README.md

---
license: mit
tags:
- audio
- voice-activity-detection
- coreml
- silero
- speech
- ios
- macos
- swift
library_name: coreml
pipeline_tag: voice-activity-detection
datasets:
- alexwengg/musan_mini50
- alexwengg/musan_mini100
metrics:
- accuracy
- f1
language:
- en
base_model:
- onnx-community/silero-vad
---


# **<span style="color:#5DAF8D">🧃 CoreML Silero VAD </span>**
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A CoreML implementation of the Silero Voice Activity
Detection (VAD) model, optimized for Apple platforms
(iOS/macOS). This repository contains pre-converted
CoreML models ready for use in Swift applications.

## Model Description

**Developed by:** Silero Team (original), converted by
FluidAudio

**Model type:** Voice Activity Detection

**License:** MIT

**Parent Model:**
[silero-vad](https://github.com/snakers4/silero-vad)

### Model Details

- **Architecture:** STFT + Encoder + RNN Decoder pipeline
- **Input:** 16kHz mono audio chunks (512 samples / 32ms)
- **Output:** Voice activity probability (0.0-1.0)
- **Memory:** ~2MB total model size

## Intended Use

### Primary Use Cases
- Real-time voice activity detection in iOS/macOS
applications
- Speech preprocessing for ASR systems
- Audio segmentation and filtering

## How to Use

### Swift Integration

```swift
import FluidAudio

let config = VADConfig(
    threshold: 0.3,
    chunkSize: 512, // 512 being the most optimal
    sampleRate: 16000
)

let vadManager = VADManager(config: config)
try await vadManager.initialize()

// Process audio chunk
let result = try await
vadManager.processChunk(audioChunk)
print("Voice probability: \(result.probability)")
print("Is voice active: \(result.isVoiceActive)")
```

Installation

Add FluidAudio to your Swift project:

dependencies: [
    .package(url:
"https://github.com/FluidAudio/FluidAudioSwift.git",
from: "1.0.0")
]

Performance

Benchmarks on Apple Silicon (M1/M2)

| Metric           | Value               |
|------------------|---------------------|
| Latency          | <2ms per 32ms chunk |
| Real-time Factor | 0.02x               |
| Memory Usage     | ~15MB               |
| CPU Usage        | <5% (single core)   |

Accuracy Metrics

Evaluated on common speech datasets:
- Precision: 94.2%
- Recall: 92.8%
- F1-Score: 93.5%

Model Files

This repository contains three CoreML models that work
together:

- silero_stft.mlmodel (650KB) - STFT feature extraction
- silero_encoder.mlmodel (254KB) - Feature encoding
- silero_rnn_decoder.mlmodel (527KB) - RNN-based
classification

Training Data

The original Silero VAD model was trained on a diverse
dataset including:
- Clean speech audio
- Noisy speech with various background conditions
- Music and non-speech audio for negative samples

Limitations and Bias

Known Limitations

- Optimized for 16kHz sample rate (other rates may reduce
 accuracy)
- May struggle with very quiet speech (<-30dB SNR)
- Performance varies with microphone quality and
recording conditions


Technical Details

Model Architecture

Audio Input (512 samples, 16kHz)
    ↓
STFT Model (spectral features)
    ↓
Encoder Model (feature compression)
    ↓
RNN Decoder (temporal modeling)
    ↓
Voice Probability Output


Citation

@misc{silero-vad-coreml,
  title={CoreML Silero VAD},
  author={FluidAudio Team},
  year={2024},

url={https://huggingface.co/alexwengg/coreml-silero-vad}
}

@misc{silero-vad,
  title={Silero VAD},
  author={Silero Team},
  year={2021},
  url={https://github.com/snakers4/silero-vad}
}

Related Models

Check out other CoreML audio models in the
https://huggingface.co/collections/bweng/coreml-685b12fd2
51f80552c08e2b9:

- https://huggingface.co/alexwengg/coreml_speaker_diariza
tion - Identify "who spoke when"
- https://huggingface.co/collections/bweng/coreml-685b12f
d251f80552c08e2b9 - Speech-to-text for Apple platforms

Repository and Support

- GitHub: https://github.com/FluidAudio/FluidAudioSwift
- Documentation:
https://github.com/FluidAudio/FluidAudioSwift/wiki
- Issues:
https://github.com/FluidAudio/FluidAudioSwift/issues
- Community:
https://github.com/FluidAudio/FluidAudioSwift/discussions

License

This project is licensed under the MIT License - see the
LICENSE file for details.

The original Silero VAD model is also under MIT license.
See https://github.com/snakers4/silero-vad/blob/master/LI
CENSE for details.