Skip to content

Latest commit

 

History

History
82 lines (71 loc) · 3.96 KB

README.md

File metadata and controls

82 lines (71 loc) · 3.96 KB
Raw

EEND-vector clustering

The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates two complementary major diarization approaches, i.e., traditional clustering-based and emerging end-to-end neural network-based approaches, to make the best of both worlds. In [1] it is shown that the EEND-vector clustering outperforms EEND when the recording is long (e.g., more than 5 min), while in [2] it is shown based on CALLHOME data that it outperforms x-vector clustering and EEND-EDA especially when the number of speakers in recordings is large.

This repository contains an example implementation of the EEND-vector clustering based on Pytorch to reproduce the results in [2], i.e., the CALLHOME experiments. For the trainer, we use Padertorch. This repository is implemented based on EEND and relies on some useful functions provided therein.

References

[1] Keisuke Kinoshita, Marc Delcroix, and Naohiro Tawara, "Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds," Proc. ICASSP, pp. 7198–7202, 2021

[2] Keisuke Kinoshita, Marc Delcroix, and Naohiro Tawara, "Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech," Proc. Interspeech, 2021 (to appear)

Citation

@inproceedings{eend-vector-clustering,
 author = {Keisuke Kinoshita and Marc Delcroix and Naohiro Tawara},
 title = {Integrating End-to-End Neural and Clustering-Based Diarization: Getting the Best of Both Worlds},
 booktitle = {{ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
 pages={7198-7202}
 year = {2021}
}

Install tools

Requirements

  • NVIDIA CUDA GPU
  • CUDA Toolkit (version == 9.2, 10.1 or 10.2)

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 Pytorch and Padertorch into 'eend' environment
  • Then, clones EEND to reference symbolic links stored under eend/, egs/ and utils/

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" (default). 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.

Run data preparation, training, inference, and scoring

cd egs/mini_librispeech/v1
CUDA_VISIBLE_DEVICES=0 ./run.sh
  • See RESULT.md and compare with your result.

CALLHOME experiment

Configuraition

  • Modify egs/callhome/v1/cmd.sh according to your job schedular. If you use your local machine, use "run.pl" (default). 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.

Run data preparation, training, inference, and scoring

cd egs/callhome/v1
CUDA_VISIBLE_DEVICES=0 ./run.sh --db_path <db_path>
# <db_path> means absolute path of the directory where the necessary LDC corpora are stored.
  • See RESULT.md and compare with your result.
  • If you want to run multi-GPU training, simply set CUDA_VISIBLE_DEVICES appropriately. This environment variable may be automatically set by your job schedular such as SLURM.