JFZhouuu / EEND-1

End-to-end Neural Diarization

PyTorch implementation for End-to-End Neural Diarization (EEND) based on Chainer implementation by Hitachi. This code implements the encoder-decoder-based attractor with self-attention version of EEND.

Usage

To run the training you can call:

python eend/train.py -c examples/train.yaml

Note that in the example you need to define the train and validation data directories as well as the output directory. The rest of the parameters are standard ones, as used in our publication. For fine-tuning, the process is similar:

python eend/train.py -c examples/adapt.yaml

In that case, you will need to provide the path where to find the trained model(s) that you want to fine-tune.

To run the inference, you can call:

python eend/infer.py -c examples/infer.yaml

Note that in the example you need to define the data, model and output directories.

Citation

In case of using the software please cite:
Federico Landini, Alicia Lozano-Diez, Mireia Diez, Lukáš Burget: From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization

@article{landini2022simulated,
  title={From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization},
  author={Landini, Federico and Lozano-Diez, Alicia and Diez, Mireia and Burget, Luk{\'a}{\v{s}}},
  journal={arXiv preprint arXiv:2204.00890},
  year={2022}
}

Contact

If you have any comment or question, please contact [e-mail]