Datasets:
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 multi-language TTS data effort.
Source
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
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
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 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 multi-language TTS data effort.