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 multi-language TTS data effort.
Source
WAXAL (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, 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
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
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 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 multi-language TTS data effort.