Improve model card with links and usage instructions

This PR updates the model card to include links to the paper, project page, and code repository. It also adds instructions on how to run inference using the official scripts from the GitHub repository and correctly sets the `pipeline_tag` metadata.

README.md

@@ -1,33 +1,65 @@
 ---
-license: apache-2.0
+datasets:
+- ShandaAI/Hive
 language:
 - en
+license: apache-2.0
+pipeline_tag: audio-to-audio
 tags:
 - audio
 - sound-separation
-- audio-to-audio
 - audiosep
-datasets:
-- ShandaAI/Hive
 ---
 
 # AudioSep-hive
 
-## Model Description
-
 **AudioSep-hive** is a data-efficient, query-based universal sound separation model trained on the [Hive dataset](https://huggingface.co/datasets/ShandaAI/Hive). By leveraging the high-quality, semantically consistent Hive dataset, this model achieves competitive separation accuracy and perceptual quality comparable to state-of-the-art models (such as SAM-Audio) while utilizing only a fraction (~0.2%) of the training data volume.
 
-This model is developed by **Shanda AI Research Tokyo** and is introduced in the paper: [A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation](https://arxiv.org/abs/2601.22599).
+- **Paper:** [A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation](https://arxiv.org/abs/2601.22599)
+- **Project Page:** https://shandaai.github.io/Hive
+- **Code Repository:** https://github.com/ShandaAI/Hive
 
 ## Model Details
 
-- **Model Type:​** Query-Based Universal Sound Separation
-- **Language(s):​** English (for text queries)
-- **License:​** Apache 2.0 (Please update if different)
-- **Trained on:​** [ShandaAI/Hive](https://huggingface.co/datasets/ShandaAI/Hive) (2,442 hours of raw audio, 19.6M mixtures)
-- **Paper:​** [arXiv:2601.22599](https://arxiv.org/abs/2601.22599)
-- **Code Repository:​** [GitHub - ShandaAI/Hive](https://github.com/ShandaAI/Hive)
+- **Model Type:** Query-Based Universal Sound Separation
+- **Language(s):** English (for text queries)
+- **License:** Apache 2.0
+- **Trained on:** [ShandaAI/Hive](https://huggingface.co/datasets/ShandaAI/Hive) (2,442 hours of raw audio, 19.6M mixtures)
 
 ## Uses
 
-The model is intended for universal sound separation tasks, allowing users to extract specific sounds from complex audio mixtures using multimodal prompts (e.g., text descriptions or audio queries). 
+The model is intended for universal sound separation tasks, allowing users to extract specific sounds from complex audio mixtures using multimodal prompts (e.g., text descriptions or audio queries). 
+
+## Usage
+
+To use this model, you can use the inference scripts provided in the official GitHub repository.
+
+### 1. Install dependencies
+
+```bash
+git clone https://github.com/ShandaAI/Hive
+cd Hive
+pip install torch torchaudio librosa pyyaml pytorch-lightning huggingface_hub gradio
+```
+
+### 2. Run Inference
+
+The following command will automatically download the configuration and checkpoints from this repository:
+
+```bash
+python infer_audiosep.py \
+  --audio_file /path/to/mixture.wav \
+  --text "acoustic guitar" \
+  --output_file /path/to/audiosep_output.wav
+```
+
+## Citation
+
+```bibtex
+@article{li2026semantically,
+  title={A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation},
+  author={Li, Kai and Cheng, Jintao and Zeng, Chang and Yan, Zijun and Wang, Helin and Su, Zixiong and Zheng, Bo and Hu, Xiaolin},
+  journal={arXiv preprint arXiv:2601.22599},
+  year={2026}
+}
+```