Although the sequence-to-sequence network with attention mechanism and neural vocoder has made great progress in the quality of speech synthesis, there are still some problems to be solved in large-scale real-time applications. For example, to avoid long sentence alignment failure while maintaining rich prosody, and to reduce the computational overhead while ensuring perceptual quality. In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet. The former uses alignment results of different attention mechanisms to guide the learning of the basic attention mechanism, and only retains the basic attention mechanism during inference. This approach can improve the performance of the text-to-feature module by absorbing the advantages of all guidance attention methods without modifying the basic inference architecture. The latter reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies. The multi-band strategy enables the LPCNet to generate sub-band signals in each inference. By predicting the sub-band signals of adjacent time in one forward operation, the multi-time strategy further decreases the number of inferences required. Due to the multi-band and multi-time strategy, the vocoder speed is increased by 2.75x on a single CPU and the MOS (mean opinion score) degradation is slight.