> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fluidinference.com/llms.txt
> Use this file to discover all available pages before exploring further.

# VAD Getting Started

> Voice activity detection with Silero VAD v6 on CoreML.

## When to Use

* **Pre-process audio before ASR** — Segment files into speech regions, skip silence. Reduces ASR processing by 30-50%.
* **Real-time speech detection** — Trigger recording or UI when user starts/stops speaking.
* **Improve diarization quality** — Filter noise before speaker embedding extraction. Reduces false speakers by 20-40%.

## Specs

| Metric      | Value                 |
| ----------- | --------------------- |
| Model       | Silero VAD v6         |
| Window size | 256ms                 |
| Memory      | Minimal (runs on CPU) |

Model: [FluidInference/silero-vad-coreml](https://huggingface.co/FluidInference/silero-vad-coreml)

## Offline Segmentation

```swift theme={null}
import FluidAudio

let manager = try await VadManager(
    config: VadConfig(defaultThreshold: 0.75)
)

let samples = try AudioConverter().resampleAudioFile(
    URL(fileURLWithPath: "audio.wav")
)

var segmentation = VadSegmentationConfig.default
segmentation.minSpeechDuration = 0.25
segmentation.minSilenceDuration = 0.4
segmentation.speechPadding = 0.12

let segments = try await manager.segmentSpeech(samples, config: segmentation)
for (index, segment) in segments.enumerated() {
    print(String(format: "Segment %02d: %.2f-%.2fs", index + 1, segment.startTime, segment.endTime))
}
```

### Get Audio Clips

```swift theme={null}
let clips = try await manager.segmentSpeechAudio(samples, config: segmentation)
print("Extracted \(clips.count) buffered segments ready for ASR")
```

### Chunk-Level Probabilities

```swift theme={null}
let results = try await manager.process(samples)
for (index, chunk) in results.enumerated() {
    print(String(format: "Chunk %02d: prob=%.3f", index, chunk.probability))
}
```

## Manual Model Loading

Stage the Core ML bundle for offline environments:

```swift theme={null}
let modelURL = URL(
    fileURLWithPath: "/opt/models/silero-vad-coreml/silero-vad-unified-256ms-v6.0.0.mlmodelc",
    isDirectory: true
)
var configuration = MLModelConfiguration()
configuration.computeUnits = .cpuOnly
let vadModel = try MLModel(contentsOf: modelURL, configuration: configuration)
let manager = VadManager(config: .default, vadModel: vadModel)
```

## Benchmarks

[VOiCES](https://iqtlabs.github.io/voices/) (25 files, clean speech):

| Metric    | Value  |
| --------- | ------ |
| Accuracy  | 96.0%  |
| Precision | 100.0% |
| Recall    | 95.8%  |
| F1-Score  | 97.9%  |
| RTFx      | 1,230x |

[MUSAN](https://www.openslr.org/17/) (2,016 files, mixed noise/music/speech):

| Metric    | Value  |
| --------- | ------ |
| Accuracy  | 94.2%  |
| Precision | 92.6%  |
| Recall    | 78.9%  |
| F1-Score  | 85.2%  |
| RTFx      | 1,221x |

## CLI

```bash theme={null}
# Offline segmentation
swift run fluidaudio vad-analyze audio.wav

# Streaming mode
swift run fluidaudio vad-analyze audio.wav --streaming --min-silence-ms 300

# Both modes
swift run fluidaudio vad-analyze audio.wav --mode both

# Benchmark
swift run fluidaudio vad-benchmark --num-files 50 --threshold 0.3
```