---
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
    num_examples: 114641
  download_size: 5500616162
  dataset_size: 5452179735
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# VocSim — Public Benchmark

[![GitHub](https://img.shields.io/badge/GitHub-vocsim%2Fbenchmark-black?logo=github)](https://github.com/vocsim/benchmark)
[![Leaderboard](https://img.shields.io/badge/%F0%9F%A4%97%20Leaderboard-vocsim%2FVocSim-green)](https://huggingface.co/spaces/vocsim/VocSim)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-blue.svg)](https://creativecommons.org/licenses/by/4.0/)

The public split of **VocSim**, a training-free benchmark for zero-shot content identity in single-source audio embeddings. VocSim probes the intrinsic geometric quality of frozen audio representations: do acoustically variable instances of the same content land near each other in embedding space, without any task-specific training?

> Basha, M., Zai, A. T., Stoll, S., & Hahnloser, R. H. R. *VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio.* ICML 2026. [arXiv:2512.10120](https://doi.org/10.48550/arXiv.2512.10120)

## What's here

- **114,641 clips** across **15 public subsets**, drawn from 19 source corpora.
- Domains: human speech (phones, words, utterances), animal vocalizations (birdsong, otter calls), environmental sounds.
- Conditions: clean to noisy, sub-100ms to multi-second, few to thousands of classes per subset.
- All audio standardized to **16 kHz mono**.
- Single-source only — no overlapping speakers or simultaneous sources — so evaluation isolates content representation from source separation.

Four additional **blind out-of-distribution subsets** (low-resource speech in Shipibo-Conibo and Chintang) are held out for server-side evaluation via the [leaderboard](https://huggingface.co/spaces/vocsim/VocSim).

## Schema

```python
{
  "audio":  {"array": np.ndarray, "sampling_rate": 16000},
  "subset": "HW1",        # source-corpus tag (see paper for the full list)
  "speaker": "spk_042",   # speaker / animal / source ID, or "N/A"
  "label":  "hello",      # ground-truth class for similarity
}
```

## Quick start

```python
from datasets import load_dataset

ds = load_dataset("vocsim/public", split="train")
print(ds[0])
```

For end-to-end evaluation (feature extraction, distance computation, P@k / GSR), use the reference pipeline at [github.com/vocsim/benchmark](https://github.com/vocsim/benchmark).

## Companion datasets

| Dataset | Purpose |
|---|---|
| [`vocsim/avian-perception-benchmark`](https://huggingface.co/datasets/vocsim/avian-perception-benchmark) | Alignment of embeddings with zebra-finch perceptual judgments |
| [`vocsim/mouse-strain-classification-benchmark`](https://huggingface.co/datasets/vocsim/mouse-strain-classification-benchmark) | C57 vs DBA USV classification |
| [`vocsim/mouse-identity-classification-benchmark`](https://huggingface.co/datasets/vocsim/mouse-identity-classification-benchmark) | Individual-mouse identification from USVs |

## Licensing

Aggregation and metadata are released under CC BY 4.0. Each source corpus retains its original license; see Appendix A.1.1 of the paper for a per-source breakdown.

## Citation

```bibtex
@inproceedings{basha2026vocsim,
  title     = {VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio},
  author    = {Basha, Maris and Zai, Anja T. and Stoll, Sabine and Hahnloser, Richard H. R.},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year      = {2026},
  doi       = {10.48550/arXiv.2512.10120}
}
```