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---
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
*Accepted to the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)*
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.
## Usage
### Clone the GitHub repo and install dependencies
```bash
git clone https://github.com/nianlonggu/WhisperSeg.git
cd WhisperSeg; pip install -r requirements.txt
```
Then in the folder "WhisperSeg", run the following python script:
```python
from model import WhisperSegmenter
import librosa
import json
segmenter = WhisperSegmenter( "nccratliri/whisperseg-animal-vad", device="cuda" )
sr = 32000
spec_time_step = 0.0025
audio, _ = librosa.load( "data/example_subset/Zebra_finch/test_adults/zebra_finch_g17y2U-f00007.wav",
sr = sr )
## Note if spec_time_step is not provided, a default value will be used by the model.
prediction = segmenter.segment( audio, sr = sr, spec_time_step = spec_time_step )
print(prediction)
```
{'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']}
Visualize the results of WhisperSeg:
```python
from audio_utils import SpecViewer
spec_viewer = SpecViewer()
spec_viewer.visualize( audio = audio, sr = sr, min_frequency= 0, prediction = prediction,
window_size=8, precision_bits=1
)
```
Run it in Google Colab:
<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>
For more details, please refer to the GitHub repository: https://github.com/nianlonggu/WhisperSeg
## Citation
When using our code or models for your work, please cite the following paper:
```
@INPROCEEDINGS{10447620,
author={Gu, Nianlong and Lee, Kanghwi and Basha, Maris and Kumar Ram, Sumit and You, Guanghao and Hahnloser, Richard H. R.},
booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection},
year={2024},
volume={},
number={},
pages={7505-7509},
keywords={Voice activity detection;Adaptation models;Animals;Transformers;Acoustics;Human voice;Spectrogram;Voice activity detection;audio segmentation;Transformer;Whisper},
doi={10.1109/ICASSP48485.2024.10447620}}
```
## Contact
nianlong.gu@uzh.ch
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