Akiki

Akiki is a lightweight Python package for streaming voice activity detection. It identifies speech in audio and returns a speech probability, a speech/silence decision, and timestamps for each 16 ms frame.

The model is included in the package. Processing runs locally, without a model download, network connection, or external service.

Install

Requires Python 3.10 or later.

pip install akiki

Or add it to a uv project:

uv add akiki

The base installation uses the bundled Rust engine on supported hosts. On other platforms, install an alternate engine with pip install 'akiki[onnx]' or pip install 'akiki[pytorch]'.

Python usage

Akiki accepts mono audio sampled at 16,000 Hz.

import soundfile as sf
from akiki import VAD

audio, sample_rate = sf.read("recording.wav", dtype="float32")

with VAD() as vad:
    for frame in vad.push(audio, sample_rate=sample_rate):
        print(frame.start_seconds, frame.end_seconds,
              frame.probability, frame.speech)

For live audio, call vad.push() repeatedly with successive audio buffers. Buffers can have any length; incomplete frames are retained for the next call. Call vad.reset() before starting a new recording. A final incomplete frame is not padded or scored.

Use VAD(threshold=0.6) to set the speech decision threshold. Every frame still includes its probability, regardless of the threshold.

Command line

akiki recording.wav
akiki recording.wav --threshold 0.6

The command prints one JSON object per complete frame, containing its timestamps, speech probability, and speech decision.

Inference engines

Linux x86_64 wheels include the compiled Rust engine for systems with glibc 2.34 or later. Pip and uv select the compatible wheel automatically; no Rust toolchain is needed to install it.

Akiki selects the Rust engine when a compatible native library is available, then tries ONNX Runtime and PyTorch if they are installed. Both are optional; the base package requires neither runtime.

To use ONNX Runtime:

pip install 'akiki[onnx]'
# or: uv add 'akiki[onnx]'

To use PyTorch:

pip install 'akiki[pytorch]'
# or: uv add 'akiki[pytorch]'

The PyTorch extra does not install ONNX Runtime. Install akiki[onnx,pytorch] if you want both alternatives. If no engine is available, Akiki raises an error with installation instructions.

Choose an engine with VAD(engine="rust"), VAD(engine="onnx"), or VAD(engine="pytorch"). The command-line equivalent is --engine rust, --engine onnx, or --engine pytorch. An explicitly selected engine raises an error if it is unavailable. Check vad.engine to see the active engine.

All engines use the same bundled model and streaming API.

License

The package and bundled Akiki model weights are licensed under Apache-2.0.