by iron-mukakin · Agent Tool · ★ 51
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A fork of Irodori-TTS. It includes tools for generating captions (such as with LLM-Studio) and merging models. The inference capabilities have also been enhanced with features like continuous generation and voice quality adjustment.
| Stars | 51 |
| Forks | 5 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 59.3549172611252/100 |
| Last Updated | 2026-08-02 |
| Created | 2026-03-02 |
| Platforms | python |
| Est. Tokens | ~13k |
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Emoji-TTS is A fork of Irodori-TTS. It includes tools for generating captions (such as with LLM-Studio) and merging models. The inference capabilities have also been enhanced with features like continuous generati. It is categorized as a Agent Tool with 51 GitHub stars.
Emoji-TTS is primarily written in Python. It covers topics such as diffusion-models, echo-tts, flow-matching.
You can find installation instructions and usage details in the Emoji-TTS GitHub repository at github.com/iron-mukakin/Emoji-TTS. The project has 51 stars and 5 forks, indicating an active community.
Emoji-TTS is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Emoji-TTS on Agent Skills Hub include VideoGraphAI, awesome-voice-agents, gpt_server. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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