# DiariZen Speaker Segmentation SDK

CPU+NPU hybrid speaker diarization segmentation inference.

## Architecture

```
Audio (16kHz mono, 4s)
  → CPU LayerNorm preprocessing
  → AX650 NPU CNN feature extractor (U16)
  → CPU WavLM Transformer + Conformer + Classifier (FP32, ONNX Runtime)
  → Frame-level log-probabilities (1, 199, 11)
```

## Requirements

- Python 3.8+
- numpy, onnxruntime, soundfile
- pyaxengine (for NPU inference)

## Usage

```python
from diarizen_sdk import DiarizenSegmenter

segmenter = DiarizenSegmenter("cnn_features.axmodel", "backend.onnx")
log_probs = segmenter(audio_array, sample_rate=16000)
# log_probs: (1, 199, 11) float32
```