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Vocal Gate VST3 & AU (Free) Total Downloads

Vocal Gate is a free AI noise gate VST3/AU plugin trained to separate clean speech from unwanted microphone artifacts. This plugin is intended for Creators and Editors. Can be used as a cough remover in your podcast/Youtube video editor, a sneeze ducker in your Twitch live stream broadcaster, a filter to block keyboard clicks and heavy breathing in your recording software, and other permutations.

Free AI Noise Gate VST3 removing coughs and sneezes in real time

Download for macOS     Download for Windows     Download for Linux

Requires: macOS 11+ (M-series)  |  Windows 10+ (x64)  |  Linux (Ubuntu 22.04+ / x64) | Release notes
Note: Installers are unsigned. On macOS, right-click Open the installer in your downloads. On Windows, press 'More info' and 'Run anyway'. On Linux, extract the .zip and place the .vst3 folder into ~/.vst3/.

Real-World Use Examples

Vocal Gate Demo

Watch the full demo on YouTube

Useful for:

  • Live Streaming: Twitch, YouTube Live, Kick, Facebook Gaming
  • Podcasting: Spotify, Apple Podcasts, Patreon
  • Video Content: YouTube VODs, TikTok, Instagram Reels

Works in:

  • Broadcasting: OBS Studio, Streamlabs, vMix
  • Video Editing: DaVinci Resolve, Adobe Premiere Pro, Final Cut Pro
  • DAWs & Audio: Reaper, Logic Pro, FL Studio, Ableton Live, Audacity

IF RECORDING/STREAMING LIVE (OBS, Streamlabs, etc.): The AI model requires a 750ms lookahead to work. You MUST delay your visual sources and desktop audio by 750ms to keep everything in sync.

E.g., How to sync in OBS:

  • Webcams: Right-click source -> Filters -> Add Video Delay (Async) -> set to 750ms.
  • Game/Screen Capture: Right-click source -> Filters -> Add Render Delay. (Note: OBS caps this at 500ms, so you must stack two Render Delay filters: one at 500ms and another at 250ms).
  • Desktop Audio: Click the gear icon in the Audio Mixer -> Advanced Audio Properties -> find your Desktop Audio and set the Sync Offset to 750ms.

Model Performance

The plugin relies on a pruned and quantised int8 ONNX model to achieve real-time inference in just ~0.3 ms per buffer.

AI_Inference

ROC

The pruned and quantised model has better performance in both inference time and ability on the test set (it is better generalised).

roc_curve_comparison

Dataset Energies

dataset_energy_comparison

Training Loss

loss_curve