README.md

---
license: mit
tags:
- speaker 
- speaker-diarization
- meeting
- wavlm
- wespeaker
- diarizen
- pyannote
- pyannote-audio-pipeline
---

## Overview
This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpeechFIT/DiariZen). The EEND component is built upon WavLM-Base+ and Conformer layers. The model was trained on far-field, single-channel audio data from the public datasets AMI, AISHELL-4, and AliMeeting. Please follow the [instructions](https://github.com/BUTSpeechFIT/DiariZen) for before use.


## Usage
```python
from diarizen.pipelines.inference import DiariZenPipeline

# load pre-trained model
diar_pipeline = DiariZenPipeline.from_pretrained("BUT-FIT/diarizen-meeting-base")
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav')

# print results
for turn, _, speaker in diar_results.itertracks(yield_label=True):
    print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")

# load pre-trained model and save RTTM result
diar_pipeline = DiariZenPipeline.from_pretrained(
        "BUT-FIT/diarizen-meeting-base",
        rttm_out_dir='.'
)
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav', sess_name='session_name')
```

## Results (SDM) 
```
Diarization Error Rate (DER):
--------------------------------------------------------------
System         Collar       AMI   AISHELL-4   AliMeeting         
--------------------------------------------------------------
Pyannote3        0s         21.1     13.9       22.8
                0.25s       13.7      7.7       13.6
--------------------------------------------------------------
Proposed         0s         15.6     12.0       17.7
                0.25s       10.2      6.1       10.2
--------------------------------------------------------------
```

## Citation
```
@inproceedings{han2025leveraging,
  title={Leveraging self-supervised learning for speaker diarization},
  author={Han, Jiangyu and Landini, Federico and Rohdin, Johan and Silnova, Anna and Diez, Mireia and Burget, Luk{\'a}{\v{s}}},
  booktitle={Proc. ICASSP},
  year={2025}
}
```