nccratliri/vad-multi-species

---
license: apache-2.0
---
# Positive Transfer Of The Whisper Speech Transformer To Human And Animal Voice Activity Detection
We proposed WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for both human and animal Voice Activity Detection (VAD). For more details, please refer to our paper

>
> [**Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection**](https://doi.org/10.1101/2023.09.30.560270)
>
> Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H. R. Hahnloser <br>
> University of Zurich and ETH Zurich

This multi-species dataset was customized for Human and Animal Voice Activity Detection (vocal segmentation) when training the multi-species WhisperSeg-large segmenter..

## Download Dataset
```python
from huggingface_hub import snapshot_download
snapshot_download('nccratliri/vad-multi-species', local_dir = "data/multi-species", repo_type="dataset" )
```

For more usage details, please refer to the GitHub repository: https://github.com/nianlonggu/WhisperSeg

## Citation
When using this dataset for your work, please cite:
```
@article {Gu2023.09.30.560270,
author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser},
title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection},
elocation-id = {2023.09.30.560270},
year = {2023},
doi = {10.1101/2023.09.30.560270},
publisher = {Cold Spring Harbor Laboratory},
abstract = {This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270},
eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf},
journal = {bioRxiv}
}
```
```
@article {Gu2023.09.30.560270,
author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser},
title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection},
elocation-id = {2023.09.30.560270},
year = {2023},
doi = {10.1101/2023.09.30.560270},
publisher = {Cold Spring Harbor Laboratory},
abstract = {This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270},
eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf},
journal = {bioRxiv}
}
```

## Contact
[e-mail]

原始信息汇总

数据集概述

数据集名称

Positive Transfer Of The Whisper Speech Transformer To Human And Animal Voice Activity Detection

数据集描述

该数据集专为人类和动物语音活动检测（语音分割）定制，用于训练多物种的WhisperSeg-large分割器。

数据集下载

python from huggingface_hub import snapshot_download snapshot_download(nccratliri/vad-multi-species, local_dir = "data/multi-species", repo_type="dataset" )

引用

当使用此数据集进行工作时，请引用以下内容：

@article {Gu2023.09.30.560270, author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser}, title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection}, elocation-id = {2023.09.30.560270}, year = {2023}, doi = {10.1101/2023.09.30.560270}, publisher = {Cold Spring Harbor Laboratory}, abstract = {This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270}, eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf}, journal = {bioRxiv} }

@article {Gu2023.09.30.560270, author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser}, title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection}, elocation-id = {2023.09.30.560270}, year = {2023}, doi = {10.1101/2023.09.30.560270}, publisher = {Cold Spring Harbor Laboratory}, abstract = {This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270}, eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf}, journal = {bioRxiv} }

联系信息

[e-mail]