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---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- audio-text-to-text
tags:
- audio-retrieval
- multimodal
- moment-retrieval
library_name: lighthouse
configs:
- config_name: default
data_files:
- split: train
path: train/*.tar
- split: valid
path: valid/*.tar
- split: test
path: test/*.tar
---
# Clotho-Moment
This repository provides wav files used in [Language-based Audio Moment Retrieval](https://arxiv.org/abs/2409.15672).
Each sample includes long audio containing some audio events with the temporal and textual annotation.
Project page: https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/
Code: https://github.com/line/lighthouse
## Split
- Train
- train/train-{000..715}.tar
- 37930 audio samples
- Valid
- valid/valid-{000..108}.tar
- 5741 audio samples
- Test
- test/test-{000..142}.tar
- 7569 audio samples
## Using Webdataset
```python
import webdataset as wds
url = "https://huggingface.co/datasets/lighthouse-emnlp2024/Clotho-Moment/resolve/main/train/train-{{001..002}}.tar"
url = f"pipe:curl -s -L {url}"
dataset = wds.WebDataset(url, shardshuffle=None).decode(wds.torch_audio)
for sample in dataset:
print(sample.keys())
```
## Citation
```bibtex
@inproceedings{munakata2025language,
title={Language-based Audio Moment Retrieval},
author={Munakata, Hokuto and Nishimura, Taichi and Nakada, Shota and Komatsu, Tatsuya},
booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2025},
organization={IEEE}
}
``` |