Datasets:

williamhtan
/

cld-multi-dataset

dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: text
      dtype: string
    - name: lang
      dtype: string
    - name: accent
      dtype: string
  splits:
    - name: train
      num_bytes: 3365528653
      num_examples: 12368
    - name: valid
      num_bytes: 413508457
      num_examples: 1546
    - name: test
      num_bytes: 399289965
      num_examples: 1546
  download_size: 4177821855
  dataset_size: 4178327075
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: valid
        path: data/valid-*
      - split: test
        path: data/test-*
license: cc-by-4.0
tags:
  - language-identification
  - spoken-language-identification
  - speech
  - low-resource
  - accent-robust
pretty_name: cld-multi-dataset
task_categories:
  - audio-classification
  - automatic-speech-recognition
language:
  - en
  - zh
  - id
  - ms
  - hi

CLD — Multilingual (5-language) Speech Dataset

Speech dataset for Convex Low-resource Accent-Robust Language Detection (CLD), covering 5 languages chosen for a deliberately challenging classification boundary. This is the multiclass division of the CLD data.

Dataset description

We curate a dataset of multilingual voice transcriptions across high-resource languages and their low-resource sub-dialects. As a primary source of transcription data we use the Common Voice (v23) dataset (Ardila et al., 2020). We supplement this with several additional dialect datasets for regional speech variance:

Singaporean English from the National Speech Corpus (NSC) — the first Singapore English corpus — provided through the Info-communications and Media Development Authority (IMDA) of Singapore. Singlish is selected because studies show it incurs particularly high error rates during voice transcription (Fong et al., 2002).
The Lahaja dataset (Sanket et al., 2024), a benchmark comprising 12.5 hours of Hindi from 132 speakers across 83 Indian districts.

We normalize and augment all audio files via time stretching, volume gain, pitch shift, and augmented background noise with MUSAN (Snyder et al., 2015).

Schema

Each split is a datasets Dataset with columns:

column	type	description
`audio`	`Audio(sampling_rate=16000)` mono	the speech clip, 16 kHz mono
`text`	`string`	reference transcription
`lang`	`string`	ISO-639-1 language code
`accent`	`string`	accent / dialect label

Multiclass setup

For the multiclass classification task we select 5 languages: English, Chinese, Indonesian, Malay, Hindi. This selection establishes a challenging classification boundary, since these languages share linguistic and geographical proximity — such regional influences often cause misidentification (e.g. Singaporean English is frequently confused with Malay or Indonesian). To maintain a low-resource setting we curate ~~16,000 training samples across these 5 languages, incorporating 24 unique accents (~~3,200 samples per language, ~666 per accent), with an 80-10-10 train/test/validation split.

Languages (5): en (English), zh (Chinese), id (Indonesian), ms (Malay), hi (Hindi)
Splits: train / valid / test

How to use

from datasets import load_dataset

ds = load_dataset("williamhtan/cld-multi-dataset")
print(ds)
sample = ds["test"][0]
print(sample["lang"], sample["text"])
audio = sample["audio"]          # {"array": np.ndarray, "sampling_rate": 16000}

Citation

If you use this dataset, please cite the CLD paper (ICML 2026) and the underlying corpora: Common Voice (Ardila et al., 2020), the National Speech Corpus (IMDA), Lahaja (Sanket et al., 2024), and MUSAN (Snyder et al., 2015).