Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
audio
audio-similarity
zero-shot-learning
representation-learning
embedding-evaluation
unsupervised-learning
License:
File size: 2,440 Bytes
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---
license: cc-by-4.0
size_categories:
- 100K<n<1M
pretty_name: VocSim
tags:
- audio
- audio-similarity
- zero-shot-learning
- representation-learning
- embedding-evaluation
- unsupervised-learning
- speech
- environmental-sounds
- animal-vocalizations
- benchmark
paperswithcode_id: audiosim
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: subset
dtype: string
- name: speaker
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 5452179735 # ~5.45 GB
num_examples: 114641
download_size: 5500616162 # ~5.5 GB
dataset_size: 5452179735 # ~5.45 GB
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# VocSim: A Training-Free Benchmark for Content Identity in Single-Source Audio Embeddings
[](https://github.com/anonymoussubmission0000/vocsim)
[-red.svg)](#)
[](https://creativecommons.org/licenses/by/4.0/)
**VocSim** evaluates how well neural audio embeddings generalize for **zero-shot audio similarity**. It tests recognizing fine-grained acoustic similarity without specific similarity training.
---
## Key Features
* **Diverse Sources:** Human speech (phones, words, utterances), birdsong, otter calls, environmental sounds.
* **Varied Conditions:** Spans clean to noisy recordings, short (<100ms) to long durations, few to many classes per subset.
* **Standardized:** All audio is 16kHz mono.
---
## Data Format
```python
{
'audio': {'array': array([...], dtype=float32), 'sampling_rate': 16000},
'subset': 'HW1', # Origin identifier
'speaker': 'spk_id', # Speaker/Animal/Source ID or 'N/A'
'label': 'hello' # Ground truth class for similarity
}
```
Train split: 114,641 public examples from 15 subsets for evaluation.
Blind Test Sets: 4 additional subsets held out privately.
## Citation
```bib
@inproceedings{vocsim_authors_2025,
title={VocSim: A Training-Free Benchmark for Content Identity in Single-Source Audio Embeddings},
author={Anonymous Authors},
booktitle={Conference/Journal},
year={2025},
url={[Link to paper upon DOI]}
}
```
## License
CC BY 4.0 - Creative Commons Attribution 4.0 International. |