Bambara-TTS Open-Source Text-to-Speech Model - Free Bambara Language Speech Synthesis

Bambara Tts

Developed by sudoping01

A text-to-speech synthesis model developed for the Bambara language, based on VITS architecture and Facebook MMS foundation model

Speech Synthesis

Transformers

Other#Bambara speech synthesis #Low-resource language TTS #African language support

Downloads 68

Release Time : 4/25/2025

Model Overview

A text-to-speech synthesis model for Bambara (Bamanankan), supporting voice generation for this language, suitable for application scenarios in Mali and other regions

Model Features

Multilingual support

Specialized in speech synthesis for Bambara (14 million speakers in Mali)

Lightweight design

Compact 145MB model optimized for CPU usage (recommended 4GB RAM)

High-quality synthesis

Provides relatively natural speech output based on VITS architecture

Cultural adaptation

Optimized for Bambara linguistic and cultural characteristics

Model Capabilities

Bambara text-to-speech conversion

16kHz sampling rate voice generation

Standard orthography processing

Use Cases

Education

Language learning aid

Provides pronunciation examples for students learning Bambara

Assistive technology

Visual impairment assistance

Converts Bambara text content into speech output

Cultural preservation

Language digitization

Helps preserve and disseminate Bambara oral culture

🚀 Bambara TTS

A text-to-speech synthesis model for Bambara (Bamanankan), a language spoken by over 14 million people primarily in Mali.

🚀 Quick Start

Bambara TTS is a text - to - speech synthesis model designed for the Bambara language. It can convert text in Bambara into natural - sounding speech.

✨ Features

Multilingual Support: Based on the MMS framework, it has the potential for multilingual capabilities.
Optimized for CPU: Can run on a CPU with 4GB RAM recommended.

📦 Installation

pip install transformers torch soundfile

💻 Usage Examples

Basic Usage

from transformers import VitsModel, AutoTokenizer
import torch

# Load model and tokenizer
model = VitsModel.from_pretrained("sudoping01/bambara-tts")
tokenizer = AutoTokenizer.from_pretrained("sudoping01/bambara-tts")

# Prepare text and generate speech
text = "An filɛ ni ye yɔrɔ minna ni an ye an sigi ka a layɛ yala an bɛ ka baara min kɛ ɛsike a kɛlen don ka Ɲɛ wa ?"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    output = model(**inputs).waveform

# Save output
waveform = output.squeeze().cpu().numpy()
sample_rate = model.config.sampling_rate
import soundfile as sf
sf.write("bambara_output.wav", waveform, sample_rate)

🔧 Technical Details

Property	Details
Architecture	VITS (Variational Inference with adversarial learning for end - to - end TTS)
Base Model	Facebook/Meta MMS
Size	145 MB
Format	PyTorch
Sampling Rate	16kHz
Language	Bambara (bm - ML)
Performance	Optimized for CPU (4GB RAM recommended)

📚 Documentation

Limitations

Loanwords and Code - Switching: Limited handling of loanwords and code - switching with French.
Dialect Variations: Variable performance across regional dialects.
Orthography Requirement: Requires standard orthography.
Prosody and Emotion: Limited prosody and emotional expression.

📄 License

The model is licensed under CC BY - NC 4.0 (Attribution - NonCommercial).

Usage Restriction: Non - commercial use only.
Attribution: Attribution required for model authors and Meta.
Cultural Respect: Use must respect Bambara language and culture.

References

@misc{bambara-tts,
  author = {sudoping01},
  title = {Text-to-Speech Model for Bambara},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/sudoping01/bambara-tts}}
}

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