HJOK/openetd-metadata

OpenETD metadata

Train / dev / test split CSVs for the OpenETD dataset released with the ACL 2026 Findings paper Speculative End-Turn Detector for Efficient Speech Chatbot Assistant (arXiv:2503.23439).

Code: https://github.com/HJ-Ok/OpenETD

Split sizes

Split	Real files	Real hours	Synthetic files	Synthetic hours
train	6,290	117.2	96,773	116.8
dev	899	16.2	12,840	15.8
test	1,798	32.4	12,868	15.7

Columns

Column	Description
file_path	Relative path to the audio file (resolve locally).
pause_times	Interval list (start, end), ... of within-speaker pauses (seconds).
gap_times	Interval list (start, end), ... of between-speaker gaps (seconds).
contains_pause	Boolean, whether the file contains any pause.
contains_gap	Boolean, whether the file contains any gap.
label	Type of the final silence (Pause or Gap); used for the binary task.
platform	(Real only) buckeye or youtube.
kfold	(Synthetic only) k-fold assignment used for pause/gap label generation.

Audio files

Audio is NOT included in this repository — we redistribute only the annotations and split assignments. To obtain the audio:

• Buckeye audio: obtain from the Buckeye Corpus maintainers under their Academic License, then place files under data/real/audio/buckeye_full/.
• YouTube audio: download with the helper script in OpenETD repository (scripts/prepare_data.sh).
• Synthetic audio: regenerate on your own Google Cloud account using data/synthetic_pipeline/generate.py in the OpenETD repository.

Quick start

from datasets import load_dataset
ds = load_dataset("HJOK/openetd-metadata", data_files={
    "real_train":  "real/train.csv",
    "real_valid":  "real/valid.csv",
    "real_test":   "real/test.csv",
    "syn_train":   "synthetic/train.csv",
    "syn_valid":   "synthetic/valid.csv",
    "syn_test":    "synthetic/test.csv",
})
print(ds["real_test"][0])

License

• Annotations (this repository): CC BY 4.0
• Code in the OpenETD GitHub repository: MIT
• External audio sources retain their original licenses (see DATA_LICENSES.md in the GitHub repo).

Citation

@inproceedings{ok2026speculativeetd,
  title     = {Speculative End-Turn Detector for Efficient Speech Chatbot Assistant},
  author    = {Ok, Hyunjong and Yoo, Suho and Lee, Jaeho},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year      = {2026},
  url       = {https://arxiv.org/abs/2503.23439}
}