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---
language:
- en
- es
- fr
- de
- it
- pt
- pl
- tr
- ru
- nl
- cs
- ar
- zh
- ja
- hu
- ko
- hi
pipeline_tag: text-to-speech
tags:
- text-to-speech
- tts
- ggml
- vulkan
- c++
- on-device
license: other
license_name: coqui-public-model-license
license_link: https://coqui.ai/cpml
base_model: coqui/XTTS-v2
---
# ATTS1HG1: High-Performance GGML Implementation of XTTS-v2
**ATTS1HG1** is a high-speed, native C++ implementation of the Coqui XTTS-v2 model, utilizing the **GGML** tensor library. It features a custom integrated HiFiGAN vocoder optimized for **Vulkan** and **CPU** inference.
<div align="center">
| **Source Code & GUI** | **Base Model** | **Backend** |
|:---:|:---:|:---:|
| [**GitHub: ATTS1HG1**](https://github.com/abbndz/ATTS1HG1) | [Coqui XTTS-v2](https://huggingface.co/coqui/XTTS-v2) | GGML / Vulkan |
</div>
## 🚀 Key Features
* **Blazing Fast:** Generates audio in **< 0.5s** on consumer GPUs (RTX 3090) and **~1.0s** on CPU.
* **Vulkan Support:** Fully optimized HiFiGAN vocoder running on Vulkan (compatible with NVIDIA, AMD, Intel iGPUs).
* **Lightweight:** Native C++ application, no heavy Python dependencies (PyTorch/TensorFlow not required at runtime).
* **Multi-Language:** Supports 17 languages.
* **Voice :** Supports 58 speaker (similar to XTTS).
## 🌍 Supported Languages
The model supports the following 17 languages:
| Code | Language | Native Name |
| :--- | :--- | :--- |
| **en** | English | English |
| **es** | Spanish | Español |
| **fr** | French | Français |
| **de** | German | Deutsch |
| **it** | Italian | Italiano |
| **pt** | Portuguese | Português |
| **pl** | Polish | Polski |
| **tr** | Turkish | Türkçe |
| **ru** | Russian | Русский |
| **nl** | Dutch | Nederlands |
| **cs** | Czech | Čeština |
| **ar** | Arabic | العربية |
| **zh** | Chinese | 中文 |
| **ja** | Japanese | 日本語 |
| **hu** | Hungarian | Magyar |
| **ko** | Korean | 한국어 |
| **hi** | Hindi | हिन्दी |
## ⚡ Performance
Benchmarks based on standard text generation ("Bonjour le monde") using the C++ client:
| Device | Backend | Latency (Total) | Note |
| :--- | :--- | :--- | :--- |
| **NVIDIA RTX 3090** | **Vulkan** | **~0.47s** | 🚀 **Recommended** |
| **Intel iGPU** | Vulkan | ~1.40s | Good for laptops |
| **CPU (Ryzen/Intel)**| CPU (AVX2)| ~1.02s | Solid fallback |
| **NVIDIA RTX 3090** | CUDA | ~1.45s | Slower on HiFiGAN due to kernel overhead |
> **Note:** The Vulkan backend is significantly faster for the HiFiGAN part of the pipeline compared to CUDA due to optimized command buffers and reduced kernel launch overhead for small convolutions.
## 🛠️ Usage
This repository contains the converted `.bin` / `.gguf` weights required by the ATTS1HG1 software.
1. Download the model files from this repository.
2. Clone and compile the software from GitHub:
```bash
git clone [https://github.com/abbndz/ATTS1HG1](https://github.com/abbndz/ATTS1HG1)
```
3. Load the model in the GUI or CLI and select **Vulkan** for best performance.
## 📜 License
This project uses the weights from **Coqui XTTS-v2**, which is licensed under the **Coqui Public Model License (CPML)**.
* **Non-commercial use:** You can use this model for personal, educational, and non-commercial projects.
* **Commercial use:** Requires a license from Coqui (check their repository for details).
The C++ code (inference engine) is available under the MIT License (see GitHub).
---
*Credits: Based on the excellent work by Coqui.ai and the GGML library by ggerganov.* |