Improve model card: add pipeline tag, links, and usage

README.md

@@ -1,14 +1,14 @@
 ---
-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
 - flowsep
-datasets:
-- ShandaAI/Hive
 ---
 
 # FlowSep-hive
@@ -17,17 +17,54 @@ datasets:
 
 **FlowSep-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).
+This model was introduced in the paper: [A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation](https://huggingface.co/papers/2601.22599).
+
+- **Developed by:** Shanda AI Research Tokyo
+- **Paper:** [Hugging Face Papers](https://huggingface.co/papers/2601.22599)
+- **Code Repository:** [GitHub - ShandaAI/Hive](https://github.com/ShandaAI/Hive)
+- **Project Page:** [https://shandaai.github.io/Hive](https://shandaai.github.io/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
+
+You can perform inference using the scripts provided in the official GitHub repository.
+
+### 1) Install dependencies
+
+```bash
+pip install torch torchaudio librosa pyyaml pytorch-lightning huggingface_hub
+```
+
+### 2) FlowSep inference
+
+Clone the [repository](https://github.com/ShandaAI/Hive) and use the `infer_flowsep.py` script, which automatically downloads the configuration and checkpoints:
+
+```bash
+python infer_flowsep.py \
+  --audio_file /path/to/mixture.wav \
+  --text "acoustic guitar" \
+  --output_file /path/to/flowsep_output.wav
+```
+
+## Citation
+
+If you find this model or the Hive dataset useful, please cite:
+
+```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}
+}
+```