Datasets:

Professor
/

kinyarwanda-speech-data

---
language: [rw]
license: cc-by-4.0
multilinguality: monolingual
task_categories: [text-to-speech, automatic-speech-recognition]
tags: [kinyarwanda, speech, tts, asr, african-languages, low-resource]
pretty_name: Kinyarwanda Speech Data (Pooled)
size_categories: [100K<n<1M]
---
# Kinyarwanda Speech Data (Pooled)

A **~989.9-hour** pooled Kinyarwanda speech corpus, combining two independently-sourced
datasets into one consistently-formatted corpus for speech modeling (TTS / ASR). Part
of the [AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort — sibling release to the Yoruba/Hausa/Igbo pools, but sourced entirely
differently: DSN African Voices, NaijaVoices, and WAXAL (the sources behind the other
three languages) don't cover Kinyarwanda at all.

## Sources

| Source | Clips | Hours | Style |
|---|---|---|---|
| [**Afrivoice Kinyarwanda**](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice_Kinyarwanda) (Digital Umuganda) | 183,159 | 983.5 h | crowdsourced spoken image descriptions, 5 domains (agriculture/health/finance/government/education) |
| [**Kinyarwanda TTS dataset**](https://huggingface.co/datasets/mbazaNLP/kinyarwanda-tts-dataset) (mbazaNLP / Digital Umuganda) | 3,992 | 6.4 h | studio-recorded, single voice actress, linguist-reviewed text |
| **Total** | **187,151** | **989.9 h** | |

All audio is standardized to **16 kHz mono FLAC** (lossless). Clips are **1–30 seconds**;
empty/garbage transcripts, near-silent clips, and undecodable audio were dropped at
ingestion.

### On Afrivoice's domain and sampling choices

Afrivoice ships six domains; a sixth, **Scripted Education, was deliberately excluded**
from this pool — its per-clip `duration` field didn't plausibly match its (much longer,
paragraph-length) transcript text, a red flag for a text-audio mismatch that wasn't
resolved before this release. The five domains used were each capped at **200 hours**
(Education came in under cap, at 183.5h, since only ~198.5h total exists for that
domain after filtering) to keep the corpus from being dominated by whichever domain
happened to have the most raw hours available (Health alone has 994h in the source
release).

## 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`) that indexes every clip:

| Column | Description |
|---|---|
| `key`, `shard` | which tar file + entry holds this clip's audio |
| `text` | transcript (native script). For Afrivoice this is `raw_text` from the source (natural capitalization/punctuation), not its lowercased/stripped `text` field, which is ASR-oriented normalization |
| `duration` | seconds |
| `source` | `afrivoice` \| `mbaza_tts` |
| `dataset_id` | integer id per source (0=afrivoice, 1=mbaza_tts) |
| `split` | `train` / `val` (250 clips held out per source for evaluation) |
| `speaker_id`, `gender` | speaker metadata where available (Afrivoice doesn't expose per-speaker IDs; mbaza_tts is single-speaker) |
| `domain` | Afrivoice's source domain (agriculture/health/financial/government/education); `null` for mbaza_tts |
| `lufs` | Afrivoice's own broadcast-standard loudness measurement (LUFS), where available — a more principled loudness signal than the `dbfs` proxy below |
| `dbfs`, `clip_ratio`, `sil_ratio` | cheap DSP quality proxies computed for every clip: loudness, fraction of clipped samples, fraction of near-silent frames |
| `has_disfluency` | always `false` here — neither source flags disfluencies the way the Nigerian-language sources' ASR transcripts do |

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd, tarfile, io, soundfile as sf
mp = hf_hub_download("Professor/kinyarwanda-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)
row = df.iloc[0]
shard_path = hf_hub_download("Professor/kinyarwanda-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 **Kinyarwanda TTS/ASR research**, in particular as pooled finetuning data for
a multilingual TTS model that doesn't natively support Kinyarwanda. The bulk of this
corpus (Afrivoice) is speech describing photographs across five institutional
domains — a fairly narrow register (descriptive, matter-of-fact) compared to natural
conversation or narrative speech; the small mbaza_tts portion is the only genuinely
TTS-purpose-built, studio-quality anchor. This is a **research aggregation**; usage
should respect the terms of each constituent source below.
## License
Both constituent sources are CC BY 4.0. Consult each source's own page for full terms:
[Afrivoice Kinyarwanda](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice_Kinyarwanda) ·
[Kinyarwanda TTS dataset](https://huggingface.co/datasets/mbazaNLP/kinyarwanda-tts-dataset).
## Citations
If you use this pooled dataset, please cite the **original sources** it draws from —
consult each source's own HuggingFace page for their preferred citation, as neither
currently ships a bibtex entry in-repo.
## Acknowledgments
Deep thanks to **Digital Umuganda** for both constituent datasets — the large-scale
Afrivoice image-description corpus across five domains, and (via the mbaza project)
the studio-quality single-speaker TTS corpus with linguist-reviewed text.
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: [rw]
	license: cc-by-4.0
	multilinguality: monolingual
	task_categories: [text-to-speech, automatic-speech-recognition]
	tags: [kinyarwanda, speech, tts, asr, african-languages, low-resource]
	pretty_name: Kinyarwanda Speech Data (Pooled)
	size_categories: [100K<n<1M]
	---

	# Kinyarwanda Speech Data (Pooled)

	A ~989.9-hour pooled Kinyarwanda speech corpus, combining two independently-sourced
	datasets into one consistently-formatted corpus for speech modeling (TTS / ASR). Part
	of the [AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
	effort — sibling release to the Yoruba/Hausa/Igbo pools, but sourced entirely
	differently: DSN African Voices, NaijaVoices, and WAXAL (the sources behind the other
	three languages) don't cover Kinyarwanda at all.

	## Sources

	\| Source \| Clips \| Hours \| Style \|
	\|---\|---\|---\|---\|
	\| [Afrivoice Kinyarwanda](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice_Kinyarwanda) (Digital Umuganda) \| 183,159 \| 983.5 h \| crowdsourced spoken image descriptions, 5 domains (agriculture/health/finance/government/education) \|
	\| [Kinyarwanda TTS dataset](https://huggingface.co/datasets/mbazaNLP/kinyarwanda-tts-dataset) (mbazaNLP / Digital Umuganda) \| 3,992 \| 6.4 h \| studio-recorded, single voice actress, linguist-reviewed text \|
	\| Total \| 187,151 \| 989.9 h \| \|

	All audio is standardized to 16 kHz mono FLAC (lossless). Clips are 1–30 seconds;
	empty/garbage transcripts, near-silent clips, and undecodable audio were dropped at
	ingestion.

	### On Afrivoice's domain and sampling choices

	Afrivoice ships six domains; a sixth, Scripted Education, was deliberately excluded
	from this pool — its per-clip `duration` field didn't plausibly match its (much longer,
	paragraph-length) transcript text, a red flag for a text-audio mismatch that wasn't
	resolved before this release. The five domains used were each capped at 200 hours
	(Education came in under cap, at 183.5h, since only ~198.5h total exists for that
	domain after filtering) to keep the corpus from being dominated by whichever domain
	happened to have the most raw hours available (Health alone has 994h in the source
	release).

	## 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`) that indexes every clip:

	\| Column \| Description \|
	\|---\|---\|
	\| `key`, `shard` \| which tar file + entry holds this clip's audio \|
	\| `text` \| transcript (native script). For Afrivoice this is `raw_text` from the source (natural capitalization/punctuation), not its lowercased/stripped `text` field, which is ASR-oriented normalization \|
	\| `duration` \| seconds \|
	\| `source` \| `afrivoice` \\| `mbaza_tts` \|
	\| `dataset_id` \| integer id per source (0=afrivoice, 1=mbaza_tts) \|
	\| `split` \| `train` / `val` (250 clips held out per source for evaluation) \|
	\| `speaker_id`, `gender` \| speaker metadata where available (Afrivoice doesn't expose per-speaker IDs; mbaza_tts is single-speaker) \|
	\| `domain` \| Afrivoice's source domain (agriculture/health/financial/government/education); `null` for mbaza_tts \|
	\| `lufs` \| Afrivoice's own broadcast-standard loudness measurement (LUFS), where available — a more principled loudness signal than the `dbfs` proxy below \|
	\| `dbfs`, `clip_ratio`, `sil_ratio` \| cheap DSP quality proxies computed for every clip: loudness, fraction of clipped samples, fraction of near-silent frames \|
	\| `has_disfluency` \| always `false` here — neither source flags disfluencies the way the Nigerian-language sources' ASR transcripts do \|

	## Usage

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

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

	row = df.iloc[0]
	shard_path = hf_hub_download("Professor/kinyarwanda-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 Kinyarwanda TTS/ASR research, in particular as pooled finetuning data for
	a multilingual TTS model that doesn't natively support Kinyarwanda. The bulk of this
	corpus (Afrivoice) is speech describing photographs across five institutional
	domains — a fairly narrow register (descriptive, matter-of-fact) compared to natural
	conversation or narrative speech; the small mbaza_tts portion is the only genuinely
	TTS-purpose-built, studio-quality anchor. This is a research aggregation; usage
	should respect the terms of each constituent source below.

	## License

	Both constituent sources are CC BY 4.0. Consult each source's own page for full terms:
	[Afrivoice Kinyarwanda](https://huggingface.co/datasets/DigitalUmuganda/Afrivoice_Kinyarwanda) ·
	[Kinyarwanda TTS dataset](https://huggingface.co/datasets/mbazaNLP/kinyarwanda-tts-dataset).

	## Citations

	If you use this pooled dataset, please cite the original sources it draws from —
	consult each source's own HuggingFace page for their preferred citation, as neither
	currently ships a bibtex entry in-repo.

	## Acknowledgments

	Deep thanks to Digital Umuganda for both constituent datasets — the large-scale
	Afrivoice image-description corpus across five domains, and (via the mbaza project)
	the studio-quality single-speaker TTS corpus with linguist-reviewed text.

	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.