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.
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
[Your paper's BibTeX entry here]