LALAL.AI vs Noise Gator

LALAL.AI uses AI to separate vocals and instrument stems, clean voice recordings, change or clone voices, and reduce echo and reverb. Its available stem types include vocals, instrumental, drums, bass, electric and acoustic guitar, piano, synthesizer, strings, and wind instruments. The web vocal remover accepts audio and video formats, including MP3, WAV, FLAC, MP4, and MOV. Fast Queue prioritizes processing, while Relaxed Queue processes as server capacity allows; both provide the same separation quality. Usage minutes are calculated by multiplying file length by the number of selected stem types. The desktop app supports Windows, macOS, and Ubuntu Linux, with batch processing for up to 20 files; Pro subscribers can optionally process locally offline. The API and VST plugin offer other ways to use the tools. The free Starter plan includes 10 Relaxed Queue minutes, but does not allow full downloads. Lite costs $7.50 a month billed annually.

Noise Gator processes microphone audio in real time to reduce noise, then sends the cleaned signal to a virtual audio device for other apps. It is free under the MIT license and runs on Windows and macOS. Discord, Teams, and Zoom are named as apps that can use the virtual device as a microphone input. DeepFilterNet is the default denoising engine, with dual-pass RNNoise as a lighter fallback. The audio pipeline also includes a highpass filter, suppression control, a voice-activity-driven noise gate, three-band EQ, and automatic gain normalization. Bluetooth headset audio is supported, and the device menu refreshes when devices connect or disconnect. The app can install VB-Cable on Windows or BlackHole on macOS automatically. Prebuilt binaries are available from GitHub Releases; the ZIP includes the executable and ONNX Runtime DLL. DeepFilterNet’s runtime and an approximately 8 MB model download on first use, with SHA256 verification. Settings are saved to a TOML file for the next launch, and headless mode supports scripted or server use. The latest listed release is v0.1.2. Building from source requires stable Rust 1.85 or later, plus MinGW-w64 or MSVC on Windows.