char5742 / flutter_silero_vad

This is an unofficial plugin for calling the Silero VAD ONNX model in Flutter.
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flutter_silero_vad

Silevo Voice Activity Detector (VAD) plugin wrapper for Flutter.

The flutter_silero_vad plugin is a robust solution for high-precision voice activity detection (VAD) in Flutter applications. Designed for easy integration using Swift and Kotlin, it leverages the Silero VAD model to accurately distinguish between speech and non-speech segments. This plugin is especially beneficial in noisy environments or for applications requiring real-time audio processing

How it works

This plugin simply calls the Silero VAD onnx model using Swift and Kotlin. The FlutterSileroVad class has only three methods: initialize, resetState, and predict.

For the initialize method, the arguments are as follows:

About resetState: Since Silero VAD is an RNN, the model has a state. Calling resetState will reset the model's state.

The predict method takes a segment of monaural audio data and determines whether or not the segment contains voice.

Step 1 Add flutter_silero_vad to your pubspec.yaml.

  flutter_silero_vad:
    git:
      url: https://github.com/char5742/flutter_silero_vad.git

Step 2 Place the Silero VAD onnx model in the assets.

Step 3:

final vad = FlutterSileroVad ();
// In Flutter, assets cannot be operated on directly from native, so if you want to use an asset, you first have to copy it locally.
onnxModelToLocal(modelPath); 
await vad.initialize(
 modelPath: modelPath,
 ...
);
final audioBuffer = Float32List(frameSize * sampleRate  / 1000); // ms
final isActive = await vad.predict(audioBuffer);
  Future<void> onnxModelToLocal(String modelPath) async {
    final data = await rootBundle.load('assets/silero_vad.onnx');
    final bytes =
        data.buffer.asUint8List(data.offsetInBytes, data.lengthInBytes);
    File(await modelPath).writeAsBytesSync(bytes);
  }

License

This project uses the following open-source packages: