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license: apache-2.0 |
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# Positive Transfer Of The Whisper Speech Transformer To Human And Animal Voice Activity Detection |
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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 |
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> [**Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection**](https://doi.org/10.1101/2023.09.30.560270) |
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> Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H. R. Hahnloser <br> |
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> University of Zurich and ETH Zurich |
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This multi-species dataset was customized for Human and Animal Voice Activity Detection (vocal segmentation) when training the multi-species WhisperSeg-large segmenter.. |
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## Download Dataset |
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```python |
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from huggingface_hub import snapshot_download |
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snapshot_download('nccratliri/vad-multi-species', local_dir = "data/multi-species", repo_type="dataset" ) |
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``` |
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For more usage details, please refer to the GitHub repository: https://github.com/nianlonggu/WhisperSeg |
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## Citation |
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When using this dataset for your work, please cite: |
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``` |
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@article {Gu2023.09.30.560270, |
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author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser}, |
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title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection}, |
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elocation-id = {2023.09.30.560270}, |
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year = {2023}, |
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doi = {10.1101/2023.09.30.560270}, |
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publisher = {Cold Spring Harbor Laboratory}, |
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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.}, |
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URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270}, |
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eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf}, |
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journal = {bioRxiv} |
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} |
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``` |
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``` |
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@article {Gu2023.09.30.560270, |
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author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser}, |
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title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection}, |
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elocation-id = {2023.09.30.560270}, |
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year = {2023}, |
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doi = {10.1101/2023.09.30.560270}, |
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publisher = {Cold Spring Harbor Laboratory}, |
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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.}, |
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URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270}, |
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eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf}, |
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journal = {bioRxiv} |
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} |
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``` |
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## Contact |
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[email protected] |
