aic-sdk - Rust Bindings for ai-coustics SDK

Rust wrapper for the ai-coustics SDK.

[!NOTE] This SDK requires a license key. Generate your key at developers.ai-coustics.com.

Installation

cargo add aic-sdk --features download-lib

download-lib fetches the matching native library during the build.

Quick Start

use aic_sdk::{include_model, ProcessorConfig, Model, Processor};

// Embed model at compile time (or use Model::from_file to load at runtime)
static MODEL: &[u8] = include_model!("/path/to/model.aicmodel");

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Get your license key from the environment variable
    let license_key = std::env::var("AIC_SDK_LICENSE")?;

    // Load the embedded model (or download manually at https://artifacts.ai-coustics.io/)
    let model = Model::from_buffer(MODEL)?;

    // Get optimal configuration based on the selected model
    let config = ProcessorConfig::optimal(&model);

    // Create a processor and initialize it
    let mut processor = Processor::new(&model, &license_key)?.with_config(&config)?;

    // Process mono audio
    let mut audio_block = vec![0.0f32; config.block_size];
    processor.process(&mut audio_block)?;

    Ok(())
}

Documentation

The full documentation is the API reference at docs.rs/aic-sdk, which also carries the long-form material:

docs::guide covers loading models, configuring a processor, voice activity detection, the analyzer and async processing.
docs::examples shows example programs that use the SDK.
docs::linking covers static, dynamic and runtime linking of the native library.

Available models are listed at artifacts.ai-coustics.io, and the product documentation lives at docs.ai-coustics.com.

License

This Rust wrapper is distributed under the Apache 2.0 license. The core C SDK is distributed under the proprietary AIC-SDK license.

NOTICE.txt lists the third-party software distributed with the SDK.

Energy-based Voice Activity Detection

An enhancement processor can report speech activity without running a separate VAD model. Create a context before processing and read its prediction after each audio block:

use aic_sdk::VadParameter;

let vad = processor.energy_vad_context();
vad.set_parameter(VadParameter::Sensitivity, 6.0)?;
processor.process(&mut audio)?;
let speech_detected = vad.is_speech_detected();