rnnoise-kmp

Kotlin Multiplatform bindings for RNNoise — a real-time noise suppression library from Xiph.Org. RNNoise uses a recurrent neural network to suppress stationary background noise while preserving speech, and doubles as a speech activity (VAD) detector.

Kotlin Multiplatform / Android:

implementation("cn.enaium.rnnoise:rnnoise-kmp:1.0.0")

JVM: the right native binary is resolved automatically — the rnnoise-kmp-jvm artifact pulls in the matching :jni-jvm-* sibling on the classpath:

• rnnoise-kmp-jni-jvm-linux-x86_64
• rnnoise-kmp-jni-jvm-linux-aarch64
• rnnoise-kmp-jni-jvm-darwin-x86_64
• rnnoise-kmp-jni-jvm-darwin-aarch64
• rnnoise-kmp-jni-jvm-windows-x86_64

NativeLoader detects os.name/os.arch at runtime, extracts the matching binary from the classpath to a temp directory, and System.loads it. No java.library.path setup is required for downstream JVM consumers. On Android the .so is loaded from the AAR's jniLibs via System.loadLibrary.

RNNoise processes 10 ms frames — 480 samples at a fixed 48 kHz sample rate:

import cn.enaium.rnnoise.createRnnoise

// 1. Create the denoiser with the built-in default model
createRnnoise().use { rnnoise ->
    // 2. Process 10 ms frames (480 samples @ 48 kHz)
    val input = FloatArray(rnnoise.frameSize)   // noisy PCM, normalized to -1.0..1.0
    val output = FloatArray(rnnoise.frameSize)
    val speechProb = rnnoise.processFrame(input, output)  // VAD probability in [0, 1]
    // `output` now contains the denoised frame
}

import cn.enaium.rnnoise.createRnnoise
import cn.enaium.rnnoise.createRnnoiseModelFromBuffer
import cn.enaium.rnnoise.createRnnoiseModelFromFilename

// From a file:
val model = createRnnoiseModelFromFilename("model.rnn")
createRnnoise(model).use { rnnoise -> /* ... */ }
model.close()  // after all denoisers using it are closed

// From a memory buffer (the buffer is copied internally):
val bytes: ByteArray = /* model bytes */
val model2 = createRnnoiseModelFromBuffer(bytes)
createRnnoise(model2).use { rnnoise -> /* ... */ }
model2.close()

fun createRnnoise(model: RnnoiseModel? = null): Rnnoise
fun createRnnoiseModelFromFilename(filename: String): RnnoiseModel
fun createRnnoiseModelFromBuffer(buffer: ByteArray): RnnoiseModel

All implementations are AutoCloseable; call close() to release the native state.

The example/ module is an Android app with a Jetpack Compose UI:

• Noise suppression switch — toggle RNNoise on/off to hear the difference
• Speech probability — live VAD output from processFrame
• Start/Stop button — real-time AudioRecord → RNNoise → AudioTrack loopback at 48 kHz

• JDK 17+
• CMake 3.16+
• Android SDK + NDK (for Android targets)
• Xcode command-line tools (for iOS/macOS/tvOS/watchOS targets)

git clone --recursive https://github.com/Enaium/rnnoise-kmp.git
cd rnnoise-kmp

The default denoising model weights are downloaded automatically from media.xiph.org during the first CMake configure and cached under jni/c_api/:

./gradlew :rnnoise-kmp:publishToMavenLocal

./gradlew :rnnoise-kmp:jvmTest        # JVM (JNI)
./gradlew :rnnoise-kmp:macosArm64Test # macOS native
./gradlew :rnnoise-kmp:linuxX64Test   # Linux native

rnnoise-kmp/
├── rnnoise/                  # Git submodule (C library)
├── jni/
│   ├── CMakeLists.txt        # JNI shared library build
│   ├── jni_bridge.cpp        # JNI bridge (C++ → JVM/Android)
│   ├── c_api/                # Downloaded model weights (build-time, gitignored)
│   └── jvm/                  # Per-OS/arch JNI publication subprojects
│       ├── darwin-aarch64, darwin-x86_64
│       ├── linux-x86_64, linux-aarch64
│       └── windows-x86_64
├── rnnoise-kmp/              # Kotlin Multiplatform module
│   ├── build.gradle.kts
│   └── src/
│       ├── commonMain/       # expect declarations + common interfaces
│       ├── commonTest/
│       ├── jvmMain/          # JVM actual (JNI) + NativeLoader
│       ├── androidMain/      # Android actual (JNI)
│       ├── nativeMain/       # Native actual (cinterop)
│       └── nativeInterop/cinterop/
├── example/                  # Android Compose demo (loopback + noise suppression)
├── scripts/                  # Native build helpers
└── .github/workflows/        # publish + test

MIT — see the LICENSE file.