Datasetpublic

KasuleTrevor/Lingala_100hrs

license: cc-by-4.0 language: - ln task_categories: - automatic-speech-recognition pretty_name: Lingala 100hrs ASR Lingala 100hrs 110.7 hours (23,539 rows) of Lingala speech with transcriptions, aggregated from three publicly available CC-BY-4.0 corpora for ASR research. Composition Counts from a full-pass audit on 2026-07-09: Source Upstream location Rows Splits AfriVoice (Lingala)… See the full description on the dataset page: https://huggingface.co/datasets/KasuleTrevor/Lingala_100hrs.

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Dataset Card

license: cc-by-4.0 language:

—ln task_categories:
—automatic-speech-recognition pretty_name: Lingala 100hrs ASR ---

Lingala 100hrs

110.7 hours (23,539 rows) of Lingala speech with transcriptions, aggregated from three publicly available CC-BY-4.0 corpora for ASR research.

Composition

Counts from a full-pass audit on 2026-07-09:

Source	Upstream location	Rows	Splits
AfriVoice (Lingala)	https://huggingface.co/datasets/DigitalUmuganda/AfriVoice	17,544	train (16,144), validation (915), test (485)
LRSC (Lingala Read Speech Corpus)	https://data.mendeley.com/datasets/28x8tc9n9k/1	2,937	train (2,489), validation (65), test (383)
FLEURS (`google/fleurs`, config `ln_cd`)	https://huggingface.co/datasets/google/fleurs	2,995	train (2,526), validation (65), test (404)

Splits

Split	Rows	Hours	Mean duration	Mean words
train	21,159	100.0	17.0 s	24.1
validation	1,045	5.1	17.6 s	24.9
test	1,335	5.6	15.1 s	19.9

Licensing

The three documented sources are all CC-BY-4.0, so this aggregation is distributed under CC-BY-4.0 with the attributions below.

Source	License	Verified against upstream on
AfriVoice (Lingala)	CC-BY-4.0 (upstream repo is gated: users must accept its access terms)	2026-07-09
LRSC	CC BY 4.0 (Mendeley Data, DOI 10.17632/28x8tc9n9k.1)	2026-07-09
FLEURS	CC-BY-4.0	2026-07-09

Dataset structure

—audio: audio (mono, 16 kHz) + sampling rate
—text: Lingala transcript
—source: upstream corpus for the row (Afrivoice, LRSC, fleurs)

Citations

LRSC:

Kimanuka, U., wa Maina, C., & Büyük, O. (2023). Speech Recognition Datasets for Low-resource Congolese Languages. AfricaNLP workshop at ICLR 2023. Data: Mendeley Data, V1, doi:10.17632/28x8tc9n9k.1

FLEURS:

Conneau, A., et al. (2022). FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech. IEEE SLT 2022.

AfriVoice:

Digital Umuganda. AfriVoice: multilingual African speech dataset (Shona, Lingala, Fulani, Malagasy, Wolof, Somali). https://huggingface.co/datasets/DigitalUmuganda/AfriVoice