sample=16	2022-11-04 21:43:28 +01:00
Mohamed Bouaziz	324bc74a58	utils_vad max duration	2022-11-04 21:42:22 +01:00