Add dataset card

README.md

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+---
+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.