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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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> |
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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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*Accepted to the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)* |
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The model "nccratliri/whisperseg-animal-vad" is the checkpoint of the multi-species WhisperSeg that was finetuned on the vocal segmentation datasets of five species. |
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## Usage |
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### Clone the GitHub repo and install dependencies |
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```bash |
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git clone https://github.com/nianlonggu/WhisperSeg.git |
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cd WhisperSeg; pip install -r requirements.txt |
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``` |
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Then in the folder "WhisperSeg", run the following python script: |
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```python |
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from model import WhisperSegmenter |
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import librosa |
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import json |
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segmenter = WhisperSegmenter( "nccratliri/whisperseg-animal-vad", device="cuda" ) |
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sr = 32000 |
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spec_time_step = 0.0025 |
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audio, _ = librosa.load( "data/example_subset/Zebra_finch/test_adults/zebra_finch_g17y2U-f00007.wav", |
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sr = sr ) |
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## Note if spec_time_step is not provided, a default value will be used by the model. |
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prediction = segmenter.segment( audio, sr = sr, spec_time_step = spec_time_step ) |
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print(prediction) |
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``` |
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{'onset': [0.01, 0.38, 0.603, 0.758, 0.912, 1.813, 1.967, 2.073, 2.838, 2.982, 3.112, 3.668, 3.828, 3.953, 5.158, 5.323, 5.467], 'offset': [0.073, 0.447, 0.673, 0.83, 1.483, 1.882, 2.037, 2.643, 2.893, 3.063, 3.283, 3.742, 3.898, 4.523, 5.223, 5.393, 6.043], 'cluster': ['zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0']} |
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Visualize the results of WhisperSeg: |
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```python |
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from audio_utils import SpecViewer |
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spec_viewer = SpecViewer() |
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spec_viewer.visualize( audio = audio, sr = sr, min_frequency= 0, prediction = prediction, |
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window_size=8, precision_bits=1 |
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) |
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``` |
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Run it in Google Colab: |
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<a href="https://colab.research.google.com/github/nianlonggu/WhisperSeg/blob/master/docs/WhisperSeg_Voice_Activity_Detection_Demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
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For more details, please refer to the GitHub repository: https://github.com/nianlonggu/WhisperSeg |
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## Citation |
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When using our code or models for your work, please cite the following paper: |
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``` |
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@INPROCEEDINGS{10447620, |
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author={Gu, Nianlong and Lee, Kanghwi and Basha, Maris and Kumar Ram, Sumit and You, Guanghao and Hahnloser, Richard H. R.}, |
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booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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title={Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection}, |
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year={2024}, |
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volume={}, |
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number={}, |
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pages={7505-7509}, |
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keywords={Voice activity detection;Adaptation models;Animals;Transformers;Acoustics;Human voice;Spectrogram;Voice activity detection;audio segmentation;Transformer;Whisper}, |
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doi={10.1109/ICASSP48485.2024.10447620}} |
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``` |
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## Contact |
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[email protected] |
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