---
language: 
  - he
tags:
  - text-to-speech
  - tts
  - hebrew
  - audio
  - fast-inference
license: mit
datasets:
  - notmax123/RanLevi40h
---

# LightBlue TTS 🇮🇱

## Model Description

LightBlue is a state-of-the-art, lightning-fast Text-to-Speech (TTS) model built from scratch specifically for Hebrew (with English support). It is designed to produce 100% native Israeli-sounding speech with perfect handling of *Nikud* (vowels) and complex homographs, without compromising on inference speed. 

It is fast enough to generate an entire 1-hour audiobook in just **3 seconds** on a modern GPU.

- **Developer:** LightBlue TTS
- **Language(s):** Hebrew (Primary), English
- **Model Type:** Text-to-Speech (TTS)
- **Demo & Website:** [https://lightbluetts.com/](https://lightbluetts.com/)
- **GitHub Repository:** [https://github.com/maxmelichov/Light-BlueTTS](https://github.com/maxmelichov/Light-BlueTTS)

## Key Features

- **Blazing Fast Inference:**
  - **1260x real-time** on an NVIDIA RTX 3090 (21 minutes of audio generated per second).
  - **35x real-time** on standard CPUs.
  - **20x real-time** on Apple M1 chips.
- **Native Hebrew Quality:** Features a real Israeli accent, correct stress placements, and native-level flow.
- **Advanced Contextual Understanding:** Passes the "Homograph Test" (e.g., correctly distinguishing between *צפה* as "watched" vs "floated", or *תרד* as "spinach" vs "go down").
- **Multiple Voices:** Includes high-quality voices like *Yonatan* (Hebrew only) and *Rotem*.

## Uses

### Direct Use
- Generating high-quality Hebrew audio from text.
- Real-time TTS applications running on standard CPUs or edge devices.
- Audiobooks, accessibility tools, virtual assistants, and automated broadcasting.

## Speed Benchmarks

LightBlue is optimized for extreme speed without sacrificing naturalness:

| Hardware | Speed | Time for 1 Hour of Audio |
| :--- | :--- | :--- |
| **NVIDIA RTX 3090** | 1260x real-time | ~3 seconds |
| **Standard CPU** | 35x real-time | ~1.7 minutes |
| **Apple M1** | 20x real-time | ~3 minutes |

## How to Get Started

To use this model, you can clone the official GitHub repository and install the requirements: