Datasets:
pretty_name: DeepASMR-NSpeech
version: 1.0.0
license: cc-by-nc-4.0
language:
- en
task_categories:
- text-to-audio
- audio-classification
tags:
- audio
- asmr
- sound-generation
- subject-verb-object
- benchmark
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: parquet/train-*.parquet
- split: test
path: parquet/test-*.parquet
DeepASMR-NSpeech
A Fine-Grained Benchmark for Non-Speech ASMR Generation
73,829 ten-second clips · 202.6 hours · 37 fine-grained actions
🎧 Interactive Demo · ⌘ Code · 📄 Paper: coming soon
Dataset summary
DeepASMR-NSpeech is a non-speech ASMR audio dataset with structured Subject-Verb-Object annotations and the SVO-AQA audio question-answering benchmark. All clips are 10 seconds long and cover 37 fine-grained sound-producing actions.
Authors: Ziyi Yang, Leying Zhang, Chenda Li, and Yanmin Qian
Auditory Cognition and Computational Acoustics Lab
Shanghai Jiao Tong University
Dataset statistics
| Metric | Train | Test | Total / cross-split |
|---|---|---|---|
| Annotated events | 12,327 | 1,163 | 13,490 |
| Ten-second clips | 72,666 | 1,163 | 73,829; 0 overlap |
| Duration | 199.4 h | 3.19 h | 202.6 h |
Quick start
from datasets import load_dataset
dataset = load_dataset("AudioCC-Lab/DeepASMR-NSpeech")
sample = dataset["train"][0]
print(sample["caption"], sample["audio"])
Data fields
Each Parquet row contains audio_id, embedded audio, caption, closed-set verb, open noun phrases subject and object, and anonymized creator_id.
The release also includes 431 human-validated SVO-AQA questions and a frozen 55-question audio-dependent subset. See the code repository for construction and evaluation instructions.
License and source-media rights
The structured annotations, dataset metadata, and SVO-AQA annotations that the authors are authorized to license are released under CC BY-NC 4.0.
The source media originates from third-party public videos. All associated rights remain with their respective original creators or other rights holders. Users must comply with applicable creator rights, platform terms, privacy requirements, and local law. Structured labels may contain model-assisted annotation noise.
Citation
@misc{yang2026deepasmrnspeech,
title = {DeepASMR-NSpeech: A Fine-Grained Benchmark for Non-Speech ASMR Generation},
author = {Yang, Ziyi and Zhang, Leying and Li, Chenda and Qian, Yanmin},
year = {2026},
note = {Dataset and code, version 1.0.0}
}