SlimSpeech: Lightweight and Efficient Text-to-Speech with Slim Rectified Flow

Recently, flow matching based speech synthesis has significantly enhanced the quality of synthesized speech while reducing the number of inference steps. In this paper, we introduce SlimSpeech, a lightweight and efficient speech synthesis system based on rectified flow. We have built upon the existing speech synthesis method utilizing the rectified flow model, modifying its structure to reduce parameters and serve as a teacher model. By refining the reflow operation, we directly derive a smaller model with a more straight sampling trajectory from the larger model, while utilizing distillation techniques to further enhance the model performance. Experimental results demonstrate that our proposed method, with significantly reduced model parameters, achieves comparable performance to larger models through one-step sampling.

Dynamic Language Group-Based MoE: Enhancing Efficiency and Flexibility for Code-Switching Speech Recognition

The Mixture of Experts (MoE) approach is ideally suited for tackling multilingual and code-switching (CS) challenges due to its multi-expert architecture. This work introduces the DLG-MoE, which is optimized for bilingual and CS scenarios. Our novel Dynamic Language Group-based MoE layer features a language router with shared weights for explicit language modeling, while independent unsupervised routers within the language group handle attributes beyond language. This structure not only enhances expert extension capabilities but also supports dynamic top-k training, allowing for flexible inference across various top-k values and improving overall performance. The model requires no pre-training and supports streaming recognition, achieving state-of-the-art (SOTA) results with unmatched flexibility compared to other methods. The Code will be released.