Silero VAD v5 CoreML
Overview
Highlights
- Hardware-accelerated inference for iOS and macOS devices
- Low-latency speech detection for real-time audio pipelines
- Reduces ASR costs by filtering non-speech segments
- Lightweight footprint compared to full-scale audio models
- MIT licensed for flexible commercial integration
Usage
# Install Hugging Face transformers
pip install transformers torch
# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("aufklarer/Silero-VAD-v5-CoreML")
tokenizer = AutoTokenizer.from_pretrained("aufklarer/Silero-VAD-v5-CoreML")
Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance:Before downloading, install huggingface_hub with:
pip install -U huggingface_hub
CLI Download
Download the full repository
huggingface-cli download aufklarer/Silero-VAD-v5-CoreML
Download a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download aufklarer/Silero-VAD-v5-CoreML config.json --local-dir ./dir
See the official docs for more CLI options
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('aufklarer/Silero-VAD-v5-CoreML')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://huggingface.co/aufklarer/Silero-VAD-v5-CoreML
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/aufklarer/Silero-VAD-v5-CoreML
Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.
PyTorch / Transformers Usage
Install Transformers
pip install -U transformers torch
Load the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('aufklarer/Silero-VAD-v5-CoreML')
tokenizer = AutoTokenizer.from_pretrained('aufklarer/Silero-VAD-v5-CoreML')
Model Download
We recommend downloading the model via the ModelScope CLI or SDK.
Guidance:Before downloading, install ModelScope with:
pip install modelscope
CLI Download
Download the full repository
modelscope download --model aufklarer/Silero-VAD-v5-CoreML
Download a single file to a local folder (e.g. README.md into ./dir)
modelscope download --model aufklarer/Silero-VAD-v5-CoreML README.md --local_dir ./dir
See the docs for more CLI options
SDK Download
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('aufklarer/Silero-VAD-v5-CoreML')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://www.modelscope.cn/aufklarer/Silero-VAD-v5-CoreML.git
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/aufklarer/Silero-VAD-v5-CoreML.git
ModelScope 模型页直接下载模型文件;无需将模型文件放在本站服务器。
Notebook Quickstart
Install the ModelScope library
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
Load the model and run inference
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
p = pipeline('text-generation', 'aufklarer/Silero-VAD-v5-CoreML')
Full Documentation
---
license: mit
tags:
- coreml
- voice-activity-detection
- silero
- vad
- neural-engine
base_model: snakers4/silero-vad
pipeline_tag: voice-activity-detection
---
Silero-VAD-v5 — CoreML
CoreML conversion of Silero VAD v5 for Apple Neural Engine.
Model Details
| Detail | Value |
|--------|-------|
| Architecture | STFT → Conv1d encoder → LSTM → decoder |
| Parameters | ~309K |
| Input | 512 samples (32ms @ 16kHz) |
| Output | Speech probability (0.0–1.0) |
| Size | ~4.2 MB |
Usage
let vad = try await SileroVADModel.fromPretrained(backend: .coreML)
let prob = vad.processChunk(samples)Variants
| Variant | Backend | Model ID |
|---------|---------|----------|
| MLX | GPU | aufklarer/Silero-VAD-v5-MLX |
| CoreML | Neural Engine | aufklarer/Silero-VAD-v5-CoreML |
Links
- Swift library: soniqo/speech-swift
- Original model: snakers4/silero-vad
---
---
- Guide: soniqo.audio/guides/vad
- Docs: soniqo.audio
- GitHub: soniqo/speech-swift