Detailed Introduction
GLM‑TTS is a text-to-speech (TTS) project released by Zai that focuses on controllable generation of emotion and speaking style. The project uses multi-reward reinforcement learning in a zero-shot setting to enhance the emotional expressiveness and naturalness of synthesized speech, enabling the model to produce speech with specified emotions or styles even for unseen examples.
Main Features
- Zero-shot emotional expression: generate speech with target emotions without specialized training samples.
- Strong controllability: multi-dimensional controls such as emotion intensity, speaking rate, and timbre.
- Multi-reward training: multiple reward signals optimize quality and emotional consistency.
- Open-source license: released under Apache-2.0 for community reuse and extension.
Use Cases
- Voice assistants and dialogue systems: provide more natural and emotionally expressive responses.
- Audiobooks and content dubbing: automatically adapt narration style to content emotion.
- Rapid prototyping for new languages/styles: quick zero-shot experiments on novel styles or languages.
- Creative tools: give creators fine-grained control over speech style synthesis.
Technical Features
- Model architecture combines extensible TTS backbones with emotion-conditioning modules.
- Training strategy employs multiple reward signals balancing perceptual quality, emotional alignment, and naturalness.
- Supports the PyTorch ecosystem for local or cloud fine-tuning and extension.
- The project is open-source on GitHub ; see the official site https://audio.z.ai for demos and documentation.