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<!-- PROJECT LOGO --> <br /> <div align="center"> <h3 align="center">Spiking-FullSubNet</h3> <p align="center"> Intel N-DNS Challenge Algorithmic Track Winner <br /> <a href="https://haoxiangsnr.github.io/spiking-fullsubnet/"><strong>Explore the docs »</strong></a> <br /> <br /> <a href="https://github.com/haoxiangsnr/spiking-fullsubnet/">View Demo</a> · <a href="https://github.com/haoxiangsnr/spiking-fullsubnet/issues">Report Bug</a> · <a href="https://github.com/haoxiangsnr/spiking-fullsubnet/issues">Request Feature</a> </p> </div> <!-- ABOUT THE PROJECT -->

About The Project

Spiking-FullSubNet

We are proud to announce that Spiking-FullSubNet has emerged as the winner of Intel N-DNS Challenge Track 1 (Algorithmic). Please refer to our brief write-up here for more details. This repository serves as the official home of the Spiking-FullSubNet implementation. Here, you will find:

<!--- We are actively working on improving the documentation, fixing bugs and removing redundancies. Please feel free to raise an issue or submit a pull request if you have suggestions for enhancements. Our team is diligently working on a comprehensive paper that will delve into the intricate details of Spiking-FullSuNet's architecture, its operational excellence, and the broad spectrum of its potential applications. Please stay tuned! -->

Updates

[2024-02-26] Currently, our repo contains two versions of the code:

  1. The frozen version, which serves as a backup for the code used in a previous competition. However, due to a restructuring in the audiozen directory, this version can no longer be directly used for inference. If you need to verify the experimental results from that time, please refer to this specific commit: 38fe020. There you will find everything you need. After switching to this commit, you can place the checkpoints from the model_zoo into the exp directory and use -M test for inference or -M train to retrain the model.

  2. The latest version of the code has undergone some restructuring and optimization to make it more understandable for readers. We've also introduced acceleate to assist with better training practices. We believe you can follow the instructions in the help documentation to run the training code directly. The pre-trained model checkpoints and a more detailed paper will be released by next weekend, so please stay tuned for that.

Documentation

See the Documentation for installation and usage. Our team is actively working to improve the documentation. Please feel free to raise an issue or submit a pull request if you have suggestions for enhancements.

License

All the code in this repository is released under the MIT License, for more details see the LICENSE file.

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