EEND (End-to-End Neural Diarization)

EEND (End-to-End Neural Diarization) is a neural-network-based speaker diarization method.

https://www.isca-speech.org/archive/Interspeech_2019/abstracts/2899.html
https://arxiv.org/abs/1909.06247 (to appear at ASRU 2019)

Install tools

Requirements

NVIDIA CUDA GPU
CUDA Toolkit (8.0 <= version <= 10.1)

Install kaldi and python environment

cd tools
make

This command builds kaldi at tools/kaldi
- if you want to use pre-build kaldi
```
cd tools
make KALDI=<existing_kaldi_root>
```
  This option make a symlink at tools/kaldi
This command extracts miniconda3 at tools/miniconda3, and creates conda envirionment named 'eend'
Then, installs Chainer and cupy into 'eend' environment
- use CUDA in /usr/local/cuda/
- if you need to specify your CUDA path
```
cd tools
make CUDA_PATH=/your/path/to/cuda-8.0
```
  This command installs cupy-cudaXX according to your CUDA version. See https://docs-cupy.chainer.org/en/stable/install.html#install-cupy

Test recipe (mini_librispeech)

Configuration

Modify egs/mini_librispeech/v1/cmd.sh according to your job schedular. If you use your local machine, use "run.pl". If you use Grid Engine, use "queue.pl" If you use SLURM, use "slurm.pl". For more information about cmd.sh see http://kaldi-asr.org/doc/queue.html.
Data preparation
```
cd egs/mini_librispeech/v1
./run_prepare_shared.sh
```
Run training, inference, and scoring
```
./run.sh
```
See RESULT.md and compare with your result.

CALLHOME two-speaker experiment

Configuraition

Modify egs/callhome/v1/cmd.sh according to your job schedular. If you use your local machine, use "run.pl". If you use Grid Engine, use "queue.pl" If you use SLURM, use "slurm.pl". For more information about cmd.sh see http://kaldi-asr.org/doc/queue.html.
Modify egs/callhome/v1/run_prepare_shared.sh according to storage paths of your copora.

Data preparation

cd egs/callhome/v1
./run_prepare_shared.sh

Self-attention-based model (latest configuration)

./run.sh

BLSTM-based model (old configuration)

local/run_blstm.sh

References

[1] Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi, Kenji Nagamatsu, Shinji Watanabe, " End-to-End Neural Speaker Diarization with Permutation-free Objectives," Proc. Interspeech, pp. 4300-4304, 2019

[2] Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi, Yawen Xue, Kenji Nagamatsu, Shinji Watanabe, " End-to-End Neural Speaker Diarization with Self-attention," arXiv preprints arXiv:1909.06247, 2019

Citation

@inproceedings{Fujita2019Interspeech,
 author={Yusuke Fujita and Naoyuki Kanda and Shota Horiguchi and Kenji Nagamatsu and Shinji Watanabe},
 title={{End-to-End Neural Speaker Diarization with Permutation-free Objectives}},
 booktitle={Interspeech},
 pages={4300--4304}
 year=2019
}

EEND (End-to-End Neural Diarization)

Install tools

Requirements

Install kaldi and python environment

Test recipe (mini_librispeech)

Configuration

Data preparation

Run training, inference, and scoring

CALLHOME two-speaker experiment

Configuraition

Data preparation

Self-attention-based model (latest configuration)

BLSTM-based model (old configuration)

References

Citation