Abstract: Speech editing and zero-shot Text-to-Speech (TTS) share a similar generative foundation conditioned on speech prompts, yet speech editing demands far stricter local acoustic consistency with surrounding unedited content. While prior work has shown that Supervised Fine-Tuning (SFT) enables TTS models to acquire functional editing capability, this approach remains fundamentally bottlenecked by imperfect paired editing data and coarse-grained optimization signals. To address these limitations, we propose CosyEdit2, a speech editing model built on a two-stage post-training framework that progresses from supervised editing initialization to editing-oriented Group Relative Policy Optimization (GRPO) over target-speech-free data. Extensive experiments demonstrate that CosyEdit2 not only substantially advances speech editing performance, but also unlocks better zero-shot TTS capability, revealing a deeper mutual relationship between the two tasks.

Contents

• Speech Editing Demo
  • Insertion Task
  • Deletion Task
  • Substitution Task
• Zero-Shot TTS Demo
• Multilingual Zero-Shot TTS Demo

Acknowledgements

This work builds upon several excellent open-source projects:

• We borrowed a lot of code from CosyVoice.
• We borrowed a lot of code from BigVGAN.
• We borrowed a lot of code from verl.
• We borrowed a lot of code from WeNet.
• The template for this demo page is adapted from FunAudioLLM.

We are deeply grateful to the authors and contributors of these projects for their outstanding work and for making their code publicly available.

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