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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**]() |
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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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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. |
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**This model is used for faster inference.** |
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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 WhisperSegmenterFast |
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import librosa |
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import json |
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segmenter = WhisperSegmenterFast( "nccratliri/whisperseg-large-ms-ct2", device="cuda" ) |
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sr = 32000 |
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min_frequency = 0 |
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spec_time_step = 0.0025 |
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min_segment_length = 0.01 |
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eps = 0.02 |
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num_trials = 3 |
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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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prediction = segmenter.segment( audio, sr = sr, min_frequency = min_frequency, spec_time_step = spec_time_step, |
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min_segment_length = min_segment_length, eps = eps,num_trials = num_trials ) |
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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= min_frequency, 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: <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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## Contact |
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[email protected] |
