CN113850291A - Text processing and model training method, device, equipment and storage medium - Google Patents
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Abstract
The disclosure provides a text processing and model training method, a text processing and model training device, text processing and model training equipment and a storage medium, and relates to the technical field of computers, in particular to the artificial intelligence fields of speech synthesis, deep learning, natural language processing and the like. The text processing method comprises the following steps: detecting a role in the text; extracting gender-related texts of the roles from the texts, wherein the gender-related texts are texts containing gender information of the roles; processing the gender-related text to determine the gender of the character. The present disclosure may determine the gender of a character in text.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to the field of artificial intelligence, such as speech synthesis, deep learning, and natural language processing, and in particular, to a method, an apparatus, a device, and a storage medium for text processing and model training.
Background
The audio book is a derivative form of the traditional book, which is a book with a playing function and taking a magnetic material as a carrier, developed along with the development of the acousto-magnetic technology, and the most common audio book is an audio novel.
In the related art, the voiced novel pronounces the content of the dialog of all characters by using the same speaker.
Disclosure of Invention
The disclosure provides a text processing and model training method, a device, equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a text processing method including: detecting a role in the text; extracting gender-related texts corresponding to the roles from the texts, wherein the gender-related texts are texts containing gender information of the roles; processing the gender-related text to determine the gender of the character.
According to another aspect of the present disclosure, there is provided a training method of a gender prediction model for determining a gender of a character of a text, the method including: obtaining training samples, the training samples comprising: training gender-related texts of roles in texts and label information of the gender-related texts, wherein the label information is used for identifying the gender corresponding to the gender-related texts; and training a gender prediction model by adopting the training samples.
According to another aspect of the present disclosure, there is provided a text processing apparatus including: the detection module is used for detecting roles in the text; the extraction module is used for extracting a gender-related text of the role from the text, wherein the gender-related text is a text containing gender information of the role; and the determining module is used for processing the text related to the gender so as to determine the gender of the role.
According to another aspect of the present disclosure, there is provided a training apparatus of an age prediction model for determining a gender of a character of a text, the apparatus including: an obtaining module, configured to obtain a training sample, where the training sample includes: training gender-related texts of roles in texts and label information of the gender-related texts, wherein the label information is used for identifying the gender corresponding to the gender-related texts; and the training module is used for training an age prediction model by adopting the training samples.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above aspects.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to any one of the above aspects.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of the above aspects.
According to the technical scheme of the disclosure, the gender of the character in the text can be determined.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;
FIG. 4 is a schematic diagram according to a fourth embodiment of the present disclosure;
FIG. 5 is a schematic diagram according to a fifth embodiment of the present disclosure;
FIG. 6 is a schematic diagram according to a sixth embodiment of the present disclosure;
FIG. 7 is a schematic diagram according to a seventh embodiment of the present disclosure;
FIG. 8 is a schematic diagram according to a seventh embodiment of the present disclosure;
fig. 9 is a schematic diagram of an electronic device for implementing any one of the text processing method or the training method of the gender prediction model according to the embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the related art, the voiced novel pronounces the content of the dialog of all characters by using the same speaker. However, different roles adopt the speaker of the proper age to pronounce the content, so that the playing effect of audio reading can be improved, and the user experience is improved.
In order to improve the playing effect of the audio book, the present disclosure provides the following embodiments.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure, which provides a text processing method, including:
101. a character in the text is detected.
102. And extracting gender-related texts of the roles from the texts, wherein the gender-related texts are texts containing gender information of the roles.
103. Processing the gender-related text to determine the gender of the character.
The text refers to the text of the audio book, and the text refers to the text of the novel by taking the audio novel as an example. In this embodiment, the style, the field, the style, the form, the length, and the like of the novel text are not limited. It is understood that the text of the audio book is not limited to the audio novel, but may be audio news, audio drama, audio learning resource, and the like.
The role refers to a speaker in the text, taking a novel text as an example, for example, a says: "weather is good today", A is the name of a person, and A is the role.
The execution main body of this embodiment may be a text processing apparatus, the apparatus may be located in an electronic device, the electronic device may be a cloud device, a server device, a client device, and the like, and a specific form of the apparatus is not limited, and may be hardware, software, or a combination of hardware and software. For software forms, web applications (web APPs), mobile applications (APPs, such as mobile phones hundreds), system applications (OS APPs, such as duerOS), and the like may be included. For the client device, which may also be referred to as a terminal device, the client device may include a mobile device (e.g., a mobile phone, a tablet computer), a wearable device (e.g., a smart watch, a smart bracelet), a smart home device (e.g., a smart television, a smart speaker), and the like.
As shown in fig. 2, a character prediction model may be used to perform prediction processing on a text to detect a character in the text.
The input of the role detection model is text, and the output is role words, such as name of a role. For example, A, B, etc. in the text can be detected by using the character detection model, and A and B respectively represent the names of people.
The role detection model may be a deep neural network model, and may be obtained by training using various related technologies, which are not described in detail herein.
After the role in the text is detected, as shown in fig. 2, the role related text may be obtained in a manner of keyword search, and then the gender related text corresponding to the role is obtained in the role related text in a manner of keyword search.
For example, if a detected character is a, the text content including a may be used as the character-related text of a, for example, one text is "a comes from the head, and the other text is" a combs a tall horse tail,. -, "a plays a basketball with a buddy, and he.
After the role-related text is obtained, the gender-related text can be obtained from the role-related text based on preset gender-related keywords (or referred to as word forest data).
Further, the gender-related text may include gender word text and reference word text, the gender word text refers to text containing gender words, the reference word text refers to text containing reference words, the gender words may include explicit gender words, such as male and female, and may also include implicit gender words, such as the "horse tail with high height combing" mentioned above. The term "meaning" includes: "he" or "her".
In some embodiments, the gender-related text comprises: gender word text and reference word text, wherein the processing the gender-related text to determine the gender of the character comprises: predicting the gender word text by adopting a gender model to determine a first gender; adopting a reference model to perform prediction processing on the pronoun text so as to determine a second gender; if the first gender is the same as the second gender, determining the gender of the character to be the same gender.
As illustrated in fig. 2, gender word text may be predicted using a gender model to determine a first gender and gender word text may be predicted using a reference model to determine a second gender. Then, the comparison module may be used to determine whether the first gender and the second gender are the same, and if the first gender and the second gender are the same, for example, if both the first gender and the second gender are female, the gender of the character is determined to be female. On the contrary, if the first gender is different from the second gender, for example, one is male and the other is female, the gender of the character can be manually labeled by sending the gender information to the manual processing module.
By comparing the gender prediction results output by the gender model and the reference model, when the gender prediction results output by the gender model and the reference model are the same, the same gender prediction result is taken as the gender of the character, so that the accuracy of the gender can be improved.
Further, the determining the first gender based on the gender information corresponding to the gender word text includes: summarizing gender scores corresponding to the plurality of gender word texts to obtain a total score of the same gender corresponding to the same gender; and taking the gender with the highest total score as the first gender.
After the gender word text is obtained, the gender word text can be processed by adopting a gender model to determine gender scores corresponding to different genders.
The input of the gender model is gender word text, and the output is gender information corresponding to the gender word text.
The gender information may be a probability value corresponding to each gender, that is, a probability value corresponding to a male and a probability value corresponding to a female.
The probability value may be used as a gender score, or the probability value may be converted into a gender score, for example, if the probability value is 10%, the score may be 10.
The summary may be an addition or other operation.
As shown in fig. 3, for example, gender information is used as gender scores and is summarized as an addition, a gender model may be used to process each gender word text to obtain a gender score corresponding to each gender word text, and then the gender scores corresponding to each gender word text are added to obtain a total score corresponding to the gender, and then the gender with the highest total score may be used as the first gender.
Generally, for a character, such as character a, the character has multiple gender word texts, such as "a with a high horse tail. Corresponding to a plurality of gender word texts, each gender word text can be processed by adopting a gender model to obtain the score of each gender word text on each gender, namely the score of each gender word text corresponding to a male and the score of each gender word text corresponding to a female are obtained.
After the score of each gender word text on each gender is obtained, the scores corresponding to a plurality of gender word texts of the same gender can be added corresponding to each gender to determine the total score of the gender.
For example, the sex word text corresponding to the role A is N, and S is usedi,1Indicates that the ith text corresponds to the sex score, S, of the malei,2Shows that the ith text corresponds to the sex of the womanAnd (3) respectively scoring, wherein the total score of the role A corresponding to the male is as follows:
the total score for women was:
then, the gender with the highest total score may be used as the first gender corresponding to the character a, for example, after calculation, the total score of the character a corresponding to the female is greater than the total score of the character a corresponding to the male, and the first gender corresponding to the character a is female.
By adopting the gender model, the gender information corresponding to the gender word text can be accurately obtained.
Further, by summarizing gender scores corresponding to a plurality of gender word texts of the same gender to obtain a total score corresponding to the gender, and taking the gender with the highest total score as the first gender, the accuracy of determining the first gender can be improved.
In the above description of the gender model, the reference model may also adopt a similar method to summarize the prediction results corresponding to a plurality of reference word texts, so as to determine the second gender.
In some embodiments, the phrase text is a plurality of pieces, the gender information corresponding to the phrase text includes different phrase scores, and the determining the second gender based on the gender information corresponding to the phrase text includes: corresponding to the same pronouns, summarizing the corresponding reference scores of the multiple pronouns texts to obtain the total score of the same pronouns; and taking the gender corresponding to the pronoun with the highest total score as the second gender.
After the pronoun text is obtained, the pronoun text can be processed by adopting a reference model so as to determine the corresponding reference scores of different pronouns.
The input of the reference model is the reference word text, and the output is the reference information corresponding to the reference word text.
The reference information may be probability values of respective referents, and the referents refer to the referents for distinguishing genders, including "he" and "her", so that the probability value corresponding to "he" and the probability value corresponding to "her" can be obtained by the reference model.
The probability value may be used as a reference score, or the probability value may be converted into a reference score, for example, if the probability value is 10%, the score may be 10 points.
As shown in fig. 4, taking the referential information as the referential score and the summary as the addition example, the referential model may be adopted to process each of the pronouncing texts respectively to obtain the referential score corresponding to each of the pronouncing texts, and then the referential scores corresponding to the same pronouncing are added to obtain the total score of the corresponding pronouncing, and then the gender corresponding to the pronouncing with the highest total score may be taken as the second gender.
Generally, for a character, such as character a, the corresponding word text is multiple, such as "he is playing basketball.", "he is going to work.", and. Corresponding to a plurality of pieces of reference word texts, each piece of reference word text can be processed by adopting a reference model to obtain the score of each piece of pronoun text on each reference word, namely, the score of each piece of reference word text corresponding to 'he' and the score of each piece of reference word text corresponding to's'.
After the scores of each pronoun text on each pronoun are obtained, the scores corresponding to a plurality of pronoun texts of the same pronoun can be added corresponding to each pronoun to determine the total score of the pronoun.
For example, the word text corresponding to the role B is M, and S is usedi,1Denoting that the ith text corresponds to a reference score, Si,2Indicating that the ith text corresponds to a reference score of "s", the total score for role B corresponding to "he" is:
the total score for "she" is:
thereafter, the total score may be scoredFor example, after calculation, the total score of the character B corresponding to "his" is greater than the total score of the character B corresponding to "her", and since the character B corresponds to a male, the second sex of the character B is a male.
By adopting the reference model, the gender information corresponding to the text of the reference word can be accurately obtained.
Furthermore, the accuracy of determining the second gender can be improved by summarizing the reference scores corresponding to a plurality of reference word texts of the same reference word to obtain the total score of the corresponding reference word, and taking the gender corresponding to the reference word with the highest total score as the second gender.
In some embodiments, the gender model comprises: the method comprises an input layer, a hidden layer, an attention layer and a classification layer, wherein the gender model is adopted to carry out prediction processing on the gender word text so as to obtain gender information corresponding to the gender word text, and the method comprises the following steps: converting the gender word text into an input vector by adopting the input layer; converting the input vector into a hidden layer vector by adopting the hidden layer; adopting the attention layer to convert the hidden layer vector into a coding vector, wherein parameters of the attention layer comprise attention weight, and the attention weight corresponding to the role appearance position is larger than the attention weight corresponding to the role appearance position; and classifying the coding vectors by adopting the classification layer to obtain gender information corresponding to the gender word text.
The hidden layer may use a pre-training language model, such as a Bidirectional Transformer Encoder (BERT) model.
Through continuous processing of each layer of the gender model, gender information corresponding to the gender word text can be obtained.
The attention layer is processed by the attention weight to the hidden layer vector, and the attention weight corresponding to the character appearance position is larger than the attention weight corresponding to the non-character appearance position, so that the attention layer can pay more attention to the appearance position of the character, and the accuracy of determining the gender information is improved. The attention weight may be determined during a training phase, and the determination process may be referred to in the related description of the training process.
By adopting the attention layer, the gender model can focus more on the position where the character appears, so that the accuracy of the gender information can be improved.
For the reference model, the above-mentioned structure may also be adopted, similar to the process of the gender model on the gender word text, the designated model is adopted to process the reference word text, and the details are not described herein.
Further, as shown in fig. 5, for the gender model, the classification layer includes a text classification layer and a name classification layer, and classifying the coding vector by using the classification layer to obtain gender information corresponding to the gender word text includes: classifying the coding vectors by adopting the text classification layer to obtain a first classification result; classifying the coding vector by adopting the name classification layer to obtain a second classification result; and fusing the first classification result and the second classification result to obtain gender information corresponding to the gender word text.
Since both the sex word text and the specified word text belong to the character-related text, and the character-related text is generally a text containing the name of the character, the name of the character is generally contained in the sex word text. When classifying the gender word text, not only text-level classification but also name-level classification can be performed, for example, "just right" in a text, generally speaking, the probability that the character name "just right" is a male is higher, that is, the probability that the male corresponding to the text is determined by the name classification layer is higher, but the probability that the female corresponding to the text is determined by the text classification layer is higher because the text classification layer is obtained based on context training.
When the first classification result and the second classification result are merged, a weighted addition method may be used. For example, if the first classification result includes the first male score S11 and the first female score S12, and the second classification result includes the second male score S21 and the second female score S22, the first male score and the second male score may be added in a weighted manner to obtain a male score output by the final gender model, that is, the male score is k 1S 11+ k 2S 21; and weighting and adding the first female score and the second female score to obtain a female score output by the final gender model, namely the female score is k 1S 12+ k 2S 22. The k1 and k2 are weighted values, and may be empirical values set according to actual requirements.
The classification layer comprises a text classification layer and a name classification layer, so that the text classification result and the name classification result can be fused, and the accuracy of the gender information is further improved.
In some embodiments, the method may further comprise: acquiring voice corresponding to the gender of the role; and performing voice playing on the dialogue content of the role by adopting the voice.
For example, voices of different speakers, such as a male voice and a female voice, may be recorded in advance corresponding to the same conversation content, and then, if the character is determined to be a male, the male voice is obtained in the voice library, and the conversation content is played using the male voice.
Alternatively, a speech synthesis technique may be employed to perform speech synthesis processing based on gender and conversation content to obtain speech of the corresponding gender and play it.
The voice playing of the conversation content is carried out by adopting the voice of the character corresponding to the gender, and the voice of the proper gender can be adopted for playing, so that the playing effect is improved.
In the embodiment of the disclosure, by determining the gender of the role in the text, the voice corresponding to the gender can be adopted based on the gender, so that the playing effect of the audio book can be improved, and the user experience can be improved.
The above description relates to a gender prediction model, which can be obtained by pre-training, and the gender prediction model can be obtained by training in the following manner.
Fig. 6 is a schematic diagram of a sixth embodiment according to the present disclosure, which provides a method for training a gender prediction model, the method including:
601. obtaining training samples, the training samples comprising: the method comprises the steps of training gender-related texts of roles in the texts and label information of the gender-related texts, wherein the label information is used for identifying the gender corresponding to the gender-related texts.
602. And training a gender prediction model by adopting the training samples.
The gender prediction model may be used in the text processing process described above, i.e., the gender prediction model is used to determine the gender of a character in text.
The gender prediction model may include: the system comprises a gender model and a reference model, wherein the corresponding gender-related texts are a gender word text and a reference word text respectively.
The training samples of the gender model may include gender word texts and corresponding label information thereof, that is, a group of training samples may be expressed as < gender word texts, label information >, and the gender model may be obtained by training a large number of training samples.
The training samples of the reference model may include a designated word text and corresponding label information thereof, that is, a set of training samples may be expressed as < the reference word text, the label information >, and the reference model may be obtained by training a large number of training samples.
Taking a novel text as an example, a large amount of novel texts can be collected, a role prediction model is adopted for the novel texts, roles in the novel texts are detected, and gender word texts and reference word texts corresponding to the roles are obtained based on keyword retrieval.
After obtaining the gender word text and the reference word text, the corresponding label information may be obtained in a manual labeling manner, for example, if a corresponding gender word text "a sticks a high ponytail", and if the label information is 1 and 0 respectively represents a female and a male, the label information corresponding to the gender word text may be labeled as 1.
For the reference word text, if "he" is included in the reference word text, the corresponding tag information may be labeled as 0, i.e., the corresponding gender is male.
The gender model and the reference model may both be deep neural network models.
In some embodiments, the gender-related text comprises: gender word text, the gender prediction model comprising: a gender model, the gender model comprising: the training sample further comprises an input layer, a hidden layer, an attention layer and a classification layer: the training of the gender prediction model by using the training samples according to the attention degree identification corresponding to the role comprises the following steps: converting the age-related text into an input vector using the input layer; converting the input vector into a hidden layer vector by adopting the hidden layer; determining attention weight of the attention layer based on the hidden layer vector and the attention degree identification, and converting the hidden layer vector into a coding vector by adopting the attention layer with the attention weight; classifying the coding vectors by adopting the classification layer to determine gender prediction information corresponding to the gender-related text; and constructing a loss function based on the gender prediction information and the label information, and training the gender model based on the loss function.
Further, the classifying layer includes a text classifying layer and a name classifying layer, and the classifying layer is adopted to classify the encoding vector to determine gender prediction information corresponding to the gender-related text, including: classifying the coding vectors by adopting the text classification layer to obtain a first classification result; classifying the coding vector by adopting the name classification layer to obtain a second classification result; and fusing the first classification result and the second classification result to obtain gender prediction information corresponding to the gender word text.
For example, during training, a gender word text is "a sticks a high horse tail, B is dad of a", if the current role is role a, the corresponding role a may set an attention degree flag different from other words, for example, the attention degree flag corresponding to role a may be set to 10, the attention degree flags of other words, such as "junior middle school", "B", etc., may be set to 8, and the attention weight of the attention layer may be controlled through different attention degree flags, so that the attention layer focuses more on the current role, for example, the attention weight corresponding to a word with a higher attention degree flag is also higher, according to the above example, the attention weight corresponding to "a" is higher than the attention weight corresponding to other words, so that the attention layer focuses more on role "a".
The results on the gender model can be seen in fig. 5. The prediction stage corresponding to fig. 5 is different from the prediction stage, in the training stage, a loss function needs to be constructed, the form of the loss function can be set as needed, based on the loss function, the model parameters can be adjusted until the loss function converges, or a preset number of iterations is reached, and the model parameters when the end condition is reached are used as a final model.
By adopting the attention layer, the gender model can focus more on the position where the character appears, so that the accuracy of the gender information can be improved.
Furthermore, the classification layer comprises a text classification layer and a name classification layer, so that the fusion of a text classification result and a name classification result can be realized, and the accuracy of the gender information is further improved.
In the embodiment of the disclosure, the gender-related text is obtained based on the training text, the label information is obtained, the gender prediction model can be trained based on the gender-related text and the label information, the gender of the character in the text can be predicted by adopting the gender prediction model, and the speaker corresponding to the gender is used for speaking the conversation content of the character, so that the voice playing effect can be improved, and the user experience can be improved.
Fig. 7 is a schematic diagram according to a seventh embodiment of the present disclosure, which provides a text processing apparatus. As shown in fig. 7, the apparatus 700 includes: a detection module 701, an extraction module 702 and a determination module 703.
The detection module 701 is used for detecting roles in the text; the extracting module 702 is configured to extract a gender-related text of the role from the text, where the gender-related text is a text containing gender information of the role; the determining module 703 is configured to process the gender-related text to determine the gender of the character.
In some embodiments, the gender-related text comprises: gender word text and reference word text, the determining module 703 includes: a first prediction unit, a second prediction unit, and a determination unit.
The first prediction unit is used for performing prediction processing on the gender word text by adopting a gender model so as to obtain gender information corresponding to the gender word text, and determining a first gender based on the gender information corresponding to the gender word text; the second prediction unit is used for performing prediction processing on the reference word text by adopting a reference model to obtain gender information corresponding to the reference word text and determining a second gender based on the gender information corresponding to the reference word text; a determining unit, configured to determine that the gender of the character is the same gender if the first gender and the second gender are the same.
In some embodiments, the gender word text is a plurality of gender words, the gender information corresponding to the gender word text includes gender scores corresponding to different genders, and the first prediction unit is specifically configured to: summarizing gender scores corresponding to the plurality of gender word texts to obtain a total score of the same gender corresponding to the same gender; and taking the gender with the highest total score as the first gender.
In some embodiments, the word-indicating text includes a plurality of pieces of word-indicating text, the gender information corresponding to the word-indicating text includes word-indicating scores corresponding to different words, and the second prediction unit is specifically configured to: corresponding to the same pronouns, summarizing the corresponding reference scores of the multiple pronouns texts to obtain the total score of the same pronouns; and taking the gender corresponding to the pronoun with the highest total score as the second gender.
In some embodiments, the gender model comprises: the first prediction unit is specifically configured to: converting the gender word text into an input vector by adopting the input layer; converting the input vector into a hidden layer vector by adopting the hidden layer; adopting the attention layer to convert the hidden layer vector into a coding vector, wherein parameters of the attention layer comprise attention weight, and the attention weight corresponding to the role appearance position is larger than the attention weight corresponding to the role appearance position; and classifying the coding vectors by adopting the classification layer to obtain gender information corresponding to the gender word text.
In some embodiments, the classification layer includes a text classification layer and a name classification layer, and the first prediction unit is further specifically configured to: classifying the coding vectors by adopting the text classification layer to obtain a first classification result; classifying the coding vector by adopting the name classification layer to obtain a second classification result; and fusing the first classification result and the second classification result to obtain gender information corresponding to the gender word text.
In some embodiments, the apparatus 700 further comprises: the acquisition module is used for acquiring the voice corresponding to the gender of the role; and the playing module is used for playing the voice of the role conversation content by adopting the voice.
In the embodiment of the disclosure, by determining the gender of the role in the text, the voice corresponding to the gender can be adopted based on the gender, so that the playing effect of the audio book can be improved, and the user experience can be improved.
Fig. 8 is a schematic diagram illustrating an eighth embodiment of the present disclosure, which provides a training apparatus for a behavior prediction model. The gender prediction model is used to determine the gender of a character of text, the apparatus 800 comprising: an acquisition module 801 and a training module 802.
The obtaining module 801 is configured to obtain a training sample, where the training sample includes: training gender-related texts of roles in texts and label information of the gender-related texts, wherein the label information is used for identifying the gender corresponding to the gender-related texts; the training module 802 is configured to train an age prediction model using the training samples.
In some embodiments, the gender-related text comprises: gender word text, the gender prediction model comprising: a gender model, the gender model comprising: the training sample further comprises an input layer, a hidden layer, an attention layer and a classification layer: the attention degree identifier corresponding to the role, the training module is specifically configured to: converting the age-related text into an input vector using the input layer; converting the input vector into a hidden layer vector by adopting the hidden layer; determining attention weight of the attention layer based on the hidden layer vector and the attention degree identification, and converting the hidden layer vector into a coding vector by adopting the attention layer with the attention weight; classifying the coding vectors by adopting the classification layer to determine gender prediction information corresponding to the gender-related text; and constructing a loss function based on the gender prediction information and the label information, and training the gender model based on the loss function.
In some embodiments, the classification layer includes a text classification layer and a name classification layer, and the training module is further specifically configured to: classifying the coding vectors by adopting the text classification layer to obtain a first classification result; classifying the coding vector by adopting the name classification layer to obtain a second classification result; and fusing the first classification result and the second classification result to obtain gender prediction information corresponding to the gender word text.
In the embodiment of the disclosure, the gender-related text is obtained based on the training text, the label information is obtained, the gender prediction model can be trained based on the gender-related text and the label information, the gender of the character in the text can be predicted by adopting the gender prediction model, and the speaker corresponding to the gender is used for speaking the conversation content of the character, so that the voice playing effect can be improved, and the user experience can be improved.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
It is to be understood that in the disclosed embodiments, the same or similar elements in different embodiments may be referenced.
It is to be understood that "first", "second", and the like in the embodiments of the present disclosure are used for distinction only, and do not indicate the degree of importance, the order of timing, and the like.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 9 illustrates a schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the electronic apparatus 900 includes a computing unit 901, which can execute various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)902 or a computer program loaded from a storage unit 909 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
A number of components in the electronic device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, optical disk, or the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 901 performs the respective methods and processes described above, such as a text processing method or a training method of a gender prediction model. For example, in some embodiments, the text processing method or the training method of the gender prediction model may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the text processing method or the training method of the gender prediction model described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured by any other suitable means (e.g. by means of firmware) to perform a text processing method or a training method of a gender prediction model.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (23)
1. A text processing method, comprising:
detecting a role in the text;
extracting gender-related texts of the roles from the texts, wherein the gender-related texts are texts containing gender information of the roles;
processing the gender-related text to determine the gender of the character.
2. The method of claim 1, wherein the gender-related text comprises: gender word text and reference word text, wherein the processing the gender-related text to determine the gender of the character comprises:
predicting the gender word text by adopting a gender model to obtain gender information corresponding to the gender word text, and determining a first gender based on the gender information corresponding to the gender word text;
adopting a reference model to perform prediction processing on the reference word text to obtain gender information corresponding to the reference word text, and determining a second gender based on the gender information corresponding to the reference word text;
if the first gender is the same as the second gender, determining the gender of the character to be the same gender.
3. The method of claim 2, wherein the gender word text is a plurality of gender words, the gender information corresponding to the gender word text comprises gender scores of different genders, and the determining a first gender based on the gender information corresponding to the gender word text comprises:
summarizing gender scores corresponding to the plurality of gender word texts to obtain a total score of the same gender corresponding to the same gender;
and taking the gender with the highest total score as the first gender.
4. The method of claim 2, wherein the pronoun text is a plurality of pieces, the gender information corresponding to the pronoun text comprises the reference scores of different pronouns, and the determining the second gender based on the gender information corresponding to the pronoun text comprises:
corresponding to the same pronouns, summarizing the corresponding reference scores of the multiple pronouns texts to obtain the total score of the same pronouns;
and taking the gender corresponding to the pronoun with the highest total score as the second gender.
5. The method of claim 2, wherein the gender model comprises: the method comprises an input layer, a hidden layer, an attention layer and a classification layer, wherein the gender model is adopted to carry out prediction processing on the gender word text so as to obtain gender information corresponding to the gender word text, and the method comprises the following steps:
converting the gender word text into an input vector by adopting the input layer;
converting the input vector into a hidden layer vector by adopting the hidden layer;
adopting the attention layer to convert the hidden layer vector into a coding vector, wherein parameters of the attention layer comprise attention weight, and the attention weight corresponding to the role appearance position is larger than the attention weight corresponding to the role appearance position;
and classifying the coding vectors by adopting the classification layer to obtain gender information corresponding to the gender word text.
6. The method of claim 5, wherein the classification layer comprises a text classification layer and a name classification layer, and classifying the encoding vector by using the classification layer to obtain gender information corresponding to the gender word text comprises:
classifying the coding vectors by adopting the text classification layer to obtain a first classification result;
classifying the coding vector by adopting the name classification layer to obtain a second classification result;
and fusing the first classification result and the second classification result to obtain gender information corresponding to the gender word text.
7. The method of any of claims 1-6, further comprising:
acquiring voice corresponding to the gender of the role;
and performing voice playing on the dialogue content of the role by adopting the voice.
8. A method of training a gender prediction model for determining the gender of a character of text, the method comprising:
obtaining training samples, the training samples comprising: training gender-related texts of roles in texts and label information of the gender-related texts, wherein the label information is used for identifying the gender corresponding to the gender-related texts;
and training a gender prediction model by adopting the training samples.
9. The method of claim 8, wherein the gender-related text comprises: gender word text, the gender prediction model comprising: a gender model, the gender model comprising: the training sample further comprises an input layer, a hidden layer, an attention layer and a classification layer: the training of the gender prediction model by using the training samples according to the attention degree identification corresponding to the role comprises the following steps:
converting the age-related text into an input vector using the input layer;
converting the input vector into a hidden layer vector by adopting the hidden layer;
determining attention weight of the attention layer based on the hidden layer vector and the attention degree identification, and converting the hidden layer vector into a coding vector by adopting the attention layer with the attention weight;
classifying the coding vectors by adopting the classification layer to determine gender prediction information corresponding to the gender-related text;
and constructing a loss function based on the gender prediction information and the label information, and training the gender model based on the loss function.
10. The method of claim 9, wherein the classification layer comprises a text classification layer and a name classification layer, and the classifying the coding vectors using the classification layer to determine gender prediction information corresponding to the gender-related text comprises:
classifying the coding vectors by adopting the text classification layer to obtain a first classification result;
classifying the coding vector by adopting the name classification layer to obtain a second classification result;
and fusing the first classification result and the second classification result to obtain gender prediction information corresponding to the gender word text.
11. A text processing apparatus comprising:
the detection module is used for detecting roles in the text;
the extraction module is used for extracting a gender-related text of the role from the text, wherein the gender-related text is a text containing gender information of the role;
and the determining module is used for processing the text related to the gender so as to determine the gender of the role.
12. The apparatus of claim 11, wherein the gender-related text comprises: gender word text and reference word text, the determining module comprising:
the first prediction unit is used for performing prediction processing on the gender word text by adopting a gender model so as to obtain gender information corresponding to the gender word text, and determining a first gender based on the gender information corresponding to the gender word text;
the second prediction unit is used for performing prediction processing on the reference word text by adopting a reference model to obtain gender information corresponding to the reference word text and determining a second gender based on the gender information corresponding to the reference word text;
a determining unit, configured to determine that the gender of the character is the same gender if the first gender and the second gender are the same.
13. The apparatus according to claim 12, wherein the gender word text is a plurality of pieces, the gender information corresponding to the gender word text includes gender scores of different genders, and the first prediction unit is specifically configured to:
summarizing gender scores corresponding to the plurality of gender word texts to obtain a total score of the same gender corresponding to the same gender;
and taking the gender with the highest total score as the first gender.
14. The apparatus according to claim 12, wherein the pronoun text is a plurality of pieces, the gender information corresponding to the pronoun text includes reference scores corresponding to different pronouns, and the second prediction unit is specifically configured to:
corresponding to the same pronouns, summarizing the corresponding reference scores of the multiple pronouns texts to obtain the total score of the same pronouns;
and taking the gender corresponding to the pronoun with the highest total score as the second gender.
15. The apparatus of claim 12, wherein the gender model comprises: the first prediction unit is specifically configured to:
converting the gender word text into an input vector by adopting the input layer;
converting the input vector into a hidden layer vector by adopting the hidden layer;
adopting the attention layer to convert the hidden layer vector into a coding vector, wherein parameters of the attention layer comprise attention weight, and the attention weight corresponding to the role appearance position is larger than the attention weight corresponding to the role appearance position;
and classifying the coding vectors by adopting the classification layer to obtain gender information corresponding to the gender word text.
16. The apparatus of claim 15, wherein the classification layers comprise a text classification layer and a name classification layer, the first prediction unit further to:
classifying the coding vectors by adopting the text classification layer to obtain a first classification result;
classifying the coding vector by adopting the name classification layer to obtain a second classification result;
and fusing the first classification result and the second classification result to obtain gender information corresponding to the gender word text.
17. The apparatus of any of claims 11-16, further comprising:
the acquisition module is used for acquiring the voice corresponding to the gender of the role;
and the playing module is used for playing the voice of the role conversation content by adopting the voice.
18. A device for training a gender prediction model for determining the gender of a character of text, the device comprising:
an obtaining module, configured to obtain a training sample, where the training sample includes: training gender-related texts of roles in texts and label information of the gender-related texts, wherein the label information is used for identifying the gender corresponding to the gender-related texts;
and the training module is used for training an age prediction model by adopting the training samples.
19. The apparatus of claim 18, wherein the gender-related text comprises: gender word text, the gender prediction model comprising: a gender model, the gender model comprising: the training sample further comprises an input layer, a hidden layer, an attention layer and a classification layer: the attention degree identifier corresponding to the role, the training module is specifically configured to:
converting the age-related text into an input vector using the input layer;
converting the input vector into a hidden layer vector by adopting the hidden layer;
determining attention weight of the attention layer based on the hidden layer vector and the attention degree identification, and converting the hidden layer vector into a coding vector by adopting the attention layer with the attention weight;
classifying the coding vectors by adopting the classification layer to determine gender prediction information corresponding to the gender-related text;
and constructing a loss function based on the gender prediction information and the label information, and training the gender model based on the loss function.
20. The apparatus of claim 19, wherein the classification layer comprises a text classification layer and a name classification layer, the training module further specific to:
classifying the coding vectors by adopting the text classification layer to obtain a first classification result;
classifying the coding vector by adopting the name classification layer to obtain a second classification result;
and fusing the first classification result and the second classification result to obtain gender prediction information corresponding to the gender word text.
21. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.
23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-10.
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