Description

️ 🖼Tool name: Inworld TTS

🔖 Tool categorization: AI-advanced Text-to-Speech (TTS) model


️ ✏What does it do?

  • Convert written text into natural and emotional spoken speech.

  • Zero-shot voice cloning for personalized voice customization and branding.

  • Control emotions and vocal style with tags like "[happy]" or "[whispering]".

  • Low latency, reaching the first voice segment in ~200 milliseconds, making it suitable for real-time interactive applications.


What does it actually deliver based on user experience?

  • Excellent sound quality that is very close to the human voice in terms of tone, rhythm, and prosody.

  • Support for multiple languages (English, Chinese, Korean, Korean, French, Spanish, etc.).

  • Text-to-speech in real-time streaming.

  • Ability to customize the voice to create a unique voice for branding or personalization.


🤖 Does it include automation?

  • Yes, it relies on AI to automatically convert text to speech.

  • Automatically control tone of voice and emotion via specific tags.

  • The architecture supports the use of real-time voice generation for live interactive applications.


💰 Pricing model:

  • Basic version: About $5 per million characters.

  • Advanced versions such as "TTS-1-Max" for high-performance or experimental tasks at a higher price.

  • Customized enterprise plans for companies that need high volume usage or advanced customizations.


🧭 How to access the tool:

  • Via the official website: Inworld AI

  • There is a "TTS Playground" to try out the models directly.

  • The API is ready to integrate with voice applications and projects.


🔗 Link to the demo or the official website:
Introducing Inworld TTS - Official Blog

Pricing Details

Categorized as an advanced AI text-to-speech (TTS) model, the tool converts written text into natural and emotional spoken speech. It also supports zero-shot voice cloning to customize the voice according to personality or brand, with the ability to control emotions and vocal style using tags such as "[happy]" or "[whispering]" or "[whispering]". It has a low latency of about 200 milliseconds to the first voice slice, making it suitable for real-time interactive applications.