| --- |
| 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. |
|
|
| [](https://arxiv.org/abs/2605.23235) |
| [](https://github.com/pilancilab/CLD) |
| [](https://pypi.org/project/jaxcld/) |
|
|
| ## 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`](https://huggingface.co/docs/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 |
|
|
| ```python |
| 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). |
|
|
