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language:
- yo
license: cc-by-nc-4.0
tags:
- text-to-speech
- tts
- f5-tts
- yoruba
- african-languages
- voice-cloning
- zero-shot-tts
library_name: f5-tts
pipeline_tag: text-to-speech
---
# F5-TTS Yorùbá — First Zero-Shot Voice Cloning TTS for Yorùbá
Fine-tuned [F5-TTS v1 Base](https://github.com/SWivid/F5-TTS) (335M params) for Yorùbá language with full tonal diacritic support.
## Highlights
- **Zero-shot voice cloning**: Clone any voice from a 5-10 second reference clip
- **Tonal language support**: Diacritic-driven pitch generation (ẹ́, ọ̀, ṣ, etc.)
- **First of its kind**: First DiT-based / flow-matching TTS model for any African language
- **Character-level tokenization**: No phonemizer needed — raw Yorùbá text with diacritics in, speech out
- **Fast inference**: ~350ms per utterance at NFE=16 on L4 GPU (RTF ≈ 0.15)
## Training Details
| | |
|---|---|
| Base model | F5-TTS v1 Base (flow-matching DiT) |
| Parameters | 335M |
| Training data | ~13.4 hours (BibleTTS + SLR86 + WAXAL) |
| Steps | 150,000 |
| GPUs | 2× A100-40GB |
| Effective batch | 38,400 frames/step |
| Learning rate | 7.5e-5 |
| Tokenizer | Character-level (no pinyin) |
| Vocab | 2,562 chars (base + Yorùbá diacritics) |
## Usage
```python
import f5_tts.model.utils as f5_utils
# CRITICAL: Must bypass pinyin conversion BEFORE any other F5-TTS import
f5_utils.convert_char_to_pinyin = lambda texts, polyphone=True: texts
from f5_tts.api import F5TTS
f5tts = F5TTS(
model="F5TTS_v1_Base",
ckpt_file="model_150000.pt",
vocab_file="vocab.txt",
device="cuda",
)
# ref_file: 5-10s WAV of any voice (add ~1s trailing silence for best results)
# ref_text: what the reference says (Yorùbá or English)
# gen_text: Yorùbá text with full diacritics
wav, sr, _ = f5tts.infer(
ref_file="reference.wav",
ref_text="text spoken in reference",
gen_text="ẹ kú àárọ̀, báwo ni àwọn ọmọ yín ṣe wà?",
speed=1.0,
nfe_step=16,
file_wave="output.wav",
)
```
### Tips for Best Results
- **Use full diacritics**: "oko" (hoe), "okó" (husband), "okò" (vehicle) are different words. Diacritics drive pitch.
- **Reference audio**: Use clean 5-10 second clips with ~1 second of trailing silence. Avoid background music/noise.
- **Reference text**: Must accurately match what is spoken in the reference audio.
- **NFE steps**: 16 recommended for best quality/speed tradeoff. Use 8 for faster inference with slight quality reduction.
- **Speed**: 1.0 recommended. Lower values (e.g. 0.85) may prevent truncation on long text but can cause slight shakiness.
- **Text cleanup**: Strip any bracket tags (e.g. `[breath]`, `[snap]`) from reference text if present.
## Training Data
| Source | Samples | Hours | Notes |
|--------|---------|-------|-------|
| [BibleTTS Yorùbá](https://masakhane-io.github.io/bibleTTS/) | 7,560 | ~8h | Studio quality, single speaker |
| [SLR86](https://openslr.org/86/) | 3,583 | ~3h | Crowdsourced, male + female |
| [WAXAL TTS](https://huggingface.co/datasets/google/WaxalNLP) | 1,492 | ~3h | Diacritics restored via Gemini |
## Limitations
- Tonal minimal pair differentiation is moderate (not perfect for all tone contrasts)
- Occasional brief audio artifacts (~5% of generations) — regenerating typically produces a clean output
- Reference audio may bleed slightly into the start of generated audio — trimming the first 200-400ms helps
- Requires full Yorùbá diacritics in input text for best results
- Reference audio quality directly affects output quality
## What's Next
- Phase 2 tonal fine-tuning (oversampled minimal pairs)
- Hausa and Igbo models using the same pipeline
- Nigerian Pidgin (non-tonal, simpler)
- Edge-optimized smaller model for on-device inference
## License
CC-BY-NC-4.0
This model is free for research and non-commercial use. For commercial licensing, contact us.
The base F5-TTS pretrained model is licensed under CC-BY-NC due to the Emilia training dataset.
## Citation
```bibtex
@misc{naijaml-f5tts-yoruba-2026,
title={F5-TTS Yorùbá: First Zero-Shot Voice Cloning TTS for Yorùbá},
author={NaijaML},
year={2026},
url={https://huggingface.co/naijaml/f5-tts-yoruba}
}
```
## Acknowledgments
Built on [F5-TTS](https://github.com/SWivid/F5-TTS) by Yushen Chen et al. Training data from [BibleTTS](https://masakhane-io.github.io/bibleTTS/), [SLR86](https://openslr.org/86/), and [Google WAXAL](https://huggingface.co/datasets/google/WaxalNLP). |