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README.md

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+---
+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]
+```

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