onVoiceDidContinue コールバックの追加とドキュメント更新

Author: helloooideeeeea

README.md

@@ -11,15 +11,14 @@ A real-time **Voice Activity Detection (VAD)** library for **Android** using **S
 ✅ **Customizable audio sample rates (8, 16, 24, 48 kHz)**  
 ✅ **Outputs WAV data with automatic sample rate conversion to 16 kHz**  
 ✅ **Lightweight and optimized for Android**  
-✅ **Available via JitPack**  
+✅ **Available via JitPack**
 
 ---
 
 ## **Sample Android App Demo**
 
 Check out the sample Android app demonstrating real-time VAD:
 
-
 [Sample Android App Demo](https://github.com/user-attachments/assets/bb66e388-b0b9-4294-8e59-322b9f65ec4a)
 
 ---
@@ -29,6 +28,7 @@ Check out the sample Android app demonstrating real-time VAD:
 ### **Using JitPack**
 
 1. **Add JitPack to `settings.gradle.kts`**
+
 ```kotlin
 dependencyResolutionManagement {
     repositories {
@@ -40,9 +40,10 @@ dependencyResolutionManagement {
 ```
 
 2. **Add the dependency to `app/build.gradle.kts`**
+
 ```kotlin
 dependencies {
-    implementation("com.github.helloooideeeeea:RealTimeCutVADLibraryForAndroid:1.0.2@aar")
+    implementation("com.github.helloooideeeeea:RealTimeCutVADLibraryForAndroid:1.0.3@aar")
 }
 ```
 
@@ -51,6 +52,7 @@ dependencies {
 ## **Usage**
 
 ### **1. Initialize VAD in `MainActivity`**
+
 ```kotlin
 import io.codeconcept.realtimecutvadlibrary.VADWrapper
 import android.os.Bundle
@@ -62,7 +64,7 @@ class MainActivity : AppCompatActivity() {
 
     override fun onCreate(savedInstanceState: Bundle?) {
         super.onCreate(savedInstanceState)
-        
+
         // Initialize VAD Wrapper
         vadWrapper = VADWrapper(this)
         vadWrapper?.setVADModel(VADWrapper.SileroModelVersion.V5)
@@ -88,6 +90,10 @@ class MainActivity : AppCompatActivity() {
             override fun onVoiceEnd(wavData: ByteArray?) {
                 Log.d("VAD", "✅ onVoiceEnd() called. wavData length: ${wavData?.size ?: 0}")
             }
+
+            override fun onVoiceDidContinue(pcmFloatData: ByteArray?) {
+                // Use this only if you need real-time VAD-detected PCM float frames.
+            }
         })
     }
 
@@ -99,10 +105,12 @@ class MainActivity : AppCompatActivity() {
 ```
 
 ### **2. Understanding `setVADCallback`**
+
 `setVADCallback` is used to register a callback that gets notified when voice activity starts or ends.
 
 - `onVoiceStart()`: Triggered when voice is detected.
 - `onVoiceEnd(wavData: ByteArray?)`: Triggered when voice stops, providing a WAV file as a byte array.
+- `onVoiceDidContinue(pcmFloatData: ByteArray?)`: Triggered during speech, providing real-time PCM float frames. Use this only if you need real-time audio data while speech is in progress.
 
 This enables real-time processing of voice input, allowing applications to act on detected speech events.
 
@@ -111,14 +119,16 @@ This enables real-time processing of voice input, allowing applications to act o
 ## Configuration Options
 
 ### Sample Rates
+
 You can set the audio sample rate using `setSamplerate`:
 
-- `.SAMPLERATE_8`  (8 kHz)
+- `.SAMPLERATE_8` (8 kHz)
 - `.SAMPLERATE_16` (16 kHz)
 - `.SAMPLERATE_24` (24 kHz)
 - `.SAMPLERATE_48` (48 kHz)
 
 ### Silero Model Versions
+
 Choose between Silero model versions:
 
 - `.v4` - Silero Model Version 4
@@ -142,39 +152,44 @@ vadWrapper.setVADThreshold(0.7F, 0.7F, 0.5F, 0.95F, 10, 57)
 ```
 
 ### **Threshold Explanation**
+
 - **Start detection probability threshold (0.7)**: The VAD model must predict speech probability above this threshold to trigger voice start.
 - **End detection probability threshold (0.7)**: The VAD model must predict speech probability below this threshold to trigger voice end.
 - **True positive ratio for voice start (0.5)**: 50% of frames in a given window must be speech for voice activity to begin.
 - **False positive ratio for voice end (0.95)**: 95% of frames in a given window must be silence for voice activity to end.
 - **Start frame count (10 frames ≈ 0.32s)**: Number of frames required to confirm voice activity.
 - **End frame count (57 frames ≈ 1.824s)**: Number of frames required to confirm silence before stopping voice detection.
 
-
 #### **Important Notes:**
+
 - **Stricter VAD Detection in Silero v5**:
-Based on observations, Silero v5 appears to apply a stricter VAD detection mechanism compared to v4. 
+  Based on observations, Silero v5 appears to apply a stricter VAD detection mechanism compared to v4.
 
 - **Differences in Speech Start Detection**:
-In Silero v4, speech is considered to have started if, within 10 frames (0.32s), **80%** of the frames exceed a VAD probability of 70%.
-In Silero v5, this condition is relaxed, and speech is considered started if **50%** of the frames within 10 frames (0.32s) exceed a VAD probability of 70%.
-Adjusting Sensitivity for Voice Activity Detection
-If you need to fine-tune the sensitivity of voice segmentation, use the following function to customize the thresholds:
+  In Silero v4, speech is considered to have started if, within 10 frames (0.32s), **80%** of the frames exceed a VAD probability of 70%.
+  In Silero v5, this condition is relaxed, and speech is considered started if **50%** of the frames within 10 frames (0.32s) exceed a VAD probability of 70%.
+  Adjusting Sensitivity for Voice Activity Detection
+  If you need to fine-tune the sensitivity of voice segmentation, use the following function to customize the thresholds:
 
 ```java
 vadWrapper?.setVADThreshold(0.7F, 0.7F, 0.5F, 0.95F, 10, 57)
 ```
+
 By adjusting these parameters, you can fine-tune the strictness of voice segmentation to better suit your application needs.
+
 - **Silero v5 Performance**:
-The performance of Silero model v5 may vary, and adjusting the thresholds might be necessary to achieve optimal results. There are also discussions on this topic, such as [this one](https://github.com/SYSTRAN/faster-whisper/issues/934#issuecomment-2439340290).
+  The performance of Silero model v5 may vary, and adjusting the thresholds might be necessary to achieve optimal results. There are also discussions on this topic, such as [this one](https://github.com/SYSTRAN/faster-whisper/issues/934#issuecomment-2439340290).
 
 ---
 
 ## Algorithm Explanation
 
 ### ONNX Runtime for Silero VAD
+
 This library leverages **ONNX Runtime (C++)** to run the Silero VAD models efficiently. By utilizing ONNX Runtime, the library achieves high-performance inference across different platforms (iOS/macOS), ensuring fast and accurate voice activity detection.
 
 ### Why Use WebRTC's Audio Processing Module (APM)?
+
 This library utilizes WebRTC's APM for several key reasons:
 
 - **High-pass Filtering**: Removes low-frequency noise.
@@ -186,15 +201,18 @@ This library utilizes WebRTC's APM for several key reasons:
 
 1. **Input Audio Configuration**: The library supports sample rates of 8 kHz, 16 kHz, 24 kHz, and 48 kHz.
 2. **Audio Preprocessing**:
+
    - The audio is split into chunks based on the sample rate.
    - APM processes these chunks with filters and gain adjustments.
    - Audio is converted to 16 kHz for Silero VAD compatibility.
 
 3. **Voice Activity Detection**:
+
    - The processed audio chunks are passed to Silero VAD.
    - VAD outputs a probability score indicating voice activity.
 
 4. **Algorithm for Voice Detection**:
+
    - **Voice Start Detection**: When the VAD probability exceeds the threshold, a pre-buffer stores audio frames to capture speech onset.
    - **Voice End Detection**: Once silence is detected over a set number of frames, recording stops, and the audio is output as WAV data.
 
@@ -218,26 +236,28 @@ config.voice_detection.enabled = false;
 ---
 
 ## **Additional Resources**
+
 - **[RealTimeCutVADCXXLibrary](https://github.com/helloooideeeeea/RealTimeCutVADCXXLibrary)**
 
 ---
 
 ## **License**
+
 This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
 
 ---
 
 ## **📌 Summary**
-| Feature | Details |
-|---------|---------|
-| **Library Name** | `RealTimeCutVADLibrary` |
-| **Platform** | Android |
-| **Voice Detection** | Real-time |
-| **Supported Models** | Silero v4 & v5 |
-| **Sample Rates** | 8kHz, 16kHz, 24kHz, 48kHz |
-| **Output Format** | WAV (16 kHz) |
-| **Noise Reduction** | WebRTC APM |
-| **Installation** | JitPack (`implementation` via Gradle) |
 
-🚀 **Now you can add real-time voice activity detection to your Android app with ease!** 🎉
+| Feature              | Details                               |
+| -------------------- | ------------------------------------- |
+| **Library Name**     | `RealTimeCutVADLibrary`               |
+| **Platform**         | Android                               |
+| **Voice Detection**  | Real-time                             |
+| **Supported Models** | Silero v4 & v5                        |
+| **Sample Rates**     | 8kHz, 16kHz, 24kHz, 48kHz             |
+| **Output Format**    | WAV (16 kHz)                          |
+| **Noise Reduction**  | WebRTC APM                            |
+| **Installation**     | JitPack (`implementation` via Gradle) |
 
+🚀 **Now you can add real-time voice activity detection to your Android app with ease!** 🎉

app/src/main/java/io/codeconcept/realtimecutvadsampleapp/MainActivity.kt

@@ -181,6 +181,10 @@ fun startVADProcessing(
             Log.d("VAD", "✅ onVoiceEnd() called. wavData length: ${wavData?.size ?: 0}")
             onStatusChange(RecordingStatus.RUNNING, wavData) // 🔹 waveAudioData を渡す
         }
+
+        override fun onVoiceDidContinue(pcmFloatData: ByteArray?) {
+            // Use this only if you need real-time VAD-detected PCM float frames.
+        }
     })
 
     val audioRecord = AudioRecord(

realtimecutvadlibrary/build.gradle.kts

@@ -80,7 +80,7 @@ val jniLibsZip = file("${projectDir}/RealTimeCutVADCXXLibrary.jniLibs.zip")
 tasks.register("downloadJniLibs") {
     doLast {
         if (!jniLibsZip.exists()) {
-            val url = URI("https://github.com/helloooideeeeea/RealTimeCutVADCXXLibrary/releases/download/v1.0.2/RealTimeCutVADCXXLibrary.jniLibs.zip").toURL()
+            val url = URI("https://github.com/helloooideeeeea/RealTimeCutVADLibraryForXCFramework/releases/download/v1.0.7/jniLibs.zip").toURL()
             println("Downloading jniLibs from $url")
 
             url.openStream().use { input ->
@@ -114,7 +114,7 @@ afterEvaluate {
 
                 groupId = "com.github.helloooideeeeea"
                 artifactId = "realtimecutvadlibrary"
-                version = "1.0.2"
+                version = "1.0.3"
 
                 pom {
                     name.set("RealTimeCutVADLibrary")

realtimecutvadlibrary/src/main/cpp/c_wrapper.h

@@ -19,14 +19,18 @@ typedef void* VADInstanceHandle;
 
 typedef void (*VoiceStartCallback)(void* context);
 typedef void (*VoiceEndCallback)(void* context, const uint8_t* wav_data, size_t wav_size);
+typedef void (*VoiceDidContinueCallback)(void* context, const uint8_t* pcm_float_data, size_t data_size);
 
 // インスタンスの作成と破棄
 VADInstanceHandle create_vad_instance();
 void destroy_vad_instance(VADInstanceHandle instance);
 
 // コールバックの設定
-void set_vad_callback(VADInstanceHandle instance, void* context,
-                      VoiceStartCallback start_cb, VoiceEndCallback end_cb);
+void set_vad_callback(VADInstanceHandle instance,
+                      void* context,
+                      VoiceStartCallback start_cb,
+                      VoiceEndCallback end_cb,
+                      VoiceDidContinueCallback continue_cb);
 
 // パラメータ設定
 void set_vad_sample_rate(VADInstanceHandle instance, int sample_rate);