CFM是一种新技术，已被证明可以改进扩散模型，Meta的Voicebox模型将CFM引入语音合成领域
Matcha-TTS是第一个开源conditional normalising flows语音合成项目，提供基于 LJSpeech 和 VCTK 数据预训练模型以供测评
Matcha-TTS有两个主要的贡献和其他建议:
1. We propose an improved encoder-decoder TTS architecture that uses a combination of 1D CNNs and Transformers in the decoder. This reduces memory consumption and is fast to evaluate, improving synthesis speed.
相对于Grad-TTS的decoder，使用了1D CNNs替换2D CNNs、并加入Transformers块
2. We train these models using optimal-transport conditional flow matching (OT-CFM) , which is a new method to learn ODEs that sample from a data distribution. Compared to conventional CNFs and score-matching probability flow ODEs, OT-CFM defines simpler paths from source to target, enabling accurate synthesis in fewer steps than DPMs.
使用Flow Matching加速技术
3. 使用旋转位置编码(rotational position embeddings) RoPE，减少存储
4. 使用MAS对齐
5. 使用snake beta激活函数
开源地址：
https://github.com/shivammehta25/Matcha-TTS
工程展示：
https://shivammehta25.github.io/Matcha-TTS/
在线推理：
https://huggingface.co/spaces/shivammehta25/Matcha-TTS
中文实现：
https://github.com/PlayVoice/Grad-TTS-Chinese
（Grad-TTS-CFM，其他优化还未集成）
原文链接：https://blog.csdn.net/weixin_48827824/article/details/129791852