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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+  - audio-classification
+  - audio-to-audio
+language:
+  - en
+tags:
+  - audio
+  - sound-separation
+  - universal-sound-separation
+  - audio-mixing
+  - audioset
+pretty_name: Hive Dataset
+size_categories:
+  - 10M<n<100M
+dataset_info:
+  features:
+    - name: mix_id
+      dtype: string
+    - name: split
+      dtype: string
+    - name: sample_rate
+      dtype: int32
+    - name: target_duration
+      dtype: float64
+    - name: num_sources
+      dtype: int32
+    - name: sources
+      sequence:
+        - name: source_id
+          dtype: string
+        - name: path
+          dtype: string
+        - name: label
+          dtype: string
+        - name: crop_start_second
+          dtype: float64
+        - name: crop_end_second
+          dtype: float64
+        - name: chunk_start_second
+          dtype: float64
+        - name: chunk_end_second
+          dtype: float64
+        - name: rms_gain
+          dtype: float64
+        - name: snr_db
+          dtype: float64
+        - name: applied_weight
+          dtype: float64
+    - name: global_normalization_factor
+      dtype: float64
+    - name: final_max_amplitude
+      dtype: float64
+  splits:
+    - name: train
+      num_examples: 5000000
+    - name: validation
+      num_examples: 500000
+    - name: test
+      num_examples: 100000
+---
+
+<h1 align="center">A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation</h1>
+<p align="center">
+  <img src="asserts/logo.png" alt="Logo" width="250"/>
+</p>
+<p align="center">
+  <strong>Kai Li<sup>*</sup>, Jintao Cheng<sup>*</sup>, Chang Zeng, Zijun Yan, Helin Wang, Zixiong Su, Bo Zheng, Xiaolin Hu</strong><br>
+    <strong>Tsinghua University, Shanda AI, Johns Hopkins University</strong><br>
+    <strong><sup>*</sup>Equal contribution</strong><br>
+    <strong>Completed during Kai Li's internship at Shanda AI.</strong><br>
+  <a href="#">📜 Arxiv 2026</a> | <a href="#">🎶 Demo</a> | <a href="#">🤗 Dataset</a>
+</p>
+
+[![visitors](https://visitor-badge.laobi.icu/badge?page_id=ShandaAI.Hive)](https://huggingface.co/datasets/ShandaAI/Hive) [![GitHub stars](https://img.shields.io/github/stars/ShandaAI/Hive?style=social)](https://github.com/ShandaAI/Hive) [![license](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
+
+## 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
+```
+
+---
+
+## ⚖️ 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.

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