Update README.md

README.md

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----
-license: mit
----
+---
+language:
+- zh
+- en
+tags:
+- llm
+- tts
+- zero-shot
+- voice-cloning
+- reinforcement-learning
+- flow-matching
+license: mit
+pipeline_tag: text-to-speech
+---
+
+# GLM-TTS: Controllable & Emotion-Expressive Zero-shot TTS
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/assets/images/logo.svg  width="50%"/>
+</div>
+
+<p align="center">
+    <a href="https://github.com/zai-org/GLM-TTS" target="_blank">💻 GitHub Repository</a>
+    &nbsp;&nbsp;|&nbsp;&nbsp;
+    <a href="https://huggingface.co/spaces/zai-org/GLM-TTS" target="_blank">🤗 Online Demo</a>
+    &nbsp;&nbsp;|&nbsp;&nbsp;
+    <a href="https://audio.z.ai/" target="_blank">🛠️ Audio.Z.AI</a>
+</p>
+
+## 📖 Model Introduction
+
+GLM-TTS is a high-quality text-to-speech (TTS) synthesis system based on large language models, supporting zero-shot voice cloning and streaming inference. The system adopts a two-stage architecture combining an LLM for speech token generation and a Flow Matching model for waveform synthesis.
+
+By introducing a **Multi-Reward Reinforcement Learning** framework, GLM-TTS significantly improves the expressiveness of generated speech, achieving more natural emotional control compared to traditional TTS systems.
+
+### Key Features
+
+* **Zero-shot Voice Cloning:** Clone any speaker's voice with just 3-10 seconds of prompt audio.
+* **RL-enhanced Emotion Control:** Utilizes a multi-reward reinforcement learning framework (GRPO) to optimize prosody and emotion.
+* **High-quality Synthesis:** Generates speech comparable to commercial systems with reduced Character Error Rate (CER).
+* **Phoneme-level Control:** Supports "Hybrid Phoneme + Text" input for precise pronunciation control (e.g., polyphones).
+* **Streaming Inference:** Supports real-time audio generation suitable for interactive applications.
+* **Bilingual Support:** Optimized for Chinese and English mixed text.
+
+## System Architecture
+
+GLM-TTS follows a two-stage design:
+
+1.  **Stage 1 (LLM):** A Llama-based model converts input text into speech token sequences.
+2.  **Stage 2 (Flow Matching):** A Flow model converts token sequences into high-quality mel-spectrograms, which are then turned into waveforms by a vocoder.
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/assets/images/architecture.png" width="60%" alt="GLM-TTS Architecture">
+</div>
+
+### Reinforcement Learning Alignment
+To tackle flat emotional expression, GLM-TTS uses a **Group Relative Policy Optimization (GRPO)** algorithm with multiple reward functions (Similarity, CER, Emotion, Laughter) to align the LLM's generation strategy.
+
+## Evaluation Results
+
+Evaluated on `seed-tts-eval`. **GLM-TTS_RL** achieves the lowest Character Error Rate (CER) while maintaining high speaker similarity.
+
+| Model | CER ↓ | SIM ↑ | Open-source |
+| :--- | :---: | :---: | :---: |
+| Seed-TTS | 1.12 | **79.6** | 🔒 No |
+| CosyVoice2 | 1.38 | 75.7 | 👐 Yes |
+| F5-TTS | 1.53 | 76.0 | 👐 Yes |
+| **GLM-TTS (Base)** | 1.03 | 76.1 | 👐 Yes |
+| **GLM-TTS_RL (Ours)** | **0.89** | 76.4 | 👐 Yes |
+
+## Quick Start
+
+### Installation
+
+```bash
+git clone [https://github.com/zai-org/GLM-TTS.git](https://github.com/zai-org/GLM-TTS.git)
+cd GLM-TTS
+pip install -r requirements.txt
+```
+
+#### Command Line Inference
+
+```bash
+python glmtts_inference.py \
+    --data=example_zh \
+    --exp_name=_test \
+    --use_cache \
+    # --phoneme # Add this flag to enable phoneme capabilities.
+```
+
+#### Shell Script Inference
+
+```bash
+bash glmtts_inference.sh
+```
+
+## Acknowledgments & Citation
+
+We thank the following open-source projects for their support:
+
+- [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) - Providing frontend processing framework and high-quality vocoder
+- [Llama](https://github.com/meta-llama/llama) - Providing basic language model architecture
+- [Vocos](https://github.com/charactr-platform/vocos) - Providing high-quality vocoder
+- [GRPO-Zero](https://github.com/policy-gradient/GRPO-Zero) - Reinforcement learning algorithm implementation inspiration
+
+If you use GLM-TTS in your research, please cite:
+
+```bibtex
+@misc{glmtts2025,
+  title={GLM-TTS: Controllable & Emotion-Expressive Zero-shot TTS with Multi-Reward Reinforcement Learning},
+  author={CogAudio Group Members},
+  year={2025},
+  publisher={Zhipu AI Inc}
+}