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README.md

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+---
+task_categories:
+- audio-classification
+tags:
+- dolphin
+- bioacoustics
+- whistle-detection
+- audio
+- spectrogram
+dataset_info:
+  config_name: review-sample
+  features:
+  - name: audio
+    dtype:
+      audio:
+        decode: false
+  - name: spectrogram
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': noise
+          '1': whistle
+  - name: file_name
+    dtype: string
+  - name: recording
+    dtype: string
+  - name: onset
+    dtype: float64
+  - name: offset
+    dtype: float64
+  splits:
+  - name: train
+    num_bytes: 37497957.0
+    num_examples: 376
+  - name: test
+    num_bytes: 10431395.0
+    num_examples: 104
+  download_size: 38245514
+  dataset_size: 47929352.0
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+- config_name: review-sample
+  data_files:
+  - split: train
+    path: review-sample/train-*
+  - split: test
+    path: review-sample/test-*
+---
+
+# OpenWhistle CNN Dataset
+
+`OpenWhistleNeurIPS26/OpenWhistle-CNN` is the public CNN dataset used for binary
+dolphin whistle detection. It contains audio windows, spectrogram images, and
+binary labels:
+
+- `noise` (`label=0`)
+- `whistle` (`label=1`)
+
+The main dataset is the complete session-disjoint dataset used for training and
+evaluation. A smaller deterministic `review-sample` config is also provided so
+reviewers can inspect representative examples quickly.
+
+## Dataset contents
+
+- Hugging Face repo: `OpenWhistleNeurIPS26/OpenWhistle-CNN`
+- Public columns: `audio`, `spectrogram`, `label`, `file_name`, `recording`, `onset`, `offset`
+
+## Full dataset splits
+
+| Split | Rows | Noise | Whistle | Sessions | Window hours |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| `train` | 53,828 | 26,914 | 26,914 | 195 | 5.980885 |
+| `validation` | 5,980 | 2,990 | 2,990 | 26 | 0.664445 |
+| `test` | 16,708 | 8,354 | 8,354 | 261 | 1.856444 |
+| **Total** | **76,516** | **38,258** | **38,258** | **482** | **8.501775** |
+
+The `train` and `validation` splits come from the non-2019/2020 pool. The
+`test` split is a manual 2019-2020 test split built from the full classification
+`all` config.
+
+## Review sample
+
+The `review-sample` config is a small deterministic subset of the same public
+dataset. It was created only to make review and manual inspection easier. It is
+not a replacement for the full dataset used for model development or reporting.
+
+### How the review sample was created
+
+The review sample was designed to preserve the structure of the full dataset
+while keeping the download small enough for quick manual inspection. The sample
+keeps the same binary label definition as the full dataset and preserves the
+train/test separation: reviewer training examples are drawn from the original
+training and validation data, while reviewer test examples are drawn only from
+the original test data.
+
+Within each reviewer split, examples were sampled separately for `noise`
+(`label=0`) and `whistle` (`label=1`) so that both classes are equally
+represented. This avoids a reviewer sample dominated by one class and makes it
+easier to inspect positives and negatives side by side. The target sizes were
+chosen to keep the same approximate train/test ratio as the full CNN dataset:
+376 examples for `train` and 104 examples for `test`, for 480 examples total.
+
+Sampling was deterministic, using seed `42`, so the same review sample can be
+rebuilt exactly from the prepared public dataset. The resulting config is named
+`review-sample`.
+
+### Review sample size
+
+| Split | Rows | Noise | Whistle | Source splits | Source rows |
+| --- | ---: | ---: | ---: | --- | ---: |
+| `train` | 376 | 188 | 188 | `train`, `validation` | 59,808 |
+| `test` | 104 | 52 | 52 | `test` | 16,708 |
+| **Total** | **480** | **240** | **240** | | |
+
+## Loading the data
+
+```python
+from datasets import load_dataset
+
+full = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-CNN")
+review = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-CNN", "review-sample")
+```

artifacts/summary.json

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+{
+  "trainval_dataset_dir": "cnn_dataset/datasets/no_2019_2020_with_session_noise_split_audio",
+  "test_dataset_dir": "cnn_dataset/datasets/classification_test_with_session_noise_audio_v3",
+  "splits": [
+    {
+      "split_name": "train",
+      "rows": 53828,
+      "positives": 26914,
+      "noise": 26914,
+      "sessions": 195,
+      "window_hours": 5.980885373055554
+    },
+    {
+      "split_name": "validation",
+      "rows": 5980,
+      "positives": 2990,
+      "noise": 2990,
+      "sessions": 26,
+      "window_hours": 0.664445029166663
+    },
+    {
+      "split_name": "test",
+      "rows": 16708,
+      "positives": 8354,
+      "noise": 8354,
+      "sessions": 261,
+      "window_hours": 1.8564444458333356
+    }
+  ],
+  "total_rows": 76516,
+  "total_window_hours": 8.501774848055552
+}

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