Silero VAD v5 — MLX
MLX-compatible weights for Silero VAD v5, converted from the official JIT model.
Model
Silero VAD v5 is a lightweight (~309K params) voice activity detection model that processes 512-sample chunks (32ms @ 16kHz) with sub-millisecond latency. It outputs a speech probability between 0 and 1 for each chunk, with LSTM state carried across chunks for streaming operation.
Architecture: STFT → 4×Conv1d+ReLU encoder → LSTM(128) → Conv1d decoder → sigmoid
Usage (Swift / MLX)
import SpeechVAD
// Load model
let vad = try await SileroVADModel.fromPretrained()
// Streaming: process 512-sample chunks
let prob = vad.processChunk(samples) // → 0.0...1.0
// Batch: detect speech segments in complete audio
let segments = vad.detectSpeech(audio: samples, sampleRate: 16000)
for seg in segments {
print("Speech: \(seg.startTime)s - \(seg.endTime)s")
}
Part of speech-swift.
Conversion
python3 scripts/convert_silero_vad.py --upload
Converts the official Silero VAD v5 JIT model via torch.hub, transposes Conv1d weights for MLX channels-last format, sums LSTM biases (bias_ih + bias_hh), and saves as safetensors.
Weight Mapping
| JIT Key | MLX Key | Shape |
|---|---|---|
_model.stft.forward_basis_buffer | stft.weight | [258, 256, 1] |
_model.encoder.{i}.reparam_conv.weight | encoder.{i}.weight | varies |
_model.encoder.{i}.reparam_conv.bias | encoder.{i}.bias | varies |
_model.decoder.rnn.weight_ih | lstm.Wx | [512, 128] |
_model.decoder.rnn.weight_hh | lstm.Wh | [512, 128] |
_model.decoder.rnn.bias_ih + bias_hh | lstm.bias | [512] |
_model.decoder.decoder.2.weight | decoder.weight | [1, 1, 128] |
_model.decoder.decoder.2.bias | decoder.bias | [1] |
License
The original Silero VAD model is released under the MIT License.
- Guide: soniqo.audio/guides/vad
- Docs: soniqo.audio
- GitHub: soniqo/speech-swift