README.md

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

# Shona Speech Data (Pooled)

A **~83.7-hour** Shona (chiShona) 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) — Shona portion: 14,809 clips, 83.7h,
source `dataset_id`/`source` = `afrivoice`.

**Why not WAXAL's `sna_asr` config, or `badrex/shona-speech`?** All three of these
Shona sources converge on the same underlying data: WAXAL's `sna_asr` (~99.2h
transcribed / 574.2h total pool), Afrivoice's own Shona (~100h transcribed / 574h
total), and `badrex/shona-speech` (~99.2h, 17,585 rows) all report near-identical
hours. A direct check confirmed this isn't coincidence: 76% of a sample of
`badrex/shona-speech`'s speaker IDs are byte-for-byte identical to Afrivoice Shona
speaker IDs. Rather than pool multiple repackagings of the same recordings, we use
Afrivoice directly as Shona's sole source — same treatment as Malagasy.

## 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 portion of
recordings are transcribed (~15% of total duration for Shona — 83.7h out of ~574h
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 — 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/shona-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)

row = df.iloc[0]
shard_path = hf_hub_download("Professor/shona-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 **Shona TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Shona. 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.