Training Infrastructure of a Text-to-Speech Generation System

Text-to-speech (TTS) models are a class of neural networks that convert written text into realistic spoken audio. TTS technology enables dynamic and personalized interactions, allowing machines to convey information in natural, human-like speech, enhancing the user experience in numerous domains.

Speech generation models have evolved rapidly, with advances in natural language processing and audio synthesisAudio synthesis is the artificial production of sound using software, often for music, sound effects, or speech generation. techniques enabling the creation of highly expressive, lifelike voices. Modern TTS models like the NVIDIA Tacotron2 and xTTS aim to produce clear and natural-sounding speech, allowing customization regarding voice tone, emotion, and even accents. These customizable aspects make TTS models valuable in applications that demand high personalization, such as voiceovers for content creation, real-time translation, and assistive technologies. Systems using these models include everything from assistants like Siri to modern LLM-based chatbots like ChatGPT.

Let’s see how to design a robust and versatile text-to-speech system. Our focus will be to design a system capable of handling text inputs and generating high-quality, legible speech from them.