Overview

The ai-coustics SDK runs speech enhancement, voice activity detection and audio analysis on CPU inside your application.

Start your integration

Process audio from your own application. Choose Python, Node.js, C, C++, Rust or WebAssembly.

Add ai-coustics to a LiveKit voice agent. Check which plugin supports your language and model.

Configure enhancement and voice activity detection in a Python Pipecat pipeline.

Create an SDK key in the developer platform. See authentication for credential setup and pricing and trial access for account terms.

Your task	Start with	What to evaluate
Isolate a primary speaker for a voice agent	Quail Voice Focus	Target speech retention, competing voices and downstream transcription
Preserve multiple speakers for speech-to-text	Quail Multi Speaker	Transcription quality on your speakers, noise and microphones
Detect speech and control turns	ai-coustics VAD	Missed speech, false triggers and turn timing
Enhance speech for human listening	Rook Multi Speaker	Listening quality and speech preservation
Analyze audio quality	Tyto	Risk scores and dimensions alongside observed call outcomes

Find exact model IDs, sample rates and frame sizes in the model reference. Try the SDK playground in your browser before installing.

Compare audio quality and runtime performance on your workload. Use the examples and compatibility matrix to set up. Before production, follow the deployment guide and review authentication, SDK telemetry and OpenTelemetry observability. Use troubleshooting to diagnose setup and runtime failures. Check the changelog when upgrading.

Connect to the documentation Model Context Protocol (MCP) server or copy a page into your coding agent.