Update README.md

README.md

@@ -2,14 +2,14 @@
 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
-
-
+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-large-ms" is the checkpoint of the multi-species WhisperSeg-large that was finetuned on the vocal segmentation datasets of five species.
 
@@ -27,19 +27,13 @@ import librosa
 import json
 segmenter = WhisperSegmenter( "nccratliri/whisperseg-large-ms", device="cuda" )
 
-sr = 32000  
-min_frequency = 0
-spec_time_step = 0.0025
-min_segment_length = 0.01
-eps = 0.02
-num_trials = 3
+sr = 32000
+spec_time_step = 0.0025  
 
-audio, _ = librosa.load( "data/example_subset/Zebra_finch/test_adults/zebra_finch_g17y2U-f00007.wav", 
+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 )
-
+## 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']}
@@ -54,24 +48,23 @@ spec_viewer.visualize( audio = audio, sr = sr, min_frequency= min_frequency, pre
 ```
 ![vis](https://github.com/nianlonggu/WhisperSeg/blob/master/assets/res_zebra_finch_adults_prediction_only.png?raw=true)
 
-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>
+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:
 ```
-@article {Gu2023.09.30.560270,
-	author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser},
-	title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection},
-	elocation-id = {2023.09.30.560270},
-	year = {2023},
-	doi = {10.1101/2023.09.30.560270},
-	publisher = {Cold Spring Harbor Laboratory},
-	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.},
-	URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270},
-	eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf},
-	journal = {bioRxiv}
-}
+@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}}
 ```
 
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