nccratliri/vad-multi-species

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

Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H. R. Hahnloser
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

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

nianlong.gu@uzh.ch