README.md

---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: voice-activity-detection
tags:
- speaker-diarization
- meeting
- wavlm
- diarizen
- echo
private: true
---

# Echo Dia (V4)

Fine-tuned DiariZen-v2 (`BUT-FIT/diarizen-wavlm-large-s80-md-v2`) on a multi-domain meeting compound.

## Training

- **Base model**: BUT-FIT/diarizen-wavlm-large-s80-md-v2
- **Training data**: 9.1 h compound (AMI 3.5h + AliMeeting 2.6h + NOTSOFAR 3.0h)
- **Strategy**: WavLM layer 23 unfrozen, lr_wavlm=2.5e-6, lr_head=1e-4
- **Augmentation**: SpecAugment (time + freq mask) + audio noise injection
- **Duration**: 60 minutes on RTX A6000 (Phase 3 winner V4)
- **Best DER val** (ES2011a, 18 min): 17.69%

## Test set DER (collar=0, with overlap)

| Dataset | DER strict | DER col=0.25 | n_meetings |
|---|---|---|---|
| AMI test | 17.34% | 13.95% | 2 |
| AliMeeting test | 14.14% | 8.66% | 5 |
| NOTSOFAR test | 13.49% | 8.38% | 5 |

## Usage

```python
import torch
from diarizen.pipelines.inference import DiariZenPipeline

# Load v2 base, then inject Echo Dia weights
pipe = DiariZenPipeline.from_pretrained("BUT-FIT/diarizen-wavlm-large-s80-md-v2")
sd = torch.load("pytorch_model.bin", map_location="cuda:0", weights_only=False)
pipe._segmentation.model.load_state_dict(sd, strict=False)

# Run
result = pipe("audio.wav")
for seg, _, spk in result.itertracks(yield_label=True):
    print(f"{seg.start:.1f}-{seg.end:.1f}  {spk}")
```

## License

CC BY-NC 4.0 (inherited from base model). Non-commercial use only.