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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](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) — 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
```python
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`](https://github.com/webdataset/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](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.
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