Abstract: An audio recognition method, a method of training an audio recognition model, and an electronic device are provided, which relate to fields of artificial intelligence, speech recognition, deep learning and natural language processing technologies. The audio recognition method includes: truncating an audio feature of target audio data to obtain at least one first audio sequence feature corresponding to a predetermined duration; obtaining, according to a peak information of the audio feature, a peak sub-information corresponding to the first audio sequence feature; performing at least one decoding operation on the first audio sequence feature to obtain a recognition result for the first audio sequence feature, a number of times the decoding operation is performed being identical to a number of peaks corresponding to the first audio sequence feature; obtaining target text data for the target audio data according to the recognition result for the at least one first audio sequence feature.

Abstract: A method for training a data processing model includes: acquiring sample data; acquiring an initial data processing model, the initial data processing model including a plurality of forward nodes for outputting a plurality of intermediate results corresponding to the sample data; determining a plurality of time-dependent features corresponding to the plurality of forward nodes; acquiring a data processing model to be trained by processing the initial data processing model based on the plurality of time-dependent features; and training the data processing model to be trained using the sample data and the plurality of intermediate results.

Abstract: The present disclosure provides a network operator processing method, apparatus, electronic device and storage medium and relates to the field of artificial intelligence such as deep learning and knowledge graph. The method may include: regarding any operator in the network, performing condition analysis on the operator respectively; regarding the operator as an operator supporting spatial reuse and found from lookup if it is determined according to an analysis result that the operator satisfies a spatial reuse condition. The solution of the present disclosure may be applied to save manpower and time costs, and improve the accuracy of the lookup result.

Abstract: The present disclosure provides a method and apparatus of fusing operators, an electronic device and a storage medium, which relates to fields of deep learning, artificial intelligence and knowledge graph. The method includes: determining operator groups to be fused, according to an operator graph to be processed, wherein each operator group of the operator groups includes at least two operators in the operator graph respectively; obtaining a fused operator corresponding to the each operator group respectively; and for the fused operator, replacing corresponding operators in the operator graph with the fused operator respectively, and coupling dependence edges of the corresponding operators to the any fused operator, wherein the corresponding operators include operators in the operator group corresponding to the fused operator.

Abstract: The present disclosure provides a speech playing method, an intelligent device and a computer readable storage medium. The method includes obtaining an object to be played; recognizing a target object type of the object to be played; obtaining a playing label set matching with the object to be played based on the target object type; in which, the playing label set is configured to represent playing rules of the object to be played; and playing the object to be played based on the playing rules represented by the playing label set.