Author: Zen and the Art of Computer Programming
Cross-platform application of speech synthesis: technical support for distributed synthesis
As an artificial intelligence expert, programmer and software architect, I am often faced with the need to convert sound to text, or text to sound. Speech synthesis technology can be applied in many scenarios, such as virtual assistants, smart speakers, conversational UI, etc. Implementing these functions usually relies on advanced speech synthesis engines. However, these engines often require significant computing resources and time to train and deploy. In order to achieve more efficient and scalable speech synthesis applications, this article will introduce a cross-platform application architecture based on distributed synthesis, aiming to achieve highly customizable personalized virtual assistants.
- introduction
1.1. Background introduction
With the popularity of smart mobile devices, people's demand for virtual assistants is increasing. As a personalized assistant for users, virtual assistants can help users complete various operations, provide information consultation, etc. To meet these needs, virtual assistants often rely on advanced speech synthesis engines. These engines convert text into natural and smooth speech, and sounds into text. However, these engines require a large amount of computing resources and time to train and deploy, and are usually not suitable for large-scale applications.
1.2. Purpose of the article
This article aims to introduce a cross-platform application architecture based on distributed composition, aiming to achieve highly customizable personalized virtual assistants. This architecture will take full advantage of distributed computing to achieve efficient and highly scalable speech synthesis applications.
1.3. Target audience
This article will focus on the following target users:
- Virtual Assistant Developers and Developers
- Software architects and developers
- Users who want to create natural and smooth voice interaction interfaces
- Technical principles and concepts
2.1. Explanation of basic concepts
- Distributed synthesis: Divide the training data into multiple parts and train on multiple computing nodes