| --- |
| license: cc-by-4.0 |
| language: en |
| task_categories: |
| - audio-classification |
| tags: |
| - end-turn-detection |
| - turn-taking |
| - spoken-dialogue |
| - conversation |
| pretty_name: OpenETD Metadata |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # 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](https://arxiv.org/abs/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](https://buckeyecorpus.osu.edu/) maintainers under their Academic License, then place files under `data/real/audio/buckeye_full/`. |
| - **YouTube audio**: download with the helper script in [OpenETD repository](https://github.com/HJ-Ok/OpenETD) (`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 |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
