tronghieuit

tronghieuit / tiny-tts

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A super-lightweight text-to-speech model with only 9M parameters and ~20 MB weights that delivers fast, efficient, and clear speech synthesis — ideal for on-device, real-time, and resource-constrained applications.

44
1
100% credibility
Found Feb 28, 2026 at 42 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

TinyTTS is an ultra-lightweight, end-to-end English text-to-speech model with only 9 million parameters that generates natural 44.1kHz audio runnable on CPUs.

How It Works

1
🌐 Discover TinyTTS

You find this lightweight tool that turns any English text into natural-sounding speech, perfect for everyday computers without fancy hardware.

2
💻 Set it up quickly

Follow simple instructions to add it to your computer, and it grabs everything it needs automatically.

3
🔤 Type your message

Enter any sentence or paragraph you want to hear spoken out loud, like a story or announcement.

4
🎤 Choose a voice

Pick from available voices, such as a clear female one, to match the style you like.

5
▶️ Generate the speech

Hit go, and in seconds it creates realistic audio faster than real-time, even on your regular computer.

✅ Enjoy your audio

Save and play the high-quality sound files for videos, apps, or just to hear text come alive effortlessly.

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Star Growth

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AI-Generated Review

What is tiny-tts?

TinyTTS is a Python text-to-speech model with only 9M parameters and ~20MB weights, delivering fast, efficient, clear English speech synthesis ideal for on-device, real-time applications. Run it via CLI like `tiny-tts --text "Hello world" --speaker female --device cpu` or Python API: `TinyTTS().speak("text", "output.wav")`. It auto-downloads checkpoints from Hugging Face and outputs 44.1kHz WAVs, solving bulky TTS woes for edge devices.

Why is it gaining traction?

This tiny TTS GitHub project echoes Parler-TTS Mini V1—crushing 50-200M param models with CPU RTF of 0.12x (8x real-time) and GPU at 0.015x, using just 127MB VRAM. ONNX export enables cross-platform deployment where PyTorch edges it on CPU speed. Developers dig the no-GPU barrier, speaker options, and Gradio demos for quick prototyping.

Who should use this?

Embedded engineers targeting IoT or mobile apps needing offline TTS, like tiny epic dungeons TTS in games. Backend devs for real-time voice in resource-limited servers. Python scripters building TTS tiny tots prototypes without cloud latency.

Verdict

Grab it for ultra-lightweight TTS needs—CLI and API shine, benchmarks deliver—but 43 stars and 1.0% credibility signal early maturity; TODOs like training code mean test lightly. Solid docs make it worth a spin over heavier alternatives.

(198 words)

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