README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

OpenWhistle Detection Finetuning

Binary whistle-vs-noise finetuning dataset for OpenWhistle.

Overview

• Task: binary whistle detection
• Labels: noise, whistle
• Total rows: 5600
• Split: session-disjoint train/validation/test with ratios 0.70/0.15/0.15

Construction

• Built from curated OpenWhistle detection clips
• Build rule: collapse every non-noise source label into whistle
• Session key: source original_path basename with audio extension removed
• Split rule: exact MILP assignment of whole sessions
• Balance rule: exact source-label targets per split

Features

• audio: audio clips stored with decode=False
• binary_label: binary class label with values noise and whistle
• name: original clip filename from the source dataset
• source_label: original source label before the binary collapse
• label: one-hot source-label tensor in order SW_Neo, SW_Luna, SW_Nikita, SW_Nana, SW_Yosefa, SW_Dana, NSW_1

Rows By Split

• train: 3920 rows
• validation: 840 rows
• test: 840 rows

Binary Label Counts

• total: noise=2800, whistle=2800
• train: noise=1960, whistle=1960
• validation: noise=420, whistle=420
• test: noise=420, whistle=420

Source Label Counts

• train: Dana=280, Luna=280, NSW_1=280, Nana=280, Neo=280, Nikita=280, Yosefa=280, noise=1960
• validation: Dana=60, Luna=60, NSW_1=60, Nana=60, Neo=60, Nikita=60, Yosefa=60, noise=420
• test: Dana=60, Luna=60, NSW_1=60, Nana=60, Neo=60, Nikita=60, Yosefa=60, noise=420

Session Leakage

• Pairwise session overlap: train__validation=0, train__test=0, validation__test=0

Example

from datasets import Audio, load_dataset

dataset = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-Detection-Finetuning")
decoded_train = dataset["train"].cast_column("audio", Audio())
sample = decoded_train[0]