626 results about "Transformation of text" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a voice generation method which comprises the following steps: constructing a multi-language voice synthesis model, obtaining plain text data and paired voice text data, and constructing an expansion vocabulary; updating a language perception embedding layer and model parameters, and converting an input text into a mark sequence; and the encoder extracts context semantic features, extracts pronunciation rule features, and the decoder fuses the features to generate an acoustic feature sequence, and converts the acoustic feature sequence into target voice data. According to the invention, the multi-language speech synthesis model is combined with the language perception embedding layer, so that the speech generation capability of a low-resource language is improved; the text conversion accuracy is improved by expanding the vocabulary, the target language learning ability is enhanced by unsupervised training, the low data environment adaptability is optimized by supervised training, and the speech naturalness and fluency are improved by feature fusion.

The invention provides a method and a device for converting a natural language statement into an SQL (Structured Query Language) and a storage medium, relates to the technical field of data processing, and is used for improving the accuracy of converting the natural language statement into the SQL statement. The method comprises the steps that semantic analysis is conducted on a data query text, a target entity and at least one query field are obtained through extraction, and the target entity indicates a query intention corresponding to the data query text; determining a comprehensive similarity between each query field and each first candidate node according to a semantic similarity and a structural similarity between each query field in the at least one query field and each first candidate node in a pre-stored knowledge graph; determining a target node corresponding to each query field according to the comprehensive similarity between each query field and each first candidate node; according to the information, the data query text can be converted into the structured query statement.

The invention relates to a speech synthesis method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring a source language dubbing audio corresponding to a source language subtitle text of a source video and a target language subtitle text translated by the source language subtitle text; extracting audio emotion features from the source language dubbing audio by using an emotion extractor, wherein the audio emotion features represent emotions expressed by the source language dubbing audio; converting the target language subtitle text into a phoneme sequence, and encoding the phoneme sequence by using a text encoder to obtain text encoding features; fusing the audio emotion features with the text coding features to obtain emotion text features; and generating a target language audio based on the emotion text features by using a decoder, the target language audio being used as a dubbing audio of the source video in the target language. Therefore, the high-quality target language audio with the source dubbing audio emotion can be automatically and efficiently generated, the cost is low, and the efficiency is high.

The invention discloses a visual language alignment-based visual narrative generation method and device, electronic equipment and a storage medium, and belongs to the technical field of image understanding. The method comprises the steps of obtaining a to-be-processed image sequence and an instruction text; inputting the to-be-processed image sequence and the instruction text into a multi-modal large model to obtain a visual narrative text output by the multi-modal large model; the multi-modal large model comprises a visual encoder, a multi-modal mapper and a large language model which are connected in sequence, and further comprises a text encoder, and the output end of the text encoder is connected with the input end of the large language model; the step of inputting the to-be-processed image sequence and the instruction text into a multi-modal large model to obtain a visual narrative text output by the multi-modal large model comprises the following steps: sequentially encoding images in the to-be-processed image sequence through the visual encoder to generate visual features; sequentially projecting the visual features to a word embedding space of the large language model for semantic alignment through the multi-modal mapper to obtain a language embedding vector; through the text encoder, converting the instruction text into an instruction embedding vector; and through the large language model, performing logical reasoning according to the language embedded vector and the instruction embedded vector, and generating the visual narrative text. According to the method, visual information and language output can be effectively aligned, and the visual correlation of multi-graph narration is improved.

The invention relates to the technical field of artificial intelligence, and discloses an interaction method and device based on voice recognition, equipment and a storage medium, and the method comprises the steps: obtaining collected user voice; a recognition model is adopted to convert the user voice into voice characters, and the identity of a speaker and the emotion of the user are obtained through recognition; obtaining the above-mentioned reply text, converting the voice text, the speaker identity and the user emotion into a prompt word text by adopting a language model, and generating a target reply text according to the above-mentioned reply text and the prompt word text; and converting the target dialogue text into target dialogue voice by adopting a text-to-voice model, and controlling a loudspeaker to play the target dialogue voice. The method can be applied to business management program systems of financial science and technology, medical treatment and the like, the problem of double limitation of intelligence and emotional interaction faced by existing human-computer interaction is solved, and the industry service quality and the user stickiness are improved.

As cybersecurity alerts are detected and logged as formatted descriptors across an organization, a text converter converts the formatted descriptors into natural language descriptors by extracting and inserting metadata fields into natural language templates corresponding to user personas for the organization. In response to an alert-based query from a user, a persona classifier predicts a persona of the user from historical chat logs and retrieves natural language descriptors for alerts related to the user and predicted persona. A prompt generator receives the retrieved natural language descriptors and generates a prompt that instructs a large language model (LLM) to respond to the user with data from the natural language descriptors. Once prompted, the LLM establishes a conversation with the user via an interface for alert resolution.

The invention discloses a Chinese named entity recognition system, method and equipment based on multi-scale features and a medium, and the method comprises the steps: decomposing an original text into a character sequence through a preprocessing module, and preprocessing characters, including removing punctuation marks and uniformly converting the characters into lower letters; a RoBERTa-WWM sub-module of a feature extraction module is responsible for converting an input text into a high-dimensional feature vector, a CNN sub-module effectively extracts local features through a sliding window mechanism, and a BiLSTM sub-module finally utilizes the advantage of bidirectional processing to obtain complete context information; decoding the hidden state representation by using a dynamic conditional random field (CRF) through a sequence tagging module to determine an optimal tag sequence; according to the method, pre-training, multi-scale feature fusion and dynamic decoding technologies are integrated, and efficient and accurate Chinese entity recognition is realized; through a unique preprocessing rule, a modular architecture design and optimized hyper-parameter configuration, the performance and robustness of the system in a complex scene are ensured.

The invention discloses an intelligent document analysis method of a knowledge base management system, and relates to the field of artificial intelligence large model multi-modal analysis. Comprising the following steps: step 1, preliminarily identifying a document format according to a document extension name, and performing corresponding analysis according to the type of the document format: performing text analysis for pdf, docx, doc and txt document formats, and extracting texts and images in the pdf document format for the pdf document format; aiming at the docx document format, analyzing the compressed package in the docx document format according to the natural sequence of the document, and extracting a text and a picture; aiming at the doc document format, identifying and extracting all text information in the doc document format; for a txt document format, character codes are recognized and processed, and various languages and symbols are extracted; the method comprises the following steps of 1, analyzing a text, 2, processing the analyzed text by utilizing a large model, namely segmenting the text, performing vectorization processing, converting the text into a numerical form which can be understood by the large model, and storing the converted text in a knowledge base for information retrieval, data analysis and knowledge mining, and 3, performing visual output on the text at the same time.

The invention discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing text-to-speech modeling. In some implementations, a computer device receives input text in a first language to convert to a desired speech in a second language. The computer device receives one or more criteria for modifying the desired speech and converts the input text to a desired text in the second language. The computer device generates audio representations of the desired text in the second language and predicts, for each of the audio representations of the desired text and using the one or more received criteria, a pitch value and a duration value. The computer device generates the desired output speech using the predicted pitch value and the predicted duration value for each of the audio representations and provides the desired speech for output.

The invention provides a multi-mode interaction mobile terminal intelligent office system and method, and the method comprises the steps: collecting and converting a voice signal through a voice input module, so as to obtain standardized audio data; the speech recognition and transcription module converts and preliminarily processes the standardized audio data to obtain text data; the intention recognition module analyzes a user intention and a key entity in the text data based on a natural language processing technology to obtain an intention recognition result; the cue word template module matches the intention recognition result to a cue word template to generate a structured instruction; the dialogue module sends the structured instruction to the large language model and analyzes structured response data returned by the large language model; the system docking and function execution module calls a target office system interface according to the structured response data so as to execute mail sending, schedule management, document processing or leave application operation; and the output and function implementation module converts the text of the operation result into a voice signal or directly outputs the voice signal, so that the problem of low office efficiency of the mobile terminal is solved.

The invention provides a video dubbing method and device, electronic equipment and a storage medium, and belongs to the technical field of video processing, and the method comprises the steps: separating a first audio in a to-be-dubbed video, and carrying out the sentence-by-sentence text conversion, and obtaining a first subtitle text with a time code; translating the first subtitle text into a second subtitle text with the same time code; normalizing the second subtitle text to obtain a third subtitle text according to the difference between the dubbing duration of the second subtitle text and the subtitle display duration; generating a third audio corresponding to the third subtitle text; and finally, synthesizing all the third audios, the background audio of the video to be dubbed and the silent video to obtain a final target video. According to the invention, under the condition that the audio duration and the subtitle display duration are different in the video dubbing process, the subtitle text is normalized, so that the damage to the original video file is avoided, and the high quality of video dubbing is ensured.

The invention discloses a voice question answering method and system based on multi-mode voice recognition and semantic understanding. The method comprises the following steps: acquiring an on-site visual image and an audio signal acquired by an integrated camera and a multi-array microphone to obtain multi-modal data; performing audio separation and conversion of a target speaker on the multi-modal data to obtain text content; performing semantic understanding and question effectiveness evaluation on the text content to obtain an evaluation result; when the evaluation result is that the text content belongs to the effective questions, generating a corresponding answer text according to the text content; converting the answer text into voice to obtain answer audio; and outputting the answer audio. By implementing the method provided by the invention, accurate voice interaction in a complex environment can be realized, the recognition accuracy, the semantic understanding ability, the user experience and the scene adaptability are remarkably improved, and the error recognition rate is reduced.

A system can receive user input data that comprises an identification of a computer infrastructure component of a group of computer infrastructure components that are associated with a user account. The system can identify information about the group of computer infrastructure components based on the identification of the computer infrastructure component. The system can convert text of the user input data into a first numerical vector. The system can create a context of the user input data based on identifying a match between the first numerical vector and a second numerical vector of a group of numerical vectors, wherein the context corresponds to a natural-language version of the second numerical vector. The system can combine the context and the information about the group of computer infrastructure components into a combined context, and input the combined context and the user input data to a large language model to produce a result.

The invention discloses a material synthesis data extraction method and system based on a knowledge enhancement large model, and relates to the related field of artificial intelligence, and the method comprises the steps: retrieving literature data, and constructing a material synthesis knowledge text by executing data denoising and OCR text conversion; performing LoRA fine tuning on the basic large model, introducing a field instruction data set to perform fine tuning learning, and determining an extraction large model; performing semantic partitioning and vectorization on the material synthesis knowledge text, constructing a multi-level retrieval framework, performing retrieval enhancement in combination with a material science knowledge base, and determining an enhanced knowledge text; and constructing a data extraction prompt, combining the enhanced knowledge text with a material to synthesize a knowledge text, inputting the knowledge text into an extraction large model, and executing knowledge extraction processing. The problem that the accuracy of data extraction is insufficient in the prior art is solved, and the effect of improving the accuracy of data extraction is achieved. Meanwhile, manual work can be replaced to complete literature analysis extraction and domain knowledge association, and support is provided for material synthesis process recommendation.

The invention discloses a phishing mail identification method and system based on text analysis and external feature detection. The method comprises the steps of obtaining a to-be-detected mail; preprocessing the first mail text to obtain a second mail text; performing text conversion on the second mail text to obtain a word vector list, and performing text feature analysis on the word vector list through a CNN-LSTM dual-channel model to obtain text features; detecting the mail link and the mail attachment through a plurality of external detection tools to obtain external verification features; dynamically fusing the text features and the external verification features through a gating weighted fusion mechanism to obtain fused features; and performing classification judgment on the fusion features to obtain a phishing mail recognition result corresponding to the to-be-detected mail. According to the method, the accuracy of phishing mail identification can be improved, phishing mail detection is effectively realized, a relatively good filtering effect is achieved, and the method can be widely applied to the technical field of network security.

The invention discloses a data security classification and grading method and system based on a large language model, and relates to the technical field of language analysis, and the method comprises the steps: converting bank internal business data of each layer into text data through text conversion, unifying the data form of multi-layer or multi-modal data into text data, and storing the text data in a database; and a reliable basis is provided for subsequent language semantic recognition. According to the method, the text data is partitioned and sorted, subsequent context language understanding is facilitated, the context window length under each service scene is set, the context semantic understanding requirement of each service scene is met, and the reliability and accuracy of semantic association are improved. The internal business data of the bank is classified and graded based on the risk value of the business scene and the sensitive value of the composite field, the accuracy and adaptability of data security classification and grading are improved, security protection strategies of different degrees are carried out for different levels of different types of data, and the requirements of bank data storage and prevention are met.

A method includes receiving acoustic signals from a user. The acoustic signals are transformed into data. The data are transmitted to a voice-to-text conversion and artificial intelligence system over a network. Information is received from the voice-to-text conversion and artificial intelligence system. The information was obtained from the data and the information is used to create a request to a hotel. A computer readable medium contains executable computer program instructions, which when executed by a data processing system, cause the data processing system to perform a process that includes; receiving acoustic signals from a user; transforming the acoustic signals into data; transmitting the data to a voice-to-text conversion and artificial intelligence system over a network; receiving information from the voice-to-text conversion and artificial intelligence system, wherein the information was obtained from the data; and using the information to create a request to a hotel through a hotel information system.

The invention relates to the technical field of large models, and particularly provides a customer service digital human interaction method, system and device and a storage medium, and the method comprises the steps: obtaining a voice signal; determining a user age stage corresponding to the voice signal according to the voice signal by using a classification model; converting the voice signal into a text sequence, and identifying the intention of the text sequence by using a large model; obtaining an answer text from a pre-constructed knowledge graph according to the intention; and converting the answer text into answer voice of a customer service digital person, and setting the attribute of the answer voice based on the age stage of the user. The voice output by the customer service digital person considers the hearing function of the user, and the interaction effect and the user experience of the customer service digital person are improved.

The invention discloses a data query method and device based on text conversion, a medium and equipment, relates to the technical field of big data and financial science and technology, and mainly aims at solving the problem that existing SQL statement query is poor in effectiveness. Comprising the steps of obtaining to-be-queried text content; the text content is labeled on the basis of a labeling model after model training is completed, text segmented words with labels are obtained, the labeling model is obtained by training a large language model on the basis of labeling samples, and the labeling samples comprise sample data with intention labels, condition labels, table and / or field labels and function labels; determining query logic matched with the tag according to a preset query statement conversion relationship, and determining a query object matched with the text segmented word based on a preset query statement word bank; and combining the query logic with the query object to obtain a query statement, and querying a query result matched with the text content based on the query statement.

Automatic annotations and technical specification generation for robotic process automation (RPA) workflows using artificial intelligence (AI) is disclosed. AI / ML models may enable smart searching of workflows and automatically generate documentation for the workflows, including descriptions of each activity, input / output parameters, and overall process explanations. Annotations and documentation may be provided for an entire complex business automation that is the sum of multiple workflows and applications. A Process Definition Document (PDD) for the business process may be generated from the RPA workflow code itself when it does not exist. Other documents, such as audit documents, compliance documents required by laws or regulations, etc. may be produced. The process may be iterative, where a generative AI model automatically converts text to RPA workflow code, a runtime automation is produced from this RPA workflow, and the other documentation is generated as well.

The invention discloses an LLMs pre-training dataset optimization method, which comprises the following steps of: selecting internal data of a dataset, and performing hidden Markov model confusion degree calculation on a text segment by segment by adopting a sliding window; and based on a hidden Markov model confusion degree calculation result, obtaining a comprehensive confusion degree score of the whole text through confusion degree weighted average calculation, and using the comprehensive confusion degree score to screen sentences with semantic chaos in the data set. According to the method, a sliding window technology and a weighted average strategy are introduced on the basis of traditional puzzle calculation, so that a comprehensive quality evaluation index can be provided for the whole text, the language model adaptation degree of the text can be evaluated more accurately, and compared with single puzzle calculation, the quality condition of the text can be reflected more comprehensively; and meanwhile, sentences with disordered semantics in the data set can be effectively screened out, a network low-quality corpus text obtained for free is converted into a high-quality and valuable corpus, the LLMs pre-training cost is effectively saved, and the model capability is improved.

The invention provides an image generation model training method and device, electronic equipment, a storage medium and a program product. The method comprises the steps of calling an image generation model to generate an image corresponding to a text sample based on the text sample; text conversion is carried out on the image tag of the text sample to obtain a first text corresponding to the image tag, and the first text is used for describing the content in the image tag; obtaining a content difference between the first text and the text sample, and generating an image mask of the image tag based on the content difference; fusing the image mask to the image tag to obtain a fused image tag; and training the image generation model based on the image and the fused image tag. According to the invention, the image generation quality of the image generation model can be effectively improved.

The invention relates to the technical field of text questions and answers, in particular to a question and answer large language model optimization method and device, equipment and a storage medium. Comprising the steps of obtaining a preset knowledge graph; the preset knowledge graph comprises a plurality of entity triples; determining a text embedding vector corresponding to each entity triple according to the two node entities and the relational data in the same entity triple; determining a problem vector, and screening all the text embedding vectors to obtain a similar feature vector corresponding to the problem vector; performing text conversion on the similar feature vector to obtain a knowledge text group, and inputting the knowledge text group and a preset question text into a preset language model to obtain a reply text of the question text; and performing matching verification on the reply text based on a preset knowledge graph, and optimizing a preset language model according to an obtained verification result. According to the method, the field adaptability of the general model to the specific field can be optimized, so that the general model is more accurate and reliable when processing the problem of the specific field.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an audio signal from input text. In one aspect, a method includes receiving a request to convert an input text to an audio signal, where the input text includes a plurality of lexical meta text inputs; generating, using a first generative neural network, a semantic representation of the lexical text input, the semantic representation comprising semantic terms representing semantic content of the lexical text input, each semantic term selected from a word list of semantic terms; generating an acoustic representation of the semantic representation using a second generative neural network and on at least the semantic representation, the acoustic representation comprising one or more respective acoustic lems representing acoustic properties of the audio signal; and processing the acoustic representation using a decoder neural network to generate an audio signal.

The invention discloses an unstructured text entity recognition and relation extraction method under small sample learning, which relates to the technical field of natural language processing, and comprises the following steps of: 1, preprocessing an unstructured text, including word segmentation, part-of-speech tagging and syntactic analysis, and converting the text into structured word vector representation; a pre-training language model is adopted to encode the text, and context semantic features of the text are obtained; 2, constructing a meta-learning model by utilizing a meta-learning algorithm based on context semantic features of the text, training through a small number of labeled samples and a large number of unlabeled samples, and learning common knowledge among different tasks; 3, based on the meta-learning model, word vectors in the text are processed, and entities including boundaries and types are recognized; and according to the identified entities, analyzing a semantic relationship between the entities, extracting a relationship type between the entities, and optimizing a dependency relationship between the entities and the relationship by adopting a dynamic planning method.

The invention relates to the technical field of artificial intelligence, and particularly provides a text similarity recognition method, system and device and a storage medium, and the method comprises the steps: carrying out word segmentation processing on a text, and converting a text unit after word segmentation into an integer index; converting each integer index into a corresponding embedding vector through an embedding matrix of a pre-training semantic recognition model, and storing the embedding vectors of the same text into the same vector set; euclidean distances of vectors of different vector sets are calculated, and weighted similarity is calculated based on the Euclidean distances by using an attention matrix. According to the method, the text is converted into the high-dimensional vector, the context semantic information and the deep meaning of the text are fully understood, and then the similarity between the vectors is calculated through the attention weight, so that the accuracy of text similarity recognition is improved.

The invention discloses a cross-reset-domain script generation method for verifying a system-on-chip, and relates to the technical field of chips, and the method comprises the steps: obtaining an original text, the original text comprises a first code, a second code and a third code, the first code represents a data signal, a clock signal and a reset signal of the system-on-chip, and the second code represents a reset signal of the system-on-chip; the second code represents an output code obtained by modeling and simulation of the system-level chip, and the third code represents a user-defined cross-reset domain verification condition; the original text is analyzed, a structured text is obtained, and the structured text comprises a clock signal and a reset signal of the system-on-chip and structured information of a port and a module; according to the method, the structured text is converted into the cross-reset-domain script for verifying the system-on-chip, so that the problem of low cross-reset-domain verification efficiency of the system-on-chip in related technologies is solved, and the cross-reset-domain verification efficiency of the system-on-chip is improved.

The invention relates to a speech recognition model training method, a speech recognition method, a speech recognition device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining a mixed text sample, and obtaining a voice sample and a tagged text sample based on the mixed text sample; performing feature extraction and coding on the voice sample to obtain a coding matrix; respectively inputting the coding matrix into a text recognition decoder and a conversion recognition decoder in a to-be-trained speech recognition model to obtain a text recognition result and a text conversion recognition result, and calculating a first model loss based on the text recognition result and the mixed text sample; calculating a second model loss based on the text conversion recognition result and the tagged text sample; and training a to-be-trained speech recognition model based on the first model loss and the second model loss to obtain a speech recognition model. The speech recognition model trained by the method can improve the recognition accuracy of the Chinese pinyin mixed speech.

The invention belongs to the technical field of artificial intelligence, and particularly discloses a retrieval enhancement generation method and system based on port equipment maintenance, and the method comprises the steps: obtaining equipment data according to a knowledge carrier of port equipment, carrying out the data preprocessing and data indexing of the equipment data, and storing the processed data in a vector database or an index; converting a fault query text input by a user into a high-dimensional real value vector by utilizing a pre-trained text embedding model; searching a fault knowledge fragment most matched with the high-dimensional real value vector in a vector database, and optimizing the fault knowledge fragment to obtain an optimal knowledge fragment set; and combining the optimal knowledge fragment set with the fault query text to generate an enhanced prompt, inputting the enhanced prompt into a large language model for reasoning analysis, and generating a fault positioning result and a processing suggestion. According to the invention, the accurate positioning of the fault mode can be realized, and the fault processing efficiency is improved.