README.md

---
language: [luo]
license: cc-by-4.0
multilinguality: monolingual
task_categories: [text-to-speech, automatic-speech-recognition]
tags: [dholuo, luo, speech, tts, asr, african-languages, low-resource]
pretty_name: Dholuo Speech Data (Pooled)
size_categories: [10K<n<100K]
---

# Dholuo Speech Data (Pooled)

A **~191.5-hour** Dholuo (Luo) speech corpus, pooled from two independent sources
and filtered to only genuinely transcribed audio. Part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort.

## Sources

- [**Anv-ke/Dholuo**](https://huggingface.co/datasets/Anv-ke/Dholuo) — African Next
  Voices, a pilot data-collection effort in Kenya led by the KenCorpus Consortium (a
  coalition of Kenyan universities and research centers), funded by the Gates
  Foundation. 91,672 clips, 186.1h, `source` = `anv_ke`. **Gated on HuggingFace** — an
  account must click through the access request on the dataset page before the API
  can download it.
- **Mozilla Common Voice 26.0**, Dholuo (`luo`) locale, accessed via the
  [Mozilla Data Collective](https://mozilladatacollective.com/) mirror
  (`datacollective` Python SDK). `train` split only. 4,033 clips, 5.3h, `source` =
  `common_voice`. Crowdsourced, community-validated read speech, CC0-1.0.

These two sources are independent (different collection institutions and
methodologies), so no overlap/deduplication concern applies here, unlike some other
sources in this collection.

## Structure and what "transcribed" means here

The source splits data into `train`/`dev`/`dev_test`; only **`train`** is used here
(`dev`/`dev_test` are held-out evaluation partitions, same policy we apply to DSN's
splits for the Nigerian-language releases).

Within `train`, two categories are pooled together:
- **Scripted** — read from a prepared script, 100% transcribed by construction. Each
  row also ships an English `translatedText` alongside the native transcript (not
  included in this release's `text` field, which is native-language only).
- **Unscripted** — topic-prompted natural speech. The large majority carries a real,
  reviewed transcript (Anv-ke's own workflow marks each as `approved`/`rejected`
  after review); only transcribed rows are included here.

**A text-encoding bug in the source, fixed during ingestion:** `unscripted`
transcripts in the raw parquet files are mojibake — UTF-8 bytes that got decoded as
Latin-1 somewhere upstream. This is fixed via a `encode('latin-1').decode('utf-8')`
round-trip that's a safe no-op on already-correct text (a genuine non-Latin-1
character can't itself be Latin-1-encoded, so the fix only fires on rows that
actually need it) — `scripted` transcripts, which are correct as shipped, pass
through unchanged.

All audio is standardized to **16 kHz mono FLAC** (lossless), 1–30 second clips.
Source audio is real WAV, embedded directly in the source's parquet files (no
WebM-mislabeling issue like some other sources in this collection).

## 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 Dholuo script) |
| `duration` | seconds |
| `source` | `anv_ke` or `common_voice` |
| `dataset_id` | always `0` |
| `split` | `train` / `val` (250 clips held out for evaluation) |
| `speaker_id` | Anv-ke's `recorder_uuid`, or Common Voice's `client_id` |
| `gender` | speaker metadata where available |
| `domain` | e.g. `scripted/Agriculture and Food`, `unscripted/Education and Technology` (Anv-ke rows only; `null` for Common Voice rows) |
| `dbfs`, `clip_ratio`, `sil_ratio` | cheap DSP quality proxies: loudness, fraction of clipped samples, fraction of near-silent frames |
| `has_disfluency` | always `false` — this source doesn't flag disfluencies |

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd, tarfile, io, soundfile as sf

mp = hf_hub_download("Professor/dholuo-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)

row = df.iloc[0]
shard_path = hf_hub_download("Professor/dholuo-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 **Dholuo TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Dholuo. Source recordings cover
2 dialects pooled together (per the upstream dataset card); dialect is not preserved
as a separate field in this release. This is a **research aggregation**; usage
should respect African Next Voices' own terms.

## License

CC BY 4.0 for the Anv-ke portion, per the upstream [Anv-ke/Dholuo](https://huggingface.co/datasets/Anv-ke/Dholuo)
release; CC0 1.0 for the Common Voice portion, per upstream Common Voice.

## Acknowledgments

Deep thanks to the **KenCorpus Consortium / African Next Voices** for the Anv-ke
portion (Gates Foundation-funded), and to **Mozilla Common Voice** and its Dholuo
contributors for the Common Voice portion, hosted via the **Mozilla Data
Collective**.

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.