shivammehta25/Matcha-TTS

What it solves

Matcha-TTS addresses the speed and efficiency challenges of ODE-based speech synthesis. It provides a non-autoregressive neural text-to-speech (TTS) system that generates highly natural-sounding speech quickly while maintaining a compact memory footprint.

How it works

The system utilizes conditional flow matching (similar to rectified flows) to accelerate the synthesis process. It is a probabilistic model that can be exported to ONNX for optimized inference, allowing for the generation of waveforms either as standalone mel-spectrograms or as end-to-end audio when a vocoder is embedded in the graph.

Who it’s for

This project is designed for developers and researchers in speech synthesis who need a fast, high-quality TTS engine that can be trained on custom datasets (such as LJ Speech) and deployed via CLI, Gradio apps, or ONNX runtime.

Highlights

• Fast Synthesis: Optimized for speed using conditional flow matching.
• ONNX Support: Ability to export checkpoints to ONNX for efficient cross-platform inference.
• Custom Training: Full pipeline provided for training on personal datasets, including normalization statistics generation.
• Flexible Inference: Supports adjustable speaking rate, sampling temperature, and ODE solver steps via CLI.