Datasetpublic

lighthouse-emnlp2024/CASTELLA_CLAP_features

CASTELLA CLAP features This repository contains audio and text features of CASTELLA dataset extracted by CLAP. Using these features, we can reproduce the audio moments retrieval using CASTELLA, which is used in lighthouse. Please also check demo page. How to Download? Run the following script: from huggingface_hub import snapshot_download repo_id = "lighthouse-emnlp2024/CASTELLA_CLAP_features" local_dir = "./" downloaded_path = snapshot_download(… See the full description on the dataset page: https://huggingface.co/datasets/lighthouse-emnlp2024/CASTELLA_CLAP_features.

sourceHugging Facecc-by-4.0updated 8mo agoView on Hugging Face

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CASTELLA CLAP features

This repository contains audio and text features of CASTELLA dataset extracted by CLAP.

—Using these features, we can reproduce the audio moments retrieval using CASTELLA, which is used in lighthouse.
—Please also check demo page.

How to Download?

Run the following script:

python

from huggingface_hub import snapshot_download

repo_id = "lighthouse-emnlp2024/CASTELLA_CLAP_features"
local_dir = "./"

downloaded_path = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    local_dir=local_dir,
    allow_patterns="*.tar.gz",
)

How to Use on Lighthouse

The .tar.gz files should be decompressed by following shell commands:

bash

mkdir -p {LIGHTHOUSE_PATH}/features/castella/clap
mkdir -p {LIGHTHOUSE_PATH}/features/castella/clap_text
tar -zxvf clap.tar.gz -C {LIGHTHOUSE_PATH}/features/castella/clap
tar -zxvf clap_text.tar.gz -C {LIGHTHOUSE_PATH}/features/castella/clap_text

Citation

bibtex

@article{munakata2025castella,
  title={CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries},
  author={Munakata, Hokuto and Takehiro, Imamura and Nishimura, Taichi and Komatsu, Tatsuya},
  journal={arXiv preprint arXiv:2511.15131},
  year={2025},
}