Merge pull request #747 from d-e-s-o/topic/ort-rc.10

Update ort dependency to 2.0.0-rc.10

.github/workflows/test.yml

@@ -24,6 +24,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install build hatchling pytest soundfile
+        pip install .[test]
 
     - name: Build package
       run: python -m build --wheel --outdir dist

README.md

@@ -1,6 +1,6 @@
 [![Mailing list : test](http://img.shields.io/badge/Email-gray.svg?style=for-the-badge&logo=gmail)](mailto:hello@silero.ai) [![Mailing list : test](http://img.shields.io/badge/Telegram-blue.svg?style=for-the-badge&logo=telegram)](https://t.me/silero_speech) [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-MIT-lightgrey.svg?style=for-the-badge)](https://github.com/snakers4/silero-vad/blob/master/LICENSE) [![downloads](https://img.shields.io/pypi/dm/silero-vad?style=for-the-badge)](https://pypi.org/project/silero-vad/) 
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb) [![Test Package](https://github.com/snakers4/silero-vad/actions/workflows/test.yml/badge.svg)](https://github.com/snakers4/silero-vad/actions/workflows/test.yml)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb) [![Test Package](https://github.com/snakers4/silero-vad/actions/workflows/test.yml/badge.svg)](https://github.com/snakers4/silero-vad/actions/workflows/test.yml) [![Pypi version](https://img.shields.io/pypi/v/silero-vad)](https://pypi.org/project/silero-vad/) [![Python version](https://img.shields.io/pypi/pyversions/silero-vad)](https://pypi.org/project/silero-vad)
 
 ![header](https://user-images.githubusercontent.com/12515440/89997349-b3523080-dc94-11ea-9906-ca2e8bc50535.png)
 

examples/c++/README.md

@@ -0,0 +1,49 @@
+# Silero-VAD V6 in C++ (based on LibTorch)
+
+This is the source code for Silero-VAD V6 in C++, utilizing LibTorch & Onnxruntime. 
+You should compare its results with the Python version. 
+Results at 16 and 8kHz have been tested. Batch and CUDA inference options are deprecated. 
+
+
+## Requirements
+- GCC 11.4.0 (GCC >= 5.1)
+- Onnxruntime 1.11.0 (other versions are also acceptable)
+- LibTorch 1.13.0 (other versions are also acceptable)
+
+## Download LibTorch
+
+```bash
+-Onnxruntime
+$wget https://github.com/microsoft/onnxruntime/releases/download/v1.11.1/onnxruntime-linux-x64-1.11.1.tgz
+$tar -xvf onnxruntime-linux-x64-1.11.1.tgz
+$ln -s onnxruntime-linux-x64-1.11.1 onnxruntime-linux    #soft-link
+
+-Libtorch
+$wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-1.13.0%2Bcpu.zip
+$unzip libtorch-shared-with-deps-1.13.0+cpu.zip
+```
+
+## Compilation
+
+```bash
+-ONNX-build
+$g++ main.cc silero.cc -I ./onnxruntime-linux/include/ -L ./onnxruntime-linux/lib/ -lonnxruntime -Wl,-rpath,./onnxruntime-linux/lib/ -o silero -std=c++14 -D_GLIBCXX_USE_CXX11_ABI=0 -DUSE_ONNX
+
+-TORCH-build
+$g++ main.cc silero.cc -I ./libtorch/include/ -I ./libtorch/include/torch/csrc/api/include -L ./libtorch/lib/ -ltorch -ltorch_cpu -lc10 -Wl,-rpath,./libtorch/lib/ -o silero -std=c++14 -D_GLIBCXX_USE_CXX11_ABI=0 -DUSE_TORCH
+```
+
+## Optional Compilation Flags
+-DUSE_TORCH
+-DUSE_ONNX
+
+## Run the Program
+To run the program, use the following command:
+
+`./silero <sample.wav> <SampleRate> <threshold>`
+`./silero aepyx.wav 16000 0.5`
+`./silero aepyx_8k.wav 8000 0.5`
+
+The sample file aepyx.wav is part of the Voxconverse dataset.
+File details: aepyx.wav is a 16kHz, 16-bit audio file.
+File details: aepyx_8k.wav is a 8kHz, 16-bit audio file.

examples/c++/main.cc

@@ -0,0 +1,61 @@
+#include <iostream>
+#include "silero.h"
+#include "wav.h"
+
+int main(int argc, char* argv[]) {
+
+	if(argc != 4){
+		std::cerr<<"Usage : "<<argv[0]<<" <wav.path> <SampleRate> <Threshold>"<<std::endl;
+		std::cerr<<"Usage : "<<argv[0]<<" sample.wav 16000 0.5"<<std::endl;
+		return 1;
+	}
+
+	std::string wav_path = argv[1];
+	float sample_rate = std::stof(argv[2]);
+	float threshold = std::stof(argv[3]);
+
+	if (sample_rate != 16000 && sample_rate != 8000) {
+		std::cout<<"Unsupported sample rate (only 16000 or 8000)."<<std::endl;
+		exit (0);
+        }
+
+	//Load Model
+#ifdef USE_TORCH
+	std::string model_path = "../../src/silero_vad/data/silero_vad.jit";
+#elif USE_ONNX
+	std::string model_path = "../../src/silero_vad/data/silero_vad.onnx";
+#endif
+	silero::VadIterator vad(model_path);
+
+        vad.threshold=threshold;	//(Default:0.5)
+	vad.sample_rate=sample_rate;	//16000Hz,8000Hz. (Default:16000)
+        vad.print_as_samples=false;	//if true, it prints time-stamp with samples. otherwise, in seconds
+					//(Default:false)
+
+	vad.SetVariables();
+
+	// Read wav
+	wav::WavReader wav_reader(wav_path);
+	std::vector<float> input_wav(wav_reader.num_samples());
+
+	for (int i = 0; i < wav_reader.num_samples(); i++)
+	{
+		input_wav[i] = static_cast<float>(*(wav_reader.data() + i));
+	}
+
+	vad.SpeechProbs(input_wav);
+
+	std::vector<silero::Interval> speeches = vad.GetSpeechTimestamps();
+	for(const auto& speech : speeches){
+		if(vad.print_as_samples){
+			std::cout<<"{'start': "<<static_cast<int>(speech.start)<<", 'end': "<<static_cast<int>(speech.end)<<"}"<<std::endl;
+		}
+		else{
+			std::cout<<"{'start': "<<speech.start<<", 'end': "<<speech.end<<"}"<<std::endl;
+		}
+	}
+
+
+	return 0;
+	}
+

examples/c++/silero.cc

@@ -0,0 +1,273 @@
+// silero.cc
+// Author      : NathanJHLee
+// Created On  : 2025-11-10
+// Description : silero 6.2 system for onnx-runtime(c++) and torch-script(c++)
+// Version     : 1.3
+
+#include "silero.h"
+
+
+namespace silero {
+
+#ifdef USE_TORCH
+	VadIterator::VadIterator(const std::string &model_path,
+			float threshold,
+			int sample_rate,
+			int window_size_ms,
+			int speech_pad_ms,
+			int min_silence_duration_ms,
+			int min_speech_duration_ms,
+			int max_duration_merge_ms,
+			bool print_as_samples)
+		: threshold(threshold), sample_rate(sample_rate), window_size_ms(window_size_ms),
+		speech_pad_ms(speech_pad_ms), min_silence_duration_ms(min_silence_duration_ms),
+		min_speech_duration_ms(min_speech_duration_ms), max_duration_merge_ms(max_duration_merge_ms),
+		print_as_samples(print_as_samples)
+	{
+		init_torch_model(model_path);
+	}
+
+	VadIterator::~VadIterator(){
+	}
+
+
+	void VadIterator::init_torch_model(const std::string& model_path) {
+		at::set_num_threads(1);
+		model = torch::jit::load(model_path);
+
+		model.eval();
+		torch::NoGradGuard no_grad;
+		std::cout<<"Silero libtorch-Model loaded successfully"<<std::endl;
+	}
+
+	void VadIterator::SpeechProbs(std::vector<float>& input_wav) {
+		int num_samples = input_wav.size();
+		int num_chunks = num_samples / window_size_samples;
+		int remainder_samples = num_samples % window_size_samples;
+		total_sample_size += num_samples;
+
+		std::vector<torch::Tensor> chunks;
+
+		for (int i = 0; i < num_chunks; i++) {
+			float* chunk_start = input_wav.data() + i * window_size_samples;
+			torch::Tensor chunk = torch::from_blob(chunk_start, {1, window_size_samples}, torch::kFloat32);
+			chunks.push_back(chunk);
+
+			if (i == num_chunks - 1 && remainder_samples > 0) {
+				int remaining_samples = num_samples - num_chunks * window_size_samples;
+				float* chunk_start_remainder = input_wav.data() + num_chunks * window_size_samples;
+				torch::Tensor remainder_chunk = torch::from_blob(chunk_start_remainder, {1, remaining_samples}, torch::kFloat32);
+				torch::Tensor padded_chunk = torch::cat({remainder_chunk, torch::zeros({1, window_size_samples - remaining_samples}, torch::kFloat32)}, 1);
+				chunks.push_back(padded_chunk);
+			}
+		}
+
+		if (!chunks.empty()) {
+			std::vector<torch::Tensor> outputs;
+			torch::Tensor batched_chunks = torch::stack(chunks);
+			for (size_t i = 0; i < chunks.size(); i++) {
+				torch::NoGradGuard no_grad;
+				std::vector<torch::jit::IValue> inputs;
+				inputs.push_back(batched_chunks[i]);
+				inputs.push_back(sample_rate);
+				torch::Tensor output = model.forward(inputs).toTensor();
+				outputs.push_back(output);
+			}
+			torch::Tensor all_outputs = torch::stack(outputs);
+			for (size_t i = 0; i < chunks.size(); i++) {
+				float output_f = all_outputs[i].item<float>();
+				outputs_prob.push_back(output_f);
+				//////To print Probs by libtorch
+				//std::cout << "Chunk " << i << " prob: " << output_f<< "\n";
+			}
+		}
+	}
+
+
+#elif USE_ONNX
+
+        VadIterator::VadIterator(const std::string &model_path, 
+			float threshold, 
+			int sample_rate, 
+			int window_size_ms, 
+			int speech_pad_ms, 
+			int min_silence_duration_ms, 
+			int min_speech_duration_ms, 
+			int max_duration_merge_ms, 
+			bool print_as_samples)
+                :sample_rate(sample_rate), threshold(threshold), window_size_ms(window_size_ms), 
+		speech_pad_ms(speech_pad_ms), min_silence_duration_ms(min_silence_duration_ms), 
+		min_speech_duration_ms(min_speech_duration_ms), max_duration_merge_ms(max_duration_merge_ms), 
+		print_as_samples(print_as_samples),
+                env(ORT_LOGGING_LEVEL_ERROR, "Vad"), session_options(), session(nullptr), allocator(), 
+		memory_info(Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeCPU)), context_samples(64), 
+		_context(64, 0.0f), current_sample(0), size_state(2 * 1 * 128), 
+		input_node_names({"input", "state", "sr"}), output_node_names({"output", "stateN"}), 
+		state_node_dims{2, 1, 128}, sr_node_dims{1}
+
+        {
+                init_onnx_model(model_path);
+        }
+        VadIterator::~VadIterator(){
+        }
+
+        void VadIterator::init_onnx_model(const std::string& model_path) {
+                int inter_threads=1;
+                int intra_threads=1;
+                session_options.SetIntraOpNumThreads(intra_threads);
+                session_options.SetInterOpNumThreads(inter_threads);
+                session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
+                session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
+		std::cout<<"Silero onnx-Model loaded successfully"<<std::endl;
+        }
+
+        float VadIterator::predict(const std::vector<float>& data_chunk) {
+                // _context와 현재 청크를 결합하여 입력 데이터 구성
+                std::vector<float> new_data(effective_window_size, 0.0f);
+                std::copy(_context.begin(), _context.end(), new_data.begin());
+                std::copy(data_chunk.begin(), data_chunk.end(), new_data.begin() + context_samples);
+                input = new_data;
+
+		Ort::Value input_ort = Ort::Value::CreateTensor<float>(
+                                memory_info, input.data(), input.size(), input_node_dims, 2);
+                Ort::Value state_ort = Ort::Value::CreateTensor<float>(
+                                memory_info, _state.data(), _state.size(), state_node_dims, 3);
+                Ort::Value sr_ort = Ort::Value::CreateTensor<int64_t>(
+                                memory_info, sr.data(), sr.size(), sr_node_dims, 1);
+                ort_inputs.clear();
+                ort_inputs.push_back(std::move(input_ort));
+                ort_inputs.push_back(std::move(state_ort));
+                ort_inputs.push_back(std::move(sr_ort));
+
+                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());
+
+                float speech_prob = ort_outputs[0].GetTensorMutableData<float>()[0]; // ONNX 출력: 첫 번째 값이 음성 확률
+
+                float* stateN = ort_outputs[1].GetTensorMutableData<float>(); // 두 번째 출력값: 상태 업데이트
+                std::memcpy(_state.data(), stateN, size_state * sizeof(float));
+
+                std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
+                // _context 업데이트: new_data의 마지막 context_samples 유지
+
+                return speech_prob;
+        }
+        void VadIterator::SpeechProbs(std::vector<float>& input_wav) {
+                reset_states();
+                total_sample_size = static_cast<int>(input_wav.size());
+                for (size_t j = 0; j < static_cast<size_t>(total_sample_size); j += window_size_samples) {
+                        if (j + window_size_samples > static_cast<size_t>(total_sample_size))
+                                break;
+                        std::vector<float> chunk(input_wav.begin() + j, input_wav.begin() + j + window_size_samples);
+                        float speech_prob = predict(chunk);
+                        outputs_prob.push_back(speech_prob);
+                }
+        }
+
+#endif
+
+        void VadIterator::reset_states() {
+                triggered = false;
+                current_sample = 0;
+                temp_end = 0;
+                outputs_prob.clear();
+                total_sample_size = 0;
+
+#ifdef USE_TORCH
+                model.run_method("reset_states"); // Reset model states if applicable
+#elif USE_ONNX
+                std::memset(_state.data(), 0, _state.size() * sizeof(float));
+                std::fill(_context.begin(), _context.end(), 0.0f);
+#endif
+	}
+
+        std::vector<Interval> VadIterator::GetSpeechTimestamps() {
+                std::vector<Interval> speeches = DoVad();
+                if(!print_as_samples){
+                        for (auto& speech : speeches) {
+                                speech.start /= sample_rate;
+                                speech.end   /= sample_rate;
+                        }
+                }
+                return speeches;
+        }
+
+        void VadIterator::SetVariables(){
+                // Initialize internal engine parameters
+                init_engine(window_size_ms);
+        }
+
+        void VadIterator::init_engine(int window_size_ms) {
+                min_silence_samples = sample_rate * min_silence_duration_ms / 1000;
+                speech_pad_samples = sample_rate * speech_pad_ms / 1000;
+                window_size_samples = sample_rate / 1000 * window_size_ms;
+                min_speech_samples = sample_rate * min_speech_duration_ms / 1000;
+#ifdef USE_ONNX
+                //for ONNX
+                context_samples=window_size_samples / 8;
+		_context.assign(context_samples, 0.0f);
+
+                effective_window_size = window_size_samples + context_samples;  // 예: 512 + 64 = 576 samples
+                input_node_dims[0] = 1;
+                input_node_dims[1] = effective_window_size;
+                _state.resize(size_state);
+                sr.resize(1);
+                sr[0] = sample_rate;
+#endif
+	}
+
+	std::vector<Interval> VadIterator::DoVad() {
+		std::vector<Interval> speeches;
+		for (size_t i = 0; i < outputs_prob.size(); ++i) {
+			float speech_prob = outputs_prob[i];
+			current_sample += window_size_samples;
+			if (speech_prob >= threshold && temp_end != 0) {
+				temp_end = 0;
+			}
+
+			if (speech_prob >= threshold) {
+				if (!triggered) {
+					triggered = true;
+					Interval segment;
+					segment.start = std::max(0, current_sample - speech_pad_samples - window_size_samples);
+					speeches.push_back(segment);
+				}
+			}else {
+				if (triggered) {
+					if (speech_prob < threshold - 0.15f) {
+						if (temp_end == 0) {
+							temp_end = current_sample;
+						}
+						if (current_sample - temp_end >= min_silence_samples) {
+							Interval& segment = speeches.back();
+							segment.end = temp_end + speech_pad_samples - window_size_samples;
+							temp_end = 0;
+							triggered = false;
+						}
+					}
+				}
+			}
+
+
+		}
+
+		if (triggered) {
+			std::cout<<"Finalizing active speech segment at stream end."<<std::endl;
+			Interval& segment = speeches.back();
+			segment.end = total_sample_size;
+			triggered = false;
+		}
+		speeches.erase(std::remove_if(speeches.begin(), speeches.end(),
+					[this](const Interval& speech) {
+					return ((speech.end - this->speech_pad_samples) - (speech.start + this->speech_pad_samples) < min_speech_samples);
+					}), speeches.end());
+
+		reset_states();
+		return speeches;
+	}
+
+
+	} // namespace silero
+

examples/c++/silero.h

@@ -0,0 +1,123 @@
+#ifndef SILERO_H
+#define SILERO_H
+
+// silero.h
+// Author      : NathanJHLee
+// Created On  : 2025-11-10
+// Description : silero 6.2 system for onnx-runtime(c++) and torch-script(c++)
+// Version     : 1.3
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include <fstream>
+#include <chrono>
+#include <algorithm>
+#include <cstring>
+
+#ifdef USE_TORCH
+#include <torch/torch.h>
+#include <torch/script.h>
+#elif USE_ONNX
+#include "onnxruntime_cxx_api.h"
+#endif
+
+namespace silero {
+
+	struct Interval {
+		float start;
+		float end;
+		int numberOfSubseg;
+
+		void initialize() {
+			start = 0;
+			end = 0;
+			numberOfSubseg = 0;
+		}
+	};
+
+	class VadIterator {
+		public:
+			VadIterator(const std::string &model_path,
+					float threshold = 0.5,
+					int sample_rate = 16000,
+					int window_size_ms = 32,
+					int speech_pad_ms = 30,
+					int min_silence_duration_ms = 100,
+					int min_speech_duration_ms = 250,
+					int max_duration_merge_ms = 300,
+					bool print_as_samples = false);
+			~VadIterator();
+
+			// Batch (non-streaming) interface (for backward compatibility)
+			void SpeechProbs(std::vector<float>& input_wav);
+			std::vector<Interval> GetSpeechTimestamps();
+			void SetVariables();
+
+			// Public parameters (can be modified by user)
+			float threshold;
+			int sample_rate;
+			int window_size_ms;
+			int min_speech_duration_ms;
+			int max_duration_merge_ms;
+			bool print_as_samples;
+
+		private:
+#ifdef  USE_TORCH
+                        torch::jit::script::Module model;
+                        void init_torch_model(const std::string& model_path);
+#elif   USE_ONNX
+                        Ort::Env env;                                    // 환경 객체
+                        Ort::SessionOptions session_options;             // 세션 옵션
+                        std::shared_ptr<Ort::Session> session;           // ONNX 세션
+                        Ort::AllocatorWithDefaultOptions allocator;      // 기본 할당자
+                        Ort::MemoryInfo memory_info;                     // 메모리 정보 (CPU)
+
+                        void init_onnx_model(const std::string& model_path);
+                        float predict(const std::vector<float>& data_chunk);
+
+                        //const int context_samples;                       // 예: 64 samples
+                        int context_samples;                       // 예: 64 samples
+                        std::vector<float> _context;                     // 초기값 모두 0
+                        int effective_window_size;
+
+                        // ONNX 입력/출력 관련 버퍼 및 노드 이름들
+                        std::vector<Ort::Value> ort_inputs;
+                        std::vector<const char*> input_node_names;
+                        std::vector<float> input;
+                        unsigned int size_state;                         // 고정값: 2*1*128
+                        std::vector<float> _state;
+                        std::vector<int64_t> sr;
+                        int64_t input_node_dims[2];                      // [1, effective_window_size]
+                        const int64_t state_node_dims[3];                // [ 2, 1, 128 ]
+                        const int64_t sr_node_dims[1];                   // [ 1 ]
+                        std::vector<Ort::Value> ort_outputs;
+                        std::vector<const char*> output_node_names;      // 기본값: [ "output", "stateN" ]
+#endif
+			std::vector<float> outputs_prob; // used in batch mode
+			int min_silence_samples;
+			int min_speech_samples;
+			int speech_pad_samples;
+			int window_size_samples;
+			int duration_merge_samples;
+			int current_sample = 0;
+			int total_sample_size = 0;
+			int min_silence_duration_ms;
+			int speech_pad_ms;
+			bool triggered = false;
+			int temp_end = 0;
+			int global_end = 0;
+			int erase_tail_count = 0;
+
+
+			void init_engine(int window_size_ms);
+			void reset_states();
+			std::vector<Interval> DoVad();
+
+
+	};
+
+} // namespace silero
+
+#endif // SILERO_H
+

examples/c++/wav.h

@@ -0,0 +1,237 @@
+// 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 wav
+
+#endif  // FRONTEND_WAV_H_
+
+

examples/cpp_libtorch_deprecated/README.md

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

examples/cpp_libtorch_deprecated/main.cc

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

examples/cpp_libtorch_deprecated/silero_torch.cc

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

examples/cpp_libtorch_deprecated/silero_torch.h

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

examples/cpp_libtorch_deprecated/wav.h

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

examples/csharp/Program.cs

@@ -21,7 +21,7 @@ class Program
                 MIN_SPEECH_DURATION_MS, MAX_SPEECH_DURATION_SECONDS, MIN_SILENCE_DURATION_MS, SPEECH_PAD_MS);
             List<SileroSpeechSegment> speechTimeList = vadDetector.GetSpeechSegmentList(new FileInfo(EXAMPLE_WAV_FILE));
             //Console.WriteLine(speechTimeList.ToJson());
-            StringBuilder sb = new StringBuilder();
+            StringBuilder sb = new();
             foreach (var speechSegment in speechTimeList)
             {
                 sb.Append($"start second: {speechSegment.StartSecond}, end second: {speechSegment.EndSecond}\n");

examples/csharp/SileroVadDetector.cs

@@ -53,28 +53,26 @@ public class SileroVadDetector
     {
         Reset();
 
-        using (var audioFile = new AudioFileReader(wavFile.FullName))
+        using var audioFile = new AudioFileReader(wavFile.FullName);
-        {
+        List<float> speechProbList = [];
-            List<float> speechProbList = new List<float>();
         this._audioLengthSamples = (int)(audioFile.Length / 2);
         float[] buffer = new float[this._windowSizeSample];
 
         while (audioFile.Read(buffer, 0, buffer.Length) > 0)
         {
-                float speechProb = _model.Call(new[] { buffer }, _samplingRate)[0];
+            float speechProb = _model.Call([buffer], _samplingRate)[0];
             speechProbList.Add(speechProb);
         }
 
         return CalculateProb(speechProbList);
     }
-    }
 
     private List<SileroSpeechSegment> CalculateProb(List<float> speechProbList)
     {
-        List<SileroSpeechSegment> result = new List<SileroSpeechSegment>();
+        List<SileroSpeechSegment> result = [];
         bool triggered = false;
         int tempEnd = 0, prevEnd = 0, nextStart = 0;
-        SileroSpeechSegment segment = new SileroSpeechSegment();
+        SileroSpeechSegment segment = new();
 
         for (int i = 0; i < speechProbList.Count; i++)
         {
@@ -164,7 +162,8 @@ public class SileroVadDetector
 
         if (segment.StartOffset != null && (_audioLengthSamples - segment.StartOffset) > _minSpeechSamples)
         {
-            segment.EndOffset = _audioLengthSamples;
+            //segment.EndOffset = _audioLengthSamples;
+            segment.EndOffset = speechProbList.Count * _windowSizeSample;
             result.Add(segment);
         }
 
@@ -182,7 +181,7 @@ public class SileroVadDetector
                 int silenceDuration = nextItem.StartOffset.Value - item.EndOffset.Value;
                 if (silenceDuration < 2 * _speechPadSamples)
                 {
-                    item.EndOffset = item.EndOffset + (silenceDuration / 2);
+                    item.EndOffset += (silenceDuration / 2);
                     nextItem.StartOffset = Math.Max(0, nextItem.StartOffset.Value - (silenceDuration / 2));
                 }
                 else
@@ -200,9 +199,9 @@ public class SileroVadDetector
         return MergeListAndCalculateSecond(result, _samplingRate);
     }
 
-    private List<SileroSpeechSegment> MergeListAndCalculateSecond(List<SileroSpeechSegment> original, int samplingRate)
+    private static List<SileroSpeechSegment> MergeListAndCalculateSecond(List<SileroSpeechSegment> original, int samplingRate)
     {
-        List<SileroSpeechSegment> result = new List<SileroSpeechSegment>();
+        List<SileroSpeechSegment> result = [];
         if (original == null || original.Count == 0)
         {
             return result;
@@ -242,7 +241,7 @@ public class SileroVadDetector
         return result;
     }
 
-    private float CalculateSecondByOffset(int offset, int samplingRate)
+    private static float CalculateSecondByOffset(int offset, int samplingRate)
     {
         float secondValue = offset * 1.0f / samplingRate;
         return (float)Math.Floor(secondValue * 1000.0f) / 1000.0f;

examples/csharp/SileroVadOnnxModel.cs

@@ -1,5 +1,6 @@
 using Microsoft.ML.OnnxRuntime;
 using Microsoft.ML.OnnxRuntime.Tensors;
+
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -14,14 +15,16 @@ namespace VADdotnet;
     private float[][] context;
     private int lastSr = 0;
     private int lastBatchSize = 0;
-        private static readonly List<int> SAMPLE_RATES = new List<int> { 8000, 16000 };
+    private static readonly List<int> SAMPLE_RATES = [8000, 16000];
 
     public SileroVadOnnxModel(string modelPath)
     {
-            var sessionOptions = new SessionOptions();
+        var sessionOptions = new SessionOptions
-            sessionOptions.InterOpNumThreads = 1;
+        {
-            sessionOptions.IntraOpNumThreads = 1;
+            InterOpNumThreads = 1,
-            sessionOptions.EnableCpuMemArena = true;
+            IntraOpNumThreads = 1,
+            EnableCpuMemArena = true
+        };
 
         session = new InferenceSession(modelPath, sessionOptions);
         ResetStates();
@@ -34,33 +37,27 @@ namespace VADdotnet;
         state[1] = new float[1][];
         state[0][0] = new float[128];
         state[1][0] = new float[128];
-            context = Array.Empty<float[]>();
+        context = [];
         lastSr = 0;
         lastBatchSize = 0;
     }
 
     public void Dispose()
     {
-            session?.Dispose();
+        GC.SuppressFinalize(this);
     }
 
-        public class ValidationResult
+    public class ValidationResult(float[][] x, int sr)
     {
-            public float[][] X { get; }
+        public float[][] X { get; } = x;
-            public int Sr { get; }
+        public int Sr { get; } = sr;
-
-            public ValidationResult(float[][] x, int sr)
-            {
-                X = x;
-                Sr = sr;
-            }
     }
 
-        private ValidationResult ValidateInput(float[][] x, int sr)
+    private static ValidationResult ValidateInput(float[][] x, int sr)
     {
         if (x.Length == 1)
         {
-                x = new float[][] { x[0] };
+            x = [x[0]];
         }
         if (x.Length > 2)
         {
@@ -186,13 +183,12 @@ namespace VADdotnet;
 
         var inputs = new List<NamedOnnxValue>
             {
-                NamedOnnxValue.CreateFromTensor("input", new DenseTensor<float>(x.SelectMany(a => a).ToArray(), new[] { x.Length, x[0].Length })),
+                NamedOnnxValue.CreateFromTensor("input", new DenseTensor<float>(x.SelectMany(a => a).ToArray(), [x.Length, x[0].Length])),
-                NamedOnnxValue.CreateFromTensor("sr", new DenseTensor<long>(new[] { (long)sr }, new[] { 1 })),
+                NamedOnnxValue.CreateFromTensor("sr", new DenseTensor<long>(new[] { (long)sr }, [1])),
-                NamedOnnxValue.CreateFromTensor("state", new DenseTensor<float>(state.SelectMany(a => a.SelectMany(b => b)).ToArray(), new[] { state.Length, state[0].Length, state[0][0].Length }))
+                NamedOnnxValue.CreateFromTensor("state", new DenseTensor<float>(state.SelectMany(a => a.SelectMany(b => b)).ToArray(), [state.Length, state[0].Length, state[0][0].Length]))
             };
 
-            using (var outputs = session.Run(inputs))
+        using var outputs = session.Run(inputs);
-            {
         var output = outputs.First(o => o.Name == "output").AsTensor<float>();
         var newState = outputs.First(o => o.Name == "stateN").AsTensor<float>();
 
@@ -214,7 +210,6 @@ namespace VADdotnet;
             }
         }
 
-                return output.ToArray();
+        return [.. output];
-            }
     }
 }

examples/csharp/VadDotNet.sln

@@ -0,0 +1,25 @@
+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio Version 17
+VisualStudioVersion = 17.14.36616.10 d17.14
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "VadDotNet", "VadDotNet.csproj", "{F36E1741-EDDB-90C7-7501-4911058F8996}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Any CPU = Debug|Any CPU
+		Release|Any CPU = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{F36E1741-EDDB-90C7-7501-4911058F8996}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{F36E1741-EDDB-90C7-7501-4911058F8996}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{F36E1741-EDDB-90C7-7501-4911058F8996}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{F36E1741-EDDB-90C7-7501-4911058F8996}.Release|Any CPU.Build.0 = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+	GlobalSection(ExtensibilityGlobals) = postSolution
+		SolutionGuid = {DFC4CEE8-1034-46B4-A5F4-D1649B3543E6}
+	EndGlobalSection
+EndGlobal

examples/java-example/pom.xml

@@ -21,10 +21,11 @@
             <version>3.8.1</version>
             <scope>test</scope>
         </dependency>
+        <!-- https://mvnrepository.com/artifact/com.microsoft.onnxruntime/onnxruntime -->
         <dependency>
             <groupId>com.microsoft.onnxruntime</groupId>
             <artifactId>onnxruntime</artifactId>
-      <version>1.16.0-rc1</version>
+            <version>1.23.1</version>
         </dependency>
     </dependencies>
 </project>

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

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

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

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

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

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

examples/rust-example/Cargo.lock

@@ -1,6 +1,6 @@
 # This file is automatically @generated by Cargo.
 # It is not intended for manual editing.
-version = 3
+version = 4
 
 [[package]]
 name = "adler"
@@ -20,6 +20,12 @@ version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
 
+[[package]]
+name = "base64ct"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0e050f626429857a27ddccb31e0aca21356bfa709c04041aefddac081a8f068a"
+
 [[package]]
 name = "bitflags"
 version = "1.3.2"
@@ -42,10 +48,16 @@ dependencies = [
 ]
 
 [[package]]
-name = "bumpalo"
+name = "byteorder"
-version = "3.16.0"
+version = "1.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c"
+checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
+
+[[package]]
+name = "bytes"
+version = "1.11.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b35204fbdc0b3f4446b89fc1ac2cf84a8a68971995d0bf2e925ec7cd960f9cb3"
 
 [[package]]
 name = "cc"
@@ -59,6 +71,22 @@ version = "1.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
 
+[[package]]
+name = "core-foundation"
+version = "0.9.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91e195e091a93c46f7102ec7818a2aa394e1e1771c3ab4825963fa03e45afb8f"
+dependencies = [
+ "core-foundation-sys",
+ "libc",
+]
+
+[[package]]
+name = "core-foundation-sys"
+version = "0.8.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "773648b94d0e5d620f64f280777445740e61fe701025087ec8b57f45c791888b"
+
 [[package]]
 name = "cpufeatures"
 version = "0.2.12"
@@ -77,12 +105,6 @@ 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"
@@ -93,6 +115,16 @@ dependencies = [
  "typenum",
 ]
 
+[[package]]
+name = "der"
+version = "0.7.10"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e7c1832837b905bbfb5101e07cc24c8deddf52f93225eee6ead5f4d63d53ddcb"
+dependencies = [
+ "pem-rfc7468",
+ "zeroize",
+]
+
 [[package]]
 name = "digest"
 version = "0.10.7"
@@ -110,9 +142,15 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "534c5cf6194dfab3db3242765c03bbe257cf92f22b38f6bc0c58d59108a820ba"
 dependencies = [
  "libc",
- "windows-sys",
+ "windows-sys 0.52.0",
 ]
 
+[[package]]
+name = "fastrand"
+version = "2.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be"
+
 [[package]]
 name = "filetime"
 version = "0.2.23"
@@ -122,7 +160,7 @@ dependencies = [
  "cfg-if",
  "libc",
  "redox_syscall",
- "windows-sys",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -136,14 +174,20 @@ dependencies = [
 ]
 
 [[package]]
-name = "form_urlencoded"
+name = "foreign-types"
-version = "1.2.1"
+version = "0.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e13624c2627564efccf4934284bdd98cbaa14e79b0b5a141218e507b3a823456"
+checksum = "f6f339eb8adc052cd2ca78910fda869aefa38d22d5cb648e6485e4d3fc06f3b1"
 dependencies = [
- "percent-encoding",
+ "foreign-types-shared",
 ]
 
+[[package]]
+name = "foreign-types-shared"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "00b0228411908ca8685dba7fc2cdd70ec9990a6e753e89b6ac91a84c40fbaf4b"
+
 [[package]]
 name = "generic-array"
 version = "0.14.7"
@@ -154,27 +198,6 @@ dependencies = [
  "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"
@@ -182,23 +205,26 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "62adaabb884c94955b19907d60019f4e145d091c75345379e70d1ee696f7854f"
 
 [[package]]
-name = "idna"
+name = "http"
-version = "0.5.0"
+version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "634d9b1461af396cad843f47fdba5597a4f9e6ddd4bfb6ff5d85028c25cb12f6"
+checksum = "e3ba2a386d7f85a81f119ad7498ebe444d2e22c2af0b86b069416ace48b3311a"
 dependencies = [
- "unicode-bidi",
+ "bytes",
- "unicode-normalization",
+ "itoa",
 ]
 
 [[package]]
-name = "js-sys"
+name = "httparse"
-version = "0.3.69"
+version = "1.10.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "29c15563dc2726973df627357ce0c9ddddbea194836909d655df6a75d2cf296d"
+checksum = "6dbf3de79e51f3d586ab4cb9d5c3e2c14aa28ed23d180cf89b4df0454a69cc87"
-dependencies = [
+
- "wasm-bindgen",
+[[package]]
-]
+name = "itoa"
+version = "1.0.17"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "92ecc6618181def0457392ccd0ee51198e065e016d1d527a7ac1b6dc7c1f09d2"
 
 [[package]]
 name = "libc"
@@ -206,16 +232,6 @@ 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"
@@ -224,9 +240,9 @@ checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"
 
 [[package]]
 name = "log"
-version = "0.4.21"
+version = "0.4.29"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c"
+checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897"
 
 [[package]]
 name = "matrixmultiply"
@@ -248,15 +264,34 @@ dependencies = [
 ]
 
 [[package]]
-name = "ndarray"
+name = "native-tls"
-version = "0.15.6"
+version = "0.2.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
+checksum = "87de3442987e9dbec73158d5c715e7ad9072fda936bb03d19d7fa10e00520f0e"
+dependencies = [
+ "libc",
+ "log",
+ "openssl",
+ "openssl-probe",
+ "openssl-sys",
+ "schannel",
+ "security-framework",
+ "security-framework-sys",
+ "tempfile",
+]
+
+[[package]]
+name = "ndarray"
+version = "0.16.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841"
 dependencies = [
  "matrixmultiply",
  "num-complex",
  "num-integer",
  "num-traits",
+ "portable-atomic",
+ "portable-atomic-util",
  "rawpointer",
 ]
 
@@ -294,33 +329,83 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
 
 [[package]]
-name = "ort"
+name = "openssl"
-version = "2.0.0-rc.2"
+version = "0.10.75"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0bc80894094c6a875bfac64415ed456fa661081a278a035e22be661305c87e14"
+checksum = "08838db121398ad17ab8531ce9de97b244589089e290a384c900cb9ff7434328"
+dependencies = [
+ "bitflags 2.5.0",
+ "cfg-if",
+ "foreign-types",
+ "libc",
+ "once_cell",
+ "openssl-macros",
+ "openssl-sys",
+]
+
+[[package]]
+name = "openssl-macros"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "openssl-probe"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d05e27ee213611ffe7d6348b942e8f942b37114c00cc03cec254295a4a17852e"
+
+[[package]]
+name = "openssl-sys"
+version = "0.9.111"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "82cab2d520aa75e3c58898289429321eb788c3106963d0dc886ec7a5f4adc321"
+dependencies = [
+ "cc",
+ "libc",
+ "pkg-config",
+ "vcpkg",
+]
+
+[[package]]
+name = "ort"
+version = "2.0.0-rc.10"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1fa7e49bd669d32d7bc2a15ec540a527e7764aec722a45467814005725bcd721"
 dependencies = [
- "half",
- "js-sys",
- "libloading",
  "ndarray",
  "ort-sys",
- "thiserror",
+ "smallvec",
  "tracing",
- "web-sys",
 ]
 
 [[package]]
 name = "ort-sys"
-version = "2.0.0-rc.2"
+version = "2.0.0-rc.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b3d9c1373fc813d3f024d394f621f4c6dde0734c79b1c17113c3bb5bf0084bbe"
+checksum = "e2aba9f5c7c479925205799216e7e5d07cc1d4fa76ea8058c60a9a30f6a4e890"
 dependencies = [
  "flate2",
+ "pkg-config",
  "sha2",
  "tar",
  "ureq",
 ]
 
+[[package]]
+name = "pem-rfc7468"
+version = "0.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "88b39c9bfcfc231068454382784bb460aae594343fb030d46e9f50a645418412"
+dependencies = [
+ "base64ct",
+]
+
 [[package]]
 name = "percent-encoding"
 version = "2.3.1"
@@ -333,6 +418,27 @@ version = "0.2.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "bda66fc9667c18cb2758a2ac84d1167245054bcf85d5d1aaa6923f45801bdd02"
 
+[[package]]
+name = "pkg-config"
+version = "0.3.32"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7edddbd0b52d732b21ad9a5fab5c704c14cd949e5e9a1ec5929a24fded1b904c"
+
+[[package]]
+name = "portable-atomic"
+version = "1.13.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f89776e4d69bb58bc6993e99ffa1d11f228b839984854c7daeb5d37f87cbe950"
+
+[[package]]
+name = "portable-atomic-util"
+version = "0.2.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d8a2f0d8d040d7848a709caf78912debcc3f33ee4b3cac47d73d1e1069e83507"
+dependencies = [
+ "portable-atomic",
+]
+
 [[package]]
 name = "proc-macro2"
 version = "1.0.84"
@@ -366,21 +472,6 @@ 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"
@@ -400,38 +491,48 @@ dependencies = [
  "errno",
  "libc",
  "linux-raw-sys",
- "windows-sys",
+ "windows-sys 0.52.0",
-]
-
-[[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"
+version = "1.13.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "976295e77ce332211c0d24d92c0e83e50f5c5f046d11082cea19f3df13a3562d"
+checksum = "21e6f2ab2928ca4291b86736a8bd920a277a399bba1589409d72154ff87c1282"
+dependencies = [
+ "zeroize",
+]
 
 [[package]]
-name = "rustls-webpki"
+name = "schannel"
-version = "0.102.4"
+version = "0.1.28"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ff448f7e92e913c4b7d4c6d8e4540a1724b319b4152b8aef6d4cf8339712b33e"
+checksum = "891d81b926048e76efe18581bf793546b4c0eaf8448d72be8de2bbee5fd166e1"
 dependencies = [
- "ring",
+ "windows-sys 0.61.2",
- "rustls-pki-types",
+]
- "untrusted",
+
+[[package]]
+name = "security-framework"
+version = "2.11.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c627723fd09706bacdb5cf41499e95098555af3c3c29d014dc3c458ef6be11c0"
+dependencies = [
+ "bitflags 2.5.0",
+ "core-foundation",
+ "core-foundation-sys",
+ "libc",
+ "security-framework-sys",
+]
+
+[[package]]
+name = "security-framework-sys"
+version = "2.15.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cc1f0cbffaac4852523ce30d8bd3c5cdc873501d96ff467ca09b6767bb8cd5c0"
+dependencies = [
+ "core-foundation-sys",
+ "libc",
 ]
 
 [[package]]
@@ -446,16 +547,21 @@ dependencies = [
 ]
 
 [[package]]
-name = "spin"
+name = "smallvec"
-version = "0.9.8"
+version = "2.0.0-alpha.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67"
+checksum = "51d44cfb396c3caf6fbfd0ab422af02631b69ddd96d2eff0b0f0724f9024051b"
 
 [[package]]
-name = "subtle"
+name = "socks"
-version = "2.5.0"
+version = "0.3.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "81cdd64d312baedb58e21336b31bc043b77e01cc99033ce76ef539f78e965ebc"
+checksum = "f0c3dbbd9ae980613c6dd8e28a9407b50509d3803b57624d5dfe8315218cd58b"
+dependencies = [
+ "byteorder",
+ "libc",
+ "winapi",
+]
 
 [[package]]
 name = "syn"
@@ -480,40 +586,18 @@ dependencies = [
 ]
 
 [[package]]
-name = "thiserror"
+name = "tempfile"
-version = "1.0.61"
+version = "3.12.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
+checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
 dependencies = [
- "thiserror-impl",
+ "cfg-if",
+ "fastrand",
+ "once_cell",
+ "rustix",
+ "windows-sys 0.59.0",
 ]
 
-[[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"
@@ -521,21 +605,9 @@ 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"
@@ -551,60 +623,54 @@ 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"
+version = "3.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d11a831e3c0b56e438a28308e7c810799e3c118417f342d30ecec080105395cd"
+checksum = "d39cb1dbab692d82a977c0392ffac19e188bd9186a9f32806f0aaa859d75585a"
 dependencies = [
  "base64",
+ "der",
  "log",
- "once_cell",
+ "native-tls",
- "rustls",
+ "percent-encoding",
  "rustls-pki-types",
- "rustls-webpki",
+ "socks",
- "url",
+ "ureq-proto",
- "webpki-roots",
+ "utf-8",
+ "webpki-root-certs",
 ]
 
 [[package]]
-name = "url"
+name = "ureq-proto"
-version = "2.5.0"
+version = "0.5.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "31e6302e3bb753d46e83516cae55ae196fc0c309407cf11ab35cc51a4c2a4633"
+checksum = "d81f9efa9df032be5934a46a068815a10a042b494b6a58cb0a1a97bb5467ed6f"
 dependencies = [
- "form_urlencoded",
+ "base64",
- "idna",
+ "http",
- "percent-encoding",
+ "httparse",
+ "log",
 ]
 
+[[package]]
+name = "utf-8"
+version = "0.7.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "09cc8ee72d2a9becf2f2febe0205bbed8fc6615b7cb429ad062dc7b7ddd036a9"
+
+[[package]]
+name = "vcpkg"
+version = "0.2.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "accd4ea62f7bb7a82fe23066fb0957d48ef677f6eeb8215f372f52e48bb32426"
+
 [[package]]
 name = "version_check"
 version = "0.9.4"
@@ -612,84 +678,42 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "49874b5167b65d7193b8aba1567f5c7d93d001cafc34600cee003eda787e483f"
 
 [[package]]
-name = "wasi"
+name = "webpki-root-certs"
-version = "0.11.0+wasi-snapshot-preview1"
+version = "1.0.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"
+checksum = "ee3e3b5f5e80bc89f30ce8d0343bf4e5f12341c51f3e26cbeecbc7c85443e85b"
-
-[[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 = "winapi"
+version = "0.3.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5c839a674fcd7a98952e593242ea400abe93992746761e38641405d28b00f419"
+dependencies = [
+ "winapi-i686-pc-windows-gnu",
+ "winapi-x86_64-pc-windows-gnu",
+]
+
+[[package]]
+name = "winapi-i686-pc-windows-gnu"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6"
+
+[[package]]
+name = "winapi-x86_64-pc-windows-gnu"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f"
+
+[[package]]
+name = "windows-link"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f0805222e57f7521d6a62e36fa9163bc891acd422f971defe97d64e70d0a4fe5"
+
 [[package]]
 name = "windows-sys"
 version = "0.52.0"
@@ -700,10 +724,28 @@ dependencies = [
 ]
 
 [[package]]
-name = "windows-targets"
+name = "windows-sys"
-version = "0.52.5"
+version = "0.59.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6f0713a46559409d202e70e28227288446bf7841d3211583a4b53e3f6d96e7eb"
+checksum = "1e38bc4d79ed67fd075bcc251a1c39b32a1776bbe92e5bef1f0bf1f8c531853b"
+dependencies = [
+ "windows-targets",
+]
+
+[[package]]
+name = "windows-sys"
+version = "0.61.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ae137229bcbd6cdf0f7b80a31df61766145077ddf49416a728b02cb3921ff3fc"
+dependencies = [
+ "windows-link",
+]
+
+[[package]]
+name = "windows-targets"
+version = "0.52.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9b724f72796e036ab90c1021d4780d4d3d648aca59e491e6b98e725b84e99973"
 dependencies = [
  "windows_aarch64_gnullvm",
  "windows_aarch64_msvc",
@@ -717,51 +759,51 @@ dependencies = [
 
 [[package]]
 name = "windows_aarch64_gnullvm"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7088eed71e8b8dda258ecc8bac5fb1153c5cffaf2578fc8ff5d61e23578d3263"
+checksum = "32a4622180e7a0ec044bb555404c800bc9fd9ec262ec147edd5989ccd0c02cd3"
 
 [[package]]
 name = "windows_aarch64_msvc"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9985fd1504e250c615ca5f281c3f7a6da76213ebd5ccc9561496568a2752afb6"
+checksum = "09ec2a7bb152e2252b53fa7803150007879548bc709c039df7627cabbd05d469"
 
 [[package]]
 name = "windows_i686_gnu"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "88ba073cf16d5372720ec942a8ccbf61626074c6d4dd2e745299726ce8b89670"
+checksum = "8e9b5ad5ab802e97eb8e295ac6720e509ee4c243f69d781394014ebfe8bbfa0b"
 
 [[package]]
 name = "windows_i686_gnullvm"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "87f4261229030a858f36b459e748ae97545d6f1ec60e5e0d6a3d32e0dc232ee9"
+checksum = "0eee52d38c090b3caa76c563b86c3a4bd71ef1a819287c19d586d7334ae8ed66"
 
 [[package]]
 name = "windows_i686_msvc"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "db3c2bf3d13d5b658be73463284eaf12830ac9a26a90c717b7f771dfe97487bf"
+checksum = "240948bc05c5e7c6dabba28bf89d89ffce3e303022809e73deaefe4f6ec56c66"
 
 [[package]]
 name = "windows_x86_64_gnu"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4e4246f76bdeff09eb48875a0fd3e2af6aada79d409d33011886d3e1581517d9"
+checksum = "147a5c80aabfbf0c7d901cb5895d1de30ef2907eb21fbbab29ca94c5b08b1a78"
 
 [[package]]
 name = "windows_x86_64_gnullvm"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "852298e482cd67c356ddd9570386e2862b5673c85bd5f88df9ab6802b334c596"
+checksum = "24d5b23dc417412679681396f2b49f3de8c1473deb516bd34410872eff51ed0d"
 
 [[package]]
 name = "windows_x86_64_msvc"
-version = "0.52.5"
+version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0"
+checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
 
 [[package]]
 name = "xattr"

examples/rust-example/Cargo.toml

@@ -4,6 +4,6 @@ version = "0.1.0"
 edition = "2021"
 
 [dependencies]
-ort = { version = "2.0.0-rc.2", features = ["load-dynamic", "ndarray"] }
+ort = { version = "=2.0.0-rc.10", features = ["ndarray"] }
-ndarray = "0.15"
+ndarray = "0.16"
 hound = "3"

examples/rust-example/src/main.rs

@@ -4,7 +4,7 @@ mod vad_iter;
 
 fn main() {
     let model_path = std::env::var("SILERO_MODEL_PATH")
-        .unwrap_or_else(|_| String::from("../../files/silero_vad.onnx"));
+        .unwrap_or_else(|_| String::from("../../src/silero_vad/data/silero_vad.onnx"));
     let audio_path = std::env::args()
         .nth(1)
         .unwrap_or_else(|| String::from("recorder.wav"));

examples/rust-example/src/silero.rs

@@ -1,12 +1,17 @@
 use crate::utils;
-use ndarray::{s, Array, Array2, ArrayBase, ArrayD, Dim, IxDynImpl, OwnedRepr};
+use ndarray::{Array, Array1, Array2, ArrayBase, ArrayD, Dim, IxDynImpl, OwnedRepr};
+use ort::session::Session;
+use ort::value::Value;
+use std::mem::take;
 use std::path::Path;
 
 #[derive(Debug)]
 pub struct Silero {
-    session: ort::Session,
+    session: Session,
     sample_rate: ArrayBase<OwnedRepr<i64>, Dim<[usize; 1]>>,
     state: ArrayBase<OwnedRepr<f32>, Dim<IxDynImpl>>,
+    context: Array1<f32>,
+    context_size: usize,
 }
 
 impl Silero {
@@ -14,18 +19,24 @@ impl Silero {
         sample_rate: utils::SampleRate,
         model_path: impl AsRef<Path>,
     ) -> Result<Self, ort::Error> {
-        let session = ort::Session::builder()?.commit_from_file(model_path)?;
+        let session = Session::builder()?.commit_from_file(model_path)?;
         let state = ArrayD::<f32>::zeros([2, 1, 128].as_slice());
-        let sample_rate = Array::from_shape_vec([1], vec![sample_rate.into()]).unwrap();
+        let sample_rate_val: i64 = sample_rate.into();
+        let context_size = if sample_rate_val == 16000 { 64 } else { 32 };
+        let context = Array1::<f32>::zeros(context_size);
+        let sample_rate = Array::from_shape_vec([1], vec![sample_rate_val]).unwrap();
         Ok(Self {
             session,
             sample_rate,
             state,
+            context,
+            context_size,
         })
     }
 
     pub fn reset(&mut self) {
         self.state = ArrayD::<f32>::zeros([2, 1, 128].as_slice());
+        self.context = Array1::<f32>::zeros(self.context_size);
     }
 
     pub fn calc_level(&mut self, audio_frame: &[i16]) -> Result<f32, ort::Error> {
@@ -33,22 +44,41 @@ impl Silero {
             .iter()
             .map(|x| (*x as f32) / (i16::MAX as f32))
             .collect::<Vec<_>>();
-        let mut frame = Array2::<f32>::from_shape_vec([1, data.len()], data).unwrap();
+
-        frame = frame.slice(s![.., ..480]).to_owned();
+        // Concatenate context with input
-        let inps = ort::inputs![
+        let mut input_with_context = Vec::with_capacity(self.context_size + data.len());
-            frame,
+        input_with_context.extend_from_slice(self.context.as_slice().unwrap());
-            std::mem::take(&mut self.state),
+        input_with_context.extend_from_slice(&data);
-            self.sample_rate.clone(),
+
-        ]?;
+        let frame =
-        let res = self
+            Array2::<f32>::from_shape_vec([1, input_with_context.len()], input_with_context)
-            .session
+                .unwrap();
-            .run(ort::SessionInputs::ValueSlice::<3>(&inps))?;
+
-        self.state = res["stateN"].try_extract_tensor().unwrap().to_owned();
+        let frame_value = Value::from_array(frame)?;
-        Ok(*res["output"]
+        let state_value = Value::from_array(take(&mut self.state))?;
-            .try_extract_raw_tensor::<f32>()
+        let sr_value = Value::from_array(self.sample_rate.clone())?;
+
+        let res = self.session.run([
+            (&frame_value).into(),
+            (&state_value).into(),
+            (&sr_value).into(),
+        ])?;
+
+        let (shape, state_data) = res["stateN"].try_extract_tensor::<f32>()?;
+        let shape_usize: Vec<usize> = shape.as_ref().iter().map(|&d| d as usize).collect();
+        self.state = ArrayD::from_shape_vec(shape_usize.as_slice(), state_data.to_vec()).unwrap();
+
+        // Update context with last context_size samples from the input
+        if data.len() >= self.context_size {
+            self.context = Array1::from_vec(data[data.len() - self.context_size..].to_vec());
+        }
+
+        let prob = *res["output"]
+            .try_extract_tensor::<f32>()
             .unwrap()
             .1
             .first()
-            .unwrap())
+            .unwrap();
+        Ok(prob)
     }
 }

examples/rust-example/src/utils.rs

@@ -36,7 +36,7 @@ pub struct VadParams {
 impl Default for VadParams {
     fn default() -> Self {
         Self {
-            frame_size: 64,
+            frame_size: 32,  // 32ms for 512 samples at 16kHz
             threshold: 0.5,
             min_silence_duration_ms: 0,
             speech_pad_ms: 64,

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 [project]
 name = "silero-vad"
-version = "6.0.0"
+version = "6.2.0"
 authors = [
   {name="Silero Team", email="hello@silero.ai"},
 ]
@@ -28,6 +28,7 @@ classifiers = [
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
+  "packaging",
   "torch>=1.12.0",
   "torchaudio>=0.12.0",
   "onnxruntime>=1.16.1",
@@ -36,3 +37,10 @@ dependencies = [
 [project.urls]
 Homepage = "https://github.com/snakers4/silero-vad"
 Issues = "https://github.com/snakers4/silero-vad/issues"
+
+[project.optional-dependencies]
+test = [
+    "pytest",
+    "soundfile",
+    "torch<2.9",
+]

src/silero_vad/tinygrad_model.py

@@ -0,0 +1,71 @@
+from tinygrad import nn
+
+
+class TinySileroVAD:
+    def __init__(self):
+        """
+        from tinygrad.nn.state import safe_load, load_state_dict
+
+        tiny_model = TinySileroVAD()
+        state_dict = safe_load('data/silero_vad_16k.safetensors')
+        load_state_dict(tiny_model, state_dict)
+        """
+        self.n_fft = 256
+        self.stride = 128
+        self.pad = 64
+        self.cutoff = int(self.n_fft // 2) + 1
+    
+        self.stft_conv = nn.Conv1d(1, 258, kernel_size=256, stride=self.stride, padding=0, bias=False)
+        self.conv1 = nn.Conv1d(129, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv1d(128, 64, kernel_size=3, stride=2, padding=1)
+        self.conv3 = nn.Conv1d(64, 64, kernel_size=3, stride=2, padding=1)
+        self.conv4 = nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1)
+
+        self.lstm_cell = nn.LSTMCell(128, 128)
+        self.final_conv = nn.Conv1d(128, 1, 1)
+
+    def __call__(self, x, state=None):
+        """
+        # full audio example:
+        import torch
+        from tinygrad import Tensor
+
+        wav = read_audio(audio_path, sampling_rate=16000).unsqueeze(0)
+        num_samples = 512
+        context_size = 64
+        context = Tensor(np.zeros((1, context_size))).float()
+        outs = []
+        state = None
+        if wav.shape[1] % num_samples:
+            pad_num = num_samples - (wav.shape[1] % num_samples)
+            wav = torch.nn.functional.pad(wav, (0, pad_num), 'constant', value=0.0)
+
+        wav = torch.nn.functional.pad(wav, (context_size, 0))
+
+        wav = Tensor(wav.numpy()).float()
+
+        for i in tqdm(range(context_size, wav.shape[1], num_samples)):
+            wavs_batch = wav[:, i-context_size:i+num_samples]
+            out_chunk, state = tiny_model(wavs_batch, state)
+            #outs.append(out_chunk.numpy())
+            outs.append(out_chunk)
+
+        predict = outs[0].cat(*outs[1:], dim=1).numpy()
+        
+        """
+        if state is not None:
+            state = (state[0], state[1])
+        x = x.pad((0, self.pad), "reflect").unsqueeze(1)
+        x = self.stft_conv(x)
+        x = (x[:, :self.cutoff, :]**2 + x[:, self.cutoff:, :]**2).sqrt()
+        x = self.conv1(x).relu()
+        x = self.conv2(x).relu()
+        x = self.conv3(x).relu()
+        x = self.conv4(x).relu().squeeze(-1)
+        h, c = self.lstm_cell(x, state)
+        x = h.unsqueeze(-1)
+        state = h.stack(c, dim=0)
+        x = x.relu()
+        x = self.final_conv(x).sigmoid()
+        x = x.squeeze(1).mean(axis=1).unsqueeze(1)
+        return x, state

src/silero_vad/utils_vad.py

@@ -2,6 +2,7 @@ import torch
 import torchaudio
 from typing import Callable, List
 import warnings
+from packaging import version
 
 languages = ['ru', 'en', 'de', 'es']
 
@@ -134,40 +135,60 @@ class Validator():
         return outs
 
 
-def read_audio(path: str,
+def read_audio(path: str, sampling_rate: int = 16000) -> torch.Tensor:
-               sampling_rate: int = 16000):
+    ta_ver = version.parse(torchaudio.__version__)
-    list_backends = torchaudio.list_audio_backends()
+    if ta_ver < version.parse("2.9"):
-
-    assert len(list_backends) > 0, 'The list of available backends is empty, please install backend manually. \
-                                    \n Recommendations: \n \tSox (UNIX OS) \n \tSoundfile (Windows OS, UNIX OS) \n \tffmpeg (Windows OS, UNIX OS)'
-
         try:
-        effects = [
+            effects = [['channels', '1'],['rate', str(sampling_rate)]]
-            ['channels', '1'],
-            ['rate', str(sampling_rate)]
-        ]
-
             wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
         except:
             wav, sr = torchaudio.load(path)
+    else:
+        try:
+            wav, sr = torchaudio.load(path)
+        except:
+            try:
+                from torchcodec.decoders import AudioDecoder
+                samples = AudioDecoder(path).get_all_samples()
+                wav = samples.data
+                sr = samples.sample_rate
+            except ImportError:
+                raise RuntimeError(
+                    f"torchaudio version {torchaudio.__version__} requires torchcodec for audio I/O. "
+                    + "Install torchcodec or pin torchaudio < 2.9"
+                )
 
-        if wav.size(0) > 1:
+    if wav.ndim > 1 and wav.size(0) > 1:
         wav = wav.mean(dim=0, keepdim=True)
 
     if sr != sampling_rate:
-            transform = torchaudio.transforms.Resample(orig_freq=sr,
+        wav = torchaudio.transforms.Resample(sr, sampling_rate)(wav)
-                                                       new_freq=sampling_rate)
-            wav = transform(wav)
-            sr = sampling_rate
 
-    assert sr == sampling_rate
     return wav.squeeze(0)
 
 
-def save_audio(path: str,
+def save_audio(path: str, tensor: torch.Tensor, sampling_rate: int = 16000):
-               tensor: torch.Tensor,
+    tensor = tensor.detach().cpu()
-               sampling_rate: int = 16000):
+    if tensor.ndim == 1:
-    torchaudio.save(path, tensor.unsqueeze(0), sampling_rate, bits_per_sample=16)
+        tensor = tensor.unsqueeze(0)
+
+    ta_ver = version.parse(torchaudio.__version__)
+
+    try:
+        torchaudio.save(path, tensor, sampling_rate, bits_per_sample=16)
+    except Exception:
+        if ta_ver >= version.parse("2.9"):
+            try:
+                from torchcodec.encoders import AudioEncoder
+                encoder = AudioEncoder(tensor, sample_rate=16000)
+                encoder.to_file(path)
+            except ImportError:
+                raise RuntimeError(
+                    f"torchaudio version {torchaudio.__version__} requires torchcodec for saving. "
+                    + "Install torchcodec or pin torchaudio < 2.9"
+                )
+        else:
+            raise
 
 
 def init_jit_model(model_path: str,
@@ -202,7 +223,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                           progress_tracking_callback: Callable[[float], None] = None,
                           neg_threshold: float = None,
                           window_size_samples: int = 512,
-                          min_silence_at_max_speech: float = 98,
+                          min_silence_at_max_speech: int = 98,
                           use_max_poss_sil_at_max_speech: bool = True):
 
     """
@@ -227,7 +248,7 @@ def get_speech_timestamps(audio: torch.Tensor,
 
     max_speech_duration_s: int (default -  inf)
         Maximum duration of speech chunks in seconds
-        Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent agressive cutting.
+        Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent aggressive cutting.
         Otherwise, they will be split aggressively just before max_speech_duration_s.
 
     min_silence_duration_ms: int (default - 100 milliseconds)
@@ -251,7 +272,7 @@ def get_speech_timestamps(audio: torch.Tensor,
     neg_threshold: float (default = threshold - 0.15)
         Negative threshold (noise or exit threshold). If model's current state is SPEECH, values BELOW this value are considered as NON-SPEECH.
 
-    min_silence_at_max_speech: float (default - 98ms)
+    min_silence_at_max_speech: int (default - 98ms)
         Minimum silence duration in ms which is used to avoid abrupt cuts when max_speech_duration_s is reached
 
     use_max_poss_sil_at_max_speech: bool (default - True)
@@ -289,7 +310,6 @@ def get_speech_timestamps(audio: torch.Tensor,
         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
-    hop_size_samples = int(window_size_samples)
 
     model.reset_states()
     min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
@@ -301,17 +321,14 @@ def get_speech_timestamps(audio: torch.Tensor,
     audio_length_samples = len(audio)
 
     speech_probs = []
-    for current_start_sample in range(0, audio_length_samples, hop_size_samples):
+    for current_start_sample in range(0, audio_length_samples, window_size_samples):
         chunk = audio[current_start_sample: current_start_sample + window_size_samples]
         if len(chunk) < window_size_samples:
             chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk))))
-        try:
         speech_prob = model(chunk, sampling_rate).item()
-        except Exception as e:
-            import ipdb; ipdb.set_trace()
         speech_probs.append(speech_prob)
-        # caculate progress and seng it to callback function
+        # calculate progress and send it to callback function
-        progress = current_start_sample + hop_size_samples
+        progress = current_start_sample + window_size_samples
         if progress > audio_length_samples:
             progress = audio_length_samples
         progress_percent = (progress / audio_length_samples) * 100
@@ -329,40 +346,53 @@ def get_speech_timestamps(audio: torch.Tensor,
     possible_ends = []
 
     for i, speech_prob in enumerate(speech_probs):
+        cur_sample = window_size_samples * i
+
+        # If speech returns after a temp_end, record candidate silence if long enough and clear temp_end
         if (speech_prob >= threshold) and temp_end:
-            if temp_end != 0:
+            sil_dur = cur_sample - temp_end
-                sil_dur = (hop_size_samples * i) - temp_end
             if sil_dur > min_silence_samples_at_max_speech:
                 possible_ends.append((temp_end, sil_dur))
             temp_end = 0
             if next_start < prev_end:
-                next_start = hop_size_samples * i
+                next_start = cur_sample
 
+        # Start of speech
         if (speech_prob >= threshold) and not triggered:
             triggered = True
-            current_speech['start'] = hop_size_samples * i
+            current_speech['start'] = cur_sample
             continue
 
-        if triggered and (hop_size_samples * i) - current_speech['start'] > max_speech_samples:
+        # Max speech length reached: decide where to cut
-            if possible_ends:
+        if triggered and (cur_sample - current_speech['start'] > max_speech_samples):
-                if use_max_poss_sil_at_max_speech:
+            if use_max_poss_sil_at_max_speech and possible_ends:
                 prev_end, dur = max(possible_ends, key=lambda x: x[1])  # use the longest possible silence segment in the current speech chunk
-                else:
-                    prev_end, dur = possible_ends[-1]   # use the last possible silence segement
                 current_speech['end'] = prev_end
                 speeches.append(current_speech)
                 current_speech = {}
                 next_start = prev_end + dur
-                if next_start < prev_end + hop_size_samples * i:  # previously reached silence (< neg_thres) and is still not speech (< thres)
+
-                    #triggered = False
+                if next_start < prev_end + cur_sample:  # previously reached silence (< neg_thres) and is still not speech (< thres)
                     current_speech['start'] = next_start
                 else:
                     triggered = False
-                    #current_speech['start'] = next_start
                 prev_end = next_start = temp_end = 0
                 possible_ends = []
             else:
-                current_speech['end'] = hop_size_samples * i
+                # Legacy max-speech cut (use_max_poss_sil_at_max_speech=False): prefer last valid silence (prev_end) if available
+                if prev_end:
+                    current_speech['end'] = prev_end
+                    speeches.append(current_speech)
+                    current_speech = {}
+                    if next_start < prev_end:
+                        triggered = False
+                    else:
+                        current_speech['start'] = next_start
+                    prev_end = next_start = temp_end = 0
+                    possible_ends = []
+                else:
+                    # No prev_end -> fallback to cutting at current sample
+                    current_speech['end'] = cur_sample
                     speeches.append(current_speech)
                     current_speech = {}
                     prev_end = next_start = temp_end = 0
@@ -370,12 +400,16 @@ def get_speech_timestamps(audio: torch.Tensor,
                     possible_ends = []
                     continue
 
+        # Silence detection while in speech
         if (speech_prob < neg_threshold) and triggered:
             if not temp_end:
-                temp_end = hop_size_samples * i
+                temp_end = cur_sample
-            # if ((hop_size_samples * i) - temp_end) > min_silence_samples_at_max_speech:  # condition to avoid cutting in very short silence
+            sil_dur_now = cur_sample - temp_end
-            #     prev_end = temp_end
+
-            if (hop_size_samples * i) - temp_end < min_silence_samples:
+            if not use_max_poss_sil_at_max_speech and sil_dur_now > min_silence_samples_at_max_speech:  # condition to avoid cutting in very short silence
+                prev_end = temp_end
+
+            if sil_dur_now < min_silence_samples:
                 continue
             else:
                 current_speech['end'] = temp_end
@@ -416,7 +450,7 @@ def get_speech_timestamps(audio: torch.Tensor,
             speech_dict['end'] *= step
 
     if visualize_probs:
-        make_visualization(speech_probs, hop_size_samples / sampling_rate)
+        make_visualization(speech_probs, window_size_samples / sampling_rate)
 
     return speeches
 
@@ -607,6 +641,8 @@ def drop_chunks(tss: List[dict],
         chunks.append((wav[cur_start: i['start']]))
         cur_start = i['end']
 
+    chunks.append(wav[cur_start:])
+
     return torch.cat(chunks)
 
 

tuning/utils.py

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