Update README.md

README.md

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----
-license: cc-by-nc-nd-4.0
----
+---
+license: cc-by-nc-nd-4.0
+language:
+  - en
+library_name: torch
+tags:
+  - audio
+  - music-generation
+  - accompaniment-generation
+  - unconditional-audio-generation
+  - pytorch
+---
+
+## AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck
+
+This is the official Hugging Face model repository for **AnyAccomp**, an accompaniment generation framework from the paper **AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck**.
+
+AnyAccomp addresses two critical challenges in accompaniment generation: **generalization** to in-the-wild singing voices and **versatility** in handling solo instrumental inputs.
+
+The core of our framework is a **quantized melodic bottleneck**, which extracts robust melodic features. A subsequent flow matching model then generates a matching accompaniment based on these features.
+
+For more details, please visit our [GitHub Repository](https://github.com/AmphionTeam/AnyAccomp).
+
+<img src="https://anyaccomp.github.io/data/framework.jpg" alt="framework" width="500">
+
+## Model Checkpoints
+
+This repository contains the three pretrained components of the AnyAccomp framework:
+
+| Model Name        | Directory                    | Description                                       |
+| ----------------- | ---------------------------- | ------------------------------------------------- |
+| **VQ**            | `./pretrained/vq`            | Extracts core melodic features from audio.        |
+| **Flow Matching** | `./pretrained/flow_matching` | Generates accompaniments from melodic features.   |
+| **Vocoder**       | `./pretrained/vocoder`       | Converts generated features into audio waveforms. |
+
+## How to use
+
+To run this model, you need to follow the steps below:
+
+1. Clone the repository and install the environment.
+2. Run the Gradio demo / Inference script.
+
+### 1. Clone and Environment
+
+In this section, follow the steps below to clone the repository and install the environment.
+
+1. Clone the repository.
+2. Install the environment following the guide below.
+
+```bash
+git clone https://github.com/AmphionTeam/AnyAccomp.git
+
+# enter the repositry directory
+cd AnyAccomp
+```
+
+### 2. Download the Pretrained Models
+
+We provide a simple Python script to download all the necessary pretrained models from Hugging Face into the correct directory.
+
+Before running the script, make sure you are in the `AnyAccomp` root directory.
+
+Run the following command:
+
+```bash
+python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='amphion/anyaccomp', local_dir='./pretrained', repo_type='model')"
+```
+
+If you have trouble connecting to Hugging Face, you can try switching to a mirror endpoint before running the command:
+
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+### 3. Install the Environment
+
+Before start installing, make sure you are under the `AnyAccomp` directory. If not, use `cd` to enter.
+
+```bash
+conda create -n anyaccomp python=3.9
+conda activate anyaccomp
+conda install -c conda-forge ffmpeg=4.0
+pip install -r requirements.txt 
+```
+
+### Run the Model
+
+Once the setup is complete, you can run the model using either the Gradio demo or the inference script.
+
+#### Run Gradio 🤗 Playground Locally
+
+You can run the following command to interact with the playground:
+
+```bash
+python gradio_app.py
+```
+
+#### Inference Script
+
+If you want to infer several audios, you can use the python inference script from folder.
+
+
+```bash
+python infer_from_folder.py
+```
+
+By default, the script loads input audio from `./example/input` and saves the results to `./example/output`. You can customize these paths in the [inference script](./anyaccomp/infer_from_folder.py).
+
+## Citation
+
+If you use AnyAccomp in your research, please cite our paper:
+
+```bibtex
+@article{zhang2025anyaccomp,
+  title={AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck},
+  author={Zhang, Junan and Zhang, Yunjia and Zhang, Xueyao and Wu, Zhizheng},
+  journal={arXiv preprint arXiv:2509.14052},
+  year={2025}
+}
+```
+