This library contains code and models to segment regions of laughter from an audio file. The models/ folder contains models trained on the Switchboard data set.
Please cite the following paper if you use this software for research:
Kimiko Ryokai, Elena Durán López, Noura Howell, Jon Gillick, and David Bamman (2018), "Capturing, Representing, and Interacting with Laughter," CHI 2018
git clone https://github.com/jrgillick/laughter-detection.git
cd laughter-detection
pip install -r requirements.txtcompute_features.py and train_model.pypython segment_laughter.py <input_audio_path> <stored_model_path> <output_folder> <threshold>(optional) <min_length>(optional) <save_to_textgrid>(optional)e.g. python segment_laughter.py my_audio_file.wav models/model.h5 my_folder 0.8 0.1
The threshold parameter adjusts the minimum probability threshold for classifying a frame as laughter. The default is 0.5, but you can experiment with settings between 0 and 1 to see what works best for your data. Lower threshold values may give more false positives but may also recover a higher percentage of laughs from your file.
The min_length parameter sets the minimum length in seconds that a laugh needs to be in order to be identified. The default value is 0.2.
If set to True, the save_to_textgrid parameter toggles writing the identified laughter intervals to a Praat TextGrid annotation file rather than saving them in separate WAV files. The default value is False.
<output_folder>