# ScriptsForVoxBlink2

**Repository Path**: jin1258804025/ScriptsForVoxBlink2

## Basic Information

- **Project Name**: ScriptsForVoxBlink2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-26
- **Last Updated**: 2025-11-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# The VoxBlink2 Dataset

The VoxBlink2 dataset is a Large Scale speaker recognition dataset with 100K+ speakers obtained from YouTube platform. This repository provides guidelines to build the corpus and relative resources to reproduce the results in our article . For more introduction, please see [cite](https://VoxBlink2.github.io). If you find this repository helpful to your research, don't forget to give us star🌟.

## Resource 
Let's start with obtaining the [resource](https://drive.google.com/drive/folders/1lzumPsnl5yEaMP9g2bFbSKINLZ-QRJVP?usp=sharing) files and decompressing tar-files.
```bash
tar -zxvf spk_info.tar.gz
tar -zxvf vb2_meta.tar.gz 
tar -zxvf asr_res.tar.gz
```

## File structure


```
% The file structure is summarized as follows: 
|---- data               
|     |---- ossi    # [Folder]evaluation protocols for open-set speaker identification
|     |---- test_vox # [Folder] evaluation protocols for speaker verification
|     |---- spk2videos	# [spk,video1,video2,...]
|---- ckpt #checkpoints for evaluation
|     |---- ecapatdnn # [Folder]
|     |---- resnet34 # [Folder]
|     |---- resnet100 # [Folder]
|     |---- resnet293 # [Folder]
|     |---- face_model # [Folder]
|---- spk_info             # video'tags of speakers：
|     |---- id000000	
|     |---- id000001	
|     |---- ...
|---- asr_res            # ASR annotations by Whisper：
|     |---- id000000	
|     |---- id000001	
|     |---- ...
|---- meta		# timestamps for video/audio cropping
|     |---- id000000	# spkid
|           |---- DwgYRqnQZHM	#videoid
|                 |---- 00000.txt	#uttid
|                 |---- ...
|           |---- ... 
|     |---- ...	
|---- face_id            # face_identification modules
|     |---- api.py # corresponding inference functions
|     |---- arcface.py # corresponding model definitions
|     |---- README.md 
|     |---- test.py # Test
|---- ossi            # video'tags of speakers：
|     |---- eval.py # recipe for evaluate openset speaker identification
|     |---- utils.py 
|     |---- example.npy # eg. Resnet34-based embedding for evaluate OSSI 
|---- audio_cropper.py	# extract audio-only segments by timestamps from downloaded audios
|---- video_cropper.py	# extract audio-visual segments by timestamps from downloaded videos
|---- downloader.py	# scripts for download videos
|---- LICENSE		# license
|---- README.md	
|---- requirement.txt			

```
## Download
The following procedures show how to construct your VoxBlink2
### Pre-requisites
1. Install **ffmpeg**:
```bash
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install ffmpeg
```
2. Install Python library:
```bash
pip install -r requirements.txt
```

3. Download videos

We provide two alternatives for you to download video or audio-only segments. We Also leverage multi-thread to facilate download process.

* For Audio-Visual
```python
python downloader.py --base_dir ${BASE_DIR} --num_workers 4 --mode video
```
* For Audio-Only
```python
python downloader.py --base_dir ${BASE_DIR} --num_workers 4 --mode audio
```

4. Crop Audio/Videos
* For Audio-Visual
```python
python cropper_video.py --save_dir_audio ${SAVE_PATH_AUDIO} --save_dir_video ${SAVE_PATH_VIDEO} --timestamp_path meta --video_root=${BASE_DIR} --num_workers 4
```
* For Audio-Only
```python
python cropper_audio.py --save_dir ${SAVE_PATH_AUDIO} --timestamp_path meta --audio_root=${BASE_DIR} --num_workers 4
```

## FID Evaluation

We provide simple scripts of our face identification model, which is adopted in curating VoxBlink2. For more, please look at [fid](https://github.com/VoxBlink2/ScriptsForVoxBlink2/tree/main/face_id).


## SV Evaluation

We provide simple scripts for model evaluation of ASV, just execute `run_eval.sh` in `asv` folder. For more, please look at [asv](https://github.com/VoxBlink2/ScriptsForVoxBlink2/tree/main/asv).

## Open-Set Speaker Identification Evaluation
We provide simple scripts for model evaluation of our proposed task: Open-Set Speaker-Identification(OSSI). just execute `run_eval_ossi.sh` in `ossi` folder. For more, please look at [ossi](https://github.com/VoxBlink2/ScriptsForVoxBlink2/tree/main/ossi).

## License

The dataset is licensed under the **CC BY-NC-SA 4.0** license. This means that you can share and adapt the dataset for non-commercial purposes as long as you provide appropriate attribution and distribute your contributions under the same license. Detailed terms can be found [here](LICENSE).

Important Note: Our released dataset only contains annotation data, including the YouTube links, time stamps and speaker labels. We do not release audio or visual data and it is the user's responsibility to decide whether and how to download the video data and whether their intended purpose with the downloaded data is legal in their country. For YouTube users with concerns regarding their videos' inclusion in our dataset, please contact us via E-mail: yuke.lin@dukekunshan.edu.cn or ming.li369@dukekunshan.edu.cn.




## Citation

Please cite the paper below if you make use of the dataset:

```
@misc{lin2024voxblink2100kspeakerrecognition,
      title={VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark}, 
      author={Yuke Lin and Ming Cheng and Fulin Zhang and Yingying Gao and Shilei Zhang and Ming Li},
      year={2024},
      eprint={2407.11510},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2407.11510}, 
}
```