F5 TTS Introduction

F5-TTS operates using a sophisticated combination of Flow Matching and Diffusion Transformer (DiT) technologies. The system processes input text by first converting it to a character sequence and padding it with filler tokens to match the length of input speech. It then uses ConvNeXt V2 blocks for text refinement before processing through its neural network architecture. The model consists of 22 layers, 16 attention heads, and 1024/2048 embedding/feed-forward network dimensions for DiT, along with 4 layers of ConvNeXt V2 components. During inference, it achieves a real-time factor (RTF) of 0.15, making it significantly faster than other state-of-the-art diffusion-based TTS models. The system has been trained on a massive 100K hours multilingual dataset, enabling it to handle multiple languages and code-switching effectively.