Datasets:
language:
- mg
license: cc-by-4.0
multilinguality: monolingual
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- malagasy
- speech
- tts
- asr
- african-languages
- low-resource
pretty_name: Malagasy Speech Data (Pooled)
size_categories:
- 10K<n<100K
Malagasy Speech Data (Pooled)
A ~179.8-hour Malagasy 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) — Malagasy portion: 34,293 clips,
179.8h, source dataset_id/source = afrivoice.
Why not WAXAL's mlg_asr config? WAXAL (google/WaxalNLP) does have a Malagasy
ASR config, but it's the same underlying data as this source: WAXAL's mlg_asr
speaker IDs are 28-character Firebase-Auth-UID-style strings that directly match
Afrivoice's speaker_id format, its own source_datasets metadata references
DigitalUmuganda/AfriVoice explicitly, and its transcripts follow the same
image-description methodology. Rather than pool both (which would just mean
training on the same recordings twice), we use Afrivoice directly as Malagasy's
sole source.
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 (~35% of total duration for Malagasy — 179.8h out of
~516h 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/malagasy-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)
row = df.iloc[0]
shard_path = hf_hub_download("Professor/malagasy-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 Malagasy TTS/ASR research, in particular as finetuning data for a multilingual TTS model that doesn't natively support Malagasy. 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.