| --- |
| task_categories: |
| - audio-classification |
| tags: |
| - audio |
| - bioacoustics |
| - dolphin |
| - whistle |
| --- |
| |
| # Dolphin whistle classification |
|
|
| Short whistle clips with metadata, fundamental-frequency tracks, F0 spectrograms, and integer class IDs in `main_category`. |
|
|
| **Whistle sub-category:** fine-grained whistle identity is carried in the **`whistle_type`** column (integer IDs). This is the primary label column for sub-category work; `main_category` groups whistles at a coarser level. |
| |
| Splits (`train`, `validation`, `test`) are disjoint by recording session. |
| |
| ## Columns |
| |
| - `audio`, `main_category`, `name`, `onset`, `offset`, `duration`, `recording_duration`, **`whistle_type`**, `whistle_name`, `f0_time`, `f0_hz`, `f0_conf`, `f0_ok`, `f0_bad_reason`, `f0_spectrogram`, `snr_db` |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| |
| # Default: one `datasets.Dataset` per split (`train`, `validation`, `test`) |
| ds = load_dataset("dolphinteam/Whistle-Classification") |
| ``` |
| |
| Load **all rows in a single dataset** (no split separation; train, validation, and test concatenated in order): |
| |
| ```python |
| from datasets import load_dataset |
| |
| full = load_dataset("dolphinteam/Whistle-Classification", split="train+validation+test") |
| ``` |
| |
| Equivalent if you already loaded the split dict and want one table: |
| |
| ```python |
| from datasets import concatenate_datasets |
| |
| ds = load_dataset("dolphinteam/Whistle-Classification") |
| full = concatenate_datasets([ds["train"], ds["validation"], ds["test"]]) |
| ``` |
| |
