fluidaudio-rs

Rust bindings for FluidAudio - a Swift library for ASR, VAD, Speaker Diarization, and TTS on Apple platforms.

Features

• ASR (Automatic Speech Recognition) - High-quality speech-to-text using Parakeet TDT models
• VAD (Voice Activity Detection) - Detect speech segments in audio

Requirements

• macOS 14+ or iOS 17+
• Apple Silicon (M1/M2/M3) recommended
• Rust 1.70+
• Swift 5.10+

Installation

Add to your Cargo.toml:

[dependencies]
fluidaudio-rs = "0.1"

Usage

Speech-to-Text (ASR)

use fluidaudio_rs::FluidAudio;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let audio = FluidAudio::new()?;

    // Check system info
    let info = audio.system_info();
    println!("Running on: {} ({})", info.chip_name, info.platform);
    println!("Apple Silicon: {}", audio.is_apple_silicon());

    // Initialize ASR (downloads models on first run)
    audio.init_asr()?;

    // Transcribe an audio file
    let result = audio.transcribe_file("audio.wav")?;
    println!("Text: {}", result.text);
    println!("Confidence: {:.2}%", result.confidence * 100.0);
    println!("Processing speed: {:.1}x realtime", result.rtfx);

    Ok(())
}

Voice Activity Detection (VAD)

use fluidaudio_rs::FluidAudio;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let audio = FluidAudio::new()?;

    // Initialize VAD with threshold (0.0-1.0)
    audio.init_vad(0.85)?;

    println!("VAD available: {}", audio.is_vad_available());

    Ok(())
}

Model Loading

First initialization downloads and compiles ML models (~500MB total). This can take 20-30 seconds as Apple's Neural Engine compiles the models. Subsequent loads use cached compilations (~1 second).

Platform Support

How it Works

This crate uses a C FFI bridge to communicate between Rust and Swift:

1. The Swift layer (FluidAudioBridge) wraps the FluidAudio library
2. C-compatible functions are exported using @_cdecl
3. Rust calls these functions through extern "C" declarations
4. The build.rs script compiles the Swift package and links it

License

MIT