Text-to-Speech Generation Systems
Understand the workings of a text-to-speech generation system along with its training and inference pipeline.
Text-to-speech (TTS) generation systems have evolved significantly, with modern AI demonstrating remarkable abilities in generating natural-sounding, human-like speech. In this lesson, we’ll look at a text-to-speech system—from data processing to deployment—to understand how these AI-powered TTS systems work and what it takes to build them responsibly. Let’s start with an overview of text-to-speech systems.
Overview of speech generation systems
At their core, text-to-speech generation systems convert input text into audible speech. While the concept is simple, building a high-quality, production-ready TTS system involves a complex interplay of several stages, as shown in the diagram below.
As depicted in the illustration above, the workflow of a text-to-speech system begins with text input (e.g., “Cat”), which is analyzed in the text processing stage to extract entities and meaning. The system encodes the processed text into phonemes (the basic units of sound) using phoneme encoding. It then predicts the duration of each phoneme, determining how long each sound will be pronounced. The phonemes and their durations are passed through a diffusion model, which generates detailed acoustic features. These features are synthesized into raw audio that represents the speech waveform. Finally, a post-processing step refines the audio output, producing clear and natural speech. This workflow ensures the generated speech is aligned with the input text and sounds realistic.
At an abstract level, typically, there are three main layers at which the text-to-speech generation system works. Let’s explore each layer as follows:
Input (prompt) processing layer: This layer acts as the system’s entry point, handling tasks like request validation to ensure the input text is in the correct format. It also performs text normalization to standardize the text (e.g., expanding abbreviations, handling numbers) and queue management to handle multiple requests efficiently.
Model service layer: This is the heart of the system, where the core TTS model resides. It’s responsible for loading the trained model, performing inference to convert the processed text into speech, and ensuring the output quality by monitoring for naturalness and clarity.
Orchestration layer: This layer manages the overall operation of the system. It handles resource allocation, ensuring that computational resources (like CPU and memory) are used efficiently. It also manages errors, gracefully handling any issues during ...