diff --git a/tools/AP_BWE_main/LICENSE b/tools/AP_BWE_main/LICENSE
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+MIT License
+
+Copyright (c) 2023 Ye-Xin Lu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/tools/AP_BWE_main/README.md b/tools/AP_BWE_main/README.md
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+# Towards High-Quality and Efficient Speech Bandwidth Extension with Parallel Amplitude and Phase Prediction
+### Ye-Xin Lu, Yang Ai, Hui-Peng Du, Zhen-Hua Ling
+
+**Abstract:**
+Speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller.
+This paper proposes a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both high-quality and efficient wideband speech waveform generation.
+The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs).
+It features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the input narrowband amplitude and phase spectra.
+To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively.
+Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in terms of speech quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz.
+In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on a single CPU.
+Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods.
+
+**We provide our implementation as open source in this repository. Audio samples can be found at the [demo website](http://yxlu-0102.github.io/AP-BWE).**
+
+
+## Pre-requisites
+0. Python >= 3.9.
+0. Clone this repository.
+0. Install python requirements. Please refer [requirements.txt](requirements.txt).
+0. Download datasets
+ 1. Download and extract the [VCTK-0.92 dataset](https://datashare.ed.ac.uk/handle/10283/3443), and move its `wav48` directory into [VCTK-Corpus-0.92](VCTK-Corpus-0.92) and rename it as `wav48_origin`.
+ 1. Trim the silence of the dataset, and the trimmed files will be saved to `wav48_silence_trimmed`.
+ ```
+ cd VCTK-Corpus-0.92
+ python flac2wav.py
+ ```
+ 1. Move all the trimmed training files from `wav48_silence_trimmed` to [wav48/train](wav48/train) following the indexes in [training.txt](VCTK-Corpus-0.92/training.txt), and move all the untrimmed test files from `wav48_origin` to [wav48/test](wav48/test) following the indexes in [test.txt](VCTK-Corpus-0.92/test.txt).
+
+## Training
+```
+cd train
+CUDA_VISIBLE_DEVICES=0 python train_16k.py --config [config file path]
+CUDA_VISIBLE_DEVICES=0 python train_48k.py --config [config file path]
+```
+Checkpoints and copies of the configuration file are saved in the `cp_model` directory by default.
+You can change the path by using the `--checkpoint_path` option.
+Here is an example:
+```
+CUDA_VISIBLE_DEVICES=0 python train_16k.py --config ../configs/config_2kto16k.json --checkpoint_path ../checkpoints/AP-BWE_2kto16k
+```
+
+## Inference
+```
+cd inference
+python inference_16k.py --checkpoint_file [generator checkpoint file path]
+python inference_48k.py --checkpoint_file [generator checkpoint file path]
+```
+You can download the [pretrained weights](https://drive.google.com/drive/folders/1IIYTf2zbJWzelu4IftKD6ooHloJ8mnZF?usp=share_link) we provide and move all the files to the `checkpoints` directory.
+
+Generated wav files are saved in `generated_files` by default.
+You can change the path by adding `--output_dir` option.
+Here is an example:
+```
+python inference_16k.py --checkpoint_file ../checkpoints/2kto16k/g_2kto16k --output_dir ../generated_files/2kto16k
+```
+
+## Model Structure
+
+
+## Comparison with other speech BWE methods
+### 2k/4k/8kHz to 16kHz
+
+
+
+
+### 8k/12k/16/24kHz to 16kHz
+
+
+
+
+## Acknowledgements
+We referred to [HiFi-GAN](https://github.com/jik876/hifi-gan) and [NSPP](https://github.com/YangAi520/NSPP) to implement this.
+
+## Citation
+```
+@article{lu2024towards,
+ title={Towards high-quality and efficient speech bandwidth extension with parallel amplitude and phase prediction},
+ author={Lu, Ye-Xin and Ai, Yang and Du, Hui-Peng and Ling, Zhen-Hua},
+ journal={arXiv preprint arXiv:2401.06387},
+ year={2024}
+}
+
+@inproceedings{lu2024multi,
+ title={Multi-Stage Speech Bandwidth Extension with Flexible Sampling Rate Control},
+ author={Lu, Ye-Xin and Ai, Yang and Sheng, Zheng-Yan and Ling, Zhen-Hua},
+ booktitle={Proc. Interspeech},
+ pages={2270--2274},
+ year={2024}
+}
+```