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Datasets:
Professor
/
luganda-speech-data

Tasks:
Text-to-Speech
Automatic Speech Recognition
Languages:
Ganda
Size:
10K<n<100K
Tags:
luganda
ganda
speech
tts
asr
african-languages
License:
Dataset card Data Studio Files
xet
 Community
luganda-speech-data
File size: 4,151 Bytes
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---
language: [lg]
license: cc0-1.0
multilinguality: monolingual
task_categories: [text-to-speech, automatic-speech-recognition]
tags: [luganda, ganda, speech, tts, asr, african-languages, common-voice]
pretty_name: Luganda Speech Data (Pooled)
size_categories: [10K<n<100K]
---

# Luganda Speech Data (Pooled)

A **~111.4-hour** Luganda speech corpus, drawn from Mozilla Common Voice and
filtered to the `train` split. Part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort.

## Source

**Mozilla Common Voice 26.0**, Luganda (`lg`) locale, accessed via the
[Mozilla Data Collective](https://mozilladatacollective.com/datasets/cmqinhruw00vinq07tohj4fd3)
mirror. 67,269 clips, 111.4h, source `dataset_id`/`source` = `common_voice`.

Common Voice is crowdsourced, scripted read speech: contributors record themselves
reading prompted sentences, and other contributors validate the recordings. The full
corpus splits into `train`/`dev`/`test`/`validated` (a superset of all three plus
extra validated-but-unsplit clips) -- only **`train`** is used here, same
held-out-partition policy we apply to DSN African Voices' splits.

**Why not WAXAL's `lug_tts` config too?** WAXAL's Luganda TTS config (~39h) is a
separate, independently-collected corpus with no evidence of overlap with Common
Voice -- unlike the Ethiopia/Shona/Lingala/Fulani cases elsewhere in this
collection, there's no matching-hours or matching-speaker-ID signal here. It remains
a candidate for a future top-up if more Luganda hours are wanted.

All audio is standardized to **16 kHz mono FLAC** (lossless), 1–30 second clips.
Source audio is MP3, decoded directly via `soundfile` (no external decoder needed).

## 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 Luganda script), from Common Voice's `sentence` field |
| `duration` | seconds |
| `source` | always `common_voice` |
| `dataset_id` | always `0` |
| `split` | `train` / `val` (250 clips held out for evaluation) |
| `speaker_id` | Common Voice's `client_id` |
| `gender` | speaker-reported gender where available (Common Voice's `gender` field, e.g. `male_masculine`/`female_feminine`) |
| `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/luganda-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)

row = df.iloc[0]
shard_path = hf_hub_download("Professor/luganda-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 **Luganda TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Luganda. Common Voice audio is
crowdsourced (variable recording equipment/environment across contributors) but
validated by community review, so quality is generally clean read speech. This is a
**research aggregation**; usage should respect Common Voice's own terms.

## License

CC0 1.0 Universal (public domain dedication), per the upstream Common Voice release.

## Acknowledgments

Deep thanks to **Mozilla Common Voice** and its Luganda-speaking contributors for
the source corpus, and to the **Mozilla Data Collective** for hosting it.

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.