| --- |
| 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. |
|
|
