Merge branch 'main' of github.com:haoxiangsnr/spiking-fullsubnet into…

… main

README.md

@@ -37,17 +37,26 @@
 
 ![Spiking-FullSubNet](./docs/source/images/project_image.png)
 
-We are proud to announce that Spiking-FullSubNet has emerged as the [winner of Intel N-DNS Challenge Track 1 (Algorithmic)](https://intel-ncl.atlassian.net/wiki/spaces/INRC/blog/2023/12/01/2027225099/INRC+Forum+12+12+2023.+Clairaudience+Intel+N-DNS+Challenge+Track+1+Algorithmic+Winner.). Please refer to our [brief write-up here](./Spiking-FullSubNet.pdf) for more details. This repository serves as the official home of the Spiking-FullSubNet implementation. Here, you will find:
+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](./Spiking-FullSubNet.pdf) 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_zoo` directory, ready to be further fine-tuned on the other datasets.
 
+<!---
 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](https://github.com/haoxiangsnr/spiking-fullsubnet/tree/38fe020cdb803d2fdc76a0df4b06311879c8e370). 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.
 
-## Upcoming Detailed Paper
 
-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!
 
 ## Documentation
 
@@ -69,4 +78,4 @@ All the code in this repository is released under the [MIT License](https://open
 [issues-shield]: https://img.shields.io/github/issues/haoxiangsnr/spiking-fullsubnet.svg?style=for-the-badge
 [issues-url]: https://github.com/haoxiangsnr/spiking-fullsubnet/issues
 [license-shield]: https://img.shields.io/github/license/haoxiangsnr/spiking-fullsubnet.svg?style=for-the-badge
-[license-url]: https://github.com/haoxiangsnr/spiking-fullsubnet/blob/master/LICENSE.txt
+[license-url]: https://github.com/haoxiangsnr/spiking-fullsubnet/blob/master/LICENSE.txt