Name: voice activity detection
Author: pyannote

Overview

Pyannote's Voice Activity Detection (VAD) is a specialized tool designed to distinguish human speech from silence or background noise in audio streams. For developers building speech-to-text pipelines or voice assistants, this model serves as a critical preprocessing layer to reduce computational overhead by filtering out non-speech segments before they hit heavier ASR engines. Unlike simple energy-based thresholds, this model handles complex acoustic environments more robustly, making it ideal for long-form audio transcription and speaker diarization workflows. It integrates easily into Python-based stacks and is released under the permissive MIT license, allowing for flexible commercial deployment and modification.

Highlights

Efficiently filters non-speech segments for ASR preprocessing
Robust performance across diverse and noisy acoustic environments
Permissive MIT license for flexible commercial integration
Optimized for long-form audio and speaker diarization pipelines

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("pyannote/voice-activity-detection")
tokenizer = AutoTokenizer.from_pretrained("pyannote/voice-activity-detection")

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

huggingface-cli download pyannote/voice-activity-detection

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download pyannote/voice-activity-detection 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('pyannote/voice-activity-detection')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/pyannote/voice-activity-detection

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/pyannote/voice-activity-detection

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('pyannote/voice-activity-detection')
tokenizer = AutoTokenizer.from_pretrained('pyannote/voice-activity-detection')

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

modelscope download --model pyannote/voice-activity-detection

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model pyannote/voice-activity-detection 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('pyannote/voice-activity-detection')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/pyannote/voice-activity-detection.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/pyannote/voice-activity-detection.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', 'pyannote/voice-activity-detection')