---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
tags:
- arxiv:2609.34839
---

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

## Paper, collection, and authors

- Paper: [OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations](https://arxiv.org/abs/2609.34839)
- Hugging Face collection: [NeurIPS 2026 OpenWhistle](https://huggingface.co/collections/dolphinteam/neurips26-openwhistle)
- This repository: [https://huggingface.co/datasets/dolphinteam/OpenWhistle-Detection-Finetuning](https://huggingface.co/datasets/dolphinteam/OpenWhistle-Detection-Finetuning)

Authors: Faadil Mustun, Chiara Semenzin, Roberto Dessi, Pablo Robin Guerrero, Pierre Orhan, Alexis Emanuelli, Emanuele Rossi, Yair Lakretz, Gonzalo de Polavieja, and German Sumbre.

## Citation

```bibtex
@misc{mustun2026openwhistle,
  title         = {OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations},
  author        = {Mustun, Faadil and Semenzin, Chiara and Dessi, Roberto and Robin Guerrero, Pablo and Orhan, Pierre and Emanuelli, Alexis and Rossi, Emanuele and Lakretz, Yair and de Polavieja, Gonzalo and Sumbre, German},
  year          = {2026},
  eprint        = {2609.34839},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2609.34839}
}
```