README.md

---
library_name: transformers
base_model: facebook/mms-tts
tags:
- text-to-speech
- vits
- mms
- multilingual
- Open-Source
- Mali
- MALIBA-AI
language:
- bm  
- son 
- dgc 
- fuf
- bbo 
- tmh 
language_bcp47:
- bm-ML  
- son-ML 
- dgc-ML 
- fuf-ML 
- bbo-ML 
- tmh-ML 
model-index:
- name: malian-tts
  results:
  - task:
      name: text-to-speech 
      type: speech-synthesis
    metrics:
    - name: Subjective Quality
      type: MOS
      value: "N/A"
pipeline_tag: text-to-speech
license: cc-by-nc-4.0
---

# MALIBA-TTS: Revolutionizing Speech Synthesis for Malian Languages 🇲🇱

MALIBA-TTS represents a breakthrough in African language technology, offering high-quality text-to-speech synthesis for six Malian languages. These models bridge a critical gap in speech technology, bringing voice synthesis capabilities to languages spoken by millions yet historically underserved by technology.

## Bridging the Digital Language Divide

Despite being spoken by over 20 million people combined, Malian languages have remained severely underrepresented in speech technology. MALIBA-TTS directly addresses this critical gap, making digital speech interfaces accessible to speakers of Bambara, Boomu, Dogon, Pular, Songhoy, and Tamasheq for the first time. This work represents a crucial step toward digital language equality.

## Table of Contents
- [Technical Specifications](#technical-specifications)
- [Transforming Access to Technology](#transforming-access-to-technology)
- [Installation](#installation)
- [Usage](#usage)
- [The MALIBA-AI Impact](#the-maliba-ai-impact)
- [Limitations](#limitations)
- [Future Development](#future-development)
- [References](#references)
- [License](#license)
- [Contributing](#contributing)

## Technical Specifications

### Model Specifications
- **Architecture**: VITS (Variational Inference with adversarial learning for end-to-end TTS)
- **Base Model**: Meta's MMS (Massively Multilingual Speech)
- **Model Size**: 145 MB per language
- **Format**: PyTorch 
- **Sampling Rate**: 16kHz
- **Audio Encoding**: 16-bit PCM
- **Languages**: Bambara, Boomu, Dogon, Pular, Songhoy, and Tamasheq


### Performance
- **Inference**: Optimized to run on CPU
- **Real-time Capability**: Generates speech with minimal latency
- **Memory Footprint**: ~4GB RAM recommended for optimal performance
- **Deployment Flexibility**: Works on standard hardware without specialized accelerators

## Transforming Access to Technology in Mali

MALIBA-TTS enables numerous applications previously unavailable to speakers of Malian languages:

- **Education**: Audio-based learning tools for literacy and education in mother tongues
- **Accessibility**: Making digital content accessible to visually impaired users
- **Healthcare**: Voice interfaces for health information in local languages
- **Cultural Preservation**: Digital narration of stories and cultural heritage
- **Mobile Access**: Voice responses for smartphone users with limited literacy
- **Public Service**: Automated voice announcements and information systems

## Installation

```
Coming soon
```

## Usage

```python
Coming soon
```

## The MALIBA-AI Impact

MALIBA-TTS is part of MALIBA-AI's broader mission to ensure "No Malian Language Left Behind." This initiative is actively transforming Mali's digital landscape by:

1. **Breaking Language Barriers**: Providing technology in languages that Malians actually speak
2. **Enabling Local Innovation**: Allowing Malian developers to build voice-based applications
3. **Preserving Cultural Heritage**: Digitizing and preserving Mali's rich oral traditions
4. **Democratizing AI**: Making cutting-edge technology accessible to all Malians regardless of literacy level
5. **Building Local Expertise**: Training Malian AI practitioners and researchers

## Limitations
[coming soon]

## Future Development

MALIBA-AI is committed to continuing this work with:

- Expansion to more Malian languages and dialects


## References
```bibtex
@misc{malian-tts,
  author = {MALIBA-AI},
  title = {Text-to-Speech Models for Six Malian Languages},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/MALIBA-AI/malian-tts}}
}
@article{kim2021conditional,
  title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech},
  author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
  journal={International Conference on Machine Learning},
  year={2021}
}
@article{meta2023mms,
  title={Scaling Speech Technology to 1,000+ Languages},
  author={A. Pratap and others},
  journal={arXiv preprint arXiv:2305.13516},
  year={2023}
}
```

## License
This project is licensed under CC BY-NC 4.0 (Attribution-NonCommercial).

### Terms of Use
- Users agree to use the model in a way that respects Malian languages and culture
- We encourage the use of these models to develop solutions that improve digital accessibility for speakers of Malian languages
- Any use of the models must acknowledge MALIBA-AI and Meta
- Commercial usage is not allowed

## Contributing

MALIBA-TTS is a project part of the MALIBA-AI initiative with the mission "No Malian Language Left Behind." We welcome contributions from:

- **Language Experts**: To improve the quality and accuracy of the models
- **Developers**: To create applications using these models
- **Researchers**: To explore technical improvements and optimizations
- **Data Contributors**: To enrich tts training data
- **Community Members**: To provide feedback and testing across dialects

To contribute, please visit [MALIBA-AI](https://huggingface.co/MALIBA-AI) or contact [coming soon] directly.

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**MALIBA-AI: Empowering Mali's Future Through Community-Driven AI Innovation**

*"No Malian Language Left Behind"*