Update README.md

README.md

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-license: apache-2.0
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+<div align="center">
+
+# 🎙️ SimWhisper-Codec
+
+### Speaking Clearly: A Simplified Whisper-Based Codec for Low-Bitrate Speech Coding
+
+<p>
+  <a href="https://zhangxinwhut.github.io/SimWhisper-Codec/"><img src="https://img.shields.io/badge/🎧_Demo-Online-brightgreen" alt="Demo"></a>
+  <a href="https://arxiv.org/pdf/2510.20504"><img src="https://img.shields.io/badge/Paper-Arxiv-red" alt="paper"></a>
+  <a href="https://huggingface.co/xxx123456/SimWhisper_Codec"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Page-yellow" alt="Hugging Face"></a>
+</p>
+
+*A semantic-first speech codec that achieves superior performance through architectural simplification rather than complex supervision.*
+
+</div>
+
+---
+
+## ✨ Highlights
+
+- 🚀 **low Bitrate**: Only **1.1 kbps** at 16 kHz sampling rate
+- 🔊 **High Quality Speech Reconstruction**: Achieving UTMOS 4.00 WER 2.75 (hubert-large-ls960-ft) sim 0.83 (wavlm_large_finetune) stoi 0.93 pesq-nb 3.29 pesq-wb 2.72 on librispeech-test-clean reconstruction (gt: WER 2.16 UTMOS 4.09)
+- 🧊 **Frozen Encoder**: No fine-tuning of Whisper encoder required
+- ⚡ **Simple & Efficient**: Architectural simplification over complex supervision
+
+## 📊 Performance
+
+| Model | Bitrate | WER ↓ | PESQ-NB ↑ | PESQ-WB ↑ | STOI ↑ | SIM ↑ | UTMOS ↑ |
+|:------|:-------:|:-----:|:---------:|:---------:|:------:|:-----:|:-------:|
+| XCodec2.0 | 0.8 kbps | 2.61 | 3.04 | 2.43 | 0.92 | 0.82 | **4.13** |
+| XY-Tokenizer | 1.0 kbps | **2.46** | 3.00 | 2.41 | 0.91 | **0.84** | 3.98 |
+| **SimWhisper-Codec** | 1.1 kbps | 2.75 | **3.29** | **2.72** | **0.93** | 0.83 | 4.00 |
+
+*Evaluated on LibriSpeech test-clean*
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+# Clone repository
+git clone https://github.com/ZhangXinWhut/SimWhisper-Codec.git && cd SimWhisper-Codec
+
+# Create and activate conda environment
+conda create -n swcodec python=3.10 -y && conda activate swcodec
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Available Models 🗂️
+
+| Model Name | Hugging Face | Training Data |
+|:----------:|:-------------:|:---------------:|
+| SimWhisper-Codec | [🤗](https://huggingface.co/xxx123456/SimWhisper_Codec) | LibriSpeech |
+
+
+### Download Model Weights
+
+You need to download the SimWhisper-Codec model weights. You can find the weights in the [SimWhisper-Codec Hugging Face repository](https://huggingface.co/xxx123456/SimWhisper_Codec).
+
+```bash
+mkdir -p ./weights && huggingface-cli download xxx123456/SimWhisper_Codec SimWhisperCodec.pt --local-dir ./weights/
+```
+
+### Inference
+
+```python
+python inference.py --input_dir /path/to/LibriSpeech/test-clean
+```
+
+The reconstructed audio files will be available in the `output_wavs/` directory.
+
+## 🙏 Acknowledgements
+
+Our codebase builds upon the [XY-Tokenizer](https://github.com/gyt1145028706/XY-Tokenizer). We thank the authors for their excellent work.
+
+## 📝 Citation
+
+If you find this work useful in your research, please cite our paper:
+
+```
+@misc{zhang2025speakingclearlysimplifiedwhisperbased,
+      title={Speaking Clearly: A Simplified Whisper-Based Codec for Low-Bitrate Speech Coding}, 
+      author={Xin Zhang and Lin Li and Xiangni Lu and Jianquan Liu and Kong Aik Lee},
+      year={2025},
+      eprint={2510.20504},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2510.20504}, 
+}
+```
+
+---
+license: apache-2.0
+---