It is crucial for ensuring production safety, improving production efficiency and equipment reliability to accurately diagnose faults in rotating machinery. With the rapid development of deep learning, the massive excellent bearing fault diagnosis methods have emerged. However, most of these methods only focus on local or global features, and as the number of network layers increases, overfitting and a large number of model parameters arise. In response to these issues, this paper proposes a lightweight framework for end-to-end fault diagnosis. The framework uses Omni-Scale block with an efficient channel attention mechanism (ECA-OS-block) to capture features at different scales, and performs global adaptive weighting to focus on critical signals via a signal attention mechanism. Then combined with a Fully Convolutional Network as dual channel to effectively extract the details of fault signals, as well as reduce the problem of over fitting and under fitting when a single model processes multi-bearing fault data. Experimental results show that the proposed approach can achieve excellent results on multiple fault datasets, and the standard deviation of the results from repeated training is small. This indicates that the model has good generalization and stability. Even with a limited number of training samples, key features of the data can still be captured. Also, the anti-interference ability is stronger than some existing models in multi-bearing systems.