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language: [myx]
license: cc-by-4.0
multilinguality: monolingual
task_categories: [text-to-speech, automatic-speech-recognition]
tags: [masaaba, lugisu, gisu, speech, tts, asr, african-languages, low-resource]
pretty_name: Masaaba Speech Data (Pooled)
size_categories: [1K<n<10K]
---
# Masaaba Speech Data (Pooled)
A **~42.7-hour** Masaaba (Lugisu/Lumasaaba) speech corpus, drawn from a single
source (WAXAL) and filtered to only genuinely transcribed audio. Part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort.
## Source
[**WAXAL**](https://huggingface.co/datasets/google/WaxalNLP) (`google/WaxalNLP`),
`mas_asr` config — crowdsourced, image-prompted speech collected via Makerere
University's "Yogera" app (the same pipeline used for WAXAL's Lusoga data). 7,947
clips, 42.7h, `source` = `waxal`.
**A naming note:** WAXAL's config code for this language is `mas`, which is also the
ISO 639-3 code for the Nilotic **Maasai** language (spoken in Kenya/Tanzania) — but
this data is actually **Masaaba/Lugisu**, a Bantu language of Uganda/Kenya, per
WAXAL's own provider documentation. It is **not** the same language as
[Anv-ke/Maasai](https://huggingface.co/datasets/Anv-ke/Maasai), which we've
separately published as `Professor/maasai-speech-data` and which is the genuine
Nilotic Maasai language. Don't confuse the two despite the shared 3-letter code.
`train`+`validation`+`test` splits are pooled together (intentional, same policy
already applied to WAXAL's `_tts` configs used elsewhere in AfroNet); the
`unlabeled` split (untranscribed audio) is excluded, consistent with AfroNet's
no-auto-transcription policy.
All audio is standardized to **16 kHz mono FLAC** (lossless), 1–30 second clips.
## Format
The dataset ships as **WebDataset-style tar shards** (`shards/shard-00000.tar` …, ~1 GB
each, one `{key}.flac` file per clip) plus a single manifest (`manifest.parquet` /
`manifest.jsonl`):
| Column | Description |
|---|---|
| `key`, `shard` | which tar file + entry holds this clip's audio |
| `text` | transcript (native script) |
| `duration` | seconds |
| `source` | always `waxal` |
| `dataset_id` | always `0` |
| `split` | `train` / `val` (250 clips held out for evaluation) |
| `speaker_id` | source-provided speaker ID |
| `gender` | speaker metadata where available |
| `domain` | always `mas_asr` |
| `dbfs`, `clip_ratio`, `sil_ratio` | cheap DSP quality proxies: loudness, fraction of clipped samples, fraction of near-silent frames |
| `has_disfluency` | always `false` |
## Usage
```python
from huggingface_hub import hf_hub_download
import pandas as pd, tarfile, io, soundfile as sf
mp = hf_hub_download("Professor/masaaba-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)
row = df.iloc[0]
shard_path = hf_hub_download("Professor/masaaba-speech-data", f"shards/{row.shard}", repo_type="dataset")
with tarfile.open(shard_path) as tar:
audio_bytes = tar.extractfile(f"{row.key}.flac").read()
arr, sr = sf.read(io.BytesIO(audio_bytes))
```
The tar shards are also directly readable by the [`webdataset`](https://github.com/webdataset/webdataset)
library for streaming training pipelines.
## Intended use & limitations
Built for **Masaaba TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Masaaba. Speech is
crowdsourced and image-prompted, with many speakers and variable recording
conditions rather than studio-controlled. This is a **research aggregation**;
usage should respect WAXAL's own terms.
## License
CC BY 4.0, per the upstream [WAXAL](https://huggingface.co/datasets/google/WaxalNLP) release.
## Acknowledgments
Deep thanks to the **WAXAL** project (Google) and Makerere University's "Yogera"
initiative, and to Masaaba-speaking contributors for the source corpus.
This dataset was pooled by **Victor Olufemi and LyngualLabs** as part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data effort.
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