---
language: [ha, yo, ig, en]
license: mit
pipeline_tag: text-to-speech
tags: [text-to-speech, voice-cloning, chatterbox, multilingual, hausa, yoruba, igbo, nigerian-english, african-languages]
base_model: ResembleAI/chatterbox
---

# Naija TTS — multilingual Hausa / Yoruba / Igbo / Nigerian English

One Chatterbox model covering four Nigerian languages, with zero-shot voice
cloning. LoRA fine-tune of Chatterbox Multilingual (MIT), with the grapheme
vocabulary extended for the characters these languages need.

Base Chatterbox ships 23 languages, of which Swahili is the only African one.
This model adds Hausa, Yoruba, Igbo and Nigerian English — the last under its
own `en-ng` tag so base English is left intact.

## Evaluation

| Language | n | CER | CER floor | WER | Speaker SIM | RTF |
|---|---|---|---|---|---|---|
| Hausa (`ha`) | 40 | 8.83 % | 4.87 % | 32.35 % | n/a | 0.680 |
| Yoruba (`yo`) | 40 | 45.02 % | 12.13 % | 77.73 % | n/a | 1.065 |
| Igbo (`ig`) | 40 | 14.87 % | 8.41 % | 42.57 % | n/a | 0.840 |
| Nigerian English (`en-ng`) | 40 | 12.27 % | 13.09 % | 31.44 % | n/a | 0.932 |

**Read each CER against its own floor**, not against zero. The floor is what
MMS-ASR scores on genuine recordings of that language — its own error rate.
A CER at the floor means the synthesized speech is as intelligible as real
speech. The floors differ substantially between languages; a raw CER comparison
across languages is meaningless.

## Usage

```python
import torchaudio as ta
import chatterbox.mtl_tts as M
from chatterbox.mtl_tts import ChatterboxMultilingualTTS

for code, name in [("ha","Hausa"), ("yo","Yoruba"), ("ig","Igbo"),
                   ("en-ng","Nigerian English")]:
    M.SUPPORTED_LANGUAGES[code] = name

model = ChatterboxMultilingualTTS.from_local("path/to/repo", device="cuda")
wav = model.generate("Sannu da zuwa.", language_id="ha",
                     audio_prompt_path="reference.wav")
ta.save("out.wav", wav, model.sr)
```

Normalize text to Unicode NFC and keep the languages' special characters as
written — for Yoruba this includes the combining tone marks.

## Limitations

- **Hausa tone** is not modelled: Boko orthography marks neither tone nor vowel
  length, so a character-input model has no signal for it. Yoruba and Igbo write
  tone with diacritics and are not subject to this.
- Long utterances can truncate (Chatterbox forces EOS on long tails).
- The corpus was capped per language for balance, so no language sees its full
  available data.
- Outputs carry Resemble's Perth watermark, inherited from the base model.

## Licence
MIT, from base Chatterbox.