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---
license: cc-by-nc-4.0
language:
- en
tags:
- music
size_categories:
- 10K<n<100K
---
# Music Grounding by Short Video E-commerce (MGSV-EC) Dataset
📄 [[Paper]](https://arxiv.org/abs/2408.16990v2)
## 📝 Dataset Summary
**MGSV-EC** is a large-scale dataset for the new task of **Music Grounding by Short Video (MGSV)**, which aims to localize a specific music segment that best serves as the background music (BGM) for a given query short video.
Unlike traditional video-to-music retrieval (V2MR), MGSV requires both identifying the relevant music track and pinpointing a precise moment from the track.
The dataset contains **53,194 short e-commerce videos** paired with **35,393 music moments**, all derived from **4,050 unique music tracks**. It supports evaluation in two modes:
- **Single-music Grounding (SmG)**: the relevant music track is known, and the task is to detect the correct segment.
- **Music-set Grounding (MsG)**: the model must retrieve the correct music track and its corresponding segment.
## 📐 Evaluation Protocol
| Mode | Sub-task | Metric |
|:--------------|:----------------------|:-------------------------------------------------|
| *Single-music* | Grounding (SmG) | mIoU |
| *Music-set* | Video-to-Music Retrieval (V2MR) | R$k$ |
| *Music-set* | Grounding (MsG) | MoR$k$ |
---
## 📊 Dataset Statistics
| **Split** | **#Music Tracks** | *Avg. Music Duration(sec)* | #Query Videos | *Avg. Video Duration(sec)* | **#Moments** |
|---------|----------------|----------------------|---------|----------------------|-----------|
| Total | 4,050 | 138.9 ± 69.6 | 53,194 | 23.9 ± 10.7 | 35,393 |
| *Train* | 3,496 | 138.3 ± 69.4 | 49,194 | 24.0 ± 10.7 | 31,660 |
| *Val* | 2,000 | 139.6 ± 70.0 | 2,000 | 22.8 ± 10.8 | 2,000 |
| *Test* | 2,000 | 139.9 ± 70.1 | 2,000 | 22.6 ± 10.7 | 2,000 |
- 🎵 Music type ratio: **~60% songs**, **~40% instrumental**
- 📹 Frame rate: 34 FPS; resolution: 1080×1920
## 📁 Data Format
Each row in the CSV file represents a query video paired with a music track and a localized music moment. The meaning of each column is as follows:
| Column Name | Description |
|:-------------|--------------|
| video_id | Unique identifier for the short query video. |
| music_id | Unique identifier for the associated music track. |
| video_start | Start time of the video segment in full video. |
| video_end | End time of the video segment in full video. |
| music_start | Start time of the music segment in full track. |
| music_end | End time of the music segment in full track. |
| music_total_duration | Total duration of the music track. |
| video_segment_duration | Duration of the video segment. |
| music_segment_duration | Duration of the music segment. |
| music_path | Relative path to the music track file. |
| video_total_duration | Total duration of the video. |
| video_width | Width of the video frame. |
| video_height | Height of the video frame. |
| video_total_frames | Total number of frames in the video. |
| video_frame_rate | Frame rate of the video. |
| video_category | Category label of the video content (e.g., "Beauty", "Food"). |
## 🧩 Feature Directory Structure
For each video-music pair, we provide pre-extracted visual and audio features for efficient training in [MGSV_feature.zip](./MGSV_feature.zip). The features are stored in the following directory structure:
```shell
[Your data feature path]
.
├── ast_feature2p5
│ ├── ast_feature/ # Audio segment features extracted by AST (Audio Spectrogram Transformer)
│ └── ast_mask/ # Segment-level masks indicating valid audio positions
└── vit_feature1
├── vit_feature/ # Frame-level visual features extracted by CLIP-ViT (ViT-B/32)
└── vit_mask/ # Frame-level masks indicating valid visual positions
```
Each .pt file corresponds to a single sample and includes:
- frame_feats: shape `[B, max_v_frames, 512]`
- frame_masks: shape `[B, max_v_frames]`, where 1 indicates valid frames, 0 for padding, used for padding control during batching
- segment_feats: shape `[B, max_snippet_num, 768]`
- segment_masks: shape `[B, max_snippet_num]`, indicating valid audio segments
Note:
- These pre-extracted features are compatible with our released PyTorch dataloader [dataloader_MGSV_EC_feature.py](./dataloader/dataloader_MGSV_EC_feature.py).
- Feature file paths are not stored in the CSV. Instead, users should specify the base directories via the following arguments:
- frame_frozen_feature_path: `[Your data feature path]/vit_feature1`
- music_frozen_feature_path: `[Your data feature path]/ast_feature2p5`
---
## 📖 Citation
If you use this dataset in your research, please cite:
```bibtex
@article{xin2024mgsv,
title={Music Grounding by Short Video},
author={Xin, Zijie and Wang, Minquan and Liu, Jingyu and Chen, Quan and Ma, Ye and Jiang, Peng and Li, Xirong},
journal={arXiv preprint arXiv:2408.16990},
year={2024}
}
```
## 📜 License
License: **CC BY-NC 4.0**
It is intended **for non-commercial academic research and educational purposes only**.
For commercial licensing or any use beyond research, please contact the authors.
📥 **Raw Vidoes/Music-tracks Access**
The raw video and music files are not publicly available due to copyright and privacy constraints.
Researchers interested in obtaining the full media content can contact **Kuaishou Technology** at: [[email protected]](mailto:[email protected]).
📬 **Contact for Issues**
For any dataset-related questions or problems (e.g., corrupted files or loading errors), please reach out to: [[email protected]](mailto:[email protected])
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