KittenTTS: Tiny Open-Source Text-to-Speech That Runs on CPU

KittenTTS is an open-source text-to-speech (TTS) project from KittenML that’s optimized for lightweight deployment and CPU inference. It’s currently in developer preview, but it’s exactly the kind of “small model” release that matters for real products: voice generation that you can actually ship without building a GPU stack.

In this guide, I’ll break down what KittenTTS is, what models are available, how to try it quickly, and how to evaluate whether it’s production-ready for your use case.

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

Table of Contents

• What is KittenTTS?
• Why KittenTTS matters (the small-model TTS shift)
• KittenTTS models (mini / micro / nano / int8)
• Quick start (install + generate speech)
• Voices available
• How to evaluate quality (a practical checklist)
• Where KittenTTS fits (use cases)
• Deployment notes (latency, CPU, Python 3.12)
• Alternatives (when to choose something else)
• Tools & platforms (official links)
• FAQ

What is KittenTTS?

KittenTTS is an open-source TTS model designed for realistic speech synthesis with a strong emphasis on small size and CPU-friendly inference. The project ships multiple model variants and exposes a simple Python API for generating audio from text.

If you’ve tried shipping TTS before, you’ll know the pain: GPU requirements, heavyweight dependencies, and slow cold-starts. KittenTTS is trying to make TTS feel more like a normal library again.

Why KittenTTS matters (the small-model TTS shift)

A lot of AI progress that actually reaches users isn’t about the biggest model—it’s about the model that’s small enough to run everywhere.

• Latency: voice that arrives late feels broken.
• Cost: generating audio at scale via APIs gets expensive.
• Portability: if your TTS needs a GPU, it won’t ship to many environments.
• Privacy: local generation can matter depending on your domain.

KittenTTS fits the “deployable AI” trend: compact models + simple integration + acceptable quality for product features.

KittenTTS models (mini / micro / nano / int8)

From the project README (v0.8), KittenTTS offers multiple model variants on Hugging Face:

• kitten-tts-mini-0.8 — 80M params (~80MB)
• kitten-tts-micro-0.8 — 40M params (~41MB)
• kitten-tts-nano-0.8 (fp32) — 15M params (~56MB)
• kitten-tts-nano-0.8 (int8) — 15M params (~19MB) (note: README mentions some minor issues for some users)

Practical rule of thumb:

• Start with mini if you want the best quality baseline.
• Move to micro if you want a smaller model without going extreme.
• Test nano-int8 if you need the smallest footprint—just validate stability.

Quick start (install + generate speech)

KittenTTS provides a wheel for quick installation:

pip install https://github.com/KittenML/KittenTTS/releases/download/0.8/kittentts-0.8.0-py3-none-any.whl

Then generate audio like this:

from kittentts import KittenTTS
import soundfile as sf

m = KittenTTS("KittenML/kitten-tts-mini-0.8")

audio = m.generate(
    "This high quality TTS model works without a GPU",
    voice="Jasper"
)

sf.write("output.wav", audio, 24000)

Voices available

The README lists these voices: Bella, Jasper, Luna, Bruno, Rosie, Hugo, Kiki, Leo.

How to evaluate quality (a practical checklist)

If you’re deciding whether KittenTTS is “good enough” for your product, don’t just test a single happy-path sentence. Use a mini evaluation set:

• Short UI prompts: “Payment failed”, “Your report is ready”, “Call ended”.
• Long-form narration: 30–90 seconds of continuous text.
• Hard mode text: numbers, currencies, dates, acronyms, names, URLs.
• Punctuation stress test: parentheses, quotes, dashes, bullet lists.
• Consistency: does the voice drift over longer paragraphs?

Also measure latency on the CPU you plan to ship on (laptop vs VPS vs edge device). TTS is one of those features where a 2–3x slowdown can completely change UX.

Where KittenTTS fits (use cases)

• Agent voice layer: LLM generates text → KittenTTS turns it into speech locally.
• Product narration: onboarding, summaries, read-aloud experiences.
• Internal tooling: voice alerts, ops summaries, accessibility upgrades.
• Edge deployments: voice on constrained machines without a GPU requirement.

Deployment notes (latency, CPU, Python 3.12)

The project notes it “works everywhere,” but it also mentions Python 3.12 and recommends using conda. If your stack is pinned to an older Python version, plan that migration before committing.

If you’re shipping this in production, treat it like any other inference component: track CPU usage, cold-start time, and memory footprint. Small models still need good packaging.

Alternatives (when to choose something else)

KittenTTS is appealing for portability, but you may want alternatives if you need:

• Enterprise-grade support and strict SLAs
• Highly controllable voices (style, emotion, speaker cloning)
• Multilingual coverage beyond your main market

In those cases, you might consider more established open-source stacks or paid APIs—but if your priority is “small + deployable + good enough,” KittenTTS is a solid test.

Tools & platforms (official links)

• GitHub: https://github.com/KittenML/KittenTTS
• Hugging Face models:
  • kitten-tts-mini-0.8
  • kitten-tts-micro-0.8
  • kitten-tts-nano-0.8-fp32
  • kitten-tts-nano-0.8-int8

FAQ

Does KittenTTS require a GPU?
No—CPU inference is a core part of the project’s pitch.

Which model should I start with?
Start with kitten-tts-mini-0.8 for quality. Once it works, test micro/nano for smaller footprint and faster downloads.

Can I use KittenTTS commercially?
It’s open source, but check the repository license before shipping in production.