DiariZen is a speaker diarization toolkit driven by AudioZen and Pyannote 3.1.
# create virtual python environment
conda create --name diarizen python=3.10
conda activate diarizen
# install diarizen
conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt && pip install -e .
# install pyannote-audio
cd pyannote-audio && pip install -e .[dev,testing]
# install dscore
git submodule init
git submodule update
- For model training, see
recipes/diar_ssl/run_stage.sh. - For model pruning, see
recipes/diar_ssl_pruning/run_stage.sh. - For inference, our model supports for Hugging Face 🤗. See below:
from diarizen.pipelines.inference import DiariZenPipeline
# load pre-trained model
diar_pipeline = DiariZenPipeline.from_pretrained("BUT-FIT/diarizen-wavlm-large-s80-md")
# apply diarization pipeline
diar_results = diar_pipeline('./example/EN2002a_30s.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}")
# start=0.0s stop=2.7s speaker_0
# start=0.8s stop=13.6s speaker_3
# start=5.8s stop=6.4s speaker_0
# ...
# load pre-trained model and save RTTM result
diar_pipeline = DiariZenPipeline.from_pretrained(
"BUT-FIT/diarizen-wavlm-large-s80-md",
rttm_out_dir='.'
)
# apply diarization pipeline
diar_results = diar_pipeline('./example/EN2002a_30s.wav', sess_name='EN2002a')We train DiariZen models on a compound dataset composed of the datasets listed in the table below, followed by structured pruning to remove redundant parameters. For the results below:
- AISHELL-4 was converted to mono using
sox in.wav -c 1 out.wav. - NOTSOFAR-1 contains only single-channel recordings, e.g.
sc_plaza_0,sc_rockfall_0. - Diarization Error Rate (DER) is evaluated without applying a collar.
- No domain adaptation is applied to any individual dataset.
- All experiments use the same clustering hyperparameters across datasets.
| Dataset | Pyannote v3.1 | DiariZen-Base-s80 | DiariZen-Large-s80 |
|---|---|---|---|
| AMI-SDM | 22.4 | 15.8 | 14.0 |
| AISHELL-4 | 12.2 | 10.7 | 9.8 |
| AliMeeting far | 24.4 | 14.1 | 12.5 |
| NOTSOFAR-1 | - | 20.3 | 17.9 |
| MSDWild | 25.3 | 17.4 | 15.6 |
| DIHARD3 full | 21.7 | 15.9 | 14.5 |
| RAMC | 22.2 | 11.4 | 11.0 |
| VoxConverse | 11.3 | 9.7 | 9.2 |
2025-06-03: Uploaded structured pruning recipes, released new pre-trained models, and updated multiple benchmark results.
If you found this work helpful, please consider citing
@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}
}
@article{han2025fine,
title={Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization},
author={Han, Jiangyu and Landini, Federico and Rohdin, Johan and Silnova, Anna and Diez, Mireia and Cernocky, Jan and Burget, Lukas},
journal={arXiv preprint arXiv:2505.24111},
year={2025}
}
@article{han2025efficient,
title={Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised Models},
author={Han, Jiangyu and P{\'a}lka, Petr and Delcroix, Marc and Landini, Federico and Rohdin, Johan and Cernock{\`y}, Jan and Burget, Luk{\'a}{\v{s}}},
journal={arXiv preprint arXiv:2506.18623},
year={2025}
}
This repository under the MIT license.
If you have any comment or question, please contact ihan@fit.vut.cz