Datasets:
File size: 13,145 Bytes
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dataset_info:
- config_name: all
features:
- name: audio
dtype:
audio:
decode: false
- name: label
dtype:
class_label:
names:
'0': NSW_3
'1': NSW_2
'2': NSW_1
'3': SW_Dana
'4': SW_Luna
'5': SW_Nana
'6': SW_Neo
'7': SW_Nikita
'8': SW_Shy
'9': SW_Yosefa
- name: name
dtype: string
- name: onset
dtype: float32
- name: offset
dtype: float32
- name: duration
dtype: float32
- name: recording_duration
dtype: float32
- name: whistle_type
dtype: int64
- name: whistle_name
dtype: string
- name: f0_time
sequence: float32
- name: f0_hz
sequence: float32
- name: f0_conf
sequence: float32
- name: f0_ok
dtype: bool
- name: f0_bad_reason
dtype: string
- name: f0_spectrogram
dtype: image
- name: snr_db
dtype: float64
splits:
- name: train
num_examples: 5848
- name: validation
num_examples: 1253
- name: test
num_examples: 1253
- config_name: all-review-sample
features:
- name: audio
dtype:
audio:
decode: false
- name: label
dtype:
class_label:
names:
'0': NSW_3
'1': NSW_2
'2': NSW_1
'3': SW_Dana
'4': SW_Luna
'5': SW_Nana
'6': SW_Neo
'7': SW_Nikita
'8': SW_Shy
'9': SW_Yosefa
- name: name
dtype: string
- name: onset
dtype: float32
- name: offset
dtype: float32
- name: duration
dtype: float32
- name: recording_duration
dtype: float32
- name: whistle_type
dtype: int64
- name: whistle_name
dtype: string
- name: f0_time
sequence: float32
- name: f0_hz
sequence: float32
- name: f0_conf
sequence: float32
- name: f0_ok
dtype: bool
- name: f0_bad_reason
dtype: string
- name: f0_spectrogram
dtype: image
- name: snr_db
dtype: float64
splits:
- name: train
num_examples: 336
- name: validation
num_examples: 72
- name: test
num_examples: 72
- config_name: balanced
features:
- name: audio
dtype:
audio:
decode: false
- name: label
dtype:
class_label:
names:
'0': NSW_1
'1': SW_Luna
'2': SW_Nana
'3': SW_Neo
'4': SW_Nikita
'5': SW_Yosefa
- name: name
dtype: string
- name: onset
dtype: float32
- name: offset
dtype: float32
- name: duration
dtype: float32
- name: recording_duration
dtype: float32
- name: whistle_type
dtype: int64
- name: whistle_name
dtype: string
- name: f0_time
sequence: float32
- name: f0_hz
sequence: float32
- name: f0_conf
sequence: float32
- name: f0_ok
dtype: bool
- name: f0_bad_reason
dtype: string
- name: f0_spectrogram
dtype: image
splits:
- name: train
num_examples: 2100
- name: validation
num_examples: 450
- name: test
num_examples: 450
- config_name: balanced-review-sample
features:
- name: audio
dtype:
audio:
decode: false
- name: label
dtype:
class_label:
names:
'0': NSW_1
'1': SW_Luna
'2': SW_Nana
'3': SW_Neo
'4': SW_Nikita
'5': SW_Yosefa
- name: name
dtype: string
- name: onset
dtype: float32
- name: offset
dtype: float32
- name: duration
dtype: float32
- name: recording_duration
dtype: float32
- name: whistle_type
dtype: int64
- name: whistle_name
dtype: string
- name: f0_time
sequence: float32
- name: f0_hz
sequence: float32
- name: f0_conf
sequence: float32
- name: f0_ok
dtype: bool
- name: f0_bad_reason
dtype: string
- name: f0_spectrogram
dtype: image
splits:
- name: train
num_examples: 336
- name: validation
num_examples: 72
- name: test
num_examples: 72
- config_name: unbalanced
features:
- name: audio
dtype:
audio:
decode: false
- name: label
dtype:
class_label:
names:
'0': NSW_3
'1': NSW_2
'2': NSW_1
'3': SW_Dana
'4': SW_Luna
'5': SW_Nana
'6': SW_Neo
'7': SW_Nikita
'8': SW_Shy
'9': SW_Yosefa
- name: name
dtype: string
- name: onset
dtype: float32
- name: offset
dtype: float32
- name: duration
dtype: float32
- name: recording_duration
dtype: float32
- name: whistle_type
dtype: int64
- name: whistle_name
dtype: string
- name: f0_time
sequence: float32
- name: f0_hz
sequence: float32
- name: f0_conf
sequence: float32
- name: f0_ok
dtype: bool
- name: f0_bad_reason
dtype: string
- name: f0_spectrogram
dtype: image
splits:
- name: train
num_examples: 2442
- name: validation
num_examples: 523
- name: test
num_examples: 523
- config_name: unbalanced-review-sample
features:
- name: audio
dtype:
audio:
decode: false
- name: label
dtype:
class_label:
names:
'0': NSW_3
'1': NSW_2
'2': NSW_1
'3': SW_Dana
'4': SW_Luna
'5': SW_Nana
'6': SW_Neo
'7': SW_Nikita
'8': SW_Shy
'9': SW_Yosefa
- name: name
dtype: string
- name: onset
dtype: float32
- name: offset
dtype: float32
- name: duration
dtype: float32
- name: recording_duration
dtype: float32
- name: whistle_type
dtype: int64
- name: whistle_name
dtype: string
- name: f0_time
sequence: float32
- name: f0_hz
sequence: float32
- name: f0_conf
sequence: float32
- name: f0_ok
dtype: bool
- name: f0_bad_reason
dtype: string
- name: f0_spectrogram
dtype: image
splits:
- name: train
num_examples: 336
- name: validation
num_examples: 72
- name: test
num_examples: 72
task_categories:
- audio-classification
tags:
- dolphin
- bioacoustics
- whistle-classification
- audio
- f0
- spectrogram
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
- split: validation
path: all/validation-*
- split: test
path: all/test-*
- config_name: all-review-sample
data_files:
- split: train
path: all-review-sample/train-*
- split: validation
path: all-review-sample/validation-*
- split: test
path: all-review-sample/test-*
- config_name: balanced
data_files:
- split: train
path: balanced/train-*
- split: validation
path: balanced/validation-*
- split: test
path: balanced/test-*
- config_name: balanced-review-sample
data_files:
- split: train
path: balanced-review-sample/train-*
- split: validation
path: balanced-review-sample/validation-*
- split: test
path: balanced-review-sample/test-*
- config_name: unbalanced
data_files:
- split: train
path: unbalanced/train-*
- split: validation
path: unbalanced/validation-*
- split: test
path: unbalanced/test-*
- config_name: unbalanced-review-sample
data_files:
- split: train
path: unbalanced-review-sample/train-*
- split: validation
path: unbalanced-review-sample/validation-*
- split: test
path: unbalanced-review-sample/test-*
---
# OpenWhistle Classification Finetuning Dataset
`OpenWhistleNeurIPS26/OpenWhistle-Classification-Finetuning` is the public
classification finetuning dataset used for dolphin whistle identity
classification. It contains short whistle clips, whistle-level metadata,
fundamental-frequency tracks, rendered F0 spectrograms, and integer class
labels.
The main reviewer-facing subset is the balanced `balanced` config. It contains
six classes:
- `NSW_1` (`label=0`)
- `SW_Luna` (`label=1`)
- `SW_Nana` (`label=2`)
- `SW_Neo` (`label=3`)
- `SW_Nikita` (`label=4`)
- `SW_Yosefa` (`label=5`)
The full dataset is split by recording session, so no session appears in more
than one of `train`, `validation`, or `test`. Smaller deterministic review
configs are also provided so reviewers can inspect representative examples
quickly without downloading the complete data first.
## Dataset Contents
- Hugging Face repo: `OpenWhistleNeurIPS26/OpenWhistle-Classification-Finetuning`
- Main balanced config: `balanced`
- Reviewer convenience config: `balanced-review-sample`
- Public columns common to all configs: `audio`, `label`, `name`, `onset`, `offset`, `duration`,
`recording_duration`, `whistle_type`, `whistle_name`, `f0_time`, `f0_hz`,
`f0_conf`, `f0_ok`, `f0_bad_reason`, `f0_spectrogram`
- The `all` and `all-review-sample` configs additionally include `snr_db`, the
estimated clip-level signal-to-noise ratio in dB.
## Balanced Dataset Splits
| Split | Rows | NSW_1 | SW_Luna | SW_Nana | SW_Neo | SW_Nikita | SW_Yosefa | Sessions |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| `train` | 2,100 | 350 | 350 | 350 | 350 | 350 | 350 | 161 |
| `validation` | 450 | 75 | 75 | 75 | 75 | 75 | 75 | 54 |
| `test` | 450 | 75 | 75 | 75 | 75 | 75 | 75 | 37 |
| **Total** | **3,000** | **500** | **500** | **500** | **500** | **500** | **500** | **252** |
The split assignment was generated with seed `42` and exact class balancing.
Session leakage checks found no overlap between any pair of splits.
## Available Subsets
The repository provides three full classification subsets and their smaller
review counterparts:
| Subset/config | Rows | Classes | Sessions | Purpose |
| --- | ---: | ---: | ---: | --- |
| `balanced` | 3,000 | 6 | 252 | Main balanced six-class finetuning dataset |
| `unbalanced` | 3,488 | 10 | 258 | Ten-class finetuning dataset with capped rare classes |
| `all` | 8,354 | 10 | 261 | Ten-class dataset preserving the full available class distribution |
| `balanced-review-sample` | 480 | 6 | Same source split design | Small reviewer sample from `balanced` |
| `unbalanced-review-sample` | 480 | 10 | Same source split design | Small reviewer sample from `unbalanced` |
| `all-review-sample` | 480 | 10 | Same source split design | Small reviewer sample from `all` |
The ten-class subsets use the following labels:
- `NSW_3`
- `NSW_2`
- `NSW_1`
- `SW_Dana`
- `SW_Luna`
- `SW_Nana`
- `SW_Neo`
- `SW_Nikita`
- `SW_Shy`
- `SW_Yosefa`
The `balanced` subset keeps the six classes listed above and is the recommended
starting point for reviewers and model finetuning. The `unbalanced` and `all`
subsets expose the broader ten-class label space for additional analysis.
## Review Samples
Review samples are small deterministic subsets of the same public dataset. They
were created only to make review and manual inspection easier. They are not a
replacement for the full configs used for model development or reporting.
### How The Review Samples Were Created
All review samples were built after the session-disjoint train/validation/test
splits were finalized. The review-sample scripts preserve the original split
assignment: reviewer training examples come only from the original `train`
split, reviewer validation examples only from `validation`, and reviewer test
examples only from `test`.
For `balanced-review-sample`, rows were sampled separately within each split and
class. Each class group was shuffled deterministically with
`numpy.default_rng(seed + split_index)` using seed `42`, then capped at 56 rows
per class for `train` and 12 rows per class for both `validation` and `test`.
This keeps the same 70/15/15 split ratio as the full `balanced` config while
keeping every class equally represented.
For `unbalanced-review-sample` and `all-review-sample`, the same deterministic
shuffle was used, but the target rows were allocated proportionally to the
source class distribution inside each split. This preserves the class imbalance
of the larger source configs while keeping the review download small.
### Review Sample Sizes
| Config | Source config | Strategy | Train | Validation | Test | Total |
| --- | --- | --- | ---: | ---: | ---: | ---: |
| `balanced-review-sample` | `balanced` | Equal rows per class within each split | 336 | 72 | 72 | 480 |
| `unbalanced-review-sample` | `unbalanced` | Proportional class distribution within each split | 336 | 72 | 72 | 480 |
| `all-review-sample` | `all` | Proportional class distribution within each split | 336 | 72 | 72 | 480 |
The reviewer-facing sample for the main balanced dataset is
`balanced-review-sample`. The other review samples are included so each full
subset has a matching small inspection subset.
## Loading The Data
```python
from datasets import load_dataset
full = load_dataset(
"OpenWhistleNeurIPS26/OpenWhistle-Classification-Finetuning",
"balanced",
)
review = load_dataset(
"OpenWhistleNeurIPS26/OpenWhistle-Classification-Finetuning",
"balanced-review-sample",
)
```
Optional broader configs can be loaded by passing `"unbalanced"` or `"all"` as
the second `load_dataset` argument. Their corresponding review configs are
`"unbalanced-review-sample"` and `"all-review-sample"`.
|