# Text2Speech: An Experimental Shortcut-Triggered Speech Synthesis Tool Based on Large Language Models

> An experimental text-to-speech tool triggered by keyboard shortcuts, using large language model technology for speech synthesis, developed with C++ and Qt 6.9, and supporting the Windows platform.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-04T01:46:38.000Z
- 最近活动: 2026-06-04T01:56:57.936Z
- 热度: 141.8
- 关键词: 文本转语音, TTS, 快捷键, LLM, Qt, C++, 语音合成, 桌面工具
- 页面链接: https://www.zingnex.cn/en/forum/thread/text2speech
- Canonical: https://www.zingnex.cn/forum/thread/text2speech
- Markdown 来源: floors_fallback

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## Introduction: Text2Speech—An Experimental Shortcut-Triggered Speech Synthesis Tool Based on LLM

Text2Speech is an open-source experimental desktop text-to-speech (TTS) tool. Its core features include shortcut triggering and large language model (LLM) driving. It is developed with C++ and Qt 6.9 and currently supports the Windows platform. The project explores new ideas for integrating LLM capabilities into desktop TTS, aiming to simplify the operation process and enhance the intelligence of speech synthesis.

## Project Background and Technical Trends

**Original Author and Source**: Maintained by IlyaLts, released on GitHub (link: https://github.com/IlyaLts/Text2Speech) on June 4, 2026.

**Project Positioning**: An experimental tool with the design concept of "fast, intelligent, experimental".

**Technical Trends**: TTS technology has gone through stages of traditional splicing/parameter synthesis → neural network TTS (e.g., WaveNet) → large-model TTS. Text2Speech embodies the trend of large-model TTS and explores the application of LLM in speech synthesis.

## Core Methods and Technical Implementation

**Shortcut Triggering**: Global shortcut triggering—select text and press the preset combination to read aloud, simplifying operations.

**LLM-Driven Architecture**: It is speculated that it may generate intermediate representations via LLM, call cloud LLM APIs, or combine semantic understanding to optimize naturalness.

**Tech Stack**: C++ (high performance), Qt 6.9 (cross-platform foundation), dependencies include liboai (OpenAI API client), nlohmann-json (JSON processing), and curl (network communication).

**Workflow**: Background shortcut monitoring → capture selected text → send to cloud LLM via liboai → receive voice data → play audio.

## Application Scenarios and Potential Value

**Accessibility Assistance**: Helps visually impaired or dyslexic users improve their computer experience.

**Content Creation Assistance**: Writers can use it for manuscript proofreading; listening to the text makes it easy to spot sentence issues.

**Multilingual Learning**: If multilingual support is available, it can be used to listen to standard pronunciations.

**Efficiency Tool Integration**: The shortcut-trigger design facilitates integration into workflows such as reading documents and processing emails.

## Limitations and Improvement Directions

**Platform Limitation**: Currently only supports Windows; needs adaptation for macOS and Linux.

**Cloud Dependency**: Requires network connection, may incur API costs, has privacy concerns; can be improved to support local lightweight models or offline mode.

**Function Completeness**: Lacks features such as voice selection, speed adjustment, multilingual support, and audio export; needs improvement.

## Conclusion and Outlook

As an experimental project, Text2Speech's value lies in exploring the technical route of combining LLM with desktop TTS and demonstrating the efficiency potential of shortcut interaction. For developers, it is a reference implementation; for users, if the platform and functions are improved, it is expected to become a practical tool. In the future, focus can be placed on improving cross-platform support, offline capabilities, and function completeness to promote the transformation of experimental technology into mature applications.