lingala
Datasets
All datasets matching “lingala”audios-lingala-annotatees
Annotated Lingala Dataset – Full Version
Description
This dataset gathers annotated Lingala audio data, intended for open-source automatic speech recognition (ASR) research and for fine-tuning Whisper-type models.
It includes:
the original audio files (viewable directly in the Hugging Face viewer)
text transcriptions
Mel spectrograms
tokenized labels
Overall statistics
Metric
Value
Total volume
5 h 0 min 18 s
Number of audio segments… See the full description on the dataset page: https://huggingface.co/datasets/Congo-digital-service/audios-lingala-annotatees.Lingala_100hrs
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)… See the full description on the dataset page: https://huggingface.co/datasets/KasuleTrevor/Lingala_100hrs.audios-lingala-annotatees-v2
Annotated Lingala Audio — canonical corpus
Annotated Lingala speech for open automatic speech recognition research and for
fine-tuning speech models.
This release is a full reconstruction of the corpus from its source
recordings and annotations. It supersedes
Congo-digital-service/audios-lingala-annotatees,
which is deprecated — see Relationship to the previous release below.
What this dataset contains
Each row is one annotated speech segment, carrying the audio… See the full description on the dataset page: https://huggingface.co/datasets/Congo-digital-service/audios-lingala-annotatees-v2.tts_lingala_malelingala-speech-datasetqwen-vl-lingala-dataset-augmented
Augmentation
This dataset derives from dataset-qwen-vl-lingala-qlora-vf (417 train / 50 test) through an augmentation step applied to the training image-text pairs, bringing the training volume to 884 examples.
Augmentation method: the 467 additional training examples compared to the source (417 → 884) are obtained mainly through controlled degradation of the input image — noise, brightness/contrast variation, light blur — rather than through synthetic content generation or text… See the full description on the dataset page: https://huggingface.co/datasets/Congo-digital-service/qwen-vl-lingala-dataset-augmented.