This project is a generative speech synthesis engine that converts text into high-fidelity human speech. It utilizes a two-stage autoregressive transformer architecture that separates semantic token prediction from acoustic detail reconstruction to balance linguistic accuracy with audio quality. The system is designed to support multilingual output and conversational AI development, enabling the generation of context-aware speech that maintains flow across multiple dialogue turns.
The platform distinguishes itself through a production-ready inference server that employs continuous batching to maximize hardware utilization and reduce latency. It includes a comprehensive voice cloning toolkit that replicates unique vocal characteristics from short reference audio samples without requiring additional model training. Users can further customize output through low-rank adaptation fine-tuning, which allows for efficient style adjustments, and speaker-specific token embeddings that manage distinct voice characteristics during multi-speaker generation.
Beyond core synthesis, the project provides a full suite of utilities for training and alignment, including reinforcement learning techniques to optimize for semantic accuracy and instruction adherence. It supports a variety of operational interfaces, including a command-line tool, a web-based dashboard, and an authenticated HTTP server for remote generation workloads. The system also includes data preparation and serialization tools to streamline the process of organizing and normalizing audio datasets for model training.
