Add C++ folder for supporting ONNX & LibTorch

examples/c++/README.md

@@ -0,0 +1,49 @@
+# Silero-VAD V5 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_
+
+