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

# Oromo Speech Data (Pooled)

A **~189.2-hour** Afaan Oromo speech corpus, drawn from a single source (Afrivoice
Ethiopia) and filtered to only genuinely transcribed audio. Part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort — sibling release to Yoruba/Hausa/Igbo/Kinyarwanda/Swahili, but Oromo (and
its four sibling Ethiopian-language releases, Amharic/Sidama/Tigrinya/Wolaytta) are
**each published independently**, not bundled into one combined "Ethiopia" dataset,
even though they share a single upstream source.

## Source

[**Afrivoice Ethiopia**](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice_Ethiopia)
(Digital Umuganda) — 38,138 clips, 189.2h, source `dataset_id`/`source` = `afrivoice`.

**Why not also use WAXAL's `orm_asr` config?** Strong circumstantial evidence points
to WAXAL's Ethiopian ASR configs (`amh_asr`/`orm_asr`/`sid_asr`/`tir_asr`/`wal_asr`)
being derived from the same underlying Digital Umuganda collection, not an
independent source: matching Firebase-style speaker-ID format between the two,
matching "describe this image" content register, and an exact match on the set of
five languages covered. Pooling both would very plausibly mean training on the same
speakers/clips twice, not doubling real coverage — see the project README for the
full reasoning.

## A note on what "transcribed" means here

Afrivoice Ethiopia collects audio across three registers — **Scripted** (read from a
pre-written script, 100% transcribed by construction), and two **Unscripted**
categories, **Expert** and general (further broken into "image prompt",
"spontaneous", "undefined" sub-categories in the raw data) — where the large
majority of recorded audio has **no transcript at all**. Only a minority of
Unscripted/Expert clips were manually transcribed by Digital Umuganda.

This dataset pools **only clips with a real, human-provided transcript** — no
auto-transcription was used to unlock the much larger untranscribed portion (several
hundred additional hours exist per language, recorded but untranscribed). That was a
deliberate choice: unverified ASR output on audio nobody has checked isn't something
we want to train a TTS model on. If that untranscribed audio is auto-transcribed and
verified in the future, it could meaningfully scale this release up.

All audio is standardized to **16 kHz mono FLAC** (lossless), 1–30 second clips.
Source audio ships as `.wav`-named files in the raw dataset, but is **actually
WebM/Opus** (confirmed via `ffprobe`) — decoded via ffmpeg during ingestion, not a
plain WAV read.

## 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), from Afrivoice's `transcription` or `script` field |
| `duration` | seconds |
| `source` | always `afrivoice` |
| `dataset_id` | always `0` |
| `split` | `train` / `val` (250 clips held out for evaluation) |
| `gender` | speaker metadata where available |
| `domain` | which register the clip came from: `scripted`, `unscripted/expert`, `unscripted/image prompt`, `unscripted/spontaneous`, or `unscripted/undefined` |
| `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/oromo-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)

row = df.iloc[0]
shard_path = hf_hub_download("Professor/oromo-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 **Oromo TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Oromo. Roughly half this
corpus is "Scripted" register (closer to natural read speech); the rest is a mix of
spontaneous/expert speech describing images, a narrower register than natural
conversation. This is a **research aggregation**; usage should respect Afrivoice
Ethiopia's own terms.

## License

CC BY 4.0, per the upstream [Afrivoice Ethiopia](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice_Ethiopia) release.

## Acknowledgments

Deep thanks to **Digital Umuganda** for the Afrivoice Ethiopia 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.