README.md

---
license: apache-2.0
language:
- en
tags:
- audio
- sound-separation
- audio-to-audio
- flowsep
datasets:
- ShandaAI/Hive
---

# FlowSep-hive

## Model Description

**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).

## 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).