kalakan-tts

Kalakan TTS

A fully custom, offline Text-to-Speech system for the Twi language.

Features

• Specialized for Twi language with proper handling of special characters (ɛ, ɔ, Ɔ, tonal markers)
• Complete text processing pipeline with Twi-specific tokenization and phoneme mapping
• High-quality acoustic models:
  • Tacotron2: Attention-based sequence-to-sequence model
  • FastSpeech2: Non-autoregressive model with explicit duration modeling
  • Transformer-TTS: Transformer-based sequence-to-sequence model
• Multiple vocoders:
  • Griffin-Lim: Simple phase reconstruction algorithm
  • HiFi-GAN: High-fidelity GAN-based vocoder
  • MelGAN: Fast GAN-based vocoder
  • WaveGlow: Flow-based generative model
• Production-ready API with REST and gRPC interfaces
• Comprehensive training infrastructure with experiment tracking and data augmentation
• Advanced dataset preparation with metadata generation, phoneme conversion, and quality control
• Optimized for both cloud and edge deployment

Installation

Basic Installation

pip install kalakan-tts

Development Installation

git clone https://github.com/kalakan-ai/kalakan-tts.git
cd kalakan-tts
pip install -e ".[dev,api,training]"

Quick Start

Text-to-Speech Synthesis

from kalakan.synthesis.synthesizer import Synthesizer

# Initialize the synthesizer with specific models
synthesizer = Synthesizer(
    acoustic_model="/path/to/acoustic_model.pt",  # or use a model name like "tacotron2", "fastspeech2", "transformer_tts"
    vocoder="/path/to/vocoder.pt",  # or use a model name like "griffin_lim", "hifigan", "melgan", "waveglow"
    device="cuda:0",  # or "cpu"
)

# Generate audio from Twi text
audio = synthesizer.synthesize(
    text="Akwaaba! Wo ho te sɛn?",
    normalize=True,
    clean=True,
    speed=1.0,  # Control speech speed (for FastSpeech2)
    pitch=1.0,  # Control pitch (for FastSpeech2)
    energy=1.0,  # Control energy/volume (for FastSpeech2)
)

# Save the audio to a file
synthesizer.save_audio(audio, "output.wav")

Command Line Interface

# Using the demo script
python demo.py --text "Akwaaba! Wo ho te sɛn?" --output output.wav --acoustic_model /path/to/acoustic_model.pt --vocoder /path/to/vocoder.pt

# Start the REST API server
python -m kalakan.api.server --host 0.0.0.0 --port 8000 --acoustic_model /path/to/acoustic_model.pt --vocoder /path/to/vocoder.pt

# Start the gRPC API server
python -m kalakan.api.grpc_api --host 0.0.0.0 --port 50051 --acoustic_model /path/to/acoustic_model.pt --vocoder /path/to/vocoder.pt

API Clients

# REST API client
python rest_client.py --text "Akwaaba! Wo ho te sɛn?" --output output.wav --host localhost --port 8000

# gRPC API client
python grpc_client.py --text "Akwaaba! Wo ho te sɛn?" --output output.wav --host localhost --port 50051

Dataset Preparation

Kalakan TTS includes a comprehensive metadata generation tool for preparing TTS datasets:

Basic Metadata Generation

# Generate basic metadata for a dataset
kalakan gen-metadata --input-dir /path/to/dataset --verbose

Advanced Features

# Generate metadata with phonemes, dataset splitting, and quality control
kalakan gen-metadata \
    --input-dir /path/to/dataset \
    --generate-phonemes \
    --split-dataset \
    --val-ratio 0.2 \
    --test-ratio 0.1 \
    --output-format both \
    --include-stats \
    --check-duplicates \
    --remove-duplicates \
    --normalize-text \
    --clean-text \
    --speaker-id "speaker_001" \
    --verbose

Key Features

• Phoneme Generation: Automatic G2P conversion for Twi text
• Text Normalization: Cleaning and standardization of text content
• Dataset Splitting: Automatic train/validation/test splits
• Quality Control: Duration filtering, duplicate detection, and validation
• Multiple Formats: CSV and JSON output support
• Comprehensive Statistics: Detailed dataset analytics

For detailed documentation, see docs/metadata_generation_guide.md.

Text Normalization

Kalakan TTS includes a powerful text normalization tool for preparing Twi text:

Basic Text Normalization

# Normalize Twi text directly
kalakan norm --text "Dr. Kwame na Prof. Ama bɛba ha 25 mu."

File Processing

# Normalize text from files with various options
kalakan norm \
    --file input.txt \
    --output normalized.txt \
    --format json \
    --show-diff \
    --verbose

Key Features

• Abbreviation Expansion: "Dr." → "doctor", "Prof." → "professor"
• Number Conversion: "25" → text representation
• Special Character Normalization: Proper handling of Twi characters (ɛ, ɔ)
• Multiple Output Formats: Text, JSON, CSV
• Batch Processing: Process multiple files at once
• Interactive File Selection: GUI file picker
• Difference Visualization: See what changed during normalization

Documentation

For detailed documentation, visit docs/.

License

Apache License 2.0