---
language: en
license: cc-by-nc-4.0
library_name: pytorch
tags:
- speaker-diarization
- ax650
- axera
- onnx
- axmodel
- wavlm
- conformer
pipeline_tag: voice-activity-detection
---

# DiariZen Speaker Segmentation - AX650 Deployment

CPU+NPU hybrid speaker diarization segmentation for AX650 NPU.

## Model

- Source: [BUT-FIT/diarizen-wavlm-large-s80-md](https://huggingface.co/BUT-FIT/diarizen-wavlm-large-s80-md)
- Architecture: WavLM-Large (pruned) + Conformer
- Task: Frame-level speaker activity segmentation (11 classes, 4s @ 16kHz)

## Pipeline

```
Audio (16kHz mono, any length)
  → CPU: resample → 4s sliding window → LayerNorm
  → AX650 NPU: CNN feature extractor (7 conv, U16, 17.7ms)
  → CPU: WavLM Transformer (24L) + Conformer (4L) + Classifier (251ms)
  → Log-probabilities (1, 199, 11) per window
```

## Performance

| Stage | Time | Hardware |
|-------|------|----------|
| CNN | 17.7 ms | AX650 NPU @1GHz |
| Backend | 251 ms | CPU ONNX Runtime |
| Total | 269 ms | 14.9x real-time |

## Accuracy

End-to-end cosine: 0.9997 vs full FP32 reference.

## Directory

```
models/          cnn_features.axmodel + backend.onnx
python/          Python SDK (diarizen_sdk)
cpp/             C++ SDK (diarizen_segmenter)
model_convert/   ONNX export + Pulsar2 compile config
reports/         SDK and simulation reports
```

## Quick Start

```bash
pip install -r python/requirements.txt
python python/diarizen_sdk/example.py audio.wav \
  --cnn-model models/cnn_features.axmodel \
  --backend-model models/backend.onnx
```

## Known Limitations

- 4s fixed window; longer audio requires sliding window stitching.
- CNN requires U16 quantization (U8 insufficient accuracy).
- Transformer + Conformer run on CPU (NPU backend limitation with WavLM attention ops).
- pos_conv_embed Conv (kernel=128, NLC layout) incompatible with NPU; included in CPU backend.