whisperseg-large-ms-ct2

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 <br> University of Zurich and ETH Zurich

The model "nccratliri/whisperseg-large-ms-ct2" is the CTranslate2 version of the multi-species WhisperSeg-large that was finetuned on the vocal segmentation datasets of five species.

This model is used for faster inference.

Usage

Clone the GitHub repo and install dependencies

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:

from model import WhisperSegmenterFast
import librosa
import json
segmenter = WhisperSegmenterFast( "nccratliri/whisperseg-large-ms-ct2", device="cuda" )

sr = 32000  
min_frequency = 0
spec_time_step = 0.0025
min_segment_length = 0.01
eps = 0.02
num_trials = 3

audio, _ = librosa.load( "data/example_subset/Zebra_finch/test_adults/zebra_finch_g17y2U-f00007.wav", 
                         sr = sr )

prediction = segmenter.segment(  audio, sr = sr, min_frequency = min_frequency, spec_time_step = spec_time_step,
                       min_segment_length = min_segment_length, eps = eps,num_trials = num_trials )
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:

from audio_utils import SpecViewer
spec_viewer = SpecViewer()
spec_viewer.visualize( audio = audio, sr = sr, min_frequency= min_frequency, 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

[email protected]