Kokoro TTS: Lightweight Open-Source Text-to-Speech Model｜Complete Guide and Overview

Introduction

In today’s rapidly evolving AI-powered speech synthesis world, a rising star is making its mark. Kokoro, a Text-to-Speech (TTS) model with only 82 million parameters, has stood out in the TTS Spaces Arena with its excellent performance and innovative design, proving that small can also be mighty.

Key Advantages of Kokoro

Impressive Performance

Kokoro v0.19 took the top spot in the single-speaker evaluation in TTS Spaces Arena. This achievement is remarkable because it outperformed many larger competitors, showing that high-quality speech synthesis doesn’t always require massive resources or complex models.

Diverse Voice Options

Kokoro offers 10 carefully designed voice packs, including:

• American English accents (e.g., Adam, Michael)
• British English accents (e.g., Bella, Sarah)
• Options with different genders and voice characteristics

Each voice pack is finely tuned to ensure clear and natural sound, suitable for a wide range of applications.

Open and Transparent Ecosystem

As an open-source project under the Apache 2.0 license, Kokoro provides developers and researchers with great flexibility:

• Commercial use allowed
• Support for further development
• Encouragement for community collaboration
• Promotion of technological innovation

Technical Details

Innovative Architecture

Kokoro uses a simple yet efficient design:

• A hybrid architecture based on StyleTTS 2 and ISTFTNet
• Decoder-only structure, removing traditional encoders
• No diffusion models, reducing computational complexity
• Optimized parameters for efficient output

Unique Training Data

Kokoro’s training process is distinctive:

• Trained on less than 100 hours of selected audio data
• Carefully sourced from legally licensed materials
• Uses public-domain audio and synthetic data from commercial TTS systems
• Ensures high data quality and copyright compliance

Cost Efficiency

Kokoro is highly cost-effective to develop:

• Trained on Vast.ai using A100 80GB GPUs
• Training costs less than $1 per hour
• Saves significantly compared to traditional cloud services

Usage Guide and Recommendations

Quick Start Tutorial

1. Online Demo:
   • Visit the demo page on Hugging Face Spaces
   • URL: hf.co/spaces/hexgrad/Kokoro-TTS
   • Enter text to experience speech synthesis instantly
2. Local Deployment:
   • Sample code provided on Google Colab
   • ONNX format support for cross-platform deployment
   • Comprehensive installation and user documentation available

Current Limitations and Future Directions

Areas for Improvement

1. Voice Cloning
   • Limited by training data size; voice cloning is not yet supported
   • Future updates may include this feature with expanded datasets
2. Dependency on External g2p Tools
   • Relies on external tools like espeak-ng for text-to-phoneme conversion
   • May affect accuracy for certain special texts
3. Application Scope
   • Performs well with long-form content
   • Conversational use cases need further improvement

Technical Support and Community Resources

For more information or support:

• Join the Discord Community
• View the Code Repository
• Try Kokoro on Hugging Face Spaces

Conclusion

Kokoro proves that in TTS technology, smart design often outweighs large-scale models. With ongoing advancements and community contributions, we look forward to more exciting developments from Kokoro in the future.

Supplementary Information

What is g2p?

g2p stands for grapheme-to-phoneme, which means “converting written text to pronunciation.”

• Grapheme: The smallest written unit in a language, such as letters in English (“a”, “b”, “c”) or Chinese characters (“一”, “二”, “三”).
• Phoneme: The smallest unit of sound that can distinguish meaning, such as /k/, /æ/, and /t/ in the word “cat.”

g2p tools help TTS systems convert written text into phonemes for accurate pronunciation, especially in languages like English where spelling and pronunciation often differ.

Why is g2p needed?

TTS systems need phoneme information to generate speech. Since spelling and pronunciation don’t always match, g2p tools bridge this gap. For example:

• The word “cat” maps straightforwardly to /kæt/.
• The word “phone,” though spelled with “ph,” is pronounced /f/, requiring a g2p tool to handle such irregularities.

What is espeak-ng?

espeak-ng is an open-source tool for g2p conversion and basic speech synthesis. Kokoro uses espeak-ng to transform text into phoneme sequences, which are then used by Kokoro to generate natural speech.

Pros and Cons of Using espeak-ng

Advantages:

• Convenience: Ready-to-use and easily integrates with Kokoro, saving development effort.
• Maturity: A well-established project with reliable performance.
• Multilingual Support: Supports various languages, providing potential for multi-language TTS.

Disadvantages:

• Accuracy Issues: May struggle with irregular words or non-standard spellings, affecting speech quality.
• External Dependency: Kokoro’s performance partly depends on espeak-ng, so issues with the tool could impact the model.