Kokoro-82M
| Property | Value |
|---|---|
| Parameter Count | 82 Million |
| License | Apache |
| Architecture | StyleTTS 2 + ISTFTNet |
| Training Cost | $1000 (1000 A100 GPU hours) |
| Paper Reference | StyleTTS 2 Paper |
What is Kokoro-82M?
Kokoro-82M is a lightweight, open-weight text-to-speech (TTS) model that delivers high-quality voice synthesis across 8 languages with 54 distinct voices. Despite its relatively small size of 82 million parameters, it achieves performance comparable to larger models while maintaining efficiency and cost-effectiveness. The model utilizes a decoder-only architecture based on StyleTTS 2 and ISTFTNet frameworks.
Implementation Details
The model is built on a hybrid architecture combining StyleTTS 2 and ISTFTNet, focusing on decoder-only implementation without diffusion or encoder components. It was trained on hundreds of hours of permissive/non-copyrighted audio data, including public domain content and synthetic audio generated by closed TTS models. The training process cost approximately $1000 using A100 80GB GPUs over 1000 hours.
- Supports 8 languages including English, Spanish, French, Hindi, Italian, Portuguese, Japanese, and Chinese
- Implements 54 distinct voice profiles
- Uses IPA phoneme labels for improved pronunciation accuracy
- Trained exclusively on permissive audio data
Core Capabilities
- Multi-language text-to-speech synthesis
- Voice style transfer and control
- Efficient inference with modest computational requirements
- Production-ready with Apache license compatibility
- Easy integration through pip installation
Frequently Asked Questions
Q: What makes this model unique?
Kokoro-82M stands out for its excellent performance-to-size ratio, supporting multiple languages and voices while maintaining a relatively small parameter count of 82M. Its Apache license and cost-efficient training make it particularly attractive for both personal and production deployments.
Q: What are the recommended use cases?
The model is suitable for a wide range of applications, from personal projects to production environments. Its lightweight nature makes it ideal for deployments where computational resources are limited, while its multi-language support enables global applications.
