Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+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).
+
+## 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)
+
+## 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).