Datasets:
language:
- plt
license: cc-by-nc-sa-4.0
pretty_name: Mimba PLT TTS Dataset (Plateau Malagasy Synthetic Speech)
task_categories:
- text-to-speech
tags:
- text-to-speech
- tts
- Plateau Malagasy
- plt
- malagasy
- african-languages
- low-resource
- voice-cloning
- synthetic-speech
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
Mimba PLT TTS Dataset — Plateau Malagasy Synthetic Speech
A clean, multi-speaker synthetic speech corpus for Plateau Malagasy (PTL), a the central dialect of the Malagasy language, the national and official language of Madagasca. Each item pairs a cleaned PLT sentence with machine-generated speech audio, intended for training and fine-tuning small, on-device text-to-speech (TTS) models for this low-resource language.
⚠️ Synthetic data. The audio is generated by a TTS model (not recorded from human speakers). See How the data was generated and Limitations.
Dataset summary
| Property | Value |
|---|---|
| Language | Plateau Malagasy (Merina) / plt (Malagasy) |
| Task | Text-to-speech (TTS) |
| Audio | Mono, 24 000 Hz, WAV bytes embedded in Parquet |
| Speakers | 4 reference voices (see Speakers) |
| Sentences | ~44 000 cleaned PLT segments |
| Total samples (target) | ~155 052 (each sentence × each speaker) |
| Source text | Derived from mimba/text2text (plt_fra) |
| Generation model | OmniVoice (k2-fsa), zero-shot clone-by-reference |
⚡ How to use (audio is stored as bytes — read this)
The audio column is not a plain array: it is stored as a struct
{"bytes": <WAV file bytes>, "path": None}. This is the standard, self-contained way to ship
audio inside Parquet, and it is exactly the internal representation of the 🤗 datasets
Audio feature. The HF web viewer shows the raw bytes, but in code you decode them in one of
two ways.
Option A — Let datasets decode it (recommended)
Cast the column to the Audio feature; decoding then happens automatically on access.
from datasets import load_dataset, Audio
ds = load_dataset("mimba/plt-tts-dataset", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=24000))
sample = ds[0]
print(sample["id"], "|", sample["speaker_id"], "|", sample["gender"])
print(sample["text"])
audio = sample["audio"] # {'array': np.float32[...], 'sampling_rate': 24000, 'path': None}
print(audio["array"].shape, audio["sampling_rate"])
Option B — Decode the bytes yourself (always works)
The bytes are a complete WAV file, so soundfile reads them directly from memory.
import io
import soundfile as sf
from datasets import load_dataset
ds = load_dataset("mimba/plt-tts-dataset", split="train")
sample = ds[0]
audio_bytes = sample["audio"]["bytes"]
array, sr = sf.read(io.BytesIO(audio_bytes)) # array: np.ndarray, sr: 24000
print(array.shape, sr)
Play it in a notebook
import IPython.display as ipd
ipd.display(ipd.Audio(array, rate=sr))
Save it to a .wav file
import soundfile as sf
sf.write("plt_sample_0.wav", array, sr)
Stream instead of downloading everything (large dataset!)
With ~264k samples, prefer streaming when you only need to iterate.
from datasets import load_dataset
import io, soundfile as sf
ds = load_dataset("mimba/plt-tts-dataset", split="train", streaming=True)
for sample in ds:
array, sr = sf.read(io.BytesIO(sample["audio"]["bytes"]))
text = sample["text"]
# ... feed (text, array) to your pipeline ...
break
Filter by speaker or gender
ds = load_dataset("mimba/plt-tts-dataset", split="train")
female_only = ds.filter(lambda x: x["gender"] == "F")
one_speaker = ds.filter(lambda x: x["speaker_id"] == "spk_m2")
Example: prepare (text, audio) pairs for TTS fine-tuning
Most token-based TTS recipes (e.g. NeuTTS / Orpheus-style) need the raw waveform so they can encode it with a neural audio codec. This dataset is ready for that:
import io, soundfile as sf
from datasets import load_dataset
ds = load_dataset("mimba/plt-tts-dataset", split="train")
def to_pair(sample):
array, sr = sf.read(io.BytesIO(sample["audio"]["bytes"]))
return {"text": sample["text"], "wav": array, "sr": sr}
# array + text are now ready to be tokenised by your codec (NeuCodec, SNAC, etc.)
example = to_pair(ds[0])
print(example["text"], example["wav"].shape, example["sr"])
Data fields
| Field | Type | Description |
|---|---|---|
id |
string |
Unique sample id, e.g. spk_m2_001234. |
speaker_id |
string |
Reference voice id (spk_m1 … spk_f2). |
gender |
string |
M or F (label of the reference voice — see caveat below). |
text |
string |
Cleaned PLT sentence (the spoken text). |
audio |
dict |
{"bytes": <WAV bytes, 24 kHz mono>, "path": None}. |
sampling_rate |
int |
Always 24000. |
duration |
float |
Audio duration in seconds. |
src_idx |
int |
Row index in the source mimba/text2text (plt_fra) for traceability. |
Because the same sentence is spoken by every speaker, you get parallel data across voices
(useful for multi-speaker training and voice cloning), and src_idx lets you map any sample
back to its original verse.
Speakers
Six distinct PLT reference voices were used for zero-shot cloning. Each voice re-speaks the full set of sentences.
speaker_id |
gender (label) |
|---|---|
spk_m1 |
M |
spk_m2 |
M |
spk_f1 |
F |
spk_f2 |
F |
⚠️ Verify gender labels. Labels come from the generation configuration and were not re-checked acoustically for every voice. If gender matters for your use case, confirm by listening, or re-derive labels with a gender classifier before relying on them.
How the data was generated
- Text comes from
mimba/text2text(plt_frasplit), itself derived from Bible translations and other PLT sources. - The PLT text was cleaned and segmented: tonal diacritics (´
ˇ ^) and the modifier apostropheʼ(U+02BC) are **preserved as letters**; straight apostrophes were corrected toʼ; editorial punctuation (« » " " [ ] ( ) … – ) and non-breaking spaces were removed; sentences with digits and very short fragments were dropped; long verses were split on strong punctuation (then commas), with every final segment ending in.,?or!`. - Audio was synthesized with OmniVoice (k2-fsa), a
multilingual zero-shot TTS model that natively supports PLT, in clone-by-reference mode
(
language_id='plt', deterministic config:num_step=32, temperatures = 0). Each sentence was rendered in each reference voice. - Output audio: float32 mono at 24 000 Hz, WAV-encoded and embedded as bytes, written in
Parquet shards (e.g.
data/spk_m2-00007.parquet).
Limitations and known issues
- Synthetic, not human. All audio is model-generated. It inherits OmniVoice's PLT pronunciation and prosody, which may not perfectly match a native speaker, and can contain TTS artifacts.
- Source-text bias. The text is predominantly of biblical/religious origin, so vocabulary, register and domain are skewed accordingly and are not representative of everyday conversation.
- Phoneme coverage depends on the source sentences; rare sounds may be under-represented.
- No human validation of every sample. Spot-check before using at scale.
Licensing considerations
license: other is set deliberately. Before redistributing or using this dataset
commercially, please verify:
- the license/copyright of the source text (sources behind
mimba/text2textmay carry their own terms), and - the licensing implications of the OmniVoice-generated audio for your intended use.
If you need a permissive, clearly-licensed corpus, confirm both of the above for your specific case.
Intended use
This dataset was built to fine-tune small, on-device TTS models (e.g. ~0.2B token-based models) so they can pronounce Plateau Malagasy and clone voices, as part of an offline mobile TTS effort for PLT. It can also serve as a base for adding expressive/non-verbal tags at a later fine-tuning stage.
Citation
If you use this dataset, please credit the Mimba project and
OmniVoice (k2-fsa) as the speech generator, and cite the
source-text dataset mimba/text2text.
Contact
For questions or contributions, please open a discussion in the "Community" tab of this repository.
BibTeX entry and citation info
@misc{
title={plt-tts-dataset: Small Out-of-domain resource for various africain languages},
author={Mimba Ngouana Fofou},
year={2026},
}