MisakiSwift

A Swift port of the Misaki grapheme-to-phoneme (G2P) library for converting English text to phonetic representations suitable for text-to-speech (TTS) engines.

Supported Platforms

• iOS 18.0+
• macOS 15.0+
• (Other Apple platforms may work as well)

Installation

Add MisakiSwift to your Swift Package Manager dependencies:

dependencies: [
    .package(url: "https://github.com/mlalma/MisakiSwift", from: "1.0.1")
]

Basic Usage

import MisakiSwift

// Create G2P converter (british = false for American English)
let g2p = EnglishG2P(british: false)

// Convert text to phonemes
let (phonemes, tokens) = g2p.phonemize(text: "Hello world!")
print(phonemes) // "həlˈO wˈɜɹld!"

Custom Phoneme Override

Use Markdown-like syntax to specify exact pronunciations in case you don't want to use fallback network:

let g2p = EnglishG2P(british: false)
let text = "[Misaki](/misˈɑki/) is a G2P engine designed for [Kokoro](/kˈOkəɹO/) models."
let (phonemes, _) = g2p.phonemize(text: text)
// "misˈɑki ɪz ɐ ʤˈitəpˈi ˈɛnʤən dəzˈInd fɔɹ kˈOkəɹO mˈɑdᵊlz."

Overview

MisakiSwift is a high-quality English G2P conversion library that transforms written text into phonemes using both dictionary-based lookup and neural network fallback. It supports British and American English pronunciations and includes advanced features like stress pattern handling and custom phoneme overrides.

Key Features

• High Accuracy: Combines extensive pronunciation dictionaries with neural network fallback for out-of-vocabulary words
• Dual Dialect Support: Supports both British and American English pronunciations
• Advanced Text Processing: Handles punctuation, numbers, acronyms, and complex formatting
• Custom Phoneme Override: Use Markdown-like syntax to specify exact pronunciations: [word](/phonemes/)
• Stress Pattern Control: Automatic stress assignment with manual override capabilities
• Apple Ecosystem Integration: Uses Apple's Natural Language framework instead of external dependencies like SpaCy

Architecture

MisakiSwift consists of several key components:

• EnglishG2P: Main conversion pipeline that orchestrates tokenization, lexicon lookup, and neural network fallback
• Lexicon: Dictionary-based pronunciation lookup using gold and silver dictionaries
• EnglishFallbackNetwork: Transformer-based model (ported to run on MLX) for phoneme prediction for out-of-vocabulary words

Key Differences from Python Misaki

1. POS Tagging: Uses Apple's NaturalLanguage framework instead of SpaCy for part-of-speech tagging
2. Neural Network: The BART-based fallback network is ported to run on MLX
3. Resource Management: All model weights and dictionaries are bundled as resources within the Swift package

Dependencies

• MLX: Machine learning framework for the neural network component
• NaturalLanguage: Apple's built-in framework for text processing and POS tagging

Model Resources

The package includes pre-trained models and dictionaries:

• BART Model Weights: Neural network weights for phoneme prediction (US and GB variants)
• Gold Dictionary: High-confidence pronunciation mappings
• Silver Dictionary: Additional pronunciation mappings with slightly lower confidence

These resources are automatically bundled with the package and loaded at runtime.