---
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](#how-the-data-was-generated) and
> [Limitations](#limitations-and-known-issues). 

---

## 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](#speakers)) |
| Sentences | ~44 000 cleaned PLT segments |
| Total samples (target) | ~155 052 (each sentence × each speaker) |
| Source text | Derived from [`mimba/text2text`](https://huggingface.co/datasets/mimba/text2text) (`plt_fra`) |
| Generation model | [OmniVoice](https://github.com/k2-fsa/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.

```python
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.

```python
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

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

### Save it to a .wav file

```python
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.

```python
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

```python
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:

```python
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`](https://huggingface.co/datasets/mimba/text2text)
  (`plt_fra` split), itself derived from Bible translations and other PLT sources.
- The PLT text was **cleaned and segmented**: tonal diacritics (´ ` ˇ ^) and the modifier
  apostrophe `ʼ` (U+02BC) are **preserved as letters**; straight apostrophes were corrected to
  `ʼ`; 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](https://github.com/k2-fsa/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:

1. the license/copyright of the **source text** (sources behind `mimba/text2text` may carry their own terms), and
2. 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)](https://github.com/k2-fsa/OmniVoice) as the speech generator, and cite the
source-text dataset [`mimba/text2text`](https://huggingface.co/datasets/mimba/text2text).

### Contact

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


### BibTeX entry and citation info

```bibtex
@misc{
      title={plt-tts-dataset: Small Out-of-domain resource for various africain languages}, 
      author={Mimba Ngouana Fofou},
      year={2026},
}
```

##### *Contact For all questions contact [@Mimba](baounabaouna@gmail.com).*