Datasets:

Professor
/

somali-speech-data

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

A **~103.2-hour** Somali speech corpus, drawn from a single source (Afrivoice) and
filtered to only genuinely transcribed audio. Part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort.

## Source

[**DigitalUmuganda/Afrivoice**](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice)
(the general, pan-African Afrivoice release — not `Afrivoice_Ethiopia`, which we've
separately ingested for 5 Ethiopian languages) — Somali portion: 22,627 clips,
103.2h, source `dataset_id`/`source` = `afrivoice`.

There is also a Somali dataset published by African Next Voices (`Anv-ke/Somali`,
502h) — a completely different organization (Kenya-based KenCorpus Consortium vs.
Rwanda-based Digital Umuganda). We use Afrivoice's Somali here; the two don't appear
to be the same underlying recordings (their total-hours figures don't match closely,
unlike the WAXAL-overlap cases described below), so Anv-ke's Somali release remains
a candidate for a future, separate addition if more Somali hours are needed.

**Why not WAXAL?** WAXAL (`google/WaxalNLP`) has no Somali config at all, so there's
no overlap question here (contrast with Shona/Lingala/Fulani/Malagasy, this source's
other languages, which we've skipped for exactly that reason — see the project
README).

## A note on what "transcribed" means here

Afrivoice pairs each audio clip with an image the speaker was prompted to describe;
`transcription` is the sentence the speaker was recorded saying. Only a **minority**
of recordings are transcribed (~19% of total duration for Somali — 103.2h out of
~536h total) — the rest was recorded but never transcribed. No auto-transcription was
used to unlock the untranscribed majority; only clips with a real, human-provided
transcript are included here.

All audio is standardized to **16 kHz mono FLAC** (lossless), 1–30 second clips.
Source audio is real WAV — unlike `Afrivoice_Ethiopia`, there's no WebM-mislabeling
bug here, and it decodes directly via `soundfile` with no ffmpeg step 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 script), from Afrivoice's `transcription` field |
| `duration` | seconds |
| `source` | always `afrivoice` |
| `dataset_id` | always `0` |
| `split` | `train` / `val` (250 clips held out for evaluation) |
| `gender` | speaker metadata where available |
| `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/somali-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)
row = df.iloc[0]
shard_path = hf_hub_download("Professor/somali-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 **Somali TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Somali. Speech is prompted by
an image-description task, a narrower register than natural conversation. This is a
**research aggregation**; usage should respect Afrivoice's own terms.

## License

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

## Acknowledgments

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

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

	# Somali Speech Data (Pooled)

	A ~103.2-hour Somali speech corpus, drawn from a single source (Afrivoice) and
	filtered to only genuinely transcribed audio. Part of the
	[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
	effort.

	## Source

	[DigitalUmuganda/Afrivoice](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice)
	(the general, pan-African Afrivoice release — not `Afrivoice_Ethiopia`, which we've
	separately ingested for 5 Ethiopian languages) — Somali portion: 22,627 clips,
	103.2h, source `dataset_id`/`source` = `afrivoice`.

	There is also a Somali dataset published by African Next Voices (`Anv-ke/Somali`,
	502h) — a completely different organization (Kenya-based KenCorpus Consortium vs.
	Rwanda-based Digital Umuganda). We use Afrivoice's Somali here; the two don't appear
	to be the same underlying recordings (their total-hours figures don't match closely,
	unlike the WAXAL-overlap cases described below), so Anv-ke's Somali release remains
	a candidate for a future, separate addition if more Somali hours are needed.

	Why not WAXAL? WAXAL (`google/WaxalNLP`) has no Somali config at all, so there's
	no overlap question here (contrast with Shona/Lingala/Fulani/Malagasy, this source's
	other languages, which we've skipped for exactly that reason — see the project
	README).

	## A note on what "transcribed" means here

	Afrivoice pairs each audio clip with an image the speaker was prompted to describe;
	`transcription` is the sentence the speaker was recorded saying. Only a minority
	of recordings are transcribed (~19% of total duration for Somali — 103.2h out of
	~536h total) — the rest was recorded but never transcribed. No auto-transcription was
	used to unlock the untranscribed majority; only clips with a real, human-provided
	transcript are included here.

	All audio is standardized to 16 kHz mono FLAC (lossless), 1–30 second clips.
	Source audio is real WAV — unlike `Afrivoice_Ethiopia`, there's no WebM-mislabeling
	bug here, and it decodes directly via `soundfile` with no ffmpeg step 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 script), from Afrivoice's `transcription` field \|
	\| `duration` \| seconds \|
	\| `source` \| always `afrivoice` \|
	\| `dataset_id` \| always `0` \|
	\| `split` \| `train` / `val` (250 clips held out for evaluation) \|
	\| `gender` \| speaker metadata where available \|
	\| `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/somali-speech-data", "manifest.parquet", repo_type="dataset")
	df = pd.read_parquet(mp)

	row = df.iloc[0]
	shard_path = hf_hub_download("Professor/somali-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 Somali TTS/ASR research, in particular as finetuning data for a
	multilingual TTS model that doesn't natively support Somali. Speech is prompted by
	an image-description task, a narrower register than natural conversation. This is a
	research aggregation; usage should respect Afrivoice's own terms.

	## License

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

	## Acknowledgments

	Deep thanks to Digital Umuganda for the Afrivoice 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.