GLM-TTS 技术报告
GLM-TTS Technical Report
AI总结：
GLM-TTS通过优化的语音分词器和多奖励强化学习框架，实现高效可控的生产级语音生成系统，同时支持参数高效定制和混合输入方案。
摘要：
本文提出了GLM-TTS，一种面向效率、可控性和高保真语音生成的生产级TTS系统。GLM-TTS采用两阶段架构，包括文本到令牌的自回归模型和令牌到波形的扩散模型。仅使用10万小时的训练数据，GLM-TTS在多个开源基准上实现了最先进的性能。为满足生产需求，GLM-TTS通过优化的语音分词器和基本频率约束，以及基于GRPO的多奖励强化学习框架，共同优化发音、说话人相似性和表达性语调。同时，系统通过参数高效LoRA基于语音定制和混合音素-文本输入方案，实现了高效可控的部署。我们的代码可在https://github.com/zai-org/GLM-TTS获取。实时语音合成演示通过Z.ai（audio.z.ai）和智谱清言应用/网页（chatglm.cn）提供。
英文摘要：
This work proposes GLM-TTS, a production-level TTS system designed for efficiency, controllability, and high-fidelity speech generation. GLM-TTS follows a two-stage architecture, consisting of a text-to-token autoregressive model and a token-to-waveform diffusion model. With only 100k hours of training data, GLM-TTS achieves state-of-the-art performance on multiple open-source benchmarks. To meet production requirements, GLM-TTS improves speech quality through an optimized speech tokenizer with fundamental frequency constraints and a GRPO-based multi-reward reinforcement learning framework that jointly optimizes pronunciation, speaker similarity, and expressive prosody. In parallel, the system enables efficient and controllable deployment via parameter-efficient LoRA-based voice customization and a hybrid phoneme-text input scheme that provides precise pronunciation control. Our code is available at https://github.com/zai-org/GLM-TTS. Real-time speech synthesis demos are provided via Z.ai (audio.z.ai), the Zhipu Qingyan app/web (chatglm.cn).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
