Welcome to the MALIBA-AI Bambara Text-to-Speech SDK! This is the first open-source TTS system specifically designed for the Bambara language. Whether you're building educational platforms, creating voice interfaces, or developing accessibility tools, this SDK provides professional grade Bambara speech synthesis with multiple authentic speakers.
MALIBA-AI is a community driven initiative focused on developing AI solutions for Mali while ensuring no Malian is left behind by technological advances. Our mission is to break down linguistic barriers by creating AI systems that understand and speak Mali's indigenous languages.
This Bambara TTS SDK represents a breakthrough in African language technology:
Install the MALIBA-AI SDK using pip:
pip install maliba_ai
For development installations:
pip install git+https://github.com/MALIBA-AI/bambara-tts.git
Get started with Bambara TTS in under a minute:
from maliba_ai.tts import BambaraTTSInference
from maliba_ai.config.speakers import Bourama
import soundfile as sf
# Initialize the TTS system
tts = BambaraTTSInference()
# Generate speech from Bambara text
text = "I ni ce! N ye MALIBA-AI ye." # "Hello! I am MALIBA-AI."
audio = tts.generate_speech(text, speaker_id=Bourama)
# Save the audio
sf.write("greeting.wav", audio, 16000)
print("Bambara speech generated successfully!")
The TTS system initializes automatically with optimal settings:
from maliba_ai.tts import BambaraTTSInference
# Standard initialization
tts = BambaraTTSInference()
# With custom Hugging Face token (if needed)
tts = BambaraTTSInference(hf_token="your_token_here")
The system automatically:
The most straightforward way to generate Bambara speech:
# Basic synthesis with default speaker (Adame)
text = "Bamanankan ye kan ɲuman ye" # "Bambara is a beautiful language"
audio = tts.generate_speech(text)
Choose from our five authentic Bambara speakers:
from maliba_ai.config.speakers import Adame, Moussa, Bourama, Modibo, Seydou
# Use different speakers
speakers_demo = [
(Adame, "I ni ce, n tɔgɔ ye Adame ye"), # "Hello, my name is Adame"
(Moussa, "N bɛ baara kɛ sɛnɛkɛla la"), # "I work in agriculture"
(Bourama, "Aw ye ɲɛnɛmako"), # "Welcome"
(Modibo, "Kalan ka di kosɛbɛ"), # "Learning is very good"
(Seydou, "Aw ka se ka baara in kɛ"), # "You can do this work"
]
for speaker, text in speakers_demo:
audio = tts.generate_speech(text, speaker_id=speaker)
print(f"Generated speech for {speaker.id}")
Note: Bourama is our most stable and accurate speaker, recommended for production applications.
Fine-tune synthesis with custom parameters (though defaults are optimized):
audio = tts.generate_speech(
text="An ka baara kɛ ɲɔgɔn fɛ", # "Let's work together"
speaker_id=Bourama,
temperature=0.8, # Keep at 0.8 for optimal results
top_k=50, # Vocabulary sampling
top_p=1.0, # Nucleus sampling
max_new_audio_tokens=2048, # Maximum audio length
output_filename="collaboration.wav" # Auto-save option
)
Our speaker system features five distinct Bambara voices:
from maliba_ai.config.speakers import Speakers
# View all available speakers
all_speakers = Speakers.get_all_speakers()
for speaker in all_speakers:
print(f"Speaker: {speaker.id}")
# Get specific speaker by name
bourama = Speakers.get_speaker_by_name("Bourama")
# Speaker recommendations:
# - Bourama: Most stable and accurate (recommended for production)
# - Adame: Natural conversational tone
# - Moussa: Clear pronunciation
# - Modibo: Expressive delivery
# - Seydou: Balanced characteristics
The SDK comes with optimized default parameters that provide the best quality:
# These defaults are optimized for Bambara - avoid changing unless necessary
temperature=0.8 # Optimal balance between consistency and naturalness
top_k=50 # Vocabulary selection size
top_p=1.0 # Nucleus sampling threshold
max_new_audio_tokens=2048 # Maximum audio sequence length
# For very short texts
audio = tts.generate_speech(
"Ce!", # "Hello!"
speaker_id=Bourama,
max_new_audio_tokens=1024 # Shorter for efficiency
)
# For experimental/creative applications only
audio = tts.generate_speech(
text,
speaker_id=Bourama,
temperature=0.7, # Slightly more conservative
top_k=40 # More focused vocabulary
)
Handle generated audio efficiently:
import numpy as np
import soundfile as sf
# Generate audio
audio = tts.generate_speech("Baara ka nɔgɔya", speaker_id=Bourama)
# Method 1: Direct file saving during generation
audio = tts.generate_speech(
"Baara ka nɔgɔya",
speaker_id=Bourama,
output_filename="work_improves.wav"
)
# Method 2: Save after generation
sf.write("output.wav", audio, 16000)
# Method 3: Audio analysis
print(f"Duration: {len(audio)/16000:.2f} seconds")
print(f"Sample rate: 16000 Hz")
print(f"Channels: 1 (mono)")
# Method 4: Audio processing
normalized_audio = audio / np.max(np.abs(audio))
Create learning materials with consistent, high-quality pronunciation:
# Bambara language lessons
lessons = [
"Dɔrɔn kelen: I ni ce - Musow kɛlɛ", # "Lesson one: Hello - Greetings"
"Dɔrɔn fila: Tɔgɔ - N tɔgɔ ye ... ye", # "Lesson two: Names - My name is ..."
"Dɔrɔn saba: Jamu - I jamu ye mun ye?", # "Lesson three: Family - What is your family name?"
]
for i, lesson in enumerate(lessons, 1):
audio = tts.generate_speech(
lesson,
speaker_id=Bourama, # Consistent speaker for lessons
output_filename=f"lesson_{i:02d}.wav"
)
print(f"Created lesson {i}")
Build Bambara-speaking applications:
# Voice assistant responses
responses = {
"greeting": "I ni ce! I bɛ se ka n wele min na?", # "Hello! What can I call you?"
"confirmation": "Awɔ, n ye a faamu", # "Yes, I understand"
"error": "Hakɛto, segin ka a fɔ", # "Sorry, please repeat"
"goodbye": "Kan bɛn na! I ni che!", # "See you later! Goodbye!"
}
def generate_assistant_voices():
for intent, response in responses.items():
audio = tts.generate_speech(
response,
speaker_id=Bourama,
output_filename=f"assistant_{intent}.wav"
)
print(f"Generated response for: {intent}")
generate_assistant_voices()
Process multiple texts efficiently:
# News headlines in Bambara
news_items = [
"Kalan yɔrɔ kura da Bamakɔ", # "New school opens in Bamako"
"Sɛnɛkɛlaw ka baara ka ɲɛ", # "Farmers' work improves"
"Fɛn kura bɛ na bamanankan na", # "New things come to Bambara"
]
def batch_process(texts, speaker=Bourama):
results = []
for i, text in enumerate(texts):
try:
audio = tts.generate_speech(
text,
speaker_id=speaker,
output_filename=f"news_{i+1:02d}.wav"
)
results.append(f"news_{i+1:02d}.wav")
print(f"Processed: {text}")
except Exception as e:
print(f"Error processing '{text}': {e}")
return results
batch_process(news_items)
# Good practices for input text
def prepare_bambara_text(text):
# Remove extra whitespace
text = " ".join(text.split())
# Ensure proper sentence ending
if not text.endswith(('.', '!', '?')):
text += '.'
return text
# Example usage
raw_text = " I ni ce "
clean_text = prepare_bambara_text(raw_text) # "I ni ce."
audio = tts.generate_speech(clean_text, speaker_id=Bourama)
def synthesize_long_text(text, speaker_id=Bourama, max_length=100):
"""Split long texts into manageable chunks"""
sentences = text.split('. ')
audio_chunks = []
for sentence in sentences:
if sentence.strip():
audio = tts.generate_speech(
sentence.strip() + '.',
speaker_id=speaker_id
)
audio_chunks.append(audio)
# Combine all audio
return np.concatenate(audio_chunks) if audio_chunks else np.array([])
# Example with long text
long_text = "Bamanankan ye kan ye min bɛ kuma Mali la. A bɛ kuma miliyɔn caman fɛ. A ka fɔcogo ka nɔgɔya don."
full_audio = synthesize_long_text(long_text)
sf.write("long_speech.wav", full_audio, 16000)
import torch
# Check GPU availability
if torch.cuda.is_available():
print(f"GPU: {torch.cuda.get_device_name()}")
print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
else:
print("Running on CPU (slower but functional)")
# For memory-constrained environments
def memory_efficient_synthesis(texts, speaker_id=Bourama):
# Process one text at a time instead of batching
for i, text in enumerate(texts):
audio = tts.generate_speech(
text,
speaker_id=speaker_id,
max_new_audio_tokens=1024, # Shorter sequences
output_filename=f"output_{i}.wav"
)
# Audio is saved, memory can be freed
print(f"Processed text {i+1}/{len(texts)}")
# Reuse the same TTS instance
tts = BambaraTTSInference() # Initialize once
# Process multiple texts
texts = ["Text 1", "Text 2", "Text 3"]
for text in texts:
audio = tts.generate_speech(text, speaker_id=Bourama)
# Don't create new instances unnecessarily
Empty audio output:
# Check text content and length
text = "I ni ce"
if not text.strip():
print("Error: Empty text provided")
elif len(text) < 3:
print("Warning: Very short text may not generate audio")
else:
audio = tts.generate_speech(text, speaker_id=Bourama)
CUDA memory errors:
# Reduce token limit for GPU memory issues
audio = tts.generate_speech(
text,
speaker_id=Bourama,
max_new_audio_tokens=1024 # Reduced from default 2048
)
Speaker validation errors:
from maliba_ai.config.settings import SPEAKER_IDS
# Verify speaker availability
def validate_speaker(speaker):
if speaker.id not in SPEAKER_IDS:
print(f"Invalid speaker. Available: {SPEAKER_IDS}")
return False
return True
# Safe speaker usage
if validate_speaker(Bourama):
audio = tts.generate_speech(text, speaker_id=Bourama)
# Monitor generation time
import time
start_time = time.time()
audio = tts.generate_speech(text, speaker_id=Bourama)
generation_time = time.time() - start_time
print(f"Generated {len(audio)/16000:.2f}s audio in {generation_time:.2f}s")
print(f"Real-time factor: {generation_time/(len(audio)/16000):.2f}x")
We welcome contributions to improve Bambara TTS! Here's how to get involved:
# Development setup
git clone https://github.com/MALIBA-AI/bambara-tts.git
cd bambara-tts
pip install -e ".[dev]"
# Run tests
pytest tests/
# Code formatting
ruff format .
isort .
Ways to contribute:
Contribution guidelines:
This project is released under the MIT License. See LICENSE for details.
MALIBA-AI ka baara kɛ ka bamanankan lakana diɲɛ kɔnɔ! (MALIBA-AI works to preserve Bambara language in the world!)
FAQs
Bambara Text-to-Speech system using Maliba-AI models
We found that maliba-ai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
/Security News
Threat actors hijacked Toptal’s GitHub org, publishing npm packages with malicious payloads that steal tokens and attempt to wipe victim systems.
Research
/Security News
Socket researchers investigate 4 malicious npm and PyPI packages with 56,000+ downloads that install surveillance malware.
Security News
The ongoing npm phishing campaign escalates as attackers hijack the popular 'is' package, embedding malware in multiple versions.