Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# MMEDIT
+
+[![arXiv](https://img.shields.io/badge/arXiv-25xx.xxxxx-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/25xx.xxxxx)
+[![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/CocoBro/MMEdit)
+[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](./LICENSE)
+
+
+## Introduction
+🟣 **MMEDIT** is a state-of-the-art audio generation model built upon the powerful [Qwen2-Audio 7B](https://huggingface.co/Qwen/Qwen2-Audio-7B). It leverages the robust audio understanding and instruction-following capabilities of the large language model to achieve precise and high-fidelity audio editing.
+
+---
+## Model Download
+| Models   | 🤗 Hugging Face |
+|-------|-------|
+| MMEdit| [MMEdit](https://huggingface.co/CocoBro/MMEdit) |
+
+download our pretrained model into ./ckpt/mmedit/
+
+---
+
+## Model Usage
+### 🔧 Dependencies and Installation
+- Python >= 3.10
+- [PyTorch >= 2.5.0](https://pytorch.org/)
+- [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads)
+- Dependent models:
+  - [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct), download into `./ckpt/qwen2-audio-7B-Instruct/`
+
+```bash
+# 1. Clone the repository
+git clone https://github.com/xycs6k8r2Anonymous/MMEdit.git
+cd MMEDIT
+
+# 2. Create environment
+conda create -n mmedit python=3.10 -y
+conda activate mmedit
+
+# 3. Install PyTorch and dependencies
+pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+pip install -r requirements.txt
+
+# Download Qwen2-Audio-7B-Instruct
+huggingface-cli download Qwen/Qwen2-Audio-7B-Instruct --local-dir ./ckpt/qwen2-audio-7B-instruct
+
+# Download MMEdit (Our Model)
+huggingface-cli download CocoBro/MMEdit --local-dir ./ckpt/mmedit
+```
+
+## 📂 Data Preparation
+
+For detailed instructions on the data pipeline, and dataset structure used for training, please refer to our separate documentation:
+
+👉 **[Data Pipeline & Preparation Guide](./datapipeline/datapipeline.md)**
+
+
+## ⚡ Quick Start
+
+
+
+
+### 1. Inference
+You can quickly generate example audio with the following code:
+
+```
+bash bash_scripts/infer_single.sh
+```
+
+The output will be save at inference/example
+
+
+---
+
+## 🚀 Usage
+
+### 1. Configuration
+Before running inference or training, please check `configs/config.yaml`. The project uses `hydra` for configuration management, allowing easy overrides via command line.
+
+### 2. Inference
+To run batch inference using the provided scripts:
+
+```bash
+cd src
+bash bash_scripts/inference.sh
+```
+
+### 3. Training
+Ensure you have downloaded the **Qwen2-Audio-7B-Instruct** checkpoint to `./ckpt/qwen2-audio-7B-instruct` and prepared your data according to the [Data Pipeline Guide](./docs/DATA_PIPELINE.md).
+
+```bash
+cd src
+# Launch distributed training
+bash bash_scripts/train_dist.sh
+```
+
+---
+
+## 📝 Todo
+- [ ] Release inference code and checkpoints.
+- [ ] Release training scripts.
+- [ ] Add HuggingFace Gradio Demo.
+- [ ] Release evaluation metrics and post-processing tools.
+
+## 🤝 Acknowledgement
+We thank the following open-source projects for their inspiration and code:
+* [Qwen2-Audio](https://github.com/QwenLM/Qwen2-Audio)
+* [Uniflowaudio](https://github.com/wsntxxn/UniFlow-Audio)
+* [AudioTime](https://github.com/wsntxxn/UniFlow-Audio)
+
+
+## 🖊️ Citation
+If you find this project useful, please cite our paper:
+
+```bibtex
+@article{mmedit2024,
+  title={MMEDIT: Audio Generation based on Qwen2-Audio 7B},
+  author={Your Name and Collaborators},
+  journal={arXiv preprint arXiv:25xx.xxxxx},
+  year={2024}
+}
+```