---
title: Hive
canonical_url: "https://www.modelscope.cn/datasets/ShandaAI/Hive"
md_url: "https://www.modelscope.cn/datasets/ShandaAI/Hive.md"
repository: ShandaAI/Hive
last_updated: 2026-02-10
license: apache-2.0
storage_size: "1.2 GB"
downloads: 71
stars: 0
---
# Hive
> Hive - ShandaAI 在 ModelScope 开源的数据集。A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation Kai Li , Jintao Cheng , Chang Zeng, Zijun Yan, Helin Wang, Zixiong Su, Bo Zheng, Xiaolin Hu Tsinghua University, Shanda AI, Johns…
ShandaAI/Hive 是 ModelScope 魔搭社区上的数据集，存储大小 1.2 GB，采用 apache-2.0 许可。
- **Repository**: ShandaAI/Hive
- **License**: apache-2.0
- **Storage size**: 1.2 GB
- **Downloads**: 71
- **Stars**: 0
- **Last updated**: 2026-02-10
Source: https://www.modelscope.cn/datasets/ShandaAI/Hive
---
A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
Kai Li*, Jintao Cheng*, Chang Zeng, Zijun Yan, Helin Wang, Zixiong Su, Bo Zheng, Xiaolin Hu
Tsinghua University, Shanda AI, Johns Hopkins University
*Equal contribution
Completed during Kai Li's internship at Shanda AI.
📜 Arxiv 2026 | 💻 Code | 🎶 Demo
## Usage
```python
from datasets import load_dataset
# Load full dataset
dataset = load_dataset("ShandaAI/Hive")
# Load specific split
train_data = load_dataset("ShandaAI/Hive", split="train")
# Streaming mode (recommended for large datasets)
dataset = load_dataset("ShandaAI/Hive", streaming=True)
```
## 📄 Dataset Description
**Hive** is a high-quality synthetic dataset designed for **Universal Sound Separation (USS)**. Unlike traditional methods relying on weakly-labeled in-the-wild data, Hive leverages an automated data collection pipeline to mine high-purity single-event segments from complex acoustic environments and synthesizes mixtures with semantically consistent constraints.
### Key Features
- **Purity over Scale**: 2.4k hours achieving competitive performance with million-hour baselines (~0.2% data scale)
- **Single-label Clean Supervision**: Rigorous semantic-acoustic alignment eliminating co-occurrence noise
- **Semantically Consistent Mixing**: Logic-based co-occurrence matrix ensuring realistic acoustic scenes
- **High Fidelity**: 44.1kHz sample rate for high-quality audio
### Dataset Scale
| Metric | Value |
|--------|-------|
| **Training Set Raw Audio** | 2,442 hours |
| **Val & Test Set Raw Audio** | 292 hours |
| **Mixed Samples** | 19.6M mixtures |
| **Total Mixed Duration** | ~22.4k hours |
| **Label Categories** | 283 classes |
| **Sample Rate** | 44.1 kHz |
| **Training Sample Duration** | 4 seconds |
| **Test Sample Duration** | 10 seconds |
### Dataset Splits
| Split | Samples | Description |
|-------|---------|-------------|
| Train | 17.5M | Training mixtures (4s duration) |
| Validation | 1.75M | Validation mixtures |
| Test | 350k | Test mixtures (10s duration) |
---
## 📂 Dataset Structure
### Directory Organization
```
hive-datasets-parquet/
├── README.md
├── train/
│ └── data.parquet
├── validation/
│ └── data.parquet
└── test/
└── data.parquet
```
Each split contains a single Parquet file with all mixture metadata. The `num_sources` field indicates the number of sources (2-5) for each mixture.
---
## 📋 Data Fields
### JSON Schema
Each JSON object contains complete generation parameters for reproducing a mixture sample:
```python
{
"mix_id": "sample_00000003",
"split": "train",
"sample_rate": 44100,
"target_duration": 4.0,
"num_sources": 2,
"sources": {
"source_id": ["s1", "s2"],
"path": ["relative/path/to/audio1", "relative/path/to/audio2"],
"label": ["Ocean", "Rain"],
"crop_start_second": [1.396, 2.5],
"crop_end_second": [5.396, 6.5],
"chunk_start_second": [35.0, 20.0],
"chunk_end_second": [45.0, 30.0],
"rms_gain": [3.546, 2.1],
"snr_db": [0.0, -3.0],
"applied_weight": [3.546, 1.487]
},
"global_normalization_factor": 0.786,
"final_max_amplitude": 0.95
}
```
### Field Descriptions
#### 1. Basic Info Fields
| Field | Type | Description |
|-------|------|-------------|
| `mix_id` | string | Unique identifier for the mixture task |
| `split` | string | Dataset partition (`train` / `validation` / `test`) |
| `sample_rate` | int32 | Audio sample rate in Hz (44100) |
| `target_duration` | float64 | Target duration in seconds (4.0 for train, 10.0 for test) |
| `num_sources` | int32 | Number of audio sources in this mixture (2-5) |
#### 2. Source Information (`sources`)
Metadata required to reproduce the mixing process for each audio source. Stored in columnar format (dict of lists) for efficient Parquet storage:
| Field | Type | Description |
|-------|------|-------------|
| `source_id` | list[string] | Source identifiers (`s1`, `s2`, ...) |
| `path` | list[string] | Relative paths to the source audio files |
| `label` | list[string] | AudioSet ontology labels for each source |
| `chunk_start_second` | list[float64] | Start times (seconds) for reading from original audio files |
| `chunk_end_second` | list[float64] | End times (seconds) for reading from original audio files |
| `crop_start_second` | list[float64] | Precise start positions (seconds) for reproducible random extraction |
| `crop_end_second` | list[float64] | Precise end positions (seconds) for reproducible random extraction |
| `rms_gain` | list[float64] | Energy normalization coefficients: $\text{target\_rms} / \text{current\_rms}$ |
| `snr_db` | list[float64] | Signal-to-noise ratios in dB assigned to each source |
| `applied_weight` | list[float64] | Final scaling weights: $\text{rms\_gain} \times 10^{(\text{snr\_db} / 20)}$ |
#### 3. Mixing Parameters
Global processing parameters after combining multiple audio sources:
| Field | Type | Description |
|-------|------|-------------|
| `global_normalization_factor` | float64 | Anti-clipping scaling coefficient: $0.95 / \text{max\_val}$ |
| `final_max_amplitude` | float64 | Maximum amplitude threshold (0.95) to prevent bit-depth overflow |
### Detailed Field Explanations
#### Cropping Logic
- `chunk_start/end_second`: Defines the reading interval from the original audio file
- `crop_start/end_second`: Records the precise random cropping position, ensuring exact reproducibility across runs
#### Energy Normalization (`rms_gain`)
Adjusts different audio sources to the same energy level:
$$\text{rms\_gain} = \frac{\text{target\_rms}}{\text{current\_rms}}$$
#### Signal-to-Noise Ratio (`snr_db`)
The SNR value assigned to each source, sampled from a predefined range using `random.uniform(snr_range[0], snr_range[1])`.
#### Applied Weight
The comprehensive scaling weight combining energy normalization and SNR adjustment:
$$\text{applied\_weight} = \text{rms\_gain} \times 10^{(\text{snr\_db} / 20)}$$
This is the final coefficient applied to the original waveform.
#### Global Normalization Factor
Prevents audio clipping after mixing:
$$\text{global\_normalization\_factor} = \frac{0.95}{\text{max\_val}}$$
Where `max_val` is the **peak amplitude (absolute value)** of the mixed signal.
---
## 🔧 Usage
### Download Metadata
```python
from datasets import load_dataset
# Load specific split and mixture type
dataset = load_dataset("ShandaAI/Hive", split="train")
```
### Generate Mixed Audio
Please refer to the [official GitHub repository](https://github.com/ShandaAI/Hive) for the complete audio generation pipeline.
```bash
# Clone the repository
git clone https://github.com/ShandaAI/Hive.git
cd Hive/hive_dataset
# Generate mixtures from metadata
python mix_from_metadata/mix_from_metadata.py \
--metadata_dir /path/to/downloaded/metadata \
--output_dir ./hive_dataset \
--dataset_paths dataset_paths.json \
--num_processes 16
```
---
## 📚 Source Datasets
Hive integrates **12 public datasets** to construct a long-tailed acoustic space:
| # | Dataset | Clips | Duration (h) | License |
|---|---------|-------|--------------|---------|
| 1 | BBC Sound Effects | 369,603 | 1,020.62 | Remix License |
| 2 | AudioSet | 326,890 | 896.61 | CC BY |
| 3 | VGGSound | 115,191 | 319.10 | CC BY 4.0 |
| 4 | MUSIC21 | 32,701 | 90.28 | YouTube Standard |
| 5 | FreeSound | 17,451 | 46.90 | CC0/BY/BY-NC |
| 6 | ClothoV2 | 14,759 | 38.19 | Non-Commercial Research |
| 7 | Voicebank-DEMAND | 12,376 | 9.94 | CC BY 4.0 |
| 8 | AVE | 3,054 | 6.91 | CC BY-NC-SA |
| 9 | SoundBible | 2,501 | 5.78 | CC BY 4.0 |
| 10 | DCASE | 1,969 | 5.46 | Academic Use |
| 11 | ESC50 | 1,433 | 1.99 | CC BY-NC 3.0 |
| 12 | FSD50K | 636 | 0.80 | Creative Commons |
| | **Total** | **898,564** | **2,442.60** | |
**Important Note**: This repository releases only **metadata** (JSON files containing mixing parameters and source references) for reproducibility. Users must independently download and prepare the source datasets according to their respective licenses.
---
## 📖 Citation
If you use this dataset, please cite:
```bibtex
@article{li2026hive,
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}
}
```
---
## ⚖️ License
This dataset metadata is released under the **Apache License 2.0**.
Please note that the source audio files are subject to their original licenses. Users must comply with the respective licenses when using the source datasets.
---
## 🙏 Acknowledgments
We extend our gratitude to the researchers and organizations who curated the foundational datasets that made Hive possible:
- **BBC Sound Effects** - Professional-grade recordings with broadcast-level fidelity
- **AudioSet** (Google) - Large-scale audio benchmark
- **VGGSound** (University of Oxford) - Real-world acoustic diversity
- **FreeSound** (MTG-UPF) - Rich crowdsourced soundscapes
- And all other contributing datasets
---
## 📬 Contact
For questions or issues, please open an issue on the [GitHub repository](https://github.com/ShandaAI/Hive) or contact the authors.