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)

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

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

• 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 https://kaldi-asr.org/doc/queue.html.

cd egs/mini_librispeech/v1
./run_prepare_shared.sh

./run.sh

• See RESULT.md and compare with your result.

• 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 https://kaldi-asr.org/doc/queue.html.
• Modify egs/callhome/v1/run_prepare_shared.sh according to storage paths of your copora.

cd egs/callhome/v1
./run_prepare_shared.sh

./run.sh

local/run_blstm.sh

[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

@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
}