Hey r/LocalLlama! 

We've been tinkering with TTS models for a while, and I'm excited to share KaniTTS – an open-source text-to-speech model we built at NineNineSix.ai. It's designed for speed and quality, hitting real-time generation on consumer GPUs while sounding natural and expressive.

# Quick overview:

* **Architecture**: Two-stage pipeline – a LiquidAI LFM2-350M backbone generates compact semantic/acoustic tokens from text (handling prosody, punctuation, etc.), then NVIDIA's NanoCodec synthesizes them into 22kHz waveforms. Trained on \~50k hours of data.
* **Performance**: On an RTX 5080, it generates 15s of audio in \~1s with only 2GB VRAM. 
* **Languages**: English-focused, but tokenizer supports Arabic, Chinese, French, German, Japanese, Korean, Spanish (fine-tune for better non-English prosody).
* **Use cases**: Conversational AI, edge devices, accessibility, or research. Batch up to 16 texts for high throughput.

It's Apache 2.0 licensed, so fork away. Check the audio comparisons on the [https://www.nineninesix.ai/n/kani-tts](https://www.nineninesix.ai/n/kani-tts) – it holds up well against ElevenLabs or Cartesia.

Repo: [https://github.com/nineninesix-ai/kani-tts](https://github.com/nineninesix-ai/kani-tts)  
Model: [https://huggingface.co/nineninesix/kani-tts-450m-0.1-pt](https://huggingface.co/nineninesix/kani-tts-450m-0.1-pt)  
Page: [https://www.nineninesix.ai/n/kani-tts](https://www.nineninesix.ai/n/kani-tts)

Feedback welcome – what's your go-to TTS setup?

KaniTTS – Fast and high-fidelity TTS with just 450M params