snapshot of https://github.com/ggml-org/whisper.cpp.git @ da54572229bcf64ba367d96c7ef15770376c4280 (2026-09-15)

examples/addon.node/.gitignore

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+.idea
+node_modules
+build

examples/addon.node/CMakeLists.txt

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+set(TARGET addon.node)
+
+# Base settings
+#==================================================================
+# env var supported by cmake-js
+add_definitions(-DNAPI_VERSION=4)
+include_directories(${CMAKE_JS_INC})
+#==================================================================
+
+add_library(${TARGET} SHARED ${CMAKE_JS_SRC} addon.cpp)
+set_target_properties(${TARGET} PROPERTIES
+    PREFIX ""
+    OUTPUT_NAME "addon"
+    SUFFIX ".node"
+    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/Release"
+)
+
+# Include N-API wrappers
+#==================================================================
+execute_process(COMMAND node -p "require('node-addon-api').include"
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE NODE_ADDON_API_DIR
+        )
+string(REPLACE "\n" "" NODE_ADDON_API_DIR ${NODE_ADDON_API_DIR})
+string(REPLACE "\"" "" NODE_ADDON_API_DIR ${NODE_ADDON_API_DIR})
+target_include_directories(${TARGET} PRIVATE ${NODE_ADDON_API_DIR})
+#==================================================================
+
+target_link_libraries(${TARGET} ${CMAKE_JS_LIB} common whisper ${CMAKE_THREAD_LIBS_INIT})
+
+if(MSVC AND CMAKE_JS_NODELIB_DEF AND CMAKE_JS_NODELIB_TARGET)
+    # Generate node.lib
+    execute_process(COMMAND ${CMAKE_AR} /def:${CMAKE_JS_NODELIB_DEF} /out:${CMAKE_JS_NODELIB_TARGET} ${CMAKE_STATIC_LINKER_FLAGS})
+endif()

examples/addon.node/README.md

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+# whisper.cpp Node.js addon
+
+This is an addon demo that can **perform whisper model reasoning in `node` and `electron` environments**, based on [cmake-js](https://github.com/cmake-js/cmake-js).
+It can be used as a reference for using the whisper.cpp project in other node projects.
+
+This addon now supports **Voice Activity Detection (VAD)** for improved transcription performance.
+
+## Install
+
+```shell
+npm install
+```
+
+## Compile
+
+Make sure it is in the project root directory and compiled with make-js.
+
+```shell
+npx cmake-js compile -T addon.node -B Release
+```
+
+For Electron addon and cmake-js options, you can see [cmake-js](https://github.com/cmake-js/cmake-js) and make very few configuration changes.
+
+> Such as appointing special cmake path:
+> ```shell
+> npx cmake-js compile -c 'xxx/cmake' -T addon.node -B Release
+> ```
+
+## Run
+
+### Basic Usage
+
+```shell
+cd examples/addon.node
+
+node index.js --language='language' --model='model-path' --fname_inp='file-path'
+```
+
+### VAD (Voice Activity Detection) Usage
+
+Run the VAD example with performance comparison:
+
+```shell
+node vad-example.js
+```
+
+## Voice Activity Detection (VAD) Support
+
+VAD can significantly improve transcription performance by only processing speech segments, which is especially beneficial for audio files with long periods of silence.
+
+### VAD Model Setup
+
+Before using VAD, download a VAD model:
+
+```shell
+# From the whisper.cpp root directory
+./models/download-vad-model.sh silero-v6.2.0
+```
+
+### VAD Parameters
+
+All VAD parameters are optional and have sensible defaults:
+
+- `vad`: Enable VAD (default: false)
+- `vad_model`: Path to VAD model file (required when VAD enabled)
+- `vad_threshold`: Speech detection threshold 0.0-1.0 (default: 0.5)
+- `vad_min_speech_duration_ms`: Min speech duration in ms (default: 250)
+- `vad_min_silence_duration_ms`: Min silence duration in ms (default: 100)
+- `vad_max_speech_duration_s`: Max speech duration in seconds (default: FLT_MAX)
+- `vad_speech_pad_ms`: Speech padding in ms (default: 30)
+- `vad_samples_overlap`: Sample overlap 0.0-1.0 (default: 0.1)
+
+### JavaScript API Example
+
+```javascript
+const path = require("path");
+const { whisper } = require(path.join(__dirname, "../../build/Release/addon.node"));
+const { promisify } = require("util");
+
+const whisperAsync = promisify(whisper);
+
+// With VAD enabled
+const vadParams = {
+  language: "en",
+  model: path.join(__dirname, "../../models/ggml-base.en.bin"),
+  fname_inp: path.join(__dirname, "../../samples/jfk.wav"),
+  vad: true,
+  vad_model: path.join(__dirname, "../../models/ggml-silero-v6.2.0.bin"),
+  vad_threshold: 0.5,
+  progress_callback: (progress) => console.log(`Progress: ${progress}%`)
+};
+
+whisperAsync(vadParams).then(result => console.log(result));
+```
+
+## Supported Parameters
+
+Both traditional whisper.cpp parameters and new VAD parameters are supported:
+
+- `language`: Language code (e.g., "en", "es", "fr")
+- `model`: Path to whisper model file
+- `fname_inp`: Path to input audio file
+- `use_gpu`: Enable GPU acceleration (default: true)
+- `flash_attn`: Enable flash attention (default: false)
+- `no_prints`: Disable console output (default: false)
+- `no_timestamps`: Disable timestamps (default: false)
+- `detect_language`: Auto-detect language (default: false)
+- `audio_ctx`: Audio context size (default: 0)
+- `max_len`: Maximum segment length (default: 0)
+- `max_context`: Maximum context size (default: -1)
+- `prompt`: Initial prompt for decoder
+- `comma_in_time`: Use comma in timestamps (default: true)
+- `print_progress`: Print progress info (default: false)
+- `progress_callback`: Progress callback function
+- VAD parameters (see above section)

examples/addon.node/__test__/whisper.spec.js

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+const { join } = require('path');
+const { whisper } = require('../../../build/Release/addon.node');
+const { promisify } = require('util');
+
+const whisperAsync = promisify(whisper);
+
+const commonParams = {
+  language: 'en',
+  model: join(__dirname, '../../../models/ggml-base.en.bin'),
+  fname_inp: join(__dirname, '../../../samples/jfk.wav'),
+  use_gpu: true,
+  flash_attn: false,
+  no_prints: true,
+  no_timestamps: false,
+  detect_language: false,
+  audio_ctx: 0,
+  max_len: 0
+};
+
+describe('Whisper.cpp Node.js addon with VAD support', () => {
+  test('Basic whisper transcription without VAD', async () => {
+    const params = {
+      ...commonParams,
+      vad: false
+    };
+
+    const result = await whisperAsync(params);
+    
+    expect(typeof result).toBe('object');
+    expect(Array.isArray(result.transcription)).toBe(true);
+    expect(result.transcription.length).toBeGreaterThan(0);
+    
+    // Check that we got some transcription text
+    const text = result.transcription.map(segment => segment[2]).join(' ');
+    expect(text.length).toBeGreaterThan(0);
+    expect(text.toLowerCase()).toContain('ask not');
+  }, 30000);
+
+  test('VAD parameters validation', async () => {
+    // Test with invalid VAD model - should return empty transcription
+    const invalidParams = {
+      ...commonParams,
+      vad: true,
+      vad_model: 'non-existent-model.bin',
+      vad_threshold: 0.5
+    };
+
+    // This should handle the error gracefully and return empty transcription
+    const result = await whisperAsync(invalidParams);
+    expect(typeof result).toBe('object');
+    expect(Array.isArray(result.transcription)).toBe(true);
+    // When VAD model doesn't exist, it should return empty transcription
+    expect(result.transcription.length).toBe(0);
+  }, 10000);
+
+  test('VAD parameter parsing', async () => {
+    // Test that VAD parameters are properly parsed (even if VAD model doesn't exist)
+    const vadParams = {
+      ...commonParams,
+      vad: false, // Disabled so no model required
+      vad_threshold: 0.7,
+      vad_min_speech_duration_ms: 300,
+      vad_min_silence_duration_ms: 150,
+      vad_max_speech_duration_s: 45.0,
+      vad_speech_pad_ms: 50,
+      vad_samples_overlap: 0.15
+    };
+
+    const result = await whisperAsync(vadParams);
+    
+    expect(typeof result).toBe('object');
+    expect(Array.isArray(result.transcription)).toBe(true);
+  }, 30000);
+
+  test('Progress callback with VAD disabled', async () => {
+    let progressCalled = false;
+    let lastProgress = 0;
+
+    const params = {
+      ...commonParams,
+      vad: false,
+      progress_callback: (progress) => {
+        progressCalled = true;
+        lastProgress = progress;
+        expect(progress).toBeGreaterThanOrEqual(0);
+        expect(progress).toBeLessThanOrEqual(100);
+      }
+    };
+
+    const result = await whisperAsync(params);
+    
+    expect(progressCalled).toBe(true);
+    expect(lastProgress).toBe(100);
+    expect(typeof result).toBe('object');
+  }, 30000);
+
+  test('Language detection without VAD', async () => {
+    const params = {
+      ...commonParams,
+      vad: false,
+      detect_language: true,
+      language: 'auto'
+    };
+
+    const result = await whisperAsync(params);
+    
+    expect(typeof result).toBe('object');
+    expect(typeof result.language).toBe('string');
+    expect(result.language.length).toBeGreaterThan(0);
+  }, 30000);
+
+  test('Basic transcription with all VAD parameters set', async () => {
+    // Test with VAD disabled but all parameters set to ensure no crashes
+    const params = {
+      ...commonParams,
+      vad: false, // Disabled so it works without VAD model
+      vad_model: '', // Empty model path
+      vad_threshold: 0.6,
+      vad_min_speech_duration_ms: 200,
+      vad_min_silence_duration_ms: 80,
+      vad_max_speech_duration_s: 25.0,
+      vad_speech_pad_ms: 40,
+      vad_samples_overlap: 0.08
+    };
+
+    const result = await whisperAsync(params);
+    
+    expect(typeof result).toBe('object');
+    expect(Array.isArray(result.transcription)).toBe(true);
+    expect(result.transcription.length).toBeGreaterThan(0);
+  }, 30000);
+});
+

examples/addon.node/addon.cpp

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+#include "napi.h"
+#include "common.h"
+#include "common-whisper.h"
+
+#include "whisper.h"
+
+#include <string>
+#include <thread>
+#include <vector>
+#include <cmath>
+#include <cstdint>
+#include <cfloat>
+
+struct whisper_params {
+    int32_t n_threads    = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t n_processors = 1;
+    int32_t offset_t_ms  = 0;
+    int32_t offset_n     = 0;
+    int32_t duration_ms  = 0;
+    int32_t max_context  = -1;
+    int32_t max_len      = 0;
+    int32_t best_of      = 5;
+    int32_t beam_size    = -1;
+    int32_t audio_ctx    = 0;
+
+    float word_thold    = 0.01f;
+    float entropy_thold = 2.4f;
+    float logprob_thold = -1.0f;
+
+    bool translate      = false;
+    bool diarize        = false;
+    bool output_txt     = false;
+    bool output_vtt     = false;
+    bool output_srt     = false;
+    bool output_wts     = false;
+    bool output_csv     = false;
+    bool print_special  = false;
+    bool print_colors   = false;
+    bool print_progress = false;
+    bool no_timestamps  = false;
+    bool no_prints      = false;
+    bool detect_language= false;
+    bool use_gpu        = true;
+    bool flash_attn     = false;
+    bool comma_in_time  = true;
+
+    std::string language = "en";
+    std::string prompt;
+    std::string model    = "../../ggml-large.bin";
+
+    std::vector<std::string> fname_inp = {};
+    std::vector<std::string> fname_out = {};
+
+    std::vector<float> pcmf32 = {}; // mono-channel F32 PCM
+
+    // Voice Activity Detection (VAD) parameters
+    bool        vad           = false;
+    std::string vad_model     = "";
+    float       vad_threshold = 0.5f;
+    int         vad_min_speech_duration_ms = 250;
+    int         vad_min_silence_duration_ms = 100;
+    float       vad_max_speech_duration_s = FLT_MAX;
+    int         vad_speech_pad_ms = 30;
+    float       vad_samples_overlap = 0.1f;
+};
+
+struct whisper_print_user_data {
+    const whisper_params * params;
+
+    const std::vector<std::vector<float>> * pcmf32s;
+};
+
+void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data) {
+    const auto & params  = *((whisper_print_user_data *) user_data)->params;
+    const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
+
+    const int n_segments = whisper_full_n_segments(ctx);
+
+    std::string speaker = "";
+
+    int64_t t0;
+    int64_t t1;
+
+    // print the last n_new segments
+    const int s0 = n_segments - n_new;
+
+    if (s0 == 0) {
+        printf("\n");
+    }
+
+    for (int i = s0; i < n_segments; i++) {
+        if (!params.no_timestamps || params.diarize) {
+            t0 = whisper_full_get_segment_t0(ctx, i);
+            t1 = whisper_full_get_segment_t1(ctx, i);
+        }
+
+        if (!params.no_timestamps && !params.no_prints) {
+            printf("[%s --> %s]  ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
+        }
+
+        if (params.diarize && pcmf32s.size() == 2) {
+            const int64_t n_samples = pcmf32s[0].size();
+
+            const int64_t is0 = timestamp_to_sample(t0, n_samples, WHISPER_SAMPLE_RATE);
+            const int64_t is1 = timestamp_to_sample(t1, n_samples, WHISPER_SAMPLE_RATE);
+
+            double energy0 = 0.0f;
+            double energy1 = 0.0f;
+
+            for (int64_t j = is0; j < is1; j++) {
+                energy0 += fabs(pcmf32s[0][j]);
+                energy1 += fabs(pcmf32s[1][j]);
+            }
+
+            if (energy0 > 1.1*energy1) {
+                speaker = "(speaker 0)";
+            } else if (energy1 > 1.1*energy0) {
+                speaker = "(speaker 1)";
+            } else {
+                speaker = "(speaker ?)";
+            }
+
+            //printf("is0 = %lld, is1 = %lld, energy0 = %f, energy1 = %f, %s\n", is0, is1, energy0, energy1, speaker.c_str());
+        }
+
+        // colorful print bug
+        //
+        if (!params.no_prints) {
+            const char * text = whisper_full_get_segment_text(ctx, i);
+            printf("%s%s", speaker.c_str(), text);
+        }
+
+
+        // with timestamps or speakers: each segment on new line
+        if ((!params.no_timestamps || params.diarize) && !params.no_prints) {
+            printf("\n");
+        }
+
+        fflush(stdout);
+    }
+}
+
+void cb_log_disable(enum ggml_log_level, const char *, void *) {}
+
+struct whisper_result {
+    std::vector<std::vector<std::string>> segments;
+    std::string language;
+};
+
+class ProgressWorker : public Napi::AsyncWorker {
+ public:
+    ProgressWorker(Napi::Function& callback, whisper_params params, Napi::Function progress_callback, Napi::Env env)
+        : Napi::AsyncWorker(callback), params(params), env(env) {
+        // Create thread-safe function
+        if (!progress_callback.IsEmpty()) {
+            tsfn = Napi::ThreadSafeFunction::New(
+                env,
+                progress_callback,
+                "Progress Callback",
+                0,
+                1
+            );
+        }
+    }
+
+    ~ProgressWorker() {
+        if (tsfn) {
+            // Make sure to release the thread-safe function on destruction
+            tsfn.Release();
+        }
+    }
+
+    void Execute() override {
+        // Use custom run function with progress callback support
+        run_with_progress(params, result);
+    }
+
+    void OnOK() override {
+        Napi::HandleScope scope(Env());
+
+        if (params.detect_language) {
+            Napi::Object resultObj = Napi::Object::New(Env());
+            resultObj.Set("language", Napi::String::New(Env(), result.language));
+            Callback().Call({Env().Null(), resultObj});
+        }
+
+        Napi::Object returnObj = Napi::Object::New(Env());
+        if (!result.language.empty()) {
+            returnObj.Set("language", Napi::String::New(Env(), result.language));
+        }
+        Napi::Array transcriptionArray = Napi::Array::New(Env(), result.segments.size());
+        for (uint64_t i = 0; i < result.segments.size(); ++i) {
+            Napi::Object tmp = Napi::Array::New(Env(), 3);
+            for (uint64_t j = 0; j < 3; ++j) {
+                tmp[j] = Napi::String::New(Env(), result.segments[i][j]);
+            }
+            transcriptionArray[i] = tmp;
+         }
+         returnObj.Set("transcription", transcriptionArray);
+         Callback().Call({Env().Null(), returnObj});
+    }
+
+    // Progress callback function - using thread-safe function
+    void OnProgress(int progress) {
+        if (tsfn) {
+            // Use thread-safe function to call JavaScript callback
+            auto callback = [progress](Napi::Env env, Napi::Function jsCallback) {
+                jsCallback.Call({Napi::Number::New(env, progress)});
+            };
+
+            tsfn.BlockingCall(callback);
+        }
+    }
+
+ private:
+    whisper_params params;
+    whisper_result result;
+    Napi::Env env;
+    Napi::ThreadSafeFunction tsfn;
+
+    // Custom run function with progress callback support
+    int run_with_progress(whisper_params &params, whisper_result & result) {
+        if (params.no_prints) {
+            whisper_log_set(cb_log_disable, NULL);
+        }
+
+        if (params.fname_inp.empty() && params.pcmf32.empty()) {
+            fprintf(stderr, "error: no input files or audio buffer specified\n");
+            return 2;
+        }
+
+        if (params.language != "auto" && whisper_lang_id(params.language.c_str()) == -1) {
+            fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+            exit(0);
+        }
+
+        // whisper init
+        struct whisper_context_params cparams = whisper_context_default_params();
+        cparams.use_gpu = params.use_gpu;
+        cparams.flash_attn = params.flash_attn;
+        struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
+
+        if (ctx == nullptr) {
+            fprintf(stderr, "error: failed to initialize whisper context\n");
+            return 3;
+        }
+
+        // If params.pcmf32 provides, set params.fname_inp as "buffer"
+        if (!params.pcmf32.empty()) {
+            fprintf(stderr, "info: using audio buffer as input\n");
+            params.fname_inp.clear();
+            params.fname_inp.emplace_back("buffer");
+        }
+
+        for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
+            const auto fname_inp = params.fname_inp[f];
+            const auto fname_out = f < (int)params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
+
+            std::vector<float> pcmf32; // mono-channel F32 PCM
+            std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
+
+            // If params.pcmf32 is empty, read input audio file
+            if (params.pcmf32.empty()) {
+                if (!::read_audio_data(fname_inp, pcmf32, pcmf32s, params.diarize)) {
+                    fprintf(stderr, "error: failed to read audio file '%s'\n", fname_inp.c_str());
+                    continue;
+                }
+            } else {
+                pcmf32 = params.pcmf32;
+            }
+
+            // Print system info
+            if (!params.no_prints) {
+                fprintf(stderr, "\n");
+                fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
+                        params.n_threads*params.n_processors, std::thread::hardware_concurrency(), whisper_print_system_info());
+            }
+
+            // Print processing info
+            if (!params.no_prints) {
+                fprintf(stderr, "\n");
+                if (!whisper_is_multilingual(ctx)) {
+                    if (params.language != "en" || params.translate) {
+                        params.language = "en";
+                        params.translate = false;
+                        fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+                    }
+                }
+                fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, lang = %s, task = %s, timestamps = %d, audio_ctx = %d ...\n",
+                        __func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE,
+                        params.n_threads, params.n_processors,
+                        params.language.c_str(),
+                        params.translate ? "translate" : "transcribe",
+                        params.no_timestamps ? 0 : 1,
+                        params.audio_ctx);
+
+                fprintf(stderr, "\n");
+            }
+
+            // Run inference
+            {
+                whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+                wparams.strategy = params.beam_size > 1 ? WHISPER_SAMPLING_BEAM_SEARCH : WHISPER_SAMPLING_GREEDY;
+
+                wparams.print_realtime   = false;
+                wparams.print_progress   = params.print_progress;
+                wparams.print_timestamps = !params.no_timestamps;
+                wparams.print_special    = params.print_special;
+                wparams.translate        = params.translate;
+                wparams.language         = params.detect_language ? "auto" : params.language.c_str();
+                wparams.detect_language  = params.detect_language;
+                wparams.n_threads        = params.n_threads;
+                wparams.n_max_text_ctx   = params.max_context >= 0 ? params.max_context : wparams.n_max_text_ctx;
+                wparams.offset_ms        = params.offset_t_ms;
+                wparams.duration_ms      = params.duration_ms;
+
+                wparams.token_timestamps = params.output_wts || params.max_len > 0;
+                wparams.thold_pt         = params.word_thold;
+                wparams.entropy_thold    = params.entropy_thold;
+                wparams.logprob_thold    = params.logprob_thold;
+                wparams.max_len          = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
+                wparams.audio_ctx        = params.audio_ctx;
+
+                wparams.greedy.best_of        = params.best_of;
+                wparams.beam_search.beam_size = params.beam_size;
+
+                wparams.initial_prompt   = params.prompt.c_str();
+
+                wparams.no_timestamps    = params.no_timestamps;
+
+                whisper_print_user_data user_data = { &params, &pcmf32s };
+
+                // This callback is called for each new segment
+                if (!wparams.print_realtime) {
+                    wparams.new_segment_callback           = whisper_print_segment_callback;
+                    wparams.new_segment_callback_user_data = &user_data;
+                }
+
+                // Set progress callback
+                wparams.progress_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, int progress, void * user_data) {
+                    ProgressWorker* worker = static_cast<ProgressWorker*>(user_data);
+                    worker->OnProgress(progress);
+                };
+                wparams.progress_callback_user_data = this;
+
+                // Set VAD parameters
+                wparams.vad            = params.vad;
+                wparams.vad_model_path = params.vad_model.c_str();
+
+                wparams.vad_params.threshold               = params.vad_threshold;
+                wparams.vad_params.min_speech_duration_ms  = params.vad_min_speech_duration_ms;
+                wparams.vad_params.min_silence_duration_ms = params.vad_min_silence_duration_ms;
+                wparams.vad_params.max_speech_duration_s   = params.vad_max_speech_duration_s;
+                wparams.vad_params.speech_pad_ms           = params.vad_speech_pad_ms;
+                wparams.vad_params.samples_overlap         = params.vad_samples_overlap;
+
+                if (whisper_full_parallel(ctx, wparams, pcmf32.data(), pcmf32.size(), params.n_processors) != 0) {
+                    fprintf(stderr, "failed to process audio\n");
+                    return 10;
+                }
+            }
+        }
+
+        if (params.detect_language || params.language == "auto") {
+            result.language = whisper_lang_str(whisper_full_lang_id(ctx));
+        }
+        const int n_segments = whisper_full_n_segments(ctx);
+        result.segments.resize(n_segments);
+
+        for (int i = 0; i < n_segments; ++i) {
+            const char * text = whisper_full_get_segment_text(ctx, i);
+            const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+            const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+            result.segments[i].emplace_back(to_timestamp(t0, params.comma_in_time));
+            result.segments[i].emplace_back(to_timestamp(t1, params.comma_in_time));
+            result.segments[i].emplace_back(text);
+        }
+
+        whisper_print_timings(ctx);
+        whisper_free(ctx);
+
+        return 0;
+    }
+};
+
+Napi::Value whisper(const Napi::CallbackInfo& info) {
+  Napi::Env env = info.Env();
+  if (info.Length() <= 0 || !info[0].IsObject()) {
+    Napi::TypeError::New(env, "object expected").ThrowAsJavaScriptException();
+  }
+  whisper_params params;
+
+  Napi::Object whisper_params = info[0].As<Napi::Object>();
+  std::string language = whisper_params.Get("language").As<Napi::String>();
+  std::string model = whisper_params.Get("model").As<Napi::String>();
+  std::string input = whisper_params.Get("fname_inp").As<Napi::String>();
+
+  bool use_gpu = true;
+  if (whisper_params.Has("use_gpu") && whisper_params.Get("use_gpu").IsBoolean()) {
+    use_gpu = whisper_params.Get("use_gpu").As<Napi::Boolean>();
+  }
+
+  bool flash_attn = false;
+  if (whisper_params.Has("flash_attn") && whisper_params.Get("flash_attn").IsBoolean()) {
+    flash_attn = whisper_params.Get("flash_attn").As<Napi::Boolean>();
+  }
+
+  bool no_prints = false;
+  if (whisper_params.Has("no_prints") && whisper_params.Get("no_prints").IsBoolean()) {
+    no_prints = whisper_params.Get("no_prints").As<Napi::Boolean>();
+  }
+
+  bool no_timestamps = false;
+  if (whisper_params.Has("no_timestamps") && whisper_params.Get("no_timestamps").IsBoolean()) {
+    no_timestamps = whisper_params.Get("no_timestamps").As<Napi::Boolean>();
+  }
+
+  bool detect_language = false;
+  if (whisper_params.Has("detect_language") && whisper_params.Get("detect_language").IsBoolean()) {
+    detect_language = whisper_params.Get("detect_language").As<Napi::Boolean>();
+  }
+
+  int32_t audio_ctx = 0;
+  if (whisper_params.Has("audio_ctx") && whisper_params.Get("audio_ctx").IsNumber()) {
+    audio_ctx = whisper_params.Get("audio_ctx").As<Napi::Number>();
+  }
+
+  bool comma_in_time = true;
+  if (whisper_params.Has("comma_in_time") && whisper_params.Get("comma_in_time").IsBoolean()) {
+    comma_in_time = whisper_params.Get("comma_in_time").As<Napi::Boolean>();
+  }
+
+  int32_t max_len = 0;
+  if (whisper_params.Has("max_len") && whisper_params.Get("max_len").IsNumber()) {
+    max_len = whisper_params.Get("max_len").As<Napi::Number>();
+  }
+
+  // Add support for max_context
+  int32_t max_context = -1;
+  if (whisper_params.Has("max_context") && whisper_params.Get("max_context").IsNumber()) {
+    max_context = whisper_params.Get("max_context").As<Napi::Number>();
+  }
+
+  // support prompt
+  std::string prompt = "";
+  if (whisper_params.Has("prompt") && whisper_params.Get("prompt").IsString()) {
+    prompt = whisper_params.Get("prompt").As<Napi::String>();
+  }
+
+  // Add support for print_progress
+  bool print_progress = false;
+  if (whisper_params.Has("print_progress") && whisper_params.Get("print_progress").IsBoolean()) {
+    print_progress = whisper_params.Get("print_progress").As<Napi::Boolean>();
+  }
+  // Add support for progress_callback
+  Napi::Function progress_callback;
+  if (whisper_params.Has("progress_callback") && whisper_params.Get("progress_callback").IsFunction()) {
+    progress_callback = whisper_params.Get("progress_callback").As<Napi::Function>();
+  }
+
+  // Add support for VAD parameters
+  bool vad = false;
+  if (whisper_params.Has("vad") && whisper_params.Get("vad").IsBoolean()) {
+    vad = whisper_params.Get("vad").As<Napi::Boolean>();
+  }
+
+  std::string vad_model = "";
+  if (whisper_params.Has("vad_model") && whisper_params.Get("vad_model").IsString()) {
+    vad_model = whisper_params.Get("vad_model").As<Napi::String>();
+  }
+
+  float vad_threshold = 0.5f;
+  if (whisper_params.Has("vad_threshold") && whisper_params.Get("vad_threshold").IsNumber()) {
+    vad_threshold = whisper_params.Get("vad_threshold").As<Napi::Number>();
+  }
+
+  int vad_min_speech_duration_ms = 250;
+  if (whisper_params.Has("vad_min_speech_duration_ms") && whisper_params.Get("vad_min_speech_duration_ms").IsNumber()) {
+    vad_min_speech_duration_ms = whisper_params.Get("vad_min_speech_duration_ms").As<Napi::Number>();
+  }
+
+  int vad_min_silence_duration_ms = 100;
+  if (whisper_params.Has("vad_min_silence_duration_ms") && whisper_params.Get("vad_min_silence_duration_ms").IsNumber()) {
+    vad_min_silence_duration_ms = whisper_params.Get("vad_min_silence_duration_ms").As<Napi::Number>();
+  }
+
+  float vad_max_speech_duration_s = FLT_MAX;
+  if (whisper_params.Has("vad_max_speech_duration_s") && whisper_params.Get("vad_max_speech_duration_s").IsNumber()) {
+    vad_max_speech_duration_s = whisper_params.Get("vad_max_speech_duration_s").As<Napi::Number>();
+  }
+
+  int vad_speech_pad_ms = 30;
+  if (whisper_params.Has("vad_speech_pad_ms") && whisper_params.Get("vad_speech_pad_ms").IsNumber()) {
+    vad_speech_pad_ms = whisper_params.Get("vad_speech_pad_ms").As<Napi::Number>();
+  }
+
+  float vad_samples_overlap = 0.1f;
+  if (whisper_params.Has("vad_samples_overlap") && whisper_params.Get("vad_samples_overlap").IsNumber()) {
+    vad_samples_overlap = whisper_params.Get("vad_samples_overlap").As<Napi::Number>();
+  }
+
+  Napi::Value pcmf32Value = whisper_params.Get("pcmf32");
+  std::vector<float> pcmf32_vec;
+  if (pcmf32Value.IsTypedArray()) {
+    Napi::Float32Array pcmf32 = pcmf32Value.As<Napi::Float32Array>();
+    size_t length = pcmf32.ElementLength();
+    pcmf32_vec.reserve(length);
+    for (size_t i = 0; i < length; i++) {
+      pcmf32_vec.push_back(pcmf32[i]);
+    }
+  }
+
+  params.language = language;
+  params.model = model;
+  params.fname_inp.emplace_back(input);
+  params.use_gpu = use_gpu;
+  params.flash_attn = flash_attn;
+  params.no_prints = no_prints;
+  params.no_timestamps = no_timestamps;
+  params.audio_ctx = audio_ctx;
+  params.pcmf32 = pcmf32_vec;
+  params.comma_in_time = comma_in_time;
+  params.max_len = max_len;
+  params.max_context = max_context;
+  params.print_progress = print_progress;
+  params.prompt = prompt;
+  params.detect_language = detect_language;
+
+  // Set VAD parameters
+  params.vad = vad;
+  params.vad_model = vad_model;
+  params.vad_threshold = vad_threshold;
+  params.vad_min_speech_duration_ms = vad_min_speech_duration_ms;
+  params.vad_min_silence_duration_ms = vad_min_silence_duration_ms;
+  params.vad_max_speech_duration_s = vad_max_speech_duration_s;
+  params.vad_speech_pad_ms = vad_speech_pad_ms;
+  params.vad_samples_overlap = vad_samples_overlap;
+
+  Napi::Function callback = info[1].As<Napi::Function>();
+  // Create a new Worker class with progress callback support
+  ProgressWorker* worker = new ProgressWorker(callback, params, progress_callback, env);
+  worker->Queue();
+  return env.Undefined();
+}
+
+
+Napi::Object Init(Napi::Env env, Napi::Object exports) {
+  exports.Set(
+      Napi::String::New(env, "whisper"),
+      Napi::Function::New(env, whisper)
+  );
+  return exports;
+}
+
+NODE_API_MODULE(whisper, Init);

examples/addon.node/index.js

@@ -0,0 +1,59 @@
+const path = require('path');
+const os = require('os');
+
+const isWindows = os.platform() === 'win32';
+const buildPath = isWindows ? "../../build/bin/Release/addon.node" : "../../build/Release/addon.node";
+
+const { whisper } = require(path.join(__dirname, buildPath));
+const { promisify } = require("util");
+
+const whisperAsync = promisify(whisper);
+
+const whisperParams = {
+  language: "en",
+  model: path.join(__dirname, "../../models/ggml-base.en.bin"),
+  fname_inp: path.join(__dirname, "../../samples/jfk.wav"),
+  use_gpu: true,
+  flash_attn: false,
+  no_prints: true,
+  comma_in_time: false,
+  translate: true,
+  no_timestamps: false,
+  detect_language: false,
+  audio_ctx: 0,
+  max_len: 0,
+  progress_callback: (progress) => {
+      console.log(`progress: ${progress}%`);
+    }
+};
+
+const arguments = process.argv.slice(2);
+const params = Object.fromEntries(
+  arguments.reduce((pre, item) => {
+    if (item.startsWith("--")) {
+      const [key, value] = item.slice(2).split("=");
+      if (key === "audio_ctx") {
+        whisperParams[key] = parseInt(value);
+      } else if (key === "detect_language") {
+        whisperParams[key] = value === "true";
+      } else {
+        whisperParams[key] = value;
+      }
+      return pre;
+    }
+    return pre;
+  }, [])
+);
+
+for (const key in params) {
+  if (whisperParams.hasOwnProperty(key)) {
+    whisperParams[key] = params[key];
+  }
+}
+
+console.log("whisperParams =", whisperParams);
+
+whisperAsync(whisperParams).then((result) => {
+  console.log();
+  console.log(result);
+});

examples/addon.node/package.json

@@ -0,0 +1,16 @@
+{
+  "name": "addon.node",
+  "version": "0.0.0",
+  "description": "",
+  "main": "index.js",
+  "author": "Qanhe Chen",
+  "license": "MIT",
+  "scripts": {
+    "test": "jest"
+  },
+  "devDependencies": {
+    "cmake-js": "^7.1.1",
+    "jest": "^29.4.0",
+    "node-addon-api": "^5.0.0"
+  }
+}

examples/addon.node/vad-example.js

@@ -0,0 +1,132 @@
+const path = require("path");
+const { whisper } = require(path.join(
+  __dirname,
+  "../../build/Release/addon.node"
+));
+const { promisify } = require("util");
+
+const whisperAsync = promisify(whisper);
+
+// Example with VAD enabled
+const vadParams = {
+  language: "en",
+  model: path.join(__dirname, "../../models/ggml-base.en.bin"),
+  fname_inp: path.join(__dirname, "../../samples/jfk.wav"),
+  use_gpu: true,
+  flash_attn: false,
+  no_prints: false,
+  comma_in_time: true,
+  translate: false,
+  no_timestamps: false,
+  detect_language: false,
+  audio_ctx: 0,
+  max_len: 0,
+  // VAD parameters
+  vad: true,
+  vad_model: path.join(__dirname, "../../models/ggml-silero-v6.2.0.bin"), // You need to download this model
+  vad_threshold: 0.5,
+  vad_min_speech_duration_ms: 250,
+  vad_min_silence_duration_ms: 100,
+  vad_max_speech_duration_s: 30.0,
+  vad_speech_pad_ms: 30,
+  vad_samples_overlap: 0.1,
+  progress_callback: (progress) => {
+    console.log(`VAD Transcription progress: ${progress}%`);
+  }
+};
+
+// Example without VAD (traditional approach)
+const traditionalParams = {
+  language: "en",
+  model: path.join(__dirname, "../../models/ggml-base.en.bin"),
+  fname_inp: path.join(__dirname, "../../samples/jfk.wav"),
+  use_gpu: true,
+  flash_attn: false,
+  no_prints: false,
+  comma_in_time: true,
+  translate: false,
+  no_timestamps: false,
+  detect_language: false,
+  audio_ctx: 0,
+  max_len: 0,
+  vad: false, // Explicitly disable VAD
+  progress_callback: (progress) => {
+    console.log(`Traditional transcription progress: ${progress}%`);
+  }
+};
+
+async function runVADExample() {
+  try {
+    console.log("=== Whisper.cpp Node.js VAD Example ===\n");
+    
+    // Check if VAD model exists
+    const fs = require('fs');
+    if (!fs.existsSync(vadParams.vad_model)) {
+      console.log("⚠️  VAD model not found. Please download the VAD model first:");
+      console.log("   ./models/download-vad-model.sh silero-v6.2.0");
+      console.log("   Or run: python models/convert-silero-vad-to-ggml.py");
+      console.log("\n   Falling back to traditional transcription without VAD...\n");
+      
+      // Run without VAD
+      console.log("🎵 Running traditional transcription...");
+      const traditionalResult = await whisperAsync(traditionalParams);
+      console.log("\n📝 Traditional transcription result:");
+      console.log(traditionalResult);
+      return;
+    }
+    
+    console.log("🎵 Running transcription with VAD enabled...");
+    console.log("VAD Parameters:");
+    console.log(`  - Threshold: ${vadParams.vad_threshold}`);
+    console.log(`  - Min speech duration: ${vadParams.vad_min_speech_duration_ms}ms`);
+    console.log(`  - Min silence duration: ${vadParams.vad_min_silence_duration_ms}ms`);
+    console.log(`  - Max speech duration: ${vadParams.vad_max_speech_duration_s}s`);
+    console.log(`  - Speech padding: ${vadParams.vad_speech_pad_ms}ms`);
+    console.log(`  - Samples overlap: ${vadParams.vad_samples_overlap}\n`);
+    
+    const startTime = Date.now();
+    const vadResult = await whisperAsync(vadParams);
+    const vadDuration = Date.now() - startTime;
+    
+    console.log("\n✅ VAD transcription completed!");
+    console.log(`⏱️  Processing time: ${vadDuration}ms`);
+    console.log("\n📝 VAD transcription result:");
+    console.log(vadResult);
+    
+    // Compare with traditional approach
+    console.log("\n🔄 Running traditional transcription for comparison...");
+    const traditionalStartTime = Date.now();
+    const traditionalResult = await whisperAsync(traditionalParams);
+    const traditionalDuration = Date.now() - traditionalStartTime;
+    
+    console.log("\n✅ Traditional transcription completed!");
+    console.log(`⏱️  Processing time: ${traditionalDuration}ms`);
+    console.log("\n📝 Traditional transcription result:");
+    console.log(traditionalResult);
+    
+    // Performance comparison
+    console.log("\n📊 Performance Comparison:");
+    console.log(`VAD:         ${vadDuration}ms`);
+    console.log(`Traditional: ${traditionalDuration}ms`);
+    const speedup = traditionalDuration / vadDuration;
+    if (speedup > 1) {
+      console.log(`🚀 VAD is ${speedup.toFixed(2)}x faster!`);
+    } else {
+      console.log(`ℹ️  Traditional approach was ${(1/speedup).toFixed(2)}x faster in this case.`);
+    }
+    
+  } catch (error) {
+    console.error("❌ Error during transcription:", error);
+  }
+}
+
+// Run the example
+if (require.main === module) {
+  runVADExample();
+}
+
+module.exports = {
+  runVADExample,
+  vadParams,
+  traditionalParams
+};