Silero VAD v5 CoreML

Provideraufklarer
Categoryvoice-activity-detection
Licensemit
Downloads18
Stars0

Overview

Silero VAD v5 CoreML is a specialized voice activity detection model optimized for Apple silicon. Unlike general-purpose audio models, this version is purpose-built to distinguish human speech from background noise with high precision and minimal latency. For developers building real-time transcription or voice-assistant pipelines on iOS and macOS, this model provides an efficient way to trigger downstream ASR (Automatic Speech Recognition) engines only when speech is present, significantly reducing CPU overhead and battery drain. It integrates directly into CoreML workflows, offering a lightweight alternative to cloud-based VAD or heavy PyTorch implementations without sacrificing accuracy.

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
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# 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:

Guidance
pip install -U huggingface_hub

CLI Download

Download the full repository

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)

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

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 Download
git lfs install
git clone https://huggingface.co/aufklarer/Silero-VAD-v5-CoreML

To skip LFS large-file downloads, use:

Skip LFS
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

Install Transformers
pip install -U transformers torch

Load the model and run inference

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:

Guidance
pip install modelscope

CLI Download

Download the full repository

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)

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

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 Download
git lfs install
git clone https://www.modelscope.cn/aufklarer/Silero-VAD-v5-CoreML.git

To skip LFS large-file downloads, use:

Skip LFS
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

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

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

来源: HuggingFace

---
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

swift
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

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