Add Silero VAD (#313)

 cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
 cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
 project(sherpa-onnx)
 project(sherpa-onnx)
 
 
-set(SHERPA_ONNX_VERSION "1.7.14")
+set(SHERPA_ONNX_VERSION "1.7.15")
 
 
 # Disable warning about
 # Disable warning about
 #
 #

@@ -136,6 +136,7 @@ class BuildExtension(build_ext):
         binaries += ["sherpa-onnx-online-websocket-server"]
         binaries += ["sherpa-onnx-online-websocket-server"]
         binaries += ["sherpa-onnx-offline-websocket-server"]
         binaries += ["sherpa-onnx-offline-websocket-server"]
         binaries += ["sherpa-onnx-online-websocket-client"]
         binaries += ["sherpa-onnx-online-websocket-client"]
+        binaries += ["sherpa-onnx-vad-microphone"]
 
 
         if is_windows():
         if is_windows():
             binaries += ["kaldi-native-fbank-core.dll"]
             binaries += ["kaldi-native-fbank-core.dll"]

+# File description
+
+- [./http_server.py](./http_server.py) It defines which files to server.
+  Files are saved in [./web](./web).
+- [non_streaming_server.py](./non_streaming_server.py) WebSocket server for
+  non-streaming models.
+- [vad-remove-non-speech-segments.py](./vad-remove-non-speech-segments.py) It uses
+  [silero-vad](https://github.com/snakers4/silero-vad) to remove non-speech
+  segments and concatenate all speech segments into a single one.

+#!/usr/bin/env python3
+
+"""
+This file shows how to remove non-speech segments
+and merge all speech segments into a large segment
+and save it to a file.
+
+Usage
+
+python3 ./vad-remove-non-speech-segments.py \
+        --silero-vad-model silero_vad.onnx
+
+Please visit
+https://github.com/snakers4/silero-vad/blob/master/files/silero_vad.onnx
+to download silero_vad.onnx
+
+For instance,
+
+wget https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx
+"""
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import sherpa_onnx
+import soundfile as sf
+
+try:
+    import sounddevice as sd
+except ImportError:
+    print("Please install sounddevice first. You can use")
+    print()
+    print("  pip install sounddevice")
+    print()
+    print("to install it")
+    sys.exit(-1)
+
+
+def assert_file_exists(filename: str):
+    assert Path(filename).is_file(), (
+        f"{filename} does not exist!\n"
+        "Please refer to "
+        "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
+    )
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--silero-vad-model",
+        type=str,
+        required=True,
+        help="Path to silero_vad.onnx",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    devices = sd.query_devices()
+    if len(devices) == 0:
+        print("No microphone devices found")
+        sys.exit(0)
+
+    print(devices)
+    default_input_device_idx = sd.default.device[0]
+    print(f'Use default device: {devices[default_input_device_idx]["name"]}')
+
+    args = get_args()
+    assert_file_exists(args.silero_vad_model)
+
+    sample_rate = 16000
+    samples_per_read = int(0.1 * sample_rate)  # 0.1 second = 100 ms
+
+    config = sherpa_onnx.VadModelConfig()
+    config.silero_vad.model = args.silero_vad_model
+    config.sample_rate = sample_rate
+
+    window_size = config.silero_vad.window_size
+
+    buffer = []
+    vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=30)
+
+    all_samples = []
+
+    print("Started! Please speak")
+
+    try:
+        with sd.InputStream(channels=1, dtype="float32", samplerate=sample_rate) as s:
+            while True:
+                samples, _ = s.read(samples_per_read)  # a blocking read
+                samples = samples.reshape(-1)
+                buffer = np.concatenate([buffer, samples])
+
+                all_samples = np.concatenate([all_samples, samples])
+
+                while len(buffer) > window_size:
+                    vad.accept_waveform(buffer[:window_size])
+                    buffer = buffer[window_size:]
+    except KeyboardInterrupt:
+        print("\nCaught Ctrl + C. Saving & Exiting")
+
+        speech_samples = []
+        while not vad.empty():
+            speech_samples.extend(vad.front.samples)
+            vad.pop()
+
+        speech_samples = np.array(speech_samples, dtype=np.float32)
+
+        filename_for_speech = time.strftime("%Y%m%d-%H%M%S-speech.wav")
+        sf.write(filename_for_speech, speech_samples, samplerate=sample_rate)
+
+        filename_for_all = time.strftime("%Y%m%d-%H%M%S-all.wav")
+        sf.write(filename_for_all, all_samples, samplerate=sample_rate)
+
+        print(f"Saved to {filename_for_speech} and {filename_for_all}")
+
+
+if __name__ == "__main__":
+    main()

@@ -56,6 +56,7 @@ def get_binaries_to_install():
     binaries += ["sherpa-onnx-online-websocket-server"]
     binaries += ["sherpa-onnx-online-websocket-server"]
     binaries += ["sherpa-onnx-offline-websocket-server"]
     binaries += ["sherpa-onnx-offline-websocket-server"]
     binaries += ["sherpa-onnx-online-websocket-client"]
     binaries += ["sherpa-onnx-online-websocket-client"]
+    binaries += ["sherpa-onnx-vad-microphone"]
     if is_windows():
     if is_windows():
         binaries += ["kaldi-native-fbank-core.dll"]
         binaries += ["kaldi-native-fbank-core.dll"]
         binaries += ["sherpa-onnx-c-api.dll"]
         binaries += ["sherpa-onnx-c-api.dll"]
@@ -95,8 +96,8 @@ setuptools.setup(
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
     ],
     ],
     entry_points={
     entry_points={
-        'console_scripts': [
-            'sherpa-onnx-cli=sherpa_onnx.cli:cli',
+        "console_scripts": [
+            "sherpa-onnx-cli=sherpa_onnx.cli:cli",
         ],
         ],
     },
     },
     license="Apache licensed, as found in the LICENSE file",
     license="Apache licensed, as found in the LICENSE file",

@@ -13,6 +13,7 @@ endif()
 set(sources
 set(sources
   base64-decode.cc
   base64-decode.cc
   cat.cc
   cat.cc
+  circular-buffer.cc
   context-graph.cc
   context-graph.cc
   endpoint.cc
   endpoint.cc
   features.cc
   features.cc
@@ -66,6 +67,8 @@ set(sources
   provider.cc
   provider.cc
   resample.cc
   resample.cc
   session.cc
   session.cc
+  silero-vad-model-config.cc
+  silero-vad-model.cc
   slice.cc
   slice.cc
   stack.cc
   stack.cc
   symbol-table.cc
   symbol-table.cc
@@ -73,6 +76,9 @@ set(sources
   transpose.cc
   transpose.cc
   unbind.cc
   unbind.cc
   utils.cc
   utils.cc
+  vad-model-config.cc
+  vad-model.cc
+  voice-activity-detector.cc
   wave-reader.cc
   wave-reader.cc
 )
 )
 
 
@@ -172,32 +178,42 @@ if(SHERPA_ONNX_ENABLE_PORTAUDIO)
     microphone.cc
     microphone.cc
   )
   )
 
 
+  add_executable(sherpa-onnx-vad-microphone
+    sherpa-onnx-vad-microphone.cc
+    microphone.cc
+  )
+
   if(BUILD_SHARED_LIBS)
   if(BUILD_SHARED_LIBS)
     set(PA_LIB portaudio)
     set(PA_LIB portaudio)
   else()
   else()
     set(PA_LIB portaudio_static)
     set(PA_LIB portaudio_static)
   endif()
   endif()
 
 
-  target_link_libraries(sherpa-onnx-microphone ${PA_LIB} sherpa-onnx-core)
-  target_link_libraries(sherpa-onnx-microphone-offline ${PA_LIB} sherpa-onnx-core)
+  set(exes
+    sherpa-onnx-microphone
+    sherpa-onnx-microphone-offline
+    sherpa-onnx-vad-microphone
+  )
+  foreach(exe IN LISTS exes)
+    target_link_libraries(${exe} ${PA_LIB} sherpa-onnx-core)
+  endforeach()
 
 
   if(NOT WIN32)
   if(NOT WIN32)
-    target_link_libraries(sherpa-onnx-microphone "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../lib")
-    target_link_libraries(sherpa-onnx-microphone "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../../../sherpa_onnx/lib")
-
-    target_link_libraries(sherpa-onnx-microphone-offline "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../lib")
-    target_link_libraries(sherpa-onnx-microphone-offline "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../../../sherpa_onnx/lib")
+    foreach(exe IN LISTS exes)
+      target_link_libraries(${exe} "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../lib")
+      target_link_libraries(${exe} "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../../../sherpa_onnx/lib")
+    endforeach()
 
 
     if(SHERPA_ONNX_ENABLE_PYTHON)
     if(SHERPA_ONNX_ENABLE_PYTHON)
-      target_link_libraries(sherpa-onnx-microphone "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../lib/python${PYTHON_VERSION}/site-packages/sherpa_onnx/lib")
-      target_link_libraries(sherpa-onnx-microphone-offline "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../lib/python${PYTHON_VERSION}/site-packages/sherpa_onnx/lib")
+
+      foreach(exe IN LISTS exes)
+        target_link_libraries(${exe} "-Wl,-rpath,${SHERPA_ONNX_RPATH_ORIGIN}/../lib/python${PYTHON_VERSION}/site-packages/sherpa_onnx/lib")
+      endforeach()
     endif()
     endif()
   endif()
   endif()
 
 
   install(
   install(
-    TARGETS
-      sherpa-onnx-microphone
-      sherpa-onnx-microphone-offline
+    TARGETS ${exes}
     DESTINATION
     DESTINATION
       bin
       bin
   )
   )
@@ -269,6 +285,7 @@ endif()
 if(SHERPA_ONNX_ENABLE_TESTS)
 if(SHERPA_ONNX_ENABLE_TESTS)
   set(sherpa_onnx_test_srcs
   set(sherpa_onnx_test_srcs
     cat-test.cc
     cat-test.cc
+    circular-buffer-test.cc
     context-graph-test.cc
     context-graph-test.cc
     packed-sequence-test.cc
     packed-sequence-test.cc
     pad-sequence-test.cc
     pad-sequence-test.cc

+# File descriptions
+
+- [./sherpa-onnx-alsa.cc](./sherpa-onnx-alsa.cc) For Linux only, especially for
+  embedded Linux, e.g., Raspberry Pi; it uses a streaming model for real-time
+  speech recognition with a microphone.
+
+- [./sherpa-onnx-microphone.cc](./sherpa-onnx-microphone.cc)
+  For Linux/Windows/macOS; it uses a streaming model for real-time speech
+  recognition with a microphone.
+
+- [./sherpa-onnx-microphone-offline.cc](./sherpa-onnx-microphone-offline.cc)
+  For Linux/Windows/macOS; it uses a non-streaming model for speech
+  recognition with a microphone.
+
+- [./sherpa-onnx.cc](./sherpa-onnx.cc)
+  It uses a streaming model to decode wave files
+
+- [./sherpa-onnx-offline.cc](./sherpa-onnx-offline.cc)
+  It uses a non-streaming model to decode wave files
+
+- [./online-websocket-server.cc](./online-websocket-server.cc)
+  WebSocket server for streaming models.
+
+- [./offline-websocket-server.cc](./offline-websocket-server.cc)
+  WebSocket server for non-streaming models.
+
+- [./sherpa-onnx-vad-microphone.cc](./sherpa-onnx-vad-microphone.cc)
+  Use silero VAD to detect speeches with a microphone.
+

+// sherpa-onnx/csrc/circular-buffer-test.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/circular-buffer.h"
+
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+TEST(CircularBuffer, Push) {
+  CircularBuffer buffer(10);
+  EXPECT_EQ(buffer.Size(), 0);
+  EXPECT_EQ(buffer.Head(), 0);
+  EXPECT_EQ(buffer.Tail(), 0);
+
+  std::vector<float> a = {0, 1, 2, 3, 4, 5};
+  buffer.Push(a.data(), a.size());
+
+  EXPECT_EQ(buffer.Size(), 6);
+  EXPECT_EQ(buffer.Head(), 0);
+  EXPECT_EQ(buffer.Tail(), 6);
+
+  auto c = buffer.Get(0, a.size());
+  EXPECT_EQ(a.size(), c.size());
+  for (int32_t i = 0; i != a.size(); ++i) {
+    EXPECT_EQ(a[i], c[i]);
+  }
+
+  std::vector<float> d = {-6, -7, -8, -9};
+  buffer.Push(d.data(), d.size());
+
+  c = buffer.Get(a.size(), d.size());
+  EXPECT_EQ(d.size(), c.size());
+  for (int32_t i = 0; i != d.size(); ++i) {
+    EXPECT_EQ(d[i], c[i]);
+  }
+}
+
+TEST(CircularBuffer, PushAndPop) {
+  CircularBuffer buffer(5);
+  std::vector<float> a = {0, 1, 2, 3};
+  buffer.Push(a.data(), a.size());
+
+  EXPECT_EQ(buffer.Size(), 4);
+  EXPECT_EQ(buffer.Head(), 0);
+  EXPECT_EQ(buffer.Tail(), 4);
+
+  buffer.Pop(2);
+
+  EXPECT_EQ(buffer.Size(), 2);
+  EXPECT_EQ(buffer.Head(), 2);
+  EXPECT_EQ(buffer.Tail(), 4);
+
+  auto c = buffer.Get(2, 2);
+  EXPECT_EQ(c.size(), 2);
+  EXPECT_EQ(c[0], 2);
+  EXPECT_EQ(c[1], 3);
+
+  a = {10, 20, 30};
+  buffer.Push(a.data(), a.size());
+  EXPECT_EQ(buffer.Size(), 5);
+  EXPECT_EQ(buffer.Head(), 2);
+  EXPECT_EQ(buffer.Tail(), 7);
+
+  c = buffer.Get(2, 5);
+  EXPECT_EQ(c.size(), 5);
+  EXPECT_EQ(c[0], 2);
+  EXPECT_EQ(c[1], 3);
+  EXPECT_EQ(c[2], 10);
+  EXPECT_EQ(c[3], 20);
+  EXPECT_EQ(c[4], 30);
+
+  c = buffer.Get(3, 4);
+  EXPECT_EQ(c.size(), 4);
+  EXPECT_EQ(c[0], 3);
+  EXPECT_EQ(c[1], 10);
+  EXPECT_EQ(c[2], 20);
+  EXPECT_EQ(c[3], 30);
+
+  c = buffer.Get(4, 3);
+  EXPECT_EQ(c.size(), 3);
+  EXPECT_EQ(c[0], 10);
+  EXPECT_EQ(c[1], 20);
+  EXPECT_EQ(c[2], 30);
+
+  buffer.Pop(4);
+  EXPECT_EQ(buffer.Size(), 1);
+  EXPECT_EQ(buffer.Head(), 6);
+  EXPECT_EQ(buffer.Tail(), 7);
+
+  c = buffer.Get(6, 1);
+  EXPECT_EQ(c.size(), 1);
+  EXPECT_EQ(c[0], 30);
+
+  a = {100, 200, 300, 400};
+  buffer.Push(a.data(), a.size());
+  EXPECT_EQ(buffer.Size(), 5);
+
+  EXPECT_EQ(buffer.Size(), 5);
+  EXPECT_EQ(buffer.Head(), 6);
+  EXPECT_EQ(buffer.Tail(), 11);
+
+  c = buffer.Get(6, 5);
+  EXPECT_EQ(c.size(), 5);
+  EXPECT_EQ(c[0], 30);
+  EXPECT_EQ(c[1], 100);
+  EXPECT_EQ(c[2], 200);
+  EXPECT_EQ(c[3], 300);
+  EXPECT_EQ(c[4], 400);
+
+  buffer.Pop(3);
+  EXPECT_EQ(buffer.Size(), 2);
+  EXPECT_EQ(buffer.Head(), 9);
+  EXPECT_EQ(buffer.Tail(), 11);
+
+  c = buffer.Get(10, 1);
+  EXPECT_EQ(c.size(), 1);
+  EXPECT_EQ(c[0], 400);
+
+  a = {1000, 2000, 3000};
+  buffer.Push(a.data(), a.size());
+
+  EXPECT_EQ(buffer.Size(), 5);
+  EXPECT_EQ(buffer.Head(), 9);
+  EXPECT_EQ(buffer.Tail(), 14);
+
+  buffer.Pop(1);
+
+  EXPECT_EQ(buffer.Size(), 4);
+  EXPECT_EQ(buffer.Head(), 10);
+  EXPECT_EQ(buffer.Tail(), 14);
+
+  a = {4000};
+
+  buffer.Push(a.data(), a.size());
+  EXPECT_EQ(buffer.Size(), 5);
+  EXPECT_EQ(buffer.Head(), 10);
+  EXPECT_EQ(buffer.Tail(), 15);
+
+  c = buffer.Get(13, 2);
+  EXPECT_EQ(c.size(), 2);
+  EXPECT_EQ(c[0], 3000);
+  EXPECT_EQ(c[1], 4000);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/circular-buffer.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/circular-buffer.h"
+
+#include <algorithm>
+
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+CircularBuffer::CircularBuffer(int32_t capacity) {
+  if (capacity <= 0) {
+    SHERPA_ONNX_LOGE("Please specify a positive capacity. Given: %d\n",
+                     capacity);
+    exit(-1);
+  }
+  buffer_.resize(capacity);
+}
+
+void CircularBuffer::Push(const float *p, int32_t n) {
+  int32_t capacity = buffer_.size();
+  int32_t size = Size();
+  if (n + size > capacity) {
+    SHERPA_ONNX_LOGE("Overflow! n: %d, size: %d, n+size: %d, capacity: %d", n,
+                     size, n + size, capacity);
+    exit(-1);
+  }
+
+  int32_t start = tail_ % capacity;
+
+  tail_ += n;
+
+  if (start + n < capacity) {
+    std::copy(p, p + n, buffer_.begin() + start);
+    return;
+  }
+
+  int32_t part1_size = capacity - start;
+
+  std::copy(p, p + part1_size, buffer_.begin() + start);
+
+  std::copy(p + part1_size, p + n, buffer_.begin());
+}
+
+std::vector<float> CircularBuffer::Get(int32_t start_index, int32_t n) const {
+  if (start_index < head_ || start_index >= tail_) {
+    SHERPA_ONNX_LOGE("Invalid start_index: %d. head_: %d, tail_: %d",
+                     start_index, head_, tail_);
+    return {};
+  }
+
+  int32_t size = Size();
+  if (n < 0 || n > size) {
+    SHERPA_ONNX_LOGE("Invalid n: %d. size: %d", n, size);
+    return {};
+  }
+
+  int32_t capacity = buffer_.size();
+
+  if (start_index - head_ + n > size) {
+    SHERPA_ONNX_LOGE("Invalid start_index: %d and n: %d. head_: %d, size: %d",
+                     start_index, n, head_, size);
+    return {};
+  }
+
+  int32_t start = start_index % capacity;
+
+  if (start + n < capacity) {
+    return {buffer_.begin() + start, buffer_.begin() + start + n};
+  }
+
+  std::vector<float> ans(n);
+
+  std::copy(buffer_.begin() + start, buffer_.end(), ans.begin());
+
+  int32_t part1_size = capacity - start;
+  int32_t part2_size = n - part1_size;
+  std::copy(buffer_.begin(), buffer_.begin() + part2_size,
+            ans.begin() + part1_size);
+
+  return ans;
+}
+
+void CircularBuffer::Pop(int32_t n) {
+  int32_t size = Size();
+  if (n < 0 || n > size) {
+    SHERPA_ONNX_LOGE("Invalid n: %d. size: %d", n, size);
+    return;
+  }
+
+  head_ += n;
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/circular-buffer.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_CIRCULAR_BUFFER_H_
+#define SHERPA_ONNX_CSRC_CIRCULAR_BUFFER_H_
+
+#include <cstdint>
+#include <vector>
+
+namespace sherpa_onnx {
+
+class CircularBuffer {
+ public:
+  // Capacity of this buffer. Should be large enough.
+  // If it is full, we just print a message and exit the program.
+  explicit CircularBuffer(int32_t capacity);
+
+  // Push an array
+  //
+  // @param p Pointer to the start address of the array
+  // @param n Number of elements in the array
+  //
+  // Note: If n + Size() > capacity, we print an error message and exit.
+  void Push(const float *p, int32_t n);
+
+  // @param start_index Should in the range [head_, tail_)
+  // @param n Number of elements to get
+  // @return Return a vector of size n containing the requested elements
+  std::vector<float> Get(int32_t start_index, int32_t n) const;
+
+  // Remove n elements from the buffer
+  //
+  // @param n Should be in the range [0, size_]
+  void Pop(int32_t n);
+
+  // Number of elements in the buffer.
+  int32_t Size() const { return tail_ - head_; }
+
+  // Current position of the head
+  int32_t Head() const { return head_; }
+
+  // Current position of the tail
+  int32_t Tail() const { return tail_; }
+
+  void Reset() {
+    head_ = 0;
+    tail_ = 0;
+  }
+
+ private:
+  std::vector<float> buffer_;
+
+  int32_t head_ = 0;  // linear index; always increasing; never wraps around
+  int32_t tail_ = 0;  // linear index, always increasing; never wraps around.
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_CIRCULAR_BUFFER_H_

@@ -76,4 +76,8 @@ Ort::SessionOptions GetSessionOptions(const OnlineLMConfig &config) {
   return GetSessionOptionsImpl(config.lm_num_threads, config.lm_provider);
   return GetSessionOptionsImpl(config.lm_num_threads, config.lm_provider);
 }
 }
 
 
+Ort::SessionOptions GetSessionOptions(const VadModelConfig &config) {
+  return GetSessionOptionsImpl(config.num_threads, config.provider);
+}
+
 }  // namespace sherpa_onnx
 }  // namespace sherpa_onnx

@@ -10,6 +10,7 @@
 #include "sherpa-onnx/csrc/offline-model-config.h"
 #include "sherpa-onnx/csrc/offline-model-config.h"
 #include "sherpa-onnx/csrc/online-lm-config.h"
 #include "sherpa-onnx/csrc/online-lm-config.h"
 #include "sherpa-onnx/csrc/online-model-config.h"
 #include "sherpa-onnx/csrc/online-model-config.h"
+#include "sherpa-onnx/csrc/vad-model-config.h"
 
 
 namespace sherpa_onnx {
 namespace sherpa_onnx {
 
 
@@ -20,6 +21,8 @@ Ort::SessionOptions GetSessionOptions(const OfflineModelConfig &config);
 Ort::SessionOptions GetSessionOptions(const OfflineLMConfig &config);
 Ort::SessionOptions GetSessionOptions(const OfflineLMConfig &config);
 
 
 Ort::SessionOptions GetSessionOptions(const OnlineLMConfig &config);
 Ort::SessionOptions GetSessionOptions(const OnlineLMConfig &config);
+
+Ort::SessionOptions GetSessionOptions(const VadModelConfig &config);
 }  // namespace sherpa_onnx
 }  // namespace sherpa_onnx
 
 
 #endif  // SHERPA_ONNX_CSRC_SESSION_H_
 #endif  // SHERPA_ONNX_CSRC_SESSION_H_

+// sherpa-onnx/csrc/sherpa-onnx-vad-microphone.cc
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#include <signal.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include <algorithm>
+#include <mutex>  // NOLINT
+
+#include "portaudio.h"  // NOLINT
+#include "sherpa-onnx/csrc/circular-buffer.h"
+#include "sherpa-onnx/csrc/microphone.h"
+#include "sherpa-onnx/csrc/voice-activity-detector.h"
+
+bool stop = false;
+std::mutex mutex;
+sherpa_onnx::CircularBuffer buffer(16000 * 60);
+
+static int32_t RecordCallback(const void *input_buffer,
+                              void * /*output_buffer*/,
+                              unsigned long frames_per_buffer,  // NOLINT
+                              const PaStreamCallbackTimeInfo * /*time_info*/,
+                              PaStreamCallbackFlags /*status_flags*/,
+                              void *user_data) {
+  std::lock_guard<std::mutex> lock(mutex);
+  buffer.Push(reinterpret_cast<const float *>(input_buffer), frames_per_buffer);
+
+  return stop ? paComplete : paContinue;
+}
+
+static void Handler(int32_t sig) {
+  stop = true;
+  fprintf(stderr, "\nCaught Ctrl + C. Exiting...\n");
+}
+
+int32_t main(int32_t argc, char *argv[]) {
+  signal(SIGINT, Handler);
+
+  const char *kUsageMessage = R"usage(
+This program shows how to use VAD in sherpa-onnx.
+
+  ./bin/sherpa-onnx-vad-microphone \
+    --silero-vad-model=/path/to/silero_vad.onnx \
+    --provider=cpu \
+    --num-threads=1
+
+Please download silero_vad.onnx from
+https://github.com/snakers4/silero-vad/blob/master/files/silero_vad.onnx
+
+For instance, use
+wget https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx
+)usage";
+
+  sherpa_onnx::ParseOptions po(kUsageMessage);
+  sherpa_onnx::VadModelConfig config;
+
+  config.Register(&po);
+  po.Read(argc, argv);
+  if (po.NumArgs() != 0) {
+    po.PrintUsage();
+    exit(EXIT_FAILURE);
+  }
+
+  fprintf(stderr, "%s\n", config.ToString().c_str());
+
+  if (!config.Validate()) {
+    fprintf(stderr, "Errors in config!\n");
+    return -1;
+  }
+
+  sherpa_onnx::Microphone mic;
+
+  PaDeviceIndex num_devices = Pa_GetDeviceCount();
+  fprintf(stderr, "Num devices: %d\n", num_devices);
+
+  PaStreamParameters param;
+
+  param.device = Pa_GetDefaultInputDevice();
+  if (param.device == paNoDevice) {
+    fprintf(stderr, "No default input device found\n");
+    exit(EXIT_FAILURE);
+  }
+  fprintf(stderr, "Use default device: %d\n", param.device);
+
+  const PaDeviceInfo *info = Pa_GetDeviceInfo(param.device);
+  fprintf(stderr, "  Name: %s\n", info->name);
+  fprintf(stderr, "  Max input channels: %d\n", info->maxInputChannels);
+
+  param.channelCount = 1;
+  param.sampleFormat = paFloat32;
+
+  param.suggestedLatency = info->defaultLowInputLatency;
+  param.hostApiSpecificStreamInfo = nullptr;
+  float sample_rate = 16000;
+
+  PaStream *stream;
+  PaError err =
+      Pa_OpenStream(&stream, &param, nullptr, /* &outputParameters, */
+                    sample_rate,
+                    0,          // frames per buffer
+                    paClipOff,  // we won't output out of range samples
+                                // so don't bother clipping them
+                    RecordCallback, &config.silero_vad.window_size);
+  if (err != paNoError) {
+    fprintf(stderr, "portaudio error: %s\n", Pa_GetErrorText(err));
+    exit(EXIT_FAILURE);
+  }
+
+  err = Pa_StartStream(stream);
+
+  auto vad = std::make_unique<sherpa_onnx::VoiceActivityDetector>(config);
+
+  fprintf(stderr, "Started\n");
+
+  if (err != paNoError) {
+    fprintf(stderr, "portaudio error: %s\n", Pa_GetErrorText(err));
+    exit(EXIT_FAILURE);
+  }
+
+  int32_t window_size = config.silero_vad.window_size;
+  bool printed = false;
+
+  while (!stop) {
+    {
+      std::lock_guard<std::mutex> lock(mutex);
+
+      while (buffer.Size() >= window_size) {
+        std::vector<float> samples = buffer.Get(buffer.Head(), window_size);
+        buffer.Pop(window_size);
+        vad->AcceptWaveform(samples.data(), samples.size());
+
+        if (vad->IsSpeechDetected() && !printed) {
+          printed = true;
+          fprintf(stderr, "\nDetected speech!\n");
+        }
+        if (!vad->IsSpeechDetected()) {
+          printed = false;
+        }
+
+        while (!vad->Empty()) {
+          float duration = vad->Front().samples.size() / sample_rate;
+          vad->Pop();
+          fprintf(stderr, "Duration: %.3f seconds\n", duration);
+        }
+      }
+    }
+    Pa_Sleep(100);  // sleep for 100ms
+  }
+
+  err = Pa_CloseStream(stream);
+  if (err != paNoError) {
+    fprintf(stderr, "portaudio error: %s\n", Pa_GetErrorText(err));
+    exit(EXIT_FAILURE);
+  }
+
+  return 0;
+}

+// sherpa-onnx/csrc/silero-vad-model-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/silero-vad-model-config.h"
+
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void SileroVadModelConfig::Register(ParseOptions *po) {
+  po->Register("silero-vad-model", &model, "Path to silero VAD ONNX model.");
+
+  po->Register("silero-vad-threshold", &threshold,
+               "Speech threshold. Silero VAD outputs speech probabilities for "
+               "each audio chunk, probabilities ABOVE this value are "
+               "considered as SPEECH. It is better to tune this parameter for "
+               "each dataset separately, but lazy "
+               "0.5 is pretty good for most datasets.");
+
+  po->Register(
+      "silero-vad-min-silence-duration", &min_silence_duration,
+      "In seconds.  In the end of each speech chunk wait for "
+      "--silero-vad-min-silence-duration seconds before separating it");
+
+  po->Register("silero-vad-min-speech-duration", &min_speech_duration,
+               "In seconds.  In the end of each silence chunk wait for "
+               "--silero-vad-min-speech-duration seconds before separating it");
+
+  po->Register(
+      "silero-vad-window-size", &window_size,
+      "In samples. Audio chunks of --silero-vad-window-size samples are fed "
+      "to the silero VAD model. WARNING! Silero VAD models were trained using "
+      "512, 1024, 1536 samples for 16000 sample rate and 256, 512, 768 samples "
+      "for 8000 sample rate. Values other than these may affect model "
+      "perfomance!");
+}
+
+bool SileroVadModelConfig::Validate() const {
+  if (model.empty()) {
+    SHERPA_ONNX_LOGE("Please provide --silero-vad-model");
+    return false;
+  }
+
+  if (!FileExists(model)) {
+    SHERPA_ONNX_LOGE("Silero vad model file %s does not exist", model.c_str());
+    return false;
+  }
+
+  if (threshold < 0.01) {
+    SHERPA_ONNX_LOGE(
+        "Please use a larger value for --silero-vad-threshold. Given: %f",
+        threshold);
+    return false;
+  }
+
+  if (threshold >= 1) {
+    SHERPA_ONNX_LOGE(
+        "Please use a smaller value for --silero-vad-threshold. Given: %f",
+        threshold);
+    return false;
+  }
+
+  return true;
+}
+
+std::string SileroVadModelConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "SilerVadModelConfig(";
+  os << "model=\"" << model << "\", ";
+  os << "threshold=" << threshold << ", ";
+  os << "min_silence_duration=" << min_silence_duration << ", ";
+  os << "min_speech_duration=" << min_speech_duration << ", ";
+  os << "window_size=" << window_size << ")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/silero-vad-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_SILERO_VAD_MODEL_CONFIG_H_
+#define SHERPA_ONNX_CSRC_SILERO_VAD_MODEL_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+
+namespace sherpa_onnx {
+
+struct SileroVadModelConfig {
+  std::string model;
+
+  // threshold to classify a segment as speech
+  //
+  // The predicted probability of a segment is larger than this
+  // value, then it is classified as speech.
+  float threshold = 0.5;
+
+  float min_silence_duration = 0.5;  // in seconds
+
+  float min_speech_duration = 0.25;  // in seconds
+
+  // 512, 1024, 1536 samples for 16000 Hz
+  // 256, 512, 768 samples for 800 Hz
+  int window_size = 512;  // in samples
+
+  SileroVadModelConfig() = default;
+
+  void Register(ParseOptions *po);
+
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_SILERO_VAD_MODEL_CONFIG_H_

+// sherpa-onnx/csrc/silero-vad-model.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/silero-vad-model.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+#include "sherpa-onnx/csrc/session.h"
+
+namespace sherpa_onnx {
+
+class SileroVadModel::Impl {
+ public:
+  explicit Impl(const VadModelConfig &config)
+      : config_(config),
+        env_(ORT_LOGGING_LEVEL_ERROR),
+        sess_opts_(GetSessionOptions(config)),
+        allocator_{} {
+    auto buf = ReadFile(config.silero_vad.model);
+    Init(buf.data(), buf.size());
+
+    sample_rate_ = config.sample_rate;
+    if (sample_rate_ != 16000) {
+      SHERPA_ONNX_LOGE("Expected sample rate 16000. Given: %d",
+                       config.sample_rate);
+      exit(-1);
+    }
+
+    min_silence_samples_ =
+        sample_rate_ * config_.silero_vad.min_silence_duration;
+
+    min_speech_samples_ = sample_rate_ * config_.silero_vad.min_speech_duration;
+  }
+
+  void Reset() {
+    // 2 - number of LSTM layer
+    // 1 - batch size
+    // 64 - hidden dim
+    std::array<int64_t, 3> shape{2, 1, 64};
+
+    Ort::Value h =
+        Ort::Value::CreateTensor<float>(allocator_, shape.data(), shape.size());
+
+    Ort::Value c =
+        Ort::Value::CreateTensor<float>(allocator_, shape.data(), shape.size());
+
+    Fill<float>(&h, 0);
+    Fill<float>(&c, 0);
+
+    states_.clear();
+
+    states_.reserve(2);
+    states_.push_back(std::move(h));
+    states_.push_back(std::move(c));
+
+    triggered_ = false;
+    current_sample_ = 0;
+    temp_start_ = 0;
+    temp_end_ = 0;
+  }
+
+  bool IsSpeech(const float *samples, int32_t n) {
+    if (n != config_.silero_vad.window_size) {
+      SHERPA_ONNX_LOGE("n: %d != window_size: %d", n,
+                       config_.silero_vad.window_size);
+      exit(-1);
+    }
+
+    auto memory_info =
+        Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
+
+    std::array<int64_t, 2> x_shape = {1, n};
+
+    Ort::Value x =
+        Ort::Value::CreateTensor(memory_info, const_cast<float *>(samples), n,
+                                 x_shape.data(), x_shape.size());
+
+    int64_t sr_shape = 1;
+    Ort::Value sr =
+        Ort::Value::CreateTensor(memory_info, &sample_rate_, 1, &sr_shape, 1);
+
+    std::array<Ort::Value, 4> inputs = {std::move(x), std::move(sr),
+                                        std::move(states_[0]),
+                                        std::move(states_[1])};
+
+    auto out =
+        sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
+                   output_names_ptr_.data(), output_names_ptr_.size());
+
+    states_[0] = std::move(out[1]);
+    states_[1] = std::move(out[2]);
+
+    float prob = out[0].GetTensorData<float>()[0];
+
+    float threshold = config_.silero_vad.threshold;
+
+    current_sample_ += config_.silero_vad.window_size;
+
+    if (prob > threshold && temp_end_ != 0) {
+      temp_end_ = 0;
+    }
+
+    if (prob > threshold && temp_start_ == 0) {
+      // start speaking, but we require that it must satisfy
+      // min_speech_duration
+      temp_start_ = current_sample_;
+      return false;
+    }
+
+    if (prob > threshold && temp_start_ != 0 && !triggered_) {
+      if (current_sample_ - temp_start_ < min_speech_samples_) {
+        return false;
+      }
+
+      triggered_ = true;
+
+      return true;
+    }
+
+    if ((prob < threshold) && !triggered_) {
+      // silence
+      temp_start_ = 0;
+      temp_end_ = 0;
+      return false;
+    }
+
+    if ((prob > threshold - 0.15) && triggered_) {
+      // speaking
+      return true;
+    }
+
+    if ((prob > threshold) && !triggered_) {
+      // start speaking
+      triggered_ = true;
+
+      return true;
+    }
+
+    if ((prob < threshold) && triggered_) {
+      // stop to speak
+      if (temp_end_ == 0) {
+        temp_end_ = current_sample_;
+      }
+
+      if (current_sample_ - temp_end_ < min_silence_samples_) {
+        // continue speaking
+        return true;
+      }
+      // stopped speaking
+      temp_start_ = 0;
+      temp_end_ = 0;
+      triggered_ = false;
+      return false;
+    }
+
+    return false;
+  }
+
+  int32_t WindowSize() const { return config_.silero_vad.window_size; }
+
+  int32_t MinSilenceDurationSamples() const { return min_silence_samples_; }
+
+  int32_t MinSpeechDurationSamples() const { return min_speech_samples_; }
+
+ private:
+  void Init(void *model_data, size_t model_data_length) {
+    sess_ = std::make_unique<Ort::Session>(env_, model_data, model_data_length,
+                                           sess_opts_);
+
+    GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
+    GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
+    Check();
+
+    Reset();
+  }
+
+  void Check() {
+    if (input_names_.size() != 4) {
+      SHERPA_ONNX_LOGE("Expect 4 inputs. Given: %d",
+                       static_cast<int32_t>(input_names_.size()));
+      exit(-1);
+    }
+
+    if (input_names_[0] != "input") {
+      SHERPA_ONNX_LOGE("Input[0]: %s. Expected: input",
+                       input_names_[0].c_str());
+      exit(-1);
+    }
+
+    if (input_names_[1] != "sr") {
+      SHERPA_ONNX_LOGE("Input[1]: %s. Expected: sr", input_names_[1].c_str());
+      exit(-1);
+    }
+
+    if (input_names_[2] != "h") {
+      SHERPA_ONNX_LOGE("Input[2]: %s. Expected: h", input_names_[2].c_str());
+      exit(-1);
+    }
+
+    if (input_names_[3] != "c") {
+      SHERPA_ONNX_LOGE("Input[3]: %s. Expected: c", input_names_[3].c_str());
+      exit(-1);
+    }
+
+    // Now for outputs
+    if (output_names_.size() != 3) {
+      SHERPA_ONNX_LOGE("Expect 3 outputs. Given: %d",
+                       static_cast<int32_t>(output_names_.size()));
+      exit(-1);
+    }
+
+    if (output_names_[0] != "output") {
+      SHERPA_ONNX_LOGE("Output[0]: %s. Expected: output",
+                       output_names_[0].c_str());
+      exit(-1);
+    }
+
+    if (output_names_[1] != "hn") {
+      SHERPA_ONNX_LOGE("Output[1]: %s. Expected: sr", output_names_[1].c_str());
+      exit(-1);
+    }
+
+    if (output_names_[2] != "cn") {
+      SHERPA_ONNX_LOGE("Output[2]: %s. Expected: sr", output_names_[2].c_str());
+      exit(-1);
+    }
+  }
+
+ private:
+  VadModelConfig config_;
+
+  Ort::Env env_;
+  Ort::SessionOptions sess_opts_;
+  Ort::AllocatorWithDefaultOptions allocator_;
+
+  std::unique_ptr<Ort::Session> sess_;
+
+  std::vector<std::string> input_names_;
+  std::vector<const char *> input_names_ptr_;
+
+  std::vector<std::string> output_names_;
+  std::vector<const char *> output_names_ptr_;
+
+  std::vector<Ort::Value> states_;
+  int64_t sample_rate_;
+  int32_t min_silence_samples_;
+  int32_t min_speech_samples_;
+
+  bool triggered_ = false;
+  int32_t current_sample_ = 0;
+  int32_t temp_start_ = 0;
+  int32_t temp_end_ = 0;
+};
+
+SileroVadModel::SileroVadModel(const VadModelConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+SileroVadModel::~SileroVadModel() = default;
+
+void SileroVadModel::Reset() { return impl_->Reset(); }
+
+bool SileroVadModel::IsSpeech(const float *samples, int32_t n) {
+  return impl_->IsSpeech(samples, n);
+}
+
+int32_t SileroVadModel::WindowSize() const { return impl_->WindowSize(); }
+
+int32_t SileroVadModel::MinSilenceDurationSamples() const {
+  return impl_->MinSilenceDurationSamples();
+}
+
+int32_t SileroVadModel::MinSpeechDurationSamples() const {
+  return impl_->MinSpeechDurationSamples();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/silero-vad-model.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_SILERO_VAD_MODEL_H_
+#define SHERPA_ONNX_CSRC_SILERO_VAD_MODEL_H_
+
+#include <memory>
+
+#include "sherpa-onnx/csrc/vad-model.h"
+
+namespace sherpa_onnx {
+
+class SileroVadModel : public VadModel {
+ public:
+  explicit SileroVadModel(const VadModelConfig &config);
+  ~SileroVadModel() override;
+
+  // reset the internal model states
+  void Reset() override;
+
+  /**
+   * @param samples Pointer to a 1-d array containing audio samples.
+   *                Each sample should be normalized to the range [-1, 1].
+   * @param n Number of samples.
+   *
+   * @return Return true if speech is detected. Return false otherwise.
+   */
+  bool IsSpeech(const float *samples, int32_t n) override;
+
+  int32_t WindowSize() const override;
+
+  int32_t MinSilenceDurationSamples() const override;
+  int32_t MinSpeechDurationSamples() const override;
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_SILERO_VAD_MODEL_H_

+// sherpa-onnx/csrc/vad-model-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/vad-model-config.h"
+
+#include <sstream>
+#include <string>
+
+namespace sherpa_onnx {
+
+void VadModelConfig::Register(ParseOptions *po) {
+  silero_vad.Register(po);
+
+  po->Register("vad-sample-rate", &sample_rate,
+               "Sample rate expected by the VAD model");
+
+  po->Register("vad-num-threads", &num_threads,
+               "Number of threads to run the VAD model");
+
+  po->Register("vad-provider", &provider,
+               "Specify a provider to run the VAD model. Supported values: "
+               "cpu, cuda, coreml");
+
+  po->Register("vad-debug", &debug,
+               "true to display debug information when loading vad models");
+}
+
+bool VadModelConfig::Validate() const { return silero_vad.Validate(); }
+
+std::string VadModelConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "VadModelConfig(";
+  os << "silero_vad=" << silero_vad.ToString() << ", ";
+  os << "sample_rate=" << sample_rate << ", ";
+  os << "num_threads=" << num_threads << ", ";
+  os << "provider=\"" << provider << "\", ";
+  os << "debug=" << (debug ? "True" : "False") << ")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/vad-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_VAD_MODEL_CONFIG_H_
+#define SHERPA_ONNX_CSRC_VAD_MODEL_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+#include "sherpa-onnx/csrc/silero-vad-model-config.h"
+
+namespace sherpa_onnx {
+
+struct VadModelConfig {
+  SileroVadModelConfig silero_vad;
+
+  int32_t sample_rate = 16000;
+  int32_t num_threads = 1;
+  std::string provider = "cpu";
+
+  // true to show debug information when loading models
+  bool debug = false;
+
+  VadModelConfig() = default;
+
+  VadModelConfig(const SileroVadModelConfig &silero_vad, int32_t sample_rate,
+                 int32_t num_threads, const std::string &provider, bool debug)
+      : silero_vad(silero_vad),
+        sample_rate(sample_rate),
+        num_threads(num_threads),
+        provider(provider),
+        debug(debug) {}
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_VAD_MODEL_CONFIG_H_

+// sherpa-onnx/csrc/vad-model.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/vad-model.h"
+
+#include "sherpa-onnx/csrc/silero-vad-model.h"
+
+namespace sherpa_onnx {
+
+std::unique_ptr<VadModel> VadModel::Create(const VadModelConfig &config) {
+  // TODO(fangjun): Support other VAD models.
+  return std::make_unique<SileroVadModel>(config);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/vad-model.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_VAD_MODEL_H_
+#define SHERPA_ONNX_CSRC_VAD_MODEL_H_
+
+#include <memory>
+
+#include "sherpa-onnx/csrc/vad-model-config.h"
+
+namespace sherpa_onnx {
+
+class VadModel {
+ public:
+  virtual ~VadModel() = default;
+
+  static std::unique_ptr<VadModel> Create(const VadModelConfig &config);
+
+  // reset the internal model states
+  virtual void Reset() = 0;
+
+  /**
+   * @param samples Pointer to a 1-d array containing audio samples.
+   *                Each sample should be normalized to the range [-1, 1].
+   * @param n Number of samples. Should be equal to WindowSize()
+   *
+   * @return Return true if speech is detected. Return false otherwise.
+   */
+  virtual bool IsSpeech(const float *samples, int32_t n) = 0;
+
+  virtual int32_t WindowSize() const = 0;
+
+  virtual int32_t MinSilenceDurationSamples() const = 0;
+  virtual int32_t MinSpeechDurationSamples() const = 0;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_VAD_MODEL_H_

+// sherpa-onnx/csrc/voice-activity-detector.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/voice-activity-detector.h"
+
+#include <queue>
+#include <utility>
+
+#include "sherpa-onnx/csrc/circular-buffer.h"
+#include "sherpa-onnx/csrc/vad-model.h"
+
+namespace sherpa_onnx {
+
+class VoiceActivityDetector::Impl {
+ public:
+  explicit Impl(const VadModelConfig &config, float buffer_size_in_seconds = 60)
+      : model_(VadModel::Create(config)),
+        config_(config),
+        buffer_(buffer_size_in_seconds * config.sample_rate) {}
+
+  void AcceptWaveform(const float *samples, int32_t n) {
+    buffer_.Push(samples, n);
+
+    bool is_speech = model_->IsSpeech(samples, n);
+    if (is_speech) {
+      if (start_ == -1) {
+        // beginning of speech
+        start_ = buffer_.Tail() - 2 * model_->WindowSize() -
+                 model_->MinSpeechDurationSamples();
+      }
+    } else {
+      // non-speech
+      if (start_ != -1) {
+        // end of speech, save the speech segment
+        int32_t end = buffer_.Tail() - model_->MinSilenceDurationSamples();
+
+        std::vector<float> samples = buffer_.Get(start_, end - start_);
+        SpeechSegment segment;
+
+        segment.start = start_;
+        segment.samples = std::move(samples);
+
+        segments_.push(std::move(segment));
+
+        buffer_.Pop(end - buffer_.Head());
+      }
+
+      start_ = -1;
+    }
+  }
+
+  bool Empty() const { return segments_.empty(); }
+
+  void Pop() { segments_.pop(); }
+
+  const SpeechSegment &Front() const { return segments_.front(); }
+
+  void Reset() {
+    std::queue<SpeechSegment>().swap(segments_);
+
+    model_->Reset();
+    buffer_.Reset();
+
+    start_ = -1;
+  }
+
+  bool IsSpeechDetected() const { return start_ != -1; }
+
+ private:
+  std::queue<SpeechSegment> segments_;
+
+  std::unique_ptr<VadModel> model_;
+  VadModelConfig config_;
+  CircularBuffer buffer_;
+
+  int32_t start_ = -1;
+};
+
+VoiceActivityDetector::VoiceActivityDetector(
+    const VadModelConfig &config, float buffer_size_in_seconds /*= 60*/)
+    : impl_(std::make_unique<Impl>(config, buffer_size_in_seconds)) {}
+
+VoiceActivityDetector::~VoiceActivityDetector() = default;
+
+void VoiceActivityDetector::AcceptWaveform(const float *samples, int32_t n) {
+  impl_->AcceptWaveform(samples, n);
+}
+
+bool VoiceActivityDetector::Empty() const { return impl_->Empty(); }
+
+void VoiceActivityDetector::Pop() { impl_->Pop(); }
+
+const SpeechSegment &VoiceActivityDetector::Front() const {
+  return impl_->Front();
+}
+
+void VoiceActivityDetector::Reset() { impl_->Reset(); }
+
+bool VoiceActivityDetector::IsSpeechDetected() const {
+  return impl_->IsSpeechDetected();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/voice-activity-detector.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_VOICE_ACTIVITY_DETECTOR_H_
+#define SHERPA_ONNX_CSRC_VOICE_ACTIVITY_DETECTOR_H_
+
+#include <memory>
+#include <vector>
+
+#include "sherpa-onnx/csrc/vad-model-config.h"
+
+namespace sherpa_onnx {
+
+struct SpeechSegment {
+  int32_t start;  // in samples
+  std::vector<float> samples;
+};
+
+class VoiceActivityDetector {
+ public:
+  explicit VoiceActivityDetector(const VadModelConfig &config,
+                                 float buffer_size_in_seconds = 60);
+  ~VoiceActivityDetector();
+
+  void AcceptWaveform(const float *samples, int32_t n);
+  bool Empty() const;
+  void Pop();
+  const SpeechSegment &Front() const;
+
+  bool IsSpeechDetected() const;
+
+  void Reset();
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_VOICE_ACTIVITY_DETECTOR_H_

 include_directories(${CMAKE_SOURCE_DIR})
 include_directories(${CMAKE_SOURCE_DIR})
 
 
 pybind11_add_module(_sherpa_onnx
 pybind11_add_module(_sherpa_onnx
+  circular-buffer.cc
   display.cc
   display.cc
   endpoint.cc
   endpoint.cc
   features.cc
   features.cc
@@ -20,6 +21,10 @@ pybind11_add_module(_sherpa_onnx
   online-stream.cc
   online-stream.cc
   online-transducer-model-config.cc
   online-transducer-model-config.cc
   sherpa-onnx.cc
   sherpa-onnx.cc
+  silero-vad-model-config.cc
+  vad-model-config.cc
+  vad-model.cc
+  voice-activity-detector.cc
 )
 )
 
 
 if(APPLE)
 if(APPLE)

+// sherpa-onnx/python/csrc/circular-buffer.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/circular-buffer.h"
+
+#include <vector>
+
+#include "sherpa-onnx/csrc/circular-buffer.h"
+
+namespace sherpa_onnx {
+
+void PybindCircularBuffer(py::module *m) {
+  using PyClass = CircularBuffer;
+  py::class_<PyClass>(*m, "CircularBuffer")
+      .def(py::init<int32_t>(), py::arg("capacity"))
+      .def(
+          "push",
+          [](PyClass &self, const std::vector<float> &samples) {
+            self.Push(samples.data(), samples.size());
+          },
+          py::arg("samples"))
+      .def("get", &PyClass::Get, py::arg("start_index"), py::arg("n"))
+      .def("pop", &PyClass::Pop, py::arg("n"))
+      .def("reset", &PyClass::Reset)
+      .def_property_readonly("size", &PyClass::Size)
+      .def_property_readonly("head", &PyClass::Head)
+      .def_property_readonly("tail", &PyClass::Tail);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/circular-buffer.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_CIRCULAR_BUFFER_H_
+#define SHERPA_ONNX_PYTHON_CSRC_CIRCULAR_BUFFER_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindCircularBuffer(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_CIRCULAR_BUFFER_H_

@@ -4,6 +4,7 @@
 
 
 #include "sherpa-onnx/python/csrc/sherpa-onnx.h"
 #include "sherpa-onnx/python/csrc/sherpa-onnx.h"
 
 
+#include "sherpa-onnx/python/csrc/circular-buffer.h"
 #include "sherpa-onnx/python/csrc/display.h"
 #include "sherpa-onnx/python/csrc/display.h"
 #include "sherpa-onnx/python/csrc/endpoint.h"
 #include "sherpa-onnx/python/csrc/endpoint.h"
 #include "sherpa-onnx/python/csrc/features.h"
 #include "sherpa-onnx/python/csrc/features.h"
@@ -15,6 +16,9 @@
 #include "sherpa-onnx/python/csrc/online-model-config.h"
 #include "sherpa-onnx/python/csrc/online-model-config.h"
 #include "sherpa-onnx/python/csrc/online-recognizer.h"
 #include "sherpa-onnx/python/csrc/online-recognizer.h"
 #include "sherpa-onnx/python/csrc/online-stream.h"
 #include "sherpa-onnx/python/csrc/online-stream.h"
+#include "sherpa-onnx/python/csrc/vad-model-config.h"
+#include "sherpa-onnx/python/csrc/vad-model.h"
+#include "sherpa-onnx/python/csrc/voice-activity-detector.h"
 
 
 namespace sherpa_onnx {
 namespace sherpa_onnx {
 
 
@@ -34,6 +38,11 @@ PYBIND11_MODULE(_sherpa_onnx, m) {
   PybindOfflineLMConfig(&m);
   PybindOfflineLMConfig(&m);
   PybindOfflineModelConfig(&m);
   PybindOfflineModelConfig(&m);
   PybindOfflineRecognizer(&m);
   PybindOfflineRecognizer(&m);
+
+  PybindVadModelConfig(&m);
+  PybindVadModel(&m);
+  PybindCircularBuffer(&m);
+  PybindVoiceActivityDetector(&m);
 }
 }
 
 
 }  // namespace sherpa_onnx
 }  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/silero-vad-model-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/silero-vad-model-config.h"
+
+#include <memory>
+#include <string>
+
+#include "sherpa-onnx/csrc/silero-vad-model-config.h"
+
+namespace sherpa_onnx {
+
+void PybindSileroVadModelConfig(py::module *m) {
+  using PyClass = SileroVadModelConfig;
+  py::class_<PyClass>(*m, "SileroVadModelConfig")
+      .def(py::init<>())
+      .def(py::init([](const std::string &model, float threshold,
+                       float min_silence_duration, float min_speech_duration,
+                       int32_t window_size) -> std::unique_ptr<PyClass> {
+             auto ans = std::make_unique<PyClass>();
+
+             ans->model = model;
+             ans->threshold = threshold;
+             ans->min_silence_duration = min_silence_duration;
+             ans->min_speech_duration = min_speech_duration;
+             ans->window_size = window_size;
+
+             return ans;
+           }),
+           py::arg("model"), py::arg("threshold") = 0.5,
+           py::arg("min_silence_duration") = 0.5,
+           py::arg("min_speech_duration") = 0.25, py::arg("window_size") = 512)
+      .def_readwrite("model", &PyClass::model)
+      .def_readwrite("threshold", &PyClass::threshold)
+      .def_readwrite("min_silence_duration", &PyClass::min_silence_duration)
+      .def_readwrite("min_speech_duration", &PyClass::min_speech_duration)
+      .def_readwrite("window_size", &PyClass::window_size)
+      .def("__str__", &PyClass::ToString)
+      .def("validate", &PyClass::Validate);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/silero-vad-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_SILERO_VAD_MODEL_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_SILERO_VAD_MODEL_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindSileroVadModelConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_SILERO_VAD_MODEL_CONFIG_H_

+// sherpa-onnx/python/csrc/vad-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/vad-model-config.h"
+
+#include <string>
+
+#include "sherpa-onnx/csrc/vad-model-config.h"
+#include "sherpa-onnx/python/csrc/silero-vad-model-config.h"
+
+namespace sherpa_onnx {
+
+void PybindVadModelConfig(py::module *m) {
+  PybindSileroVadModelConfig(m);
+
+  using PyClass = VadModelConfig;
+  py::class_<PyClass>(*m, "VadModelConfig")
+      .def(py::init<>())
+      .def(py::init<const SileroVadModelConfig &, int32_t, int32_t,
+                    const std::string &, bool>(),
+           py::arg("silero_vad"), py::arg("sample_rate") = 16000,
+           py::arg("num_threads") = 1, py::arg("provider") = "cpu",
+           py::arg("debug") = false)
+      .def_readwrite("silero_vad", &PyClass::silero_vad)
+      .def_readwrite("sample_rate", &PyClass::sample_rate)
+      .def_readwrite("num_threads", &PyClass::num_threads)
+      .def_readwrite("provider", &PyClass::provider)
+      .def_readwrite("debug", &PyClass::debug)
+      .def("__str__", &PyClass::ToString)
+      .def("validate", &PyClass::Validate);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/vad-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_VAD_MODEL_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_VAD_MODEL_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindVadModelConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_VAD_MODEL_CONFIG_H_

+// sherpa-onnx/python/csrc/vad-model.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/vad-model.h"
+
+#include <vector>
+
+#include "sherpa-onnx/csrc/vad-model.h"
+
+namespace sherpa_onnx {
+
+void PybindVadModel(py::module *m) {
+  using PyClass = VadModel;
+  py::class_<PyClass>(*m, "VadModel")
+      .def_static("create", &PyClass::Create, py::arg("config"))
+      .def("reset", &PyClass::Reset)
+      .def(
+          "is_speech",
+          [](PyClass &self, const std::vector<float> &samples) -> bool {
+            return self.IsSpeech(samples.data(), samples.size());
+          },
+          py::arg("samples"))
+      .def("window_size", &PyClass::WindowSize)
+      .def("min_silence_duration_samples", &PyClass::MinSilenceDurationSamples)
+      .def("min_speech_duration_samples", &PyClass::MinSpeechDurationSamples);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/vad-model.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_VAD_MODEL_H_
+#define SHERPA_ONNX_PYTHON_CSRC_VAD_MODEL_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindVadModel(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_VAD_MODEL_H_

+// sherpa-onnx/python/csrc/voice-activity-detector.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/voice-activity-detector.h"
+
+#include <vector>
+
+#include "sherpa-onnx/csrc/voice-activity-detector.h"
+
+namespace sherpa_onnx {
+
+void PybindSpeechSegment(py::module *m) {
+  using PyClass = SpeechSegment;
+  py::class_<PyClass>(*m, "SpeechSegment")
+      .def_property_readonly("start",
+                             [](const PyClass &self) { return self.start; })
+      .def_property_readonly("samples",
+                             [](const PyClass &self) { return self.samples; });
+}
+
+void PybindVoiceActivityDetector(py::module *m) {
+  PybindSpeechSegment(m);
+  using PyClass = VoiceActivityDetector;
+  py::class_<PyClass>(*m, "VoiceActivityDetector")
+      .def(py::init<const VadModelConfig &, float>(), py::arg("config"),
+           py::arg("buffer_size_in_seconds") = 60)
+      .def(
+          "accept_waveform",
+          [](PyClass &self, const std::vector<float> &samples) {
+            self.AcceptWaveform(samples.data(), samples.size());
+          },
+          py::arg("samples"))
+      .def("empty", &PyClass::Empty)
+      .def("pop", &PyClass::Pop)
+      .def("is_speech_detected", &PyClass::IsSpeechDetected)
+      .def("reset", &PyClass::Reset)
+      .def_property_readonly("front", &PyClass::Front);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/voice-activity-detector.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_VOICE_ACTIVITY_DETECTOR_H_
+#define SHERPA_ONNX_PYTHON_CSRC_VOICE_ACTIVITY_DETECTOR_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindVoiceActivityDetector(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_VOICE_ACTIVITY_DETECTOR_H_

 from typing import Dict, List, Optional
 from typing import Dict, List, Optional
 
 
-from _sherpa_onnx import Display, OfflineStream, OnlineStream
+from _sherpa_onnx import (
+    CircularBuffer,
+    Display,
+    OfflineStream,
+    OnlineStream,
+    SileroVadModelConfig,
+    SpeechSegment,
+    VadModel,
+    VadModelConfig,
+    VoiceActivityDetector,
+)
 
 
 from .offline_recognizer import OfflineRecognizer
 from .offline_recognizer import OfflineRecognizer
 from .online_recognizer import OnlineRecognizer
 from .online_recognizer import OnlineRecognizer