KittenTTS – KittenML’s Open-Source Lightweight Text-to-Speech Model

What is KittenTTS？

KittenTTS is a lightweight, open-source text-to-speech (TTS) model developed by the KittenML team. Featuring an extremely small model size (only 25 MB) and strong CPU optimization, it runs on low-power devices without requiring a GPU. KittenTTS offers 8 preset voices (4 male, 4 female) and supports multiple languages (currently focused on English). It can be integrated into various applications via ONNX/PyTorch formats. The model downloads and caches its weights locally on first run, enabling offline speech generation thereafter, making it ideal for offline scenarios.

Main Features of KittenTTS

Lightweight Design: At only 25 MB and around 15 million parameters, it is one of the smallest open-source TTS models available, making it suitable for resource-constrained devices.
CPU Optimization: Runs in real time on Raspberry Pi, low-power embedded devices, or mobile platforms without GPU support, lowering hardware requirements.
Multiple Voices: Offers 8 preset voices (4 male, 4 female), allowing users to choose different voice styles as needed.
Low-Latency Inference: Optimized for real-time interaction scenarios, with fast response times for hardware-triggered speech playback.
Offline Capability: Downloads and caches model weights locally on first run, enabling speech generation without an internet connection — ideal for network-restricted environments.
Openness & Compatibility: Supports ONNX and PyTorch formats, allowing easy integration into Python, web applications, and embedded systems.

Technical Principles of KittenTTS

Model Compression: Uses techniques such as knowledge distillation and parameter pruning to reduce traditional hundred-megabyte-level TTS models to just 25 MB, while preserving speech naturalness and quality.
CPU Inference Optimization: Accelerated via ONNX Runtime to eliminate GPU dependency, enabling efficient performance on CPUs and making it suitable for low-power devices.
End-to-End Neural Speech Synthesis: Maps text directly to speech waveforms without complex intermediate steps, balancing efficiency with naturalness to improve overall speech output.
Offline Caching Mechanism: Downloads and stores model weights locally on first run, ensuring stable performance without internet access and enhancing practicality.

Project Repository

GitHub: https://github.com/KittenML/KittenTTS

Application Scenarios

Offline Voice Assistants: For in-car navigation, outdoor devices, and other offline environments, ensuring reliable voice prompts and interactions without internet access.
Educational Programming Tools: When integrated with visual programming platforms (e.g., KittenBlock), students can easily create voice-controlled robots or storytelling machines, making learning more engaging.
Assistive Technology: Enables localized screen readers for visually impaired users, reducing cloud dependency and privacy risks while providing safe and reliable voice assistance.
Mobile Applications: Its lightweight, low-power nature makes it ideal for mobile apps to deliver voice announcements, personal assistants, and more.
Smart Toys: Adds voice interaction to children’s toys, enhancing interactivity, entertainment, and overall user experience.