---
language: 
  - dyu
tags:
  - text-to-speech
  - tts
  - vits
  - coqui-tts
  - dioula
  - jula
  - african-languages
  - ivory-coast
  - burkina-faso
license: apache-2.0
model-index:
- name: Dioula TTS (VITS)
  results: []
---

# 🎙️ Dioula Text-to-Speech (VITS)

This is a **Text-to-Speech (TTS)** model trained for the **Dioula (Jula)** language, a Manding language spoken by over 20 million people in West Africa (Ivory Coast, Burkina Faso, Mali). 

This model is built using the **VITS** architecture via the [Coqui TTS](https://github.com/coqui-ai/TTS) framework.

## 👨‍💻 Developer Information

- **Developed by:** [Soumana Dama](https://soumanadama.netlify.app/) — Full stack developer & AI engineer - Founder & Lead AI Engineer at [Scoinvestigator AI](https://www.scoinvestigator.com/)
- **LinkedIn:** [Soumana Dama](https://www.linkedin.com/in/soumana-dama-445096253/)
- **GitHub:** [Damasoumana1](https://github.com/Damasoumana1)

## 📊 Model Details

- **Architecture:** VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech)
- **Framework:** `TTS==0.20.6` (Coqui TTS)
- **Parameters:** 83.05 Million (83,048,044)
- **Language:** Dioula (`dyu`)
- **Sample Rate:** 16,000 Hz
- **Status:** *Work in Progress — v5 (Best model at step 193,972 | Last checkpoint: 229,500 steps)*

## 📈 Training Metrics

### v5 — Best Model (Step 193,972) · June 28, 2026

| Metric | Value | vs v4 | Description |
|---|---|---|---|
| **Avg Loss Discriminator** (`avg_loss_disc`) | `2.45` | ✅ 2.59 → 2.45 | Discriminator loss (real vs fake audio). |
| **Avg Loss Generator** (`avg_loss_0`) | `2.45` | ✅ 2.59 → 2.45 | Overall generator loss. |
| **Avg Loss Mel/KL** (`avg_loss_1`) | `41.26` | ⚠️ 40.71 → 41.26 | Mel spectrogram + KL divergence loss. |
| **Avg Loss Duration** (`avg_loss_duration`) | `2.49` | ✅ 2.50 → 2.49 | Phoneme/character duration prediction. |
| **Avg Loss Feat** (`avg_loss_feat`) | `6.15` | ↗️ 5.36 → 6.15 | Feature matching loss. |
| **Epoch** | `8` | — | Total training epochs completed. |
| **Last Checkpoint** | `229,500 steps` | — | Available for resuming training (v6). |
| **Parameters** | `83,048,044` | — | Total model parameters. |

*(Training curves, alignment plots and spectrograms are available in the TensorBoard logs in this repository.)*

### v4 — Best Model (Step 102,972) · Archived

| Metric | Value | Description |
|---|---|---|
| **Avg Loss Discriminator** (`avg_loss_disc`) | `2.59` | Discriminator loss (real vs fake audio). |
| **Avg Loss Generator** (`avg_loss_0`) | `2.59` | Overall generator loss. |
| **Avg Loss Mel/KL** (`avg_loss_1`) | `40.71` | Mel spectrogram + KL divergence loss. |
| **Avg Loss Duration** (`avg_loss_duration`) | `2.50` | Phoneme/character duration prediction. |
| **Avg Loss Feat** (`avg_loss_feat`) | `5.36` | Feature matching loss. |

## 🚀 How to Use (Inference)

You can use this model to generate Dioula speech from text using Python.

### 1. Install Dependencies
```bash
pip install TTS==0.20.6
```

### 2. Python Script
```python
import os
from TTS.api import TTS

# Initialize the TTS API with the path to your downloaded model and config
model_path = "best_model.pth" # or checkpoint_xxxx.pth
config_path = "config.json"

tts = TTS(model_path=model_path, config_path=config_path, progress_bar=False, gpu=False)

# The text you want to synthesize in Dioula
text_to_speak = "I ni cɛ, i ka kɛnɛ wa ?"

# Generate the audio file
output_path = "output_dioula.wav"
tts.tts_to_file(text=text_to_speak, file_path=output_path)

print(f"Audio saved to {output_path}")
```

## 📚 Dataset
This model was trained on a custom Dioula dataset consisting of approximately 9,000 verified audio samples and transcripts, prepared specifically for speech processing tasks.

## ⚠️ Limitations
- **Vocabulary:** Some French-specific accented characters (`é`, `è`, `ô`, `ç`) might not be fully supported in this version's vocabulary and may be ignored during synthesis. It is recommended to use clean Dioula orthography.
- **Intonation:** As the model is still training, long sentences might lack perfect natural prosody.

## ⚖️ License & Citation

### License

This model is built upon the VITS architecture via the Coqui TTS framework. The model weights are released under the [CC BY-NC 4.0 License](https://creativecommons.org/licenses/by-nc/4.0/) (free for academic and research purposes). The dataset, methodology, and this specific implementation are intended strictly for non-commercial research purposes. If you intend to use this model for commercial applications, please contact the author.

### Citation

If you use this model or the Audio Dioula Project concept in your research, please cite it as follows:

```bibtex
@misc{soumanadama2026dioula_tts,
  author = {Soumana Dama},
  title = {Audio Dioula Project - Multilingual AI Assistant for Education in Burkina Faso},
  year = {2026},
  note = {Master's Project: Applied Artificial Intelligence in the African Context}
}
```