KittenML/KittenTTS

Health v2 · maintenance only

Adoption not part of the score

15403 stars · 885 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

KittenTTS is an open-source, ultra-lightweight text-to-speech library built on ONNX, with models ranging from 15M to 80M parameters (25-80 MB on disk). It delivers high-quality 24 kHz voice synthesis on CPU without requiring a GPU, making it suitable for edge deployment.

Use cases

• generate speech from text on a CPU without a GPU
• add text-to-speech to an edge device or embedded system
• run a tiny TTS model under 25MB
• synthesize voice audio for an app offline
• convert written text into natural-sounding speech in Python
• deploy lightweight voice synthesis on resource-constrained hardware

When to choose

• you need TTS inference on CPU-only machines or edge devices
• model size and memory footprint are critical constraints
• you want a simple Python API with built-in voices and speed control
• you prefer an Apache-2.0 licensed open-source TTS solution

When to avoid

• you need production-stable APIs - the project is a developer preview and APIs may change
• you need extensive voice cloning or many built-in voices (only 8 voices available)
• you require very high-fidelity studio-grade speech from large models
• you need non-ONNX runtimes or GPU-optimized training pipelines

Facets

library · maturity active

tts speech-recognition machine-learning llm-inference speech-processing machine-learning artificial-intelligence python cross-platform onnx lightweight edge-deployment voice-synthesis cpu-inference audio cpu

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For agents

markdown · JSON · MCP: product_card(name="KittenML/KittenTTS")