README.md

---
license: cc-by-4.0
language:
- ln
pretty_name: "Lingala Read Speech Corpus (LRSC)"
task_categories:
- automatic-speech-recognition
tags:
- lingala
- bantu-languages
- congolese-languages
- low-resource
- automatic-speech-recognition
- read-speech
- speech
size_categories:
- 1K<n<10K
---

# Lingala Read Speech Corpus (LRSC)

This repository provides a Hugging Face-ready repackaging of the **Lingala Read Speech Corpus (LRSC)**, the labelled Lingala subset of *Speech Recognition Datasets for Congolese Languages*.

The goal of this repackaging by **Bantu Languages Initiative** is to make the annotated LRSC subset easier to load, inspect, cite, and use with the Hugging Face `datasets` library for ASR research on Lingala and under-resourced Bantu languages.

This is **not** the original publication repository. The original dataset was published on Mendeley Data by Kimanuka, wa Maina and Büyük.

## Dataset summary

- Language: Lingala (`ln`)
- Task: Automatic Speech Recognition (ASR)
- Speech type: read speech
- Total duration in this HF package: **5.01 hours**
- Number of utterances: **2940**
- Number of speakers detected from filenames: **31**
- Audio sampling rates in original files: 16000 Hz: 2940
- Channels in original files: 1 channel(s): 2940

## Splits

The original LRSC release contained train/validation style manifests. For easier use in modern Hugging Face ASR workflows, this repository provides deterministic `train`, `validation`, and `test` splits.

When possible, the split is speaker-level, so the same detected speaker ID does not appear in multiple splits.

| split      |   utterances |   speakers |   hours |   ratio_hours |
|:-----------|-------------:|-----------:|--------:|--------------:|
| test       |          463 |          3 |   0.863 |      0.172428 |
| train      |         1924 |         25 |   3.221 |      0.643556 |
| validation |          553 |          3 |   0.921 |      0.184016 |

## Speaker metadata

The `speaker_id` is extracted from the audio filename prefix. The `gender` field is manually annotated from representative audio samples and should be interpreted as **perceived voice category**, not as a biological attribute.

| gender   |   speakers |   utterances |   hours |
|:---------|-----------:|-------------:|--------:|
| female   |         20 |         1892 |   3.255 |
| male     |         11 |         1048 |   1.75  |

## Character inventory

The original `dict.ltr.txt` file is included in this repository as `dict.ltr.txt`, because the corpus uses characters and diacritics beyond a minimal Latin alphabet. This is important for CTC-style ASR experiments and for preserving Lingala tonal/orthographic information.

Characters found in `dict.ltr.txt`:

`’`, `x`, `a`, `w`, `à`, `f`, `-`, `q`, `5`, `ç`, `d`, `î`, `j`, `e`, `0`, `g`, `s`, `o`, `c`, `'`, `h`, `3`, `t`, `l`, `ǎ`, `r`, `ε`, `ê`, `8`, `y`, `n`, `|`, `u`, `ɔ`, `z`, `ɛ`, `k`, `m`, `å`, `v`, `i`, `p`, `b`

## Data fields

Each example contains:

- `utterance_id`: unique utterance identifier, derived from the audio filename.
- `audio`: audio file decoded by Hugging Face `datasets`.
- `transcription`: original text transcription.
- `speaker_id`: speaker identifier extracted from filename.
- `gender`: perceived voice category (`female`, `male`, `unknown`).
- `language`: ISO-like language code, here `ln`.
- `language_name`: `Lingala`.
- `duration_s`: duration in seconds.
- `sampling_rate`: original file sampling rate.
- `num_channels`: number of channels in the original file.
- `original_split`: original LRSC split before this HF repackaging.

## Relation to other Lingala ASR resources

LRSC is complementary to larger African speech datasets such as `google/WaxalNLP`. While WaxalNLP provides a much larger multilingual ASR/TTS resource, LRSC is useful as a compact supervised Lingala benchmark with carefully paired audio and text. It can be especially useful for quick experiments, sanity checks, low-resource ASR baselines, and evaluation of models trained on larger Lingala speech resources.

## License

The original dataset page indicates a **CC BY 4.0** license. This repackaging is therefore distributed under **CC BY 4.0**.

Users must cite the original authors and dataset when using this corpus.

## Citation

Please cite the original dataset:

```bibtex
@dataset{kimanuka_2023_congolese_speech,
  author    = {Kimanuka, Ussen and wa Maina, Ciira and Büyük, Osman},
  title     = {Speech Recognition Datasets for Congolese Languages},
  year      = {2023},
  publisher = {Mendeley Data},
  version   = {V1},
  doi       = {10.17632/28x8tc9n9k.1}
}
```

## Limitations
 - LRSC is a small read-speech corpus and should not be treated as fully representative of all Lingala accents, dialects, spontaneous speech, or noisy real-world speech.
 - Speaker IDs are inferred from filenames.
 - The gender field is manually added in this repackaging as perceived voice category and may be uncertain.
 - The train/validation/test splits are newly generated for Hugging Face convenience; the original_split column preserves the original split provenance.

This Hugging Face version was prepared by Bantu Languages Initiative to improve accessibility for the ASR and African NLP research community.