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LaughterSegmentation

★ 66 Open GitHub ↗

2024 Latest laughter detection & segmentaion model. Paper: "Robust Laughter Segmentation with Automatic Diverse Data Synthesis", Interspeech 2024

// repository documentation

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# Laughter Segmentation

## Overview
You can extract a exact segment of laughter from various talking audio using trained model and code. You can also train your own model.

Code, annotations, and model are described in the following paper:
[Taisei Omine, Kenta Akita, and Reiji Tsuruno, "Robust Laughter Segmentation with Automatic Diverse Data Synthesis", Interspeech 2024.](https://doi.org/10.21437/Interspeech.2024-1644)

## Installation
```Batchfile
git clone https://github.com/omine-me/LaughterSegmentation.git
cd LaughterSegmentation
python -m pip install -r requirements.txt
# ↓ Depends on your environment. See https://pytorch.org/get-started/locally/
python -m pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121
```
Run in Venv environment is recommended. Also, download `model.safetensors` from [Huggingface](https://huggingface.co/omine-me/LaughterSegmentation/tree/main) (1.26 GB) and place it in `models` directory and make sure the name is `model.safetensors`.

Python<=3.11 is required ([#2](https://github.com/omine-me/LaughterSegmentation/issues/2)).

Tested on Windows 11 with GeForce RTX 2060 SUPER.

## Usage
1. Prepare audio file.
1. Open Terminal and go to the directory where `inference.py` is located.
1. Run `python inference.py --audio_path audio.wav`. You have to change *audio.wav* to your own audio path. You can use common audio format like `mp3`, `wav`, `opus`, etc. 16kHz wav audio is faster. If the audio fails to load, run the following command and also download FFmpeg and add it to the PATH.
    ```Batchfile
    python -m pip uninstall pysoundfile
    python -m pip uninstall soundfile
    python -m pip install soundfile
    ```
1. If you want to change output directory, use  `--output_dir` option. If you want to use your own model, use `--model_path` option.
1. Result will be saved in output directory in json format. To visualize the results, you can use [this site](https://omine-me.github.io/AudioDatasetChecker/compare.html) (not perfect because it's for debugging).

## Training
Read [README](/train/README.md) in train directory.

## Evaluation (Includes our evaluation dataset)
Read [README](/evaluation/README.md) in evaluavtion directory.

## License
This repository is MIT-licensed, but [the publicly available trained model](https://huggingface.co/omine-me/LaughterSegmentation/tree/main) is currently available for research use only.

## Citation
Cite as: `Omine, T., Akita, K., Tsuruno, R. (2024) Robust Laughter Segmentation with Automatic Diverse Data Synthesis. Proc. Interspeech 2024, 4748-4752, doi: 10.21437/Interspeech.2024-1644`

or
```
@inproceedings{omine24_interspeech,
  title     = {Robust Laughter Segmentation with Automatic Diverse Data Synthesis},
  author    = {Taisei Omine and Kenta Akita and Reiji Tsuruno},
  year      = {2024},
  booktitle = {Interspeech 2024},
  pages     = {4748--4752},
  doi       = {10.21437/Interspeech.2024-1644},
}
```

## Contact
Use Issues or reach out my [X(Twitter)](https://x.com/mineBeReal).

LaughterSegmentation

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