Intel N-DNS Challenge Algorithmic Track Winner
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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:
- A PyTorch-based implementation of the Spiking-FullSubNet model.
- Scripts for training the model and evaluating its performance.
- The pre-trained models in the
model_zoodirectory, ready to be further fine-tuned on the other datasets.
[2024-02-26] Currently, our repo contains two versions of the code:
-
The frozen version, which serves as a backup for the code used in a previous competition. However, due to a restructuring in the
audiozendirectory, 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 themodel_zoointo theexpdirectory and use-M testfor inference or-M trainto retrain the model. -
The latest version of the code has undergone some restructuring and optimization to make it more understandable for readers. We've also introduced
acceleateto 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.
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.
All the code in this repository is released under the MIT License, for more details see the LICENSE file.
