Metadata-Version: 2.4
Name: akiki
Version: 0.0.1
Summary: Local streaming voice activity detection with Akiki VAD
Author: Akiki AI
License-Expression: Apache-2.0
Project-URL: Homepage, https://huggingface.co/Akiki-AI
Keywords: vad,audio,speech,voice-activity-detection
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: onnxruntime>=1.17
Requires-Dist: soundfile>=0.12
Dynamic: license-file

# Akiki

Run streaming voice activity detection locally with a small bundled Akiki VAD
model. No model download, network connection or PyTorch is needed at runtime.

```bash
pip install akiki
akiki --version
akiki recording.wav
```

Audio must be mono at 16,000 Hz. The CLI prints one JSON object per complete
16 ms frame: its start, end, speech probability and threshold decision.

```python
import soundfile as sf
from akiki import VAD

audio, sample_rate = sf.read("recording.wav", dtype="float32")
vad = VAD()
for frame in vad.push(audio, sample_rate=sample_rate):
    print(frame)
vad.reset()  # Start a new recording.
```

`push()` accepts arbitrary buffer sizes and retains incomplete frames between
calls. A trailing incomplete frame is left pending; it is not padded or scored.
Use `VAD(threshold=0.6)` or `akiki recording.wav --threshold 0.6` to change the
threshold. Probabilities are always returned.

Version 0.0.1 bundles the experimental **candidate-1, seed 1729** model, with
210,561 parameters and a 16 ms output hop. It is an initial functional release;
future versions will supply the production Akiki VAD models. Training provenance
and checksums are included in `akiki/models/MODEL_CARD.md` and the model metadata.

Code and bundled Akiki weights are provided under Apache-2.0. Source audio and
the independent teacher retain their original licences; neither is distributed
in this package.

To publish this release from the project directory:

```bash
uv build
uv publish dist/akiki-0.0.1*
```

Authenticate to PyPI locally when prompted. The project contains no credentials.
