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README.md

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+---
+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.