Ming Tao
Publications
SoulX-Singer: Towards High-Quality Zero-Shot Singing Voice Synthesis
While recent years have witnessed rapid progress in speech synthesis, open-source singing voice synthesis (SVS) systems still face significant barriers to industrial deployment, particularly in terms of robustness and zero-shot generalization. In this report, we introduce SoulX-Singer, a high-quality open-source SVS system designed with practical deployment considerations in mind. SoulX-Singer supports controllable singing generation conditioned on either symbolic musical scores (MIDI) or melodic representations, enabling flexible and expressive control in real-world production workflows. Trained on more than 42,000 hours of vocal data, the system supports Mandarin Chinese, English, and Cantonese and consistently achieves state-of-the-art synthesis quality across languages under diverse musical conditions. Furthermore, to enable reliable evaluation of zero-shot SVS performance in practical scenarios, we construct SoulX-Singer-Eval, a dedicated benchmark with strict training-test disentanglement, facilitating systematic assessment in zero-shot settings.
Safe-FedLLM: Delving into the Safety of Federated Large Language Models
Federated learning (FL) addresses data privacy and silo issues in large language models (LLMs). Most prior work focuses on improving the training efficiency of federated LLMs. However, security in open environments is overlooked, particularly defenses against malicious clients. To investigate the safety of LLMs during FL, we conduct preliminary experiments to analyze potential attack surfaces and defensible characteristics from the perspective of Low-Rank Adaptation (LoRA) weights. We find two key properties of FL: 1) LLMs are vulnerable to attacks from malicious clients in FL, and 2) LoRA weights exhibit distinct behavioral patterns that can be filtered through simple classifiers. Based on these properties, we propose Safe-FedLLM, a probe-based defense framework for federated LLMs, constructing defenses across three dimensions: Step-Level, Client-Level, and Shadow-Level. The core concept of Safe-FedLLM is to perform probe-based discrimination on the LoRA weights locally trained by each client during FL, treating them as high-dimensional behavioral features and using lightweight classification models to determine whether they possess malicious attributes. Extensive experiments demonstrate that Safe-FedLLM effectively enhances the defense capability of federated LLMs without compromising performance on benign data. Notably, our method effectively suppresses malicious data impact without significant impact on training speed, and remains effective even with many malicious clients. Our code is available at: https://github.com/dmqx/Safe-FedLLM.