Spark TTS is an open-source, PyTorch-based text-to-speech inference system that leverages large language models to produce highly natural, intelligible speech from text input. It uses an efficient single-stream architecture where speech tokens are directly reconstructed from the predictions of an LLM, removing the need for external acoustic models or complex vocoders and making the generation pipeline cleaner and faster. The project supports zero-shot voice cloning, meaning it can imitate a new speaker’s voice without dedicated training for that specific voice, and works across languages, including English and Chinese, even in cross-lingual code-switching scenarios. Spark-TTS allows users to control speech characteristics like gender, pitch, and speaking rate to customize synthesized output and support virtual speaker creation.
Features
- LLM-based speech synthesis with direct token reconstruction
- Zero-shot voice cloning for new voices without dedicated training
- Cross-lingual and code-switching support
- Controllable speech parameters (gender, pitch, speed)
- CLI and web UI for inference workflows
- High-performance deployment options (e.g., Triton)