Huawei Technologies Co., Limited

Liqun Deng

Tao Li

Jiansheng Wei

Junhua Zhu

Wenhua Sun

Guowei Huang

Yang Zhang

Yulong Zeng

Yasheng Wang

Yuezhi Hu

Zhanlei Yang

Nianzu Zheng

Disong Wang

Anqi Cui

The present disclosure relates to text data processing methods and apparatuses. One example method includes obtaining target text, where a phoneme of the target text includes a first phoneme and a second phoneme that are adjacent to each other. Feature extraction is performed on the first phoneme and the second phoneme to obtain a first audio feature of the first phoneme and a second audio feature of the second phoneme. By using a target recurrent neural network (RNN) and based on the first audio feature, first speech data corresponding to the first phoneme is obtained. By using the target RNN and based on the second audio feature, second speech data corresponding to the second phoneme is obtained. By using a vocoder and based on the first speech data and the second speech data, audio corresponding to the first phoneme and audio corresponding to the second phoneme are obtained.

Text data processing method and apparatus

A missing semantics complementing method in the field of natural language processing in the artificial intelligence field is provided. The method includes: obtaining a question statement and a historical dialog statement; resolving a to-be-resolved item in the question statement based on the historical dialog statement and location information of the to-be-resolved item, to obtain a resolved question statement; determining whether a component in the question statement is ellipted, and if a component in the question statement is ellipted, complementing the ellipted component based on the historical dialog statement, to obtain a question statement after ellipsis resolution; merging the resolved question statement and the question statement after ellipsis resolution, to obtain a merged question statement; and determining a target complemented question statement from the resolved question statement, the question statement after ellipsis resolution, and the merged question statement.

Missing semantics complementing method and apparatus

In a speech synthesis method, an emotion intensity feature vector is set for a target synthesis text, an acoustic feature vector corresponding to an emotion intensity is generated based on the emotion intensity feature vector by using an acoustic model, and a speech corresponding to the emotion intensity is synthesized based on the acoustic feature vector. The emotion intensity feature vector is continuously adjustable, and emotion speeches of different intensities can be generated based on values of different emotion intensity feature vectors, so that emotion types of a synthesized speech are more diversified. This application may be applied to a human-computer interaction process in the artificial intelligence (AI) field, to perform intelligent emotion speech synthesis.

Speech synthesis method and device

A speech synthesis method and a speech synthesis apparatus to synthesize speeches of different emotional intensities in the field of artificial intelligence, where the method includes obtaining a target emotional type and a target emotional intensity parameter that correspond to an input text, determining a corresponding target emotional acoustic model based on the target emotional type and the target emotional intensity parameter, inputting a text feature of the input text into the target emotional acoustic model to obtain an acoustic feature of the input text, and synthesizing a target emotional speech based on the acoustic feature of the input text.

Speech synthesis method and speech synthesis apparatus

The present disclosure relates to computing resource allocation methods, devices, and systems. One example system includes a management node and a target computing node. The management node is configured to obtain M computing tasks and establish a resource assessment model, and send one or more computing tasks of the M computing tasks and information about the resource assessment model to the target computing node. The target computing node is configured to receive the one or more computing tasks and the information about the resource assessment model, substitute input data of a particular computing stage of a target task into the resource assessment model to compute a resource size required for the particular computing stage, and compute the input data by using a computing resource that is of the resource size and that is in a preset resource pool.

Mapreduce job resource sizing using assessment models

The present disclosure relates to a resource allocation method for gene analysis. In one example method, a parameter value that is of the target chromosome region and that is used for resource allocation is obtained according to a sequenced read in a target chromosome region. A computing resource is allocated, according to the parameter value that is of the target chromosome region and that is used for resource allocation, to an operation in a cleansing and variant calling task that is in the gene analysis and that is performed on the sequenced read in the target chromosome region.

Resource allocation method and apparatus for gene analysis

A method for scheduling a data flow task and an apparatus. The method includes: preprocessing a data flow task to obtain at least one subtask; classifying the subtask into a central processing unit (CPU) task group, a graphics processing unit (GPU) task group, or a to-be-determined task group; allocating the subtask to a working node; when the subtask belongs to the CPU task group, determining that a CPU executes the subtask; when the subtask belongs to the GPU task group, determining that a GPU executes the subtask; or when the subtask belongs to the to-be-determined task group, determining, according to costs of executing the subtask by a CPU and a GPU, a running platform (e.g., the CPU or the GPU) executes the subtask, where the cost includes duration of executing the subtask.

Method for scheduling data flow task and apparatus

A metadata updating method based on columnar storage in a distributed file system includes acquiring to-be-updated metadata in a data table, splitting data records of the data table into multiple row groups on a row basis, converting the data table into global file metadata and multiple row group files, where the row group file includes an actual data block, a data index block, a local metadata block, a metadata index block, and a file footer, determining whether the to-be-updated metadata belongs to the global file metadata, updating local metadata when the to-be-updated metadata does not belong to the global file metadata, and adding an updated local metadata block, an updated metadata index block, and an updated file footer to the multiple row group files according to updated local metadata. Dynamic updating of metadata saves time of executing an updating operation of this type and needed computing resources.

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Shenzhen, China

Liqun Deng