skadoosh

A modular, lightning-fast, fully local voice agent framework in Rust — targeting sub-150 ms from end-of-speech endpointing to first audio out on capable hardware.

Mic in. Silero VAD listens. Whisper transcribes. A local LLM streams a reply. Kokoro speaks each clause as soon as it lands. Interrupt it mid-sentence and it shuts up instantly. No cloud required — but any OpenAI-compatible API works.

Use it as a binary (skadoosh), or as a library/SDK with pluggable engines and your choice of modalities — voice, text, files, or any mix.

flowchart LR
  MIC["cpal mic callback<br/>(RT thread, zero alloc/locks)"] --> RB["lock-free ringbuf<br/>f32 @ 16 kHz, ~30 s"]
  RB --> VAD["Silero VAD (ort)<br/>512-sample frames + segmenter"]
  VAD -->|"speech segment"| STT["whisper-rs tiny.en<br/>(dedicated thread)"]
  STT -->|"text"| LLM["LLM stream (reqwest SSE)<br/>OpenAI-compatible, Ollama default"]
  LLM --> SPLIT["clause splitter<br/>. ? ! , + max-len flush"]
  SPLIT -->|"clauses"| TTS["TTS (ort)<br/>Kokoro-82M / sine mock"]
  TTS -->|"24 kHz clips"| PB["cpal playback<br/>resample → device"]
  VAD -.->|"speech while playing"| INT["barge-in: cancel turn token<br/>+ lock-free playback flush"]
  INT -.-> LLM
  INT -.-> TTS
  INT -.-> PB

Why skadoosh

• Streaming at every stage after STT — the LLM token stream is split into clauses (., ?, !, ,), each clause is synthesized the moment it arrives, and playback starts on the first clause. You never wait for the full reply.
• Barge-in that actually works — speak while the agent is talking and the turn is cancelled (LLM stream aborted via CancellationToken) and the playback ring buffer is flushed within one output callback period (~5–10 ms), lock-free.
• Real-time safe audio edge — the cpal callbacks never allocate or lock; they push/pull through ringbuf SPSC queues. Everything else is ordinary tokio tasks on bounded mpsc channels.
• An SDK, not just a binary — swap any engine (SttEngine / LlmBackend / TtsEngine traits), drive text or audio turns programmatically, and subscribe to a live AgentEvent stream.
• Every modality combination — mic↔speaker voice agent, text REPL, wav-in selftest, text-to-speech one-shots, voice-in/text-out — all headless-testable.
• #![forbid(unsafe_code)], zero OpenSSL (rustls everywhere).

Install

# as a library
cargo add skadoosh

# as a binary (needs cmake + clang + ALSA headers, see Requirements)
cargo install skadoosh

60-second SDK quickstart

The full voice agent is five lines (examples/voice_agent.rs):

use skadoosh::{Agent, Config, Result};

fn main() -> Result<()> {
    let agent = Agent::builder().config(Config::default()).build()?;
    println!("skadoosh voice agent — speak; ctrl-c to quit");
    agent.run()
}

Drive text turns and watch events — no models or audio devices needed if you plug in mocks (see examples/mock_agent.rs, which runs green with zero models, zero servers, zero audio devices):

use skadoosh::{Agent, AgentEvent, Config, Result};

fn main() -> Result<()> {
    let mut agent = Agent::builder().config(Config::default()).build()?;
    let mut events = agent.events();
    std::thread::spawn(move || {
        while let Ok(event) = events.blocking_recv() {
            if let AgentEvent::Clause(c) = event {
                print!("{c}"); // reply clauses as they stream
            }
        }
    });
    let reply = agent.text_turn("Explain barge-in in one sentence.")?;
    println!("\n(full reply: {reply})");
    Ok(())
}

Bring your own engine

Every stage is a trait. Implement LlmBackend to talk to your own serving stack, SttEngine for a different recognizer, TtsEngine for a different voice — and inject them:

let agent = Agent::builder()
    .config(config)
    .stt(Box::new(MyRecognizer::new()?))
    .llm(Box::new(MyServingStack::new()))
    .tts(Box::new(MyVoice::load()?))
    .build()?;

skadoosh::stt::MockStt and skadoosh::tts::MockTts ship in-crate so your own integration tests need no models, no servers, and no audio hardware.

Modalities

The same engine traits and pipeline machinery power every cell of the matrix.

Latency budget

The sub-150 ms target applies to endpoint → first audio (STT + LLM time-to-first-token + first-clause TTS + playback start). The 300 ms endpoint window (configurable via --silence-ms) sits on top and is the dominant fixed cost of knowing you finished talking.

These are measured, not assumed: every turn emits an AgentEvent::StageLatency (and logs a per-stage breakdown), and --selftest prints the full table. Total speech-end → first-audio is hardware-dependent.

Requirements

• Rust ≥ 1.88 (the ort 2.0 rc line requires it)
• Build deps: cmake, a C/C++ toolchain, clang/libclang (whisper.cpp bindings), pkg-config, ALSA headers on Linux (sudo apt install build-essential cmake clang libclang-dev pkg-config libasound2-dev)
• Ollama (default) or any OpenAI-compatible server; use --api-key for hosted providers
• Optional: espeak-ng — required at runtime only for the real Kokoro TTS engine (the MockTts fallback needs nothing)
• By default the first build downloads a prebuilt ONNX Runtime (one-time, cached). To link a system ONNX Runtime instead: skadoosh = { version = "0.2", default-features = false, features = ["load-dynamic"] }

Quickstart (binary)

# 1. LLM backend
ollama pull qwen2.5:0.5b
ollama serve   # listens on http://localhost:11434

# 2. Models + test fixture (Silero VAD ~2 MB, whisper tiny.en ~74 MB)
./scripts/download_models.sh
# real neural TTS (optional, ~320 MB, needs espeak-ng):
./scripts/download_models.sh --with-kokoro
sudo apt install espeak-ng

# 3. Run the agent
skadoosh            # or: cargo run --release

Talk. It answers. Talk over it. It stops.

Useful variations:

skadoosh --list-devices                          # enumerate audio I/O
skadoosh --input-device "USB Mic" --output-device "Headphones"
skadoosh --silence-ms 250 --vad-threshold 0.6    # snappier / stricter endpointing
skadoosh --mock-tts                              # pipeline demo with zero TTS model
skadoosh --repl                                  # text mode (same brain, no audio)
skadoosh --say "Systems nominal." --out-wav status.wav
skadoosh --llm-url https://api.openai.com/v1 --llm-model gpt-4o-mini --api-key sk-...

Every flag falls back to a SKADOOSH_* environment variable (SKADOOSH_LLM_MODEL=qwen2.5:1.5b skadoosh). --api-key is never logged.

No audio hardware? Run the selftest

--selftest drives the real VAD → STT → LLM → TTS chain from a wav file and writes the synthesized reply to selftest_out.wav — no mic or speaker needed:

skadoosh --selftest tests/data/jfk.wav --mock-tts

skadoosh selftest — latency report
  vad segmentation                     65 ms
  stt (whisper)                       677 ms
  llm time-to-first-token               0 ms   (loopback mock)
  llm first clause                      0 ms
  tts first clip                        0 ms   (MockTts)
  total                               761 ms

(measured on an 8-core cloud box with a mock LLM and MockTts; your numbers with a warm Ollama + Kokoro will differ — that's the point of the table.)

Configuration

Examples

How barge-in works

The VAD never stops listening, even during playback. A speech onset while the speaker is active (with a 64 ms hangover to reject clicks) makes the orchestrator:

1. cancel the per-turn CancellationToken — the LLM SSE stream and any queued clauses die immediately (partial replies are discarded from history, since you never heard them);
2. bump a lock-free flush epoch — the playback callback notices on its next period and clears the ring itself, so audio stops within ~5–10 ms with no locks on the real-time thread.

Your new utterance is already accumulating in the segmenter and flows to Whisper while the old turn unwinds. Stale-turn clips are dropped defensively via turn IDs, and a TurnCancelled event fires so your UI can react.

Note: barge-in assumes headphones. With speakers, the agent's own output will trigger the VAD (no echo cancellation yet).

Architecture map

Testing

The default suite is fully headless and runs everywhere (110+ tests):

cargo test          # real VAD + Whisper on jfk.wav, mock LLM SSE, e2e selftest,
                    # SDK/mock-engine tests, binary-level SIGINT tests, ...
cargo clippy --all-targets -- -D warnings
cargo fmt --check

Opt-in gates:

• SKADOOSH_KOKORO_TESTS=1 — real Kokoro synthesis (needs download_models.sh --with-kokoro + espeak-ng)
• SKADOOSH_AUDIO_TESTS=1 — real cpal device capture/playback

Skipped tests always print why and name the variable that enables them.

Roadmap

• Wake word / push-to-talk modes
• GPU execution providers (CUDA/CoreML) for ort sessions
• Echo cancellation for speakerphone barge-in
• misaki-quality G2P (v1 phonemizes with espeak-ng + normalization — expect occasional mispronunciations)
• More Kokoro voices exposed via CLI; voice/speed flags
• Vision/document input modality via multimodal LLM backends
• Tool calling / multi-turn agents

License

Dual-licensed under MIT or Apache-2.0, at your option.