OpenWhistleNeurIPS26/OpenWhistle-Pretraining
OpenWhistle Pretraining Dataset OpenWhistleNeurIPS26/OpenWhistle-Pretraining is the public unlabeled audio dataset used for OpenWhistle pretraining. It contains 96 kHz dolphin acoustic segments with timing and recording metadata, but no whistle/noise labels. The main default config is the complete pretraining dataset. A smaller deterministic review-sample config is also provided so reviewers can inspect representative examples quickly without downloading the full dataset.… See the full description on the dataset page: https://huggingface.co/datasets/OpenWhistleNeurIPS26/OpenWhistle-Pretraining.
OpenWhistle Pretraining Dataset
OpenWhistleNeurIPS26/OpenWhistle-Pretraining is the public unlabeled audio dataset used for OpenWhistle pretraining. It contains 96 kHz dolphin acoustic segments with timing and recording metadata, but no whistle/noise labels.
The main default config is the complete pretraining dataset. A smaller deterministic review-sample config is also provided so reviewers can inspect representative examples quickly without downloading the full dataset.
Dataset contents
- Hugging Face repo:
OpenWhistleNeurIPS26/OpenWhistle-Pretraining - Public columns:
audio,start_time,end_time,duration,year,hydrophone - Sampling rate: 96 kHz, mono audio
- Labels: none; this dataset is intended for unsupervised or self-supervised pretraining
Full dataset splits
Full dataset coverage by year
Full dataset coverage by hydrophone
Full dataset coverage by year and hydrophone
Review sample
The review-sample config is a small deterministic subset of the same public dataset. It was created only to make review and manual inspection easier. It is not a replacement for the full dataset used for model development or reporting.
How the review sample was created
The review sample was designed to preserve the structure of the full pretraining dataset while keeping the download small enough for quick manual inspection. Because the pretraining dataset is unlabeled, the sample is not class-balanced. Instead, it preserves the original train and validation split names and uses the same audio and metadata columns as the full dataset.
The target size was set to 480 rows, following the same review-sample size used for the OpenWhistle CNN dataset. Rows were allocated across splits to keep the same approximate train/validation ratio as the full pretraining dataset: 430 examples for train and 50 examples for validation.
For each split, the dataset was streamed from OpenWhistleNeurIPS26/OpenWhistle-Pretraining, the audio column was cast to Audio(sampling_rate=96000, mono=True, decode=False), and rows were shuffled with a fixed seed and a finite shuffle buffer before taking the requested number of examples. Sampling used base seed 42, shuffle buffer size 64, and split-specific seeds derived from the split order (train=42, validation=43). The resulting config is named review-sample.
Review sample size
Review sample coverage
Loading the data
from datasets import load_dataset
full = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-Pretraining")
review = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-Pretraining", "review-sample")