Datasets:

iNLP-Lab
/

GlobeAudio

language:
  - en
  - zh
  - ru
  - th
  - bn
license: cc-by-nc-4.0
multilinguality:
  - multilingual
pretty_name: GlobeAudio
task_categories:
  - audio-text-to-text
dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 44100
    - name: question
      dtype: string
    - name: option1
      dtype: string
    - name: option2
      dtype: string
    - name: option3
      dtype: string
    - name: option4
      dtype: string
    - name: target
      dtype: string
  download_size: 1554418131
  dataset_size: 1554462926
tags:
  - audio
  - in-the-wild
  - compound-task
configs:
  - config_name: en
    data_files:
      - split: train
        path: data/en/en-*
    default: true
  - config_name: zh
    data_files:
      - split: train
        path: data/zh/zh-*
  - config_name: sg
    data_files:
      - split: train
        path: data/sg/sg-*
  - config_name: ru
    data_files:
      - split: train
        path: data/ru/ru-*
  - config_name: th
    data_files:
      - split: train
        path: data/th/th-*
  - config_name: bn
    data_files:
      - split: train
        path: data/bn/bn-*

GlobeAudio Dataset

This is the dataset for the paper GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models.

Dataset Description
Language Selection
Dataset Quality
Dataset Breakdown
Sample Usage

Dataset Description

GlobeAudio is a multilingual, multicultural benchmark for assessing naturalistic audio understanding, comprising 5,637 human-authored and rigorously verified MCQs across six typologically diverse languages, namely English (United States), Chinese (China), Thai (Thailand), Russian (Russia), Bengali (India) and Singlish (Singapore). The dataset comprises of naturally occurring audio clips from online media that reflect real-life speech across diverse contexts, and are curated to rigorously examine the ability of multilingual systems to handle acoustic and sociolinguistic variability.

Language Selection

The 6 carefully selected languages span multiple language families (Indo-European, Sino-Tibetan, Tai-Kadai and creole varieties), covers a wide range of geographic regions, and reflects substantial variation in resource availability and phonological characteristics. They also differ markedly in script systems and spoken characteristics -- the inclusion of Singlish enables the evaluation of naturally occuring code-mixing and multilingual speech within a single variety, capturing linguistic phenomena that are common in real-world audio but rarely represented in existing benchmarks.

Dataset Quality

For all selected languages, data annotation is performed exclusively by native speakers for whom the language is their first language, in order to ensure high linguistic fidelity, culturally appropriate interpretation and reliable construction of questions and distractors. Consequently, a two-stage quality control process was conducted involving random sampling and cross-checking, producing a final dataset with an inter-annotator agreement of 95.5%, together with verified and consistent gold labels.

Dataset Composition

Language (Code)	CC Size	Isochrony	Genus	Script	Clip Length (s)	QA Length (Q/A)	Total
English (en)	42.60	High, Str	Germanic	Latin	25.72	53.8 / 22.9	1,274
Russian (ru)	6.15	High, Str	Slavic	Cyrillic	25.69	37.8 / 22.2	924
Chinese (zh)	4.99	Mid, Syl	Sinitic	Hanzi	24.88	13.5 / 5.0	1,072
Thai (th)	0.37	Mid, Syl	Tai	Thai	24.82	29.3 / 13.0	1,145
Bengali (bn)	0.10	High, Syl	Indo-Aryan	Bengali	25.47	37.6 / 18.3	719
Singlish (sg)	--	Low, Syl	--	Latin	25.54	73.3 / 34.9	503
Total					25.35	40.9 / 19.4	5,637

Isochrony is categorized by syllable complexity and rhythmic unit (Str = stress-timed, Syl = syllable-timed).

Each audio clip is approximately 20-40 seconds long, and contains naturally occurring audio that reflect real-life auditory settings spanning both formal and informal contexts.

Sample Usage

The datasets library lets you load and preprocess datasets in Python at scale. You may load the dataset to your local drive with load_dataset.

For example, to download the English config, specify the corresponding language config name (i.e., "en" for English):

from datasets import load_dataset

# load a specific language eg. "en", "ru", "zh", "sg", "th", "bn"
dataset = load_dataset("iNLP-Lab/GlobeAudio", "en")