Awesome
Mamba-based Segmentation Model for Speaker Diarization
Alexis Plaquet, Naohiro Tawara, Marc Delcroix, Shota Horiguchi, Atsushi Ando, and Shoko Araki
Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization, as attention-based models have unsuitable memory requirements for long-form audio, and traditional RNN capabilities are too limited. In this paper, we propose to assess the potential of Mamba for diarization by comparing the state-of-the-art neural segmentation of the pyannote.audio pipeline with our proposed Mamba-based variant. Mamba's stronger processing capabilities allow usage of longer local windows, which significantly improve diarization quality by making the speaker embedding extraction more reliable. We find Mamba to be a superior alternative to both traditional RNN and the tested attention-based model. Our proposed Mamba-based system achieves state-of-the-art performance on three widely used diarization datasets.
Citations
@misc{plaquet2024mambabasedsegmentationmodelspeaker,
title={Mamba-based Segmentation Model for Speaker Diarization},
author={Alexis Plaquet and Naohiro Tawara and Marc Delcroix and Shota Horiguchi and Atsushi Ando and Shoko Araki},
year={2024},
eprint={2410.06459},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2410.06459},
}
Repository contents
- The source code relevant for running and evaluating Mamba-based models in
src/
- Tutorial notebooks in
tutorials/
:- Setting up the environment
- Training a Mamba-based segmentation model from scratch
- Evaluating the full pipeline (TODO)
- The subset splits used for each dataset in
databases/
(in pyannote.audio format) - The predictions outputted by the model (in
eval_rttms.zip
files) and the detail of all computed metrics in.csv
files, all contained inresults/
Installation
You can install the plaqntt
package using pip.
- Clone this repository and open a terminal in the same folder as this file.
- Run
pip install -e .
License
Please refer to the LICENSE file for details.