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nccratliri/
whisperseg-large-ms

Name: whisperseg-large-ms
Availability: InStock

transformers

pytorch

whisper

automatic-speech-recognition

apache-2.0

endpoints_compatible

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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

Accepted to the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)

The model "nccratliri/whisperseg-large-ms" is the checkpoint of the multi-species WhisperSeg-large 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-large-ms", 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:

Open In Colab

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