A ready-to-train corpus for StyleTTS2 on Plateau Malagasy (PLT), pairing clean audio, original text, and IPA-phonemized text for one or more reference speakers. Built as a unified, self-contained HuggingFace dataset so that training notebooks can load a single source of truth — audio, transcription and speaker metadata in one place — without juggling multiple files or repositories.

⚠️ Derived from synthetic audio. The audio in this corpus originates from mimba/plt-tts-dataset, which was generated by OmniVoice. It is not human-recorded speech. See Limitations.

Dataset summary

Property	Value
Language	Plateau Malagasy (Merina) / `plt`
Task	Text-to-speech (TTS) training — StyleTTS2 Stage 1 & 2
Audio	Mono, 24 000 Hz, PCM-16 WAV bytes embedded in Parquet
Speakers	Extensible — currently `spk_m2` (id=0), `spk_f1` (id=2)
Samples	~38 700 per speaker (derived from `mimba/plt-tts-dataset`)
Phonemization	IPA via tafitaribhi (PLT G2P), mode PHRASE, normalized with `normalize_plt`
Source audio	`mimba/plt-tts-dataset`
Source text	Same as above — ultimately from `mimba/text2text` (`plt_fra`)

⚡ How to use

The audio column is stored as {"bytes": <WAV file bytes>, "path": None} — identical to the standard 🤗 datasets Audio feature layout.

Option A — Let `datasets` decode it (recommended)

from datasets import load_dataset, Audio

ds = load_dataset("mimba/styletts2-plt-corpus", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=24000))

sample = ds[0]
print(sample["speaker_name"], "|", sample["speaker_id"])
print("original  :", sample["text_original"])
print("phonemized:", sample["text_phonemized"])

audio = sample["audio"]   # {'array': np.float32[...], 'sampling_rate': 24000, 'path': None}
print(audio["array"].shape, audio["sampling_rate"])

Option B — Decode the bytes yourself

import io, soundfile as sf
from datasets import load_dataset

ds = load_dataset("mimba/styletts2-plt-corpus", split="train")
sample = ds[0]

array, sr = sf.read(io.BytesIO(sample["audio"]["bytes"]))
print(array.shape, sr)   # e.g. (57600,) 24000

Play it in a notebook

import IPython.display as ipd
ipd.display(ipd.Audio(array, rate=sr))

Filter by speaker

ds = load_dataset("mimba/styletts2-plt-corpus", split="train")

spk_m2 = ds.filter(lambda x: x["speaker_name"] == "spk_m2")
spk_f1 = ds.filter(lambda x: x["speaker_name"] == "spk_f1")

Build `train_list.txt` / `val_list.txt` for StyleTTS2

StyleTTS2's meldataset.py expects wav_path|phonemes|speaker_id filelists. This corpus is designed to produce them with a single iteration, without any extra phonemization step:

import io, os, soundfile as sf
from datasets import load_dataset

ds = load_dataset("mimba/styletts2-plt-corpus", split="train")
os.makedirs("wavs", exist_ok=True)
lines = []

for i, sample in enumerate(ds):
    uid = f"{sample['speaker_name']}_{i:06d}.wav"
    with open(f"wavs/{uid}", "wb") as f:
        f.write(sample["audio"]["bytes"])
    lines.append(f"{uid}|{sample['text_phonemized']}|{sample['speaker_id']}")

# split 95/5 train/val
n_val = max(4, len(lines) // 20)
open("Data/train_list.txt", "w").write("\n".join(lines[:-n_val]))
open("Data/val_list.txt",   "w").write("\n".join(lines[-n_val:]))

Stream (large corpus)

from datasets import load_dataset

ds = load_dataset("mimba/styletts2-plt-corpus", split="train", streaming=True)
for sample in ds:
    print(sample["text_phonemized"])
    break

Data fields

Field	Type	Description
`audio`	`dict`	`{"bytes": <WAV bytes, 24 kHz mono, PCM-16>, "path": None}`
`sample_rate`	`int`	Always `24000`
`text_original`	`string`	Original PLT sentence before normalization
`text_phonemized`	`string`	IPA phonemization (tafitaribhi, mode PHRASE, post-normalized)
`speaker_id`	`int`	Integer speaker index (0 = `spk_m2`, 1 = `spk_m1`, 2 = `spk_f1`, 3 = `spk_f2`, …)
`speaker_name`	`string`	Human-readable speaker id (e.g. `spk_m2`)

Chunk files are named chunk_{speaker_name}_{index:05d}.parquet — one speaker's data never overwrites another's, making it safe to add new speakers to the same repository without any migration.

Speakers

`speaker_name`	`speaker_id`	Gender (label)	Source
`spk_m2`	0	M	`mimba/plt-tts-dataset` (spk_m2 shards)
`spk_f1`	1	F	`mimba/plt-tts-dataset` (spk_f1 shards)

⚠️ Gender labels are inherited from the source dataset and were not re-verified acoustically. Confirm by listening if gender is relevant to your use case.

How the data was produced

Source audio — the raw waveforms come from mimba/plt-tts-dataset, itself generated by OmniVoice in clone-by-reference mode (language_id='plt'). Only the target speakers are included here.
Audio processing — each waveform is resampled to 24 000 Hz if needed, converted to float32 mono, peak-normalized to 0.95, and re-encoded as PCM-16 WAV bytes. Entries with zero-length audio (corrupted source) are silently dropped.
Text normalization — normalize_plt converts numbers to PLT words (e.g. 25 → dimy amby roapolo), expands common abbreviations, and normalizes punctuation. Digits embedded in usernames or hashtags (e.g. Zaw2, #100Africanmyths) are left untouched by a word-boundary guard (\b\d+\b) to avoid producing nonsense words.
Phonemization — normalized text is converted to IPA by tafitaribhi (a PLT-specific G2P engine), called in phrase mode (full sentence at once, preserving cross-word prosody). Curly apostrophes (U+2019) produced by the phonemizer are normalized to straight apostrophes to match the symbol vocabulary used by PL-BERT and StyleTTS2. Characters outside the 55-symbol IPA vocabulary are stripped.
Storage — examples are written in Parquet shards of 1 000 rows each, scoped by speaker (chunk_{speaker_name}_{index:05d}.parquet), and pushed incrementally to this repository. The OOD_texts.txt file (50 000 IPA sentences from mimba/pl-bert-phonemized-plt) is also included for use as StyleTTS2's out-of-distribution text reference.

Limitations and known issues

Synthetic, not human. Audio inherits OmniVoice's PLT pronunciation and prosody artifacts; it is not a substitute for real recorded speech.
Source-text bias. The underlying text is predominantly biblical/religious in register; everyday conversational vocabulary is under-represented.
Phonemization is approximate. tafitaribhi models PLT G2P rules and may make errors on rare words, loanwords, or numerals not caught by normalize_plt.
No per-sample human validation. Spot-check alignment between audio and text_phonemized before training at scale.
IPA vocabulary fixed at 55 symbols. Characters outside this set are stripped; very rare IPA symbols may be lost.

Relation to other Mimba datasets

Dataset	Role
`mimba/text2text`	Original PLT source text
`mimba/plt-tts-dataset`	Raw multi-speaker synthetic audio (4 speakers, full corpus)
`mimba/pl-bert`	3.9M PLT sentences for PL-BERT pre-training
`mimba/pl-bert-phonemized-plt`	PL-BERT corpus phonemized in phrase mode
`mimba/pl-bert-plt-final`	PL-BERT training-ready dataset + vocabularies (`phoneme_symbols.pkl`, `word_vocab.pkl`)
`mimba/styletts2-plt-corpus` ← this dataset	StyleTTS2-ready corpus (audio + IPA + speaker_id)

Intended use

This dataset was built to train StyleTTS2 (Stage 1 and Stage 2) on Plateau Malagasy, as part of the Mimba offline TTS project for low-resource African languages. The text_phonemized column is directly consumable by StyleTTS2's meldataset.py without any additional phonemization step.

It can also serve as a fine-tuning corpus for any IPA-based TTS architecture that operates at 24 kHz.

Adding a new speaker

The corpus is designed to be extended without breaking existing data. To add a new speaker (e.g. spk_m3, id=4):

Run the preparation notebook with TARGET_SPK = 'spk_m3' and SPEAKER_ID = 4.
New chunks will be written as chunk_spk_m1_{index:05d}.parquet — a different prefix from existing speakers, so no overwrite is possible.
Training notebooks filter by speaker_name before downloading, so they only pull the chunks they need.

License

cc-by-nc-sa-4.0 — non-commercial use, attribution required, share-alike. Before commercial use, verify the licensing of the source text (mimba/text2text) and OmniVoice-generated audio independently.

Citation

If you use this corpus, please credit the Mimba project and cite the source datasets:

@misc{mimba2026styletts2pltcorpus,
  title   = {Mimba StyleTTS2 PLT Corpus: A Plateau Malagasy TTS Training Corpus},
  author  = {Mimba Ngouana Fofou},
  year    = {2026},
}

Contact

For questions or contributions, open a discussion in the "Community" tab of this repository.

Contact: @Mimba

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Mimba StyleTTS2 PLT Corpus — Plateau Malagasy TTS Training Corpus