Hey guys,

I wanted to share a small project I've been working on to solve a personal pain point: TinyTTS.

We all love our massive 70B+ LLMs, but when building local voice assistants, running a heavy TTS framework alongside them often eats up way too much precious VRAM and compute. I wanted something absurdly small and fast that "just works" locally.

TL;DR Specs:

Size: ~9 Million parameters

Disk footprint: ~20 MB checkpoint (G.pth)

Speed (CPU): ~0.45s to generate 3.7s of audio (~8x faster than real-time)

Speed (GPU - RTX 4060): ~0.056s (~67x faster than real-time)

Peak VRAM: ~126 MB

License: Apache 2.0 (Open Weights)

Why TinyTTS? It is designed specifically for edge devices, CPU-only setups, or situations where your GPU is entirely occupied by your LLM. It's fully self-contained, meaning you don't need to run a complex pipeline of multiple models just to get audio out.

How to use it? I made sure it’s completely plug-and-play with a simple Python API. Even better, on your first run, it will automatically download the tiny 20MB model from Hugging Face into your cache for you.

pip install git+https://github.com/tronghieuit/tiny-tts.git

Python API:

from tiny_tts import TinyTTS

# Auto-detects device (CPU/CUDA) and downloads the 20MB checkpoint

tts = TinyTTS()

tts.speak("The weather is nice today, and I feel very relaxed.", output_path="output.wav")

CLI:

tiny-tts --text "Local AI is the future" --device cpu

Links:

GitHub: https://github.com/tronghieuit/tiny-tts

Gradio Web Demo: Try it on HF Spaces here

Hugging Face Model: backtracking/tiny-tts

What's next? I plan to clean up and publish the training code soon so the community can fine-tune it easily. I am also looking into adding ultra-lightweight zero-shot voice cloning.

Would love to hear your feedback or see if anyone manages to run this on a literal potato! Let me know what you think.

If you find this project helpful, please give it a on GitHub.