File size: 2,244 Bytes
113d2e4 cdd24f1 113d2e4 cdd24f1 113d2e4 cdd24f1 113d2e4 cdd24f1 113d2e4 cdd24f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ---
datasets:
- ShandaAI/Hive
language:
- en
license: apache-2.0
pipeline_tag: audio-to-audio
tags:
- audio
- sound-separation
- audiosep
---
# AudioSep-hive
**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.
- **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
- **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).
## 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}
}
``` |