Professor/lusoga-speech-data

Lusoga Speech Data (Pooled)

A ~39.0-hour Lusoga 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), sog_asr config — crowdsourced, image-prompted speech collected via Makerere University's "Yogera" app (the same pipeline used for WAXAL's Masaaba data). 6,723 clips, 39.0h, source = waxal.

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 sog_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/lusoga-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)

row = df.iloc[0]
shard_path = hf_hub_download("Professor/lusoga-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 Lusoga TTS/ASR research, in particular as finetuning data for a multilingual TTS model that doesn't natively support Lusoga. Speech is crowdsourced and image-prompted, with many speakers and variable recording conditions rather than studio-controlled. At ~39h, this is AfroNet's smallest published language so far -- useful as a warm-start/finetuning base, but thinner than most of the collection. 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 Lusoga-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.