| --- |
| dataset_info: |
| - config_name: rs |
| features: |
| - name: audio |
| dtype: audio |
| - name: id |
| dtype: string |
| - name: original_source |
| dtype: string |
| - name: num_repetitions |
| dtype: int64 |
| - name: events |
| list: |
| - name: start |
| dtype: float64 |
| - name: end |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 23562818259 |
| num_examples: 73592 |
| - name: validation |
| num_bytes: 2670310759 |
| num_examples: 8340 |
| - name: test |
| num_bytes: 6551552046 |
| num_examples: 20462 |
| download_size: 32785679931 |
| dataset_size: 32784681064 |
| - config_name: rs-features |
| features: |
| - name: id |
| dtype: string |
| - name: spectrogram |
| dtype: |
| array2_d: |
| shape: |
| - 257 |
| - 624 |
| dtype: float32 |
| - name: tssm |
| dtype: |
| array2_d: |
| shape: |
| - 499 |
| - 499 |
| dtype: float32 |
| splits: |
| - name: train |
| num_bytes: 120729000656 |
| num_examples: 73592 |
| - name: validation |
| num_bytes: 13681920120 |
| num_examples: 8340 |
| - name: test |
| num_bytes: 33568279316 |
| num_examples: 20462 |
| download_size: 168149591715 |
| dataset_size: 167979200092 |
| - config_name: rsn |
| features: |
| - name: audio |
| dtype: audio |
| - name: id |
| dtype: string |
| - name: original_source |
| dtype: string |
| - name: num_repetitions |
| dtype: int64 |
| - name: events |
| list: |
| - name: start |
| dtype: float64 |
| - name: end |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 23562836723 |
| num_examples: 73592 |
| - name: validation |
| num_bytes: 2670317047 |
| num_examples: 8340 |
| - name: test |
| num_bytes: 6551560014 |
| num_examples: 20462 |
| download_size: 32785827437 |
| dataset_size: 32784713784 |
| - config_name: rsn-features |
| features: |
| - name: id |
| dtype: string |
| - name: spectrogram |
| dtype: |
| array2_d: |
| shape: |
| - 257 |
| - 624 |
| dtype: float32 |
| - name: tssm |
| dtype: |
| array2_d: |
| shape: |
| - 499 |
| - 499 |
| dtype: float32 |
| splits: |
| - name: train |
| num_bytes: 120729000656 |
| num_examples: 73592 |
| - name: validation |
| num_bytes: 13681920120 |
| num_examples: 8340 |
| - name: test |
| num_bytes: 33568279316 |
| num_examples: 20462 |
| download_size: 168149591715 |
| dataset_size: 167979200092 |
| - config_name: rvn |
| features: |
| - name: audio |
| dtype: audio |
| - name: id |
| dtype: string |
| - name: original_source |
| dtype: string |
| - name: num_repetitions |
| dtype: int64 |
| - name: events |
| list: |
| - name: start |
| dtype: float64 |
| - name: end |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 23562836723 |
| num_examples: 73592 |
| - name: validation |
| num_bytes: 2670317047 |
| num_examples: 8340 |
| - name: test |
| num_bytes: 6551560014 |
| num_examples: 20462 |
| download_size: 32785827233 |
| dataset_size: 32784713784 |
| - config_name: rvn-features |
| features: |
| - name: id |
| dtype: string |
| - name: spectrogram |
| dtype: |
| array2_d: |
| shape: |
| - 257 |
| - 624 |
| dtype: float32 |
| - name: tssm |
| dtype: |
| array2_d: |
| shape: |
| - 499 |
| - 499 |
| dtype: float32 |
| splits: |
| - name: train |
| num_bytes: 120729000656 |
| num_examples: 73592 |
| - name: validation |
| num_bytes: 13681920120 |
| num_examples: 8340 |
| - name: test |
| num_bytes: 33568279316 |
| num_examples: 20462 |
| download_size: 168149591715 |
| dataset_size: 167979200092 |
| configs: |
| - config_name: rs |
| data_files: |
| - split: train |
| path: rs/train-* |
| - split: validation |
| path: rs/validation-* |
| - split: test |
| path: rs/test-* |
| - config_name: rs-features |
| data_files: |
| - split: train |
| path: rs-features/train-* |
| - split: validation |
| path: rs-features/validation-* |
| - split: test |
| path: rs-features/test-* |
| - config_name: rsn |
| data_files: |
| - split: train |
| path: rsn/train-* |
| - split: validation |
| path: rsn/validation-* |
| - split: test |
| path: rsn/test-* |
| - config_name: rsn-features |
| data_files: |
| - split: train |
| path: rsn-features/train-* |
| - split: validation |
| path: rsn-features/validation-* |
| - split: test |
| path: rsn-features/test-* |
| - config_name: rvn |
| data_files: |
| - split: train |
| path: rvn/train-* |
| - split: validation |
| path: rvn/validation-* |
| - split: test |
| path: rvn/test-* |
| - config_name: rvn-features |
| data_files: |
| - split: train |
| path: rvn-features/train-* |
| - split: validation |
| path: rvn-features/validation-* |
| - split: test |
| path: rvn-features/test-* |
| license: cc-by-nc-4.0 |
| --- |
| # Dataset Card for RepeatSynth |
|
|
| Synthetic portion of the audio datasets referenced in *Class-Agnostic Audio Repetition Counting*. |
|
|
| Code: https://github.com/Hamozwa/audio-counting |
|
|
| ## Dataset Summary |
|
|
| RS, RSN, and RVN are synthetic datasets containing varying levels of background noise. Every sample is uniformly 10 seconds long and contains between 0 and 8 repetition events. |
|
|
| | Config | Description | |
| |---|---| |
| | `rs` | RepeatSound — clean, no added background noise | |
| | `rsn` | RepeatSoundNoisy — background noise added | |
| | `rvn` | RepeatVeryNoisy — the noisiest condition | |
|
|
| Each config has `train`, `validation`, and `test` splits. `rs-features`, `rsn-features`, and `rvn-features` hold precomputed spectrograms and self-similarity matrices for the same clips (see "Feature configs" below) — most users only need the main configs above. |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("Hamozwa/RepeatSynth", "rsn", split="train") |
| ``` |
|
|
| ## Dataset Structure |
|
|
| | Field | Type | Description | Why it's here | |
| |---|---|---|---| |
| | `audio` | `Audio` | The 10-second wav clip | The actual training/test input | |
| | `id` | `string` | Original filename stem | Lets you cross-reference against the `-features` configs or reconstruct the original file layout | |
| | `original_source` | `string` | The FSD50K clip this sample's repeated event was built from | Required for attribution — FSD50K licenses each clip individually, so this is what you check to find the license of the audio actually used in a given sample | |
| | `num_repetitions` | `int` | Ground-truth repetition count (0–8) | The label WavCounter and TSSMCounter are trained/evaluated against | |
| | `events` | `list[{start: float, end: float}]` | Onset/offset (seconds) of each repeated event within the clip | Used to build the target heatmap WavCounter regresses against, rather than just a single count — this is what lets the model localize events in time, not just count them | |
|
|
| ## Feature configs |
|
|
| `rs-features`/`rsn-features`/`rvn-features` contain `id`, `spectrogram`, and `tssm` (temporal self-similarity matrix) columns: the precomputed inputs TSSMCounter and spectrogram baseline actually train on, joinable back to the main config via `id`. These are included for convenience and exact reproducibility, but are fully derivable from the `audio` column above via the preprocessing code in the linked repo, so they're kept as separate configs rather than bundled into the main table. |
|
|
| ## Dataset Sources |
|
|
| Synthetic clips were generated by combining two source collections: |
|
|
| - FSD50K — https://zenodo.org/records/4060432 — provided the audio for each repeated event |
| - MUSAN — http://openslr.org/ — used as background noise |
|
|
| ## Licensing |
|
|
| This is a derivative work built from two source collections, each with different terms. Both require attribution. |
|
|
| **FSD50K** licenses each clip individually rather than as a whole collection (CC0, CC-BY, CC-BY-NC, or CC Sampling+, depending on the clip). Since every sample here is built from one specific FSD50K clip, the `original_source` field lets you look up that clip's exact license via FSD50K's own `dev_clips_info_FSD50K.json` / `eval_clips_info_FSD50K.json` mapping files. If you plan to use or redistribute a subset commercially, check the license of every clip referenced by `original_source` first — some are CC-BY-NC and restrict commercial reuse. |
|
|
| **MUSAN** is released under CC BY 4.0 (https://openslr.org/17/) — attribution required, otherwise unrestricted. |
|
|
| ## Citation |
|
|
| ```bibtex |
| [Your paper's BibTeX entry here] |
| ``` |
