LightBlue

README.md

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+---
+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:

backbone.onnx

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source.txt

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stats.npz

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