---
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

```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]
```