| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| --- |
| |
| # OpenWhistle Detection Finetuning |
|
|
| Expert-annotated whistle-type detection dataset for OpenWhistle. Each example is a fixed-length 0.5 s audio window labeled with the whistle types present in that window; background/no-whistle windows are represented by an all-zero target vector. |
|
|
| ## Overview |
|
|
| - Task: multi-label whistle-type detection on fixed-length audio windows |
| - Target vector: `label`, with one binary decision per whistle type in the order `SW_Neo, SW_Luna, SW_Nikita, SW_Nana, SW_Yosefa, SW_Dana, NSW_1` |
| - Background: no-whistle windows use an all-zero `label` vector |
| - Convenience binary label: `binary_label`, derived from `label`, with values `noise` and `whistle` |
| - Total rows: `5600` |
| - Class balance: `400` examples per whistle type across 7 classes, plus `2800` background windows |
| - Split: session-disjoint `train`/`validation`/`test` with ratios 0.70/0.15/0.15 |
|
|
| ## Construction |
|
|
| - Built from the expert-annotated OpenWhistle benchmark subset |
| - Multi-label target: encode each whistle type present in the window in the fixed-length `label` vector |
| - Binary target: derive `whistle` when at least one `label` dimension is active, otherwise `noise` |
| - 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` |
| - `label`: multi-label whistle-type vector in order `SW_Neo, SW_Luna, SW_Nikita, SW_Nana, SW_Yosefa, SW_Dana, NSW_1` |
| - `binary_label`: derived binary class label with values `noise` and `whistle` |
| - `name`: original clip filename from the source dataset |
| - `source_label`: original source label before deriving the binary label |
|
|
| ## 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 |
|
|
| ```python |
| 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] |
| |
