System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a media file selected by a user; means for analyzing voice of the received media file and converting the voice into text data; means for translating the text data into a specified language; means for generating voice data with a specified voice quality based on the translated text data; and means for incorporating the generated voice data into the media file and streaming the media file in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's information society, language barriers are a major obstacle to obtaining information. In particular, information provided in Japanese is limited on the Internet, making it difficult for Japanese people to easily obtain information from overseas. There is an urgent need to eliminate this digital divide and enable users to enjoy content from various countries in their own native language. In addition, real-time speech translation requires advanced technology and enormous computing resources, and an effective method for achieving this is needed. [Means for solving the problem]

[0005] The present invention provides a system that translates a media file selected by a user in real time and plays it back in a specified voice quality. This system solves the above-mentioned problems by including: means for receiving a media file selected by a user; means for analyzing the audio of the received media file and converting it into text data; means for translating the text data into a specified translation language; means for generating audio data in a specified voice quality based on the translated text data; and means for incorporating the generated audio data into a media file and streaming it in real time. Furthermore, the system includes means for notifying the user of the upload status of the media file and means for buffering the generated audio data during playback, providing the user with a smooth, real-time translation experience.

[0006] "User" refers to an individual or organization that uses the system, and is a user who performs operations such as uploading media files and specifying languages ​​on the system.

[0007] "Media files" refer to digital files containing information content such as audio and video, and are files that are the subject of translation in this system.

[0008] "Means for receiving" refers to the function or process for incorporating user-selected media files into the system.

[0009] "Means for analyzing audio" refers to the functions and technologies for analyzing the audio information in a media file and converting it into character data (text data).

[0010] "Means for converting into text data" refers to the functions and technologies for formalizing the results of voice analysis as text information.

[0011] "Means of translation" refers to the functions and technologies for converting text data into a specified language.

[0012] "Means for generating voice data" refers to the functions and technologies for synthesizing voice files with a specified voice quality based on translated text information.

[0013] "Means for streaming in real time" refers to the functionality and technology for instantly transferring and playing back media files containing generated audio data to a user's device.

[0014] "Means for notifying users of upload status" refers to features and technologies that provide users with progress information about the upload process of a media file.

[0015] "Buffering means" refers to a function or technology that temporarily stores data and prevents interruptions in playback in order to smoothly play back generated audio data. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] overview

[0039] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0040] composition

[0041] The system is broadly composed of the following components:

[0042] 1. User's device: The device where the user selects media files and uploads them to the server.

[0043] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0044] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0045] Program processing overview

[0046] 1. Select and upload media files

[0047] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[0048] The terminal uploads the media file along with this information to the server.

[0049] 2. Receiving media files and analyzing audio

[0050] The server transfers the media files received from the terminal to the analysis module.

[0051] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[0052] 3. Language Translation

[0053] The server sends the analyzed text data to a translation engine and converts it into the specified target language.

[0054] The translation engine performs the translation work and generates translated text data.

[0055] 4. Speech generation and voice conversion

[0056] The server passes the translated text data to a generative AI model, which generates audio data with the specified voice quality.

[0057] The generative AI model creates the audio data and returns it to the server.

[0058] 5. Regenerate media and prepare for streaming

[0059] The server incorporates the generated audio data into the original media file and converts it into a streaming format in real time.

[0060] The server prepares to send streaming data to the terminal.

[0061] 6. User Media Viewing

[0062] The device receives streaming data from the server and buffers it for smooth playback.

[0063] The user watches and listens to the translated media content.

[0064] Specific examples

[0065] Example 1: Watching an English documentary in Japanese

[0066] 1. The user selects an English documentary video from their device and specifies the Japanese translation and desired voice quality (for example, a gentle female voice).

[0067] 2. The device uploads the video file and translation request parameters to the server.

[0068] 3. The server receives the video file, analyzes the audio using the analysis module, and converts the English audio into text data.

[0069] 4. The server translates the English text data into Japanese using a translation engine.

[0070] 5. The server uses the generated AI model to convert the Japanese text data into voice data with the specified voice quality.

[0071] 6. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0072] 7. The device receives the streaming data and the user watches the documentary video in Japanese.

[0073] Example 2: Listening to a French radio program in German

[0074] 1. The user selects an audio file of a French radio program and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[0075] 2. The terminal uploads the audio file and translation request parameters to the server.

[0076] 3. The server receives the audio file, analyzes the audio using the analysis module, and converts the French audio into text data.

[0077] 4. The server translates the French text data into German using a translation engine.

[0078] 5. The server uses the generative AI model to convert the German text data into audio data with the specified voice quality.

[0079] 6. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0080] 7. The device receives the streaming data and the user listens to the radio program in German.

[0081] Thus, the present invention is a system that provides a new viewing experience to a user by translating a media file selected by the user into another language in real time and playing it back in a specified voice quality.

[0082] The processing flow will be explained below.

[0083] Step 1:

[0084] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[0085] Step 2:

[0086] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[0087] Step 3:

[0088] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[0089] Step 4:

[0090] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[0091] Step 5:

[0092] The server passes the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[0093] Step 6:

[0094] The server passes the translated text data to the generative AI model, which generates voice data in the voice quality specified by the user. The generative AI model then creates synthetic voice data in the specified voice quality based on the translated text.

[0095] Step 7:

[0096] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[0097] Step 8:

[0098] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[0099] Step 9:

[0100] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[0101] Step 10:

[0102] Users can watch and listen to translated media content on their devices in their preferred language and voice quality, allowing them to enjoy foreign content in their native language, such as Japanese, in real time.

[0103] Through these steps, the system provides fast and accurate real-time translation and speech generation, bringing a new listening experience to users, while also providing a user-friendly interface and smooth operation.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] Conventional systems have the drawback of being complex and difficult to process in real time, as they require users to translate selected media files into other languages ​​and play them in a specified voice quality. Furthermore, the quality of the voice generation is inconsistent, resulting in an inconsistent user experience.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for using an artificial intelligence model to generate audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into the original media file and converting it into a streaming format in real time for transmission, and means for the terminal to receive the streaming data and buffer it for playback. This streamlines the process from media file translation to audio playback, allowing users to enjoy a consistent, high-quality audio experience.

[0109] A "user" is a person who selects a media file and specifies translation requests and audio playback.

[0110] A "terminal" is a device operated by a user that selects and uploads media files, and receives and buffers streaming data.

[0111] A "server" is a computing device that parses media files, translates them, generates audio, and transmits streaming data.

[0112] A "media file" is a file of digital content, including audio and video.

[0113] "Analysis" refers to the process of converting the audio data in a media file into text data.

[0114] "Text data" refers to character information converted from audio data.

[0115] "Translation" refers to the process of converting text data into a specified language.

[0116] The "translated language" is the language to be translated into, and is the output language desired by the user.

[0117] "Voice quality" refers to the characteristics of the voice when generating voice data, and is the voice characteristics (for example, gender, tone of voice, etc.) designated by the user.

[0118] "Artificial intelligence model" refers to a machine learning model used to generate audio data from text data.

[0119] "Generation" refers to the process of creating audio data using an artificial intelligence model.

[0120] "Audio data" refers to digital data of the generated audio.

[0121] "Embedding" refers to the process of integrating generated audio data into the original media file.

[0122] "Real-time" refers to providing immediate processing and results without delay.

[0123] "Streaming" refers to the continuous delivery and playback of media files over the Internet.

[0124] "Buffering" is the process of temporarily storing a portion of streaming data to ensure smooth playback.

[0125] MODE FOR CARRYING OUT THE INVENTION

[0126] Overall structure

[0127] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0128] Hardware and software used

[0129] The main hardware and software used to implement the system are as follows:

[0130] User devices: PCs, smartphones, tablets, etc.

[0131] Server: Cloud or on-premise server with high-performance computing resources

[0132] Speech analysis library: Google Speech-to-Text API, Microsoft Azure Speech-to-Text, etc.

[0133] Translation libraries: Google Translate API, DeepL, etc.

[0134] Speech generation libraries: such as OpenAI's speech generation API

[0135] Media editing libraries: FFmpeg, etc.

[0136] Process Overview

[0137] 1. Select and upload media files

[0138] The user selects a media file through the device's UI and specifies the source language, target language, and desired voice quality.

[0139] The terminal packages the selected media file and the translation request parameters and sends the package to the server in an HTTP request.

[0140] 2. Receiving media files and analyzing audio

[0141] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters.

[0142] An analysis module (for example, Google Speech-to-Text API) in the server converts the audio data of the media file into text data.

[0143] 3. Language Translation

[0144] The server sends the parsed text data to a translation library (e.g., Google Translate API or DeepL), which translates the text data into the specified target language.

[0145] 4. Speech generation and voice conversion

[0146] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API), which generates voice data with the specified voice quality.

[0147] 5. Regenerate media and prepare for streaming

[0148] The server embeds the generated audio data into the original media file, converts it into a streaming format (e.g., HLS or DASH) in real time, and transmits it.

[0149] 6. User Media Viewing

[0150] The terminal buffers the streaming data received from the server, allowing the user to watch the media file with smoothly translated audio.

[0151] Specific examples

[0152] Example 1: Watching an English documentary in Japanese

[0153] 1. The user selects an English documentary video on the device's UI and specifies the Japanese translation and desired voice quality (e.g., a soft female voice).

[0154] 2. The device combines the video file and translation request parameters into an HTTP request and uploads it to the server.

[0155] 3. The server receives the video file and converts the audio into text data using the Google Speech-to-Text API.

[0156] 4. The server uses the Google Translate API to translate the English text into Japanese.

[0157] 5. The server uses OpenAI's speech generation API to convert the Japanese text into audio data with the specified voice quality.

[0158] 6. The server uses FFmpeg to incorporate the new Japanese audio into the original video, convert it to HLS format, and generate a streaming URL for playback.

[0159] 7. The device receives the streaming URL and the user watches the documentary video with the new Japanese audio.

[0160] Example 2: Listening to a French radio program in German

[0161] 1. The user selects a French radio program on the device and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[0162] 2. The device bundles the audio file and translation request parameters into an HTTP request and uploads it to the server.

[0163] 3. The server receives the audio file and converts it into text using the Google Speech-to-Text API.

[0164] 4. The server uses DeepL to translate the French text into German.

[0165] 5. The server uses OpenAI's speech generation API to convert the German text into audio data with the specified voice quality.

[0166] 6. The server uses FFmpeg to embed the new German audio into the original audio file, convert it to HLS format, and generate a streaming URL.

[0167] 7. The device receives the streaming URL and the user listens to the radio program with the new German voice.

[0168] This allows users to experience real-time translation of selected media files and playback in the voice quality they specify.

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1: Select and upload media files

[0171] The user selects a media file through the device's UI. As input, the user specifies the media file (e.g., a video file), the source language, the target language, and the desired voice quality.

[0172] The device packages the selected media file and the translation request parameters and creates an HTTP request. The output is an HTTP request that is sent to the server.

[0173] The device executes this HTTP request and uploads the translation request along with the media file to the server.

[0174] Step 2: Receiving media files and analyzing audio

[0175] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters. The input is the HTTP request, and the output is the media file and translation request parameters.

[0176] The server uses an analysis module (e.g., Google Speech-to-Text API) to convert the audio data of the media file into text data. The input is the media file, and the output is text data.

[0177] The server temporarily stores the converted text data and prepares it for the next processing step.

[0178] Step 3: Language Translation

[0179] The server sends the parsed text data to a translation library (e.g., Google Translate API, DeepL). The input is the text data and translation request parameters, and the output is the translated text data.

[0180] The translation library converts the text data into the specified target language and generates translated text data.

[0181] The server receives the translated text data and temporarily stores it.

[0182] Step 4: Speech generation and voice conversion

[0183] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API). The input is the translated text data and voice quality information, and the output is the generated voice data.

[0184] The generative AI model generates voice data with the specified voice quality based on the text data and returns it to the server.

[0185] The server receives the generated audio data and stores it for further processing steps.

[0186] Step 5: Regenerate your media and prepare it for streaming

[0187] The server embeds the generated audio data into the original media file using a media editing library (e.g. FFmpeg). The input is the original media file and the generated audio data, and the output is a new media file.

[0188] The server converts the new media file, including the audio, into a streaming format (e.g., HLS, DASH) in real time. The input is the new media file, and the output is the streaming data.

[0189] The server generates a URL for the streaming data and prepares it for transmission to the terminal.

[0190] Step 6: User Views Media

[0191] The device obtains streaming data using the streaming URL received from the server. The input is the streaming URL, and the output is the streaming data.

[0192] The device receives the streaming data and buffers it for playback. The input is the streaming data and the output is the buffered data.

[0193] The user watches the media content with the translated voice through the terminal.

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] Conventional media translation systems have struggled to translate and generate speech in real time that matches the user's desired language and voice quality. They also lack features to improve the user experience, such as buffering streaming data and upload status notifications. The objective of this invention is to solve these problems and provide a system that supports multiple languages ​​and plays media in real time with a voice quality specified by the user.

[0197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0198] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, means for allowing a user to specify a desired language and voice quality when uploading an arbitrary media file, and means for buffering the received streaming data to achieve smooth playback, thereby enabling a user to watch or listen to media content in a desired language and voice quality in real time.

[0199] "Means for receiving user-selected media files" refers to the devices and software used to transmit user-specified multimedia files to the server and for the server to receive them.

[0200] "Means for analyzing the audio of received media files and converting it into text data" refers to devices or software that extract the audio data from media files and convert it into text data using voice recognition technology.

[0201] "Means for translating text data into a specified target language" refers to a translation engine or software for converting parsed character data into another specified language.

[0202] "Means for generating voice data with a specified voice quality based on translated text data" refers to a generative AI model or voice synthesis technology that generates a voice file with a voice quality specified by the user based on translated text data.

[0203] "Means for incorporating generated audio data into a media file and streaming it in real time" means equipment or software that integrates newly generated audio data with the original media file and transmits and plays it to a user in real time.

[0204] "A means by which a user can specify a desired language and voice quality when uploading any media file" means an interface or software configuration feature that allows a user to select or specify the language into which a media file is to be translated and the voice quality to be used when uploading a media file.

[0205] "Means for buffering received streaming data to ensure smooth playback" refers to technology or software that temporarily stores streaming data transmitted in real time for playback, enabling uninterrupted playback.

[0206] This invention provides a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0207] 1. System Configuration

[0208] User's device

[0209] The user's device is used to select media files and enter the necessary information. In this example, a smartphone (iOS or Android) is used. The smartphone app is built with React Native and provides an interface where users can upload media files and specify the desired translation language and voice quality.

[0210] server

[0211] The server is built on Node.js and Express and offers the following features:

[0212] Receiving media files

[0213] Speech analysis and text conversion (using FFmpeg)

[0214] Translation (using Google Translate API or AWS Translate)

[0215] Speech generation (built in Python and using TensorFlow to run generative AI models such as Tacotron2 and WaveGlow)

[0216] Sending Streaming Data

[0217] Generative AI Models

[0218] The generative AI model generates voice data based on the translated text data in a voice quality specified by the user, specifically using Tacotron2 and WaveGlow.

[0219] 2. System Operation

[0220] Selecting and uploading media files

[0221] Using a smartphone app, users can select any media file and specify the desired translation language and voice quality, and this information is then uploaded from the device to the server.

[0222] Receiving media files and analyzing audio

[0223] The server receives the uploaded media file and first begins audio analysis. For audio analysis, it uses FFmpeg to extract the audio data from the media file and converts it into text data using speech recognition technology.

[0224] Language Translation

[0225] The server sends the text data to Google Translate API or AWS Translate, which translates it into the specified language, generating translated text data.

[0226] Speech generation and voice conversion

[0227] The translated text data is sent to a generative AI model (Tacotron2, WaveGlow), which generates voice data with the specified voice quality. This voice data is then returned to the server.

[0228] Regenerate media and prepare it for streaming

[0229] The generated audio data is embedded into the original media file, and the regenerated media file is converted into a streaming format in real time. The structured streaming data is then sent to the user's smartphone app.

[0230] User media viewing

[0231] The smartphone app receives and buffers the streaming data, allowing users to watch and listen to the media file in real time, integrated with audio generated in the specified translation language and voice quality.

[0232] 3. Specific Examples

[0233] Example 1: Watching a French movie in Japanese

[0234] 1. A user selects a French movie file on a smartphone app and specifies a Japanese translation and a "calm male voice."

[0235] 2. The movie file is uploaded to the server and audio analysis is performed.

[0236] 3. The French audio data is converted into text and then translated into Japanese using a translation API.

[0237] 4. The generative AI model generates speech with the specified voice quality based on the Japanese text.

[0238] 5. The generated audio is integrated into the original movie file and sent to the app as streaming data.

[0239] 6. Users can smoothly watch movies converted into Japanese audio.

[0240] Example 2: Example of a prompt statement

[0241] An example prompt for watching a French movie in Japanese is:

[0242] Upload file: france_movie.mp4

[0243] Source language: French

[0244] Target language: Japanese

[0245] Voice quality: Gentle male voice

[0246] As described above, this system allows users to enjoy media content in different languages ​​in real time with the voice quality of their choice through simple operations.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] The user uses the terminal to select any media file. An input interface is displayed, allowing the user to specify the desired translation language and voice quality. The user enters this information and clicks the upload button. The inputs at this time are the media file, source language, target language, and desired voice quality. Based on this input, the terminal sends the media file and the specified information to the server.

[0250] Step 2:

[0251] The server receives media files uploaded by users. It checks the contents of the received media files and prepares them for the next step. Specific operations include saving the files and retrieving metadata (file name, size, format, etc.). The input in this case is the uploaded media file, and the output is the saved media file and its metadata.

[0252] Step 3:

[0253] The server extracts audio data from the stored media files. This process uses FFmpeg. The extracted audio data is then analyzed and converted into text data using speech recognition technology. This involves decoding the audio file and converting it using a speech recognition API (such as the Google Speech-to-Text API). The input is audio data, and the output is text data of the audio.

[0254] Step 4:

[0255] The server translates the obtained text data into the specified target language. Specifically, it translates the text data using Google Translate API or AWS Translate. The input is the original text data and the specified target language, and the output is the translated text data.

[0256] Step 5:

[0257] The server uses a generative AI model (such as Tacotron2 or WaveGlow) to generate voice data with a specified voice quality based on the translated text data. Specifically, the translated text data is input into the generative AI model to generate voice data. The input at this time is the translated text data and the specified voice quality, and the output is the generated voice data.

[0258] Step 6:

[0259] The server then incorporates the generated audio data into the original media file. Specifically, it uses FFmpeg to integrate the audio data into the media file and convert it into a streaming format. The input is the generated audio data and the original media file, and the output is the converted streaming file.

[0260] Step 7:

[0261] The server prepares to send streaming data to the terminal. Specifically, it uses a streaming protocol (such as HLS or DASH) to prepare the streaming data and starts sending it to the terminal. The input at this time is the converted streaming file, and the output is the streaming data.

[0262] Step 8:

[0263] The terminal buffers the streaming data received from the server in order to play it back. Once buffering is complete, it starts playing the media to the user. Specifically, it temporarily stores the received data and performs playback processing. The input at this time is streaming data, and the output is playback media that the user can view.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] MODE FOR CARRYING OUT THE INVENTION

[0266] overview

[0267] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[0268] composition

[0269] The system is broadly composed of the following components:

[0270] 1. User's device: The device where the user selects media files and uploads them to the server.

[0271] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0272] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0273] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions, voice, etc. and sends that information to the server.

[0274] Program processing overview

[0275] 1. Select and upload media files

[0276] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[0277] The terminal sends the media file and the translation request parameters to the server.

[0278] 2. Emotion recognition

[0279] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[0280] 3. Receiving media files and analyzing audio

[0281] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[0282] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[0283] 4. Language Translation

[0284] The server automatically selects a translation language that is particularly suited to the user's emotion based on the recognized user emotion and the input translation request parameters.

[0285] The server passes the text data to a translation engine, which converts it into the selected target language in real time.

[0286] 5. Speech generation and voice conversion

[0287] The server passes the translated text data to a generative AI model, which generates voice data with the optimal voice quality based on the recognized user emotions.

[0288] The generative AI model creates the audio data and returns it to the server.

[0289] 6. Regenerate media and prepare for streaming

[0290] The server incorporates the generated audio data into the original media file, adding it as a new audio track.

[0291] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time.

[0292] 7. User Media Viewing

[0293] The terminal receives the streaming data from the server and buffers it.

[0294] Users can view and listen to translated media content in their preferred language and voice quality.

[0295] Specific examples

[0296] Example 1: Emotion-based video translation

[0297] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, but the emotion engine recognizes the emotion of "surprise" while watching.

[0298] 2. The device uploads the video file and translation request parameters to the server.

[0299] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[0300] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[0301] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0302] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[0303] Example 2: Emotion-based audio content

[0304] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[0305] 2. The terminal uploads the audio file and translation request parameters to the server.

[0306] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[0307] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[0308] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0309] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[0310] The system of the present invention provides a more personalized viewing experience by realizing real-time translation and speech generation that reflects the user's emotions.

[0311] The processing flow will be explained below.

[0312] Step 1:

[0313] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[0314] Step 2:

[0315] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[0316] Step 3:

[0317] The emotion engine monitors input data such as the user's facial expressions and voice, recognizes the user's emotions in real time, and sends the recognized emotion data to the server.

[0318] Step 4:

[0319] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[0320] Step 5:

[0321] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[0322] Step 6:

[0323] The server transmits the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[0324] Step 7:

[0325] The server takes into consideration the user's emotional data received from the emotion engine and sends it to the generative AI model to generate voice data based on the translated text data in a specified voice quality or a voice quality appropriate for the emotion.

[0326] Step 8:

[0327] The generative AI model generates voice data based on the translated text data in a voice quality that matches the specified voice quality or emotion.

[0328] Step 9:

[0329] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[0330] Step 10:

[0331] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[0332] Step 11:

[0333] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[0334] Step 12:

[0335] Users can watch and listen to translated media content on their devices in their preferred language and voice quality. Based on data from the emotion engine, the content is played in a specific voice quality and context that matches the user's emotions.

[0336] Through these steps, the system provides fast and accurate real-time translation and speech generation, enabling a new viewing experience that reflects users' emotions, and allowing users to enjoy foreign content in their native language in real time.

[0337] Example 2

[0338] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0339] Conventional translation systems could analyze the audio of a user-selected media file, convert it into text data, and translate it. However, the translated audio was fixed to a single voice quality, making it difficult to reproduce audio that reflected the user's emotions. Furthermore, they did not offer real-time streaming or dynamic voice quality changes in response to changes in the user's emotions. This resulted in a lack of a personalized listening experience and a lack of realism and familiarity in the translated audio.

[0340] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving a media file selected by a user; means for analyzing the audio of the received media file and converting it into text data; means for translating the text data into a specified translated language; means for generating audio data with a specified voice quality based on the translated text data; means for incorporating the generated audio data into a media file and streaming it in real time; and means for recognizing the user's emotions based on an emotion engine and dynamically changing the voice quality and the translated language. This makes it possible to generate audio that reflects the user's emotions and dynamically change the voice quality in real time, thereby providing a more personalized viewing experience.

[0341] A "user" is an entity that uses the system to select media files and direct translation and speech generation.

[0342] A "media file" is a digital data file containing audio or video that is selected by a user and that the system processes.

[0343] The "means for receiving" is a mechanism for capturing media files uploaded by users onto the server.

[0344] "Means for analyzing audio and converting it to text data" refers to algorithms or software that extract the audio portion of a media file and convert it into text format.

[0345] The "means for translating" is the algorithm or software used to convert the converted text data into the specified target language.

[0346] "Means for generating voice data" refers to algorithms or software, such as a generative AI model, that generates voice data with a specified voice quality based on translated text data.

[0347] The "means for embedding and streaming" is a mechanism for embedding the generated audio data into the original media file and transmitting it to the user's terminal in real time.

[0348] An "emotion engine" is software or a device that analyzes a user's facial expressions and voice to recognize emotions and provide that information to the system.

[0349] "Voice quality" refers to the characteristics of the timbre and tone of the generated voice data, and is an element that can be changed according to emotions and user instructions.

[0350] "Means for dynamic change" refers to a mechanism that allows the system to change settings and conditions in real time depending on the situation.

[0351] "Real-time" means that the system processes and provides results immediately with little to no delay.

[0352] MODE FOR CARRYING OUT THE INVENTION

[0353] Hardware and software used

[0354] Hardware: User devices (PCs, smartphones), servers (high-performance computing resources)

[0355] Software: Generative AI model, emotion recognition engine, translation engine, voice analysis module, streaming server

[0356] Detailed Description

[0357] This invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions, making it possible to dynamically change the voice quality and translation language of audio data based on the user's emotions.

[0358] System Configuration and Operation

[0359] 1. Select media files

[0360] The user uses the device to select the media file they want to translate. For example, the user selects the video file "lecture.mp4" from their PC.

[0361] The user inputs the source language (e.g., English), target language (e.g., Japanese), and desired voice quality (e.g., a gentle voice) through a dedicated interface.

[0362] 2. Upload media files

[0363] The terminal packages the selected media file and the translation request parameters and sends them to the server.

[0364] 3. Emotion recognition

[0365] The emotion engine monitors the user's facial expressions and voice in real time. For example, if the user makes a "surprise" expression, the emotion engine will recognize this.

[0366] The emotion engine transmits the recognized emotion information to the server.

[0367] 4. Receiving media files and analyzing audio

[0368] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[0369] The audio analysis module in the server analyzes the audio in the media file and converts it into text data. For example, the audio "Hello, welcome to the lecture" extracted from "lecture.mp4" is converted into the text "Hello, welcome to the lecture."

[0370] 5. Language Translation

[0371] The server selects the most suitable post-translation language based on the emotion information received from the emotion engine and the translation request parameters input by the user.

[0372] The server passes the text data to the translation engine, which translates it in real time. For example, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture."

[0373] 6. Speech generation and voice conversion

[0374] The server passes the translated text data to a generative AI model, which then generates speech data in the optimal voice quality based on the recognized emotion. For example, the translated text "Hello, welcome to the lecture" is generated in a voice quality appropriate for the emotion "surprise."

[0375] The generative AI model generates voice data and returns it to the server, for example, "Voice data: Hello, welcome to the lecture (surprised voice)."

[0376] 7. Regenerate media and prepare for streaming

[0377] The server incorporates the generated audio data into the original media file (e.g., video) and adds it as a new audio track.

[0378] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time, for example, converting the new media file into HLS (HTTP Live Streaming) format.

[0379] 8. User Viewing

[0380] The device receives streaming data from the server and buffers it. For example, it receives a new media file, "lecture_translated.m3u8."

[0381] Users can watch and listen to media content translated in their preferred language and voice quality. For example, they can watch a video with a voice saying, "Hello, welcome to the lecture (surprised voice)."

[0382] Specific examples

[0383] Example 1: Emotion-based video translation

[0384] 1. The user selects an English educational video from their device and specifies the Japanese translation and desired voice quality.

[0385] 2. The device uploads the video file "lecture.mp4" and the translation request parameters to the server.

[0386] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[0387] 4. The server analyzes the video file, translates the audio from English to Japanese, and changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[0388] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0389] 6. The device receives the streaming data, and the user watches the Japanese video with an astonishing voice quality.

[0390] Example 2: Emotion-based audio content

[0391] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[0392] 2. The terminal uploads the audio file and translation request parameters to the server.

[0393] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[0394] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[0395] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0396] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[0397] This system enables real-time translation and speech generation that reflects the user's emotions, providing a more personalized viewing experience.

[0398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0399] Step 1: Select and upload media files

[0400] The user uses a terminal to select a media file to be translated. The input data is the selected media file and translation request parameters (e.g., source language, target language, desired voice quality). For example, the user selects the video file "lecture.mp4" from their PC, specifies English as the source language, Japanese as the target language, and a soft voice as the desired voice quality. This information is packaged on the terminal and uploaded to the server. The output is the media file received on the server and the translation request parameters.

[0401] Step 2: Emotion Recognition

[0402] The emotion engine monitors the user's facial expressions and voice in real time. The input data is the user's facial expressions and voice. For example, if the user makes a "surprise" expression, the emotion engine recognizes this and records "surprise" as emotional information. The emotion engine sends the recorded emotional information to the server. The output is the emotional information sent to the server.

[0403] Step 3: Receiving media files and analyzing audio

[0404] The server saves the media file and translation request parameters received from the terminal. The input data is the media file "lecture.mp4" sent from the terminal and the translation request parameters (source language: English, target language: Japanese, desired voice quality: gentle). The audio analysis module in the server extracts the audio portion of the media file and converts that audio data into text data. Specifically, the audio extracted from "lecture.mp4" "Hello, welcome to the lecture" is converted into "text: Hello, welcome to the lecture". The output is the audio converted into text data.

[0405] Step 4: Language Translation

[0406] The server selects the most appropriate target language for translation based on the emotional information received from the emotion engine and the translation request parameters entered by the user. The input data is the text data "Hello, welcome to the lecture", the emotional information "surprise", and the translation request parameters (target language: Japanese). The server passes this text data to the translation engine, which translates it in real time. Specifically, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture". The output is the translated Japanese text data.

[0407] Step 5: Speech generation and voice conversion

[0408] The server passes the translated text data to a generative AI model, which generates speech data with the optimal voice quality based on the emotional information. The input data is the translated Japanese text data "Hello, welcome to the lecture," the emotional information "surprise," and the desired voice quality (gentle voice). Specifically, the translated text "Hello, welcome to the lecture" is generated with an emphasized voice quality appropriate for the emotion of "surprise." The generative AI model generates speech data and returns it to the server. The output is the generated speech data "Hello, welcome to the lecture (surprised voice)."

[0409] Step 6: Regenerate your media and prepare it for streaming

[0410] The server incorporates the generated audio data into the original media file and adds it as a new audio track. The input data is the generated audio data "Hello, welcome to the lecture (surprised voice)" and the original media file "lecture.mp4." Specifically, a new Japanese audio track is added to the original media file. The server converts the new media file into a streaming format and prepares it for transmission to the device in real time. For example, it converts the new media file into HLS (HTTP Live Streaming) format. The output is a new media file converted into a streaming format.

[0411] Step 7: User Viewing

[0412] The device receives streaming data from the server and buffers it. The input data is a new streaming media file, "lecture_translated.m3u8." The user watches and listens to the translated media content in their preferred language and voice quality. Specifically, the user watches the video with a voice saying, "Hello, welcome to the lecture (surprised voice)." The output is the user's viewing experience.

[0413] (Application example 2)

[0414] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0415] Conventional media file translation systems simply translate text data into different languages ​​and generate audio data in a specified voice quality. As a result, they do not provide a personalized viewing experience that reflects the user's emotions. There is a need for a system that can provide a more interactive and emotionally sensitive media experience by dynamically changing the voice quality and translation language of audio data according to the user's emotions.

[0416] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0417] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, and means for recognizing the user's emotions and reflecting that information in the audio data generation process, thereby enabling audio generation that reflects the user's emotions and real-time streaming of media files.

[0418] A "user" is an individual or organization that uses the system.

[0419] A "media file" is a digital data file that contains audio, video, or both.

[0420] The "receiving means" is a device or software that has the function of importing the specified media file into the server.

[0421] The "means for analyzing audio" refers to a technology or device that has the function of converting audio data in a received media file into text data.

[0422] "Text data" is character information analyzed from speech.

[0423] The "translation means" refers to software or a device that has the function of converting text data into a specified target language.

[0424] The "translated language" is the target language into which the original text data has been converted.

[0425] "Voice quality" refers to the characteristics of the sound or tone of the voice data.

[0426] "Means for generating voice data" refers to a technology or device that creates voice data with a specified voice quality based on translated text data.

[0427] An "incorporating means" is a technology or device that has the ability to add generated audio data to the original media file.

[0428] "Real-time streaming means" refers to a technology or device that has the ability to deliver generated media files to users without delay.

[0429] "Means for recognizing emotions" refers to technology or devices that have the function of determining the emotional state of a user from their facial expressions and voice.

[0430] "Means for reflecting in the voice data generation process" refers to a technology or device that reflects the recognized emotional information in the generation of voice.

[0431] overview

[0432] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[0433] System Configuration

[0434] The system is broadly composed of the following components:

[0435] 1. Device: The device where the user selects media files and uploads them to the server. This includes smartphones, tablets, PCs, etc.

[0436] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0437] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0438] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions and voice, and sends that information to the server. It includes input devices such as a camera and microphone.

[0439] Specific operation of the system

[0440] 1. Select and upload media files

[0441] The user selects a media file from the terminal and inputs the source language, target language, and desired voice quality information. The terminal then sends the media file and translation request parameters to the server.

[0442] 2. Emotion recognition

[0443] Using the device's built-in camera and microphone, the emotion engine recognizes the user's emotions from their facial expressions and voice, and sends the recognition results to the server.

[0444] 3. Speech analysis and translation

[0445] The server stores the received media file and prepares it for passing to the analysis module, which analyzes the audio of the media file and converts it into text data. The text data is then passed to the translation engine, which dynamically converts it into a translated language that matches the user's sentiment.

[0446] 4. Speech Generation

[0447] The translated text data is passed to a generative AI model, which generates new voice data with a voice quality that matches the user's emotions.

[0448] 5. Media Reassembly and Streaming

[0449] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, and the new media file is converted into a streaming format in real time and sent to the device.

[0450] Specific examples

[0451] Example 1: Emotion-based video translation

[0452] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, and while watching, the emotion engine recognizes the emotion of "surprise."

[0453] 2. The device uploads the video file and translation request parameters to the server.

[0454] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[0455] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[0456] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0457] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[0458] Prompt Sentence Examples

[0459] Example 1 (translation using the emotion of joy)

[0460] "The user is showing a happy expression. Select the English video 'Science Discovery' and translate it into Japanese. Generate it in a bright and friendly voice quality."

[0461] Example 2 (Audio content translation using surprise emotion)

[0462] "Translate the French podcast 'Le Monde Aujourd'hui' into English, selected by a user who expressed surprise. Generate an energetic voice."

[0463] Such a system will enable personalized translation and speech generation that is sensitive to the user's emotions, providing a richer and more interactive viewing experience.

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] The user selects a media file using a terminal and inputs the source language, target language, and desired voice quality information. The input data includes the media file, translation parameters, and voice quality parameters. The terminal sends this data to the server. The output is the media file and parameter information transferred to the server.

[0467] Step 2:

[0468] The device uses a built-in camera and microphone to transmit the user's facial expressions and voice in real time to the emotion engine. The emotion engine analyzes the data obtained from these input devices and recognizes the user's emotions. The output is the recognized emotion information, which is sent to the server.

[0469] Step 3:

[0470] The server stores the received media files and prepares them for passing to the audio analysis module, which converts the audio data in the media files into text data. The input is the media file and the output is text data.

[0471] Step 4:

[0472] The server passes the text data to a translation engine, which converts it into the specified target language. The user's emotional information is also used as input, and a translation appropriate to the emotion is performed. The input is text data and emotional information, and the output is translated text data.

[0473] Step 5:

[0474] The server passes the translated text data to a generative AI model, which generates voice data with the specified voice quality. The voice quality is adjusted based on emotional information. The input is the translated text data and emotional information, and the output is the generated voice data.

[0475] Step 6:

[0476] The server embeds the generated audio data into the original media file and adds it as a new audio track. This operation creates a new media file. The inputs are the original media file and the generated audio data, and the output is the new media file.

[0477] Step 7:

[0478] The server converts the new media file into a streaming format and sends it to the device in real time. The device receives the streaming data and buffers it for the user to view. The input is the new media file, and the output is the streaming data.

[0479] In this way, a system is realized that provides a personalized viewing experience based on the user's emotions.

[0480] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0481] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0482] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0486] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0487] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0488] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0489] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0490] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0491] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0492] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0493] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0494] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0495] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0496] MODE FOR CARRYING OUT THE INVENTION

[0497] overview

[0498] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0499] composition

[0500] The system is broadly composed of the following components:

[0501] 1. User's device: The device where the user selects media files and uploads them to the server.

[0502] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0503] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0504] Program processing overview

[0505] 1. Select and upload media files

[0506] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[0507] The terminal uploads the media file along with this information to the server.

[0508] 2. Receiving media files and analyzing audio

[0509] The server transfers the media files received from the terminal to the analysis module.

[0510] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[0511] 3. Language Translation

[0512] The server sends the analyzed text data to a translation engine and converts it into the specified target language.

[0513] The translation engine performs the translation work and generates translated text data.

[0514] 4. Speech generation and voice conversion

[0515] The server passes the translated text data to a generative AI model, which generates audio data with the specified voice quality.

[0516] The generative AI model creates the audio data and returns it to the server.

[0517] 5. Regenerate media and prepare for streaming

[0518] The server incorporates the generated audio data into the original media file and converts it into a streaming format in real time.

[0519] The server prepares to send streaming data to the terminal.

[0520] 6. User Media Viewing

[0521] The device receives streaming data from the server and buffers it for smooth playback.

[0522] The user watches and listens to the translated media content.

[0523] Specific examples

[0524] Example 1: Watching an English documentary in Japanese

[0525] 1. The user selects an English documentary video from their device and specifies the Japanese translation and desired voice quality (for example, a gentle female voice).

[0526] 2. The device uploads the video file and translation request parameters to the server.

[0527] 3. The server receives the video file, analyzes the audio using the analysis module, and converts the English audio into text data.

[0528] 4. The server translates the English text data into Japanese using a translation engine.

[0529] 5. The server uses the generated AI model to convert the Japanese text data into voice data with the specified voice quality.

[0530] 6. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0531] 7. The device receives the streaming data and the user watches the documentary video in Japanese.

[0532] Example 2: Listening to a French radio program in German

[0533] 1. The user selects an audio file of a French radio program and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[0534] 2. The terminal uploads the audio file and translation request parameters to the server.

[0535] 3. The server receives the audio file, analyzes the audio using the analysis module, and converts the French audio into text data.

[0536] 4. The server translates the French text data into German using a translation engine.

[0537] 5. The server uses the generative AI model to convert the German text data into audio data with the specified voice quality.

[0538] 6. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0539] 7. The device receives the streaming data and the user listens to the radio program in German.

[0540] Thus, the present invention is a system that provides a new viewing experience to a user by translating a media file selected by the user into another language in real time and playing it back in a specified voice quality.

[0541] The processing flow will be explained below.

[0542] Step 1:

[0543] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[0544] Step 2:

[0545] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[0546] Step 3:

[0547] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[0548] Step 4:

[0549] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[0550] Step 5:

[0551] The server passes the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[0552] Step 6:

[0553] The server passes the translated text data to the generative AI model, which generates voice data in the voice quality specified by the user. The generative AI model then creates synthetic voice data in the specified voice quality based on the translated text.

[0554] Step 7:

[0555] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[0556] Step 8:

[0557] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[0558] Step 9:

[0559] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[0560] Step 10:

[0561] Users can watch and listen to translated media content on their devices in their preferred language and voice quality, allowing them to enjoy foreign content in their native language, such as Japanese, in real time.

[0562] Through these steps, the system provides fast and accurate real-time translation and speech generation, bringing a new listening experience to users, while also providing a user-friendly interface and smooth operation.

[0563] Example 1

[0564] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0565] Conventional systems have the drawback of being complex and difficult to process in real time, as they require users to translate selected media files into other languages ​​and play them in a specified voice quality. Furthermore, the quality of the voice generation is inconsistent, resulting in an inconsistent user experience.

[0566] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0567] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for using an artificial intelligence model to generate audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into the original media file and converting it into a streaming format in real time for transmission, and means for the terminal to receive the streaming data and buffer it for playback. This streamlines the process from media file translation to audio playback, allowing users to enjoy a consistent, high-quality audio experience.

[0568] A "user" is a person who selects a media file and specifies translation requests and audio playback.

[0569] A "terminal" is a device operated by a user that selects and uploads media files, and receives and buffers streaming data.

[0570] A "server" is a computing device that parses media files, translates them, generates audio, and transmits streaming data.

[0571] A "media file" is a file of digital content, including audio and video.

[0572] "Analysis" refers to the process of converting the audio data in a media file into text data.

[0573] "Text data" refers to character information converted from audio data.

[0574] "Translation" refers to the process of converting text data into a specified language.

[0575] The "translated language" is the language to be translated into, and is the output language desired by the user.

[0576] "Voice quality" refers to the characteristics of the voice when generating voice data, and is the voice characteristics (for example, gender, tone of voice, etc.) designated by the user.

[0577] "Artificial intelligence model" refers to a machine learning model used to generate audio data from text data.

[0578] "Generation" refers to the process of creating audio data using an artificial intelligence model.

[0579] "Audio data" refers to digital data of the generated audio.

[0580] "Embedding" refers to the process of integrating generated audio data into the original media file.

[0581] "Real-time" refers to providing immediate processing and results without delay.

[0582] "Streaming" refers to the continuous delivery and playback of media files over the Internet.

[0583] "Buffering" is the process of temporarily storing a portion of streaming data to ensure smooth playback.

[0584] MODE FOR CARRYING OUT THE INVENTION

[0585] Overall structure

[0586] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0587] Hardware and software used

[0588] The main hardware and software used to implement the system are as follows:

[0589] User devices: PCs, smartphones, tablets, etc.

[0590] Server: Cloud or on-premise server with high-performance computing resources

[0591] Speech analysis library: Google Speech-to-Text API, Microsoft Azure Speech-to-Text, etc.

[0592] Translation libraries: Google Translate API, DeepL, etc.

[0593] Speech generation libraries: such as OpenAI's speech generation API

[0594] Media editing libraries: FFmpeg, etc.

[0595] Process Overview

[0596] 1. Select and upload media files

[0597] The user selects a media file through the device's UI and specifies the source language, target language, and desired voice quality.

[0598] The terminal packages the selected media file and the translation request parameters and sends the package to the server in an HTTP request.

[0599] 2. Receiving media files and analyzing audio

[0600] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters.

[0601] An analysis module (for example, Google Speech-to-Text API) in the server converts the audio data of the media file into text data.

[0602] 3. Language Translation

[0603] The server sends the parsed text data to a translation library (e.g., Google Translate API or DeepL), which translates the text data into the specified target language.

[0604] 4. Speech generation and voice conversion

[0605] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API), which generates voice data with the specified voice quality.

[0606] 5. Regenerate media and prepare for streaming

[0607] The server embeds the generated audio data into the original media file, converts it into a streaming format (e.g., HLS or DASH) in real time, and transmits it.

[0608] 6. User Media Viewing

[0609] The terminal buffers the streaming data received from the server, allowing the user to watch the media file with smoothly translated audio.

[0610] Specific examples

[0611] Example 1: Watching an English documentary in Japanese

[0612] 1. The user selects an English documentary video on the device's UI and specifies the Japanese translation and desired voice quality (e.g., a soft female voice).

[0613] 2. The device combines the video file and translation request parameters into an HTTP request and uploads it to the server.

[0614] 3. The server receives the video file and converts the audio into text data using the Google Speech-to-Text API.

[0615] 4. The server uses the Google Translate API to translate the English text into Japanese.

[0616] 5. The server uses OpenAI's speech generation API to convert the Japanese text into audio data with the specified voice quality.

[0617] 6. The server uses FFmpeg to incorporate the new Japanese audio into the original video, convert it to HLS format, and generate a streaming URL for playback.

[0618] 7. The device receives the streaming URL and the user watches the documentary video with the new Japanese audio.

[0619] Example 2: Listening to a French radio program in German

[0620] 1. The user selects a French radio program on the device and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[0621] 2. The device bundles the audio file and translation request parameters into an HTTP request and uploads it to the server.

[0622] 3. The server receives the audio file and converts it into text using the Google Speech-to-Text API.

[0623] 4. The server uses DeepL to translate the French text into German.

[0624] 5. The server uses OpenAI's speech generation API to convert the German text into audio data with the specified voice quality.

[0625] 6. The server uses FFmpeg to embed the new German audio into the original audio file, convert it to HLS format, and generate a streaming URL.

[0626] 7. The device receives the streaming URL and the user listens to the radio program with the new German voice.

[0627] This allows users to experience real-time translation of selected media files and playback in the voice quality they specify.

[0628] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0629] Step 1: Select and upload media files

[0630] The user selects a media file through the device's UI. As input, the user specifies the media file (e.g., a video file), the source language, the target language, and the desired voice quality.

[0631] The device packages the selected media file and the translation request parameters and creates an HTTP request. The output is an HTTP request that is sent to the server.

[0632] The device executes this HTTP request and uploads the translation request along with the media file to the server.

[0633] Step 2: Receiving media files and analyzing audio

[0634] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters. The input is the HTTP request, and the output is the media file and translation request parameters.

[0635] The server uses an analysis module (e.g., Google Speech-to-Text API) to convert the audio data of the media file into text data. The input is the media file, and the output is text data.

[0636] The server temporarily stores the converted text data and prepares it for the next processing step.

[0637] Step 3: Language Translation

[0638] The server sends the parsed text data to a translation library (e.g., Google Translate API, DeepL). The input is the text data and translation request parameters, and the output is the translated text data.

[0639] The translation library converts the text data into the specified target language and generates translated text data.

[0640] The server receives the translated text data and temporarily stores it.

[0641] Step 4: Speech generation and voice conversion

[0642] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API). The input is the translated text data and voice quality information, and the output is the generated voice data.

[0643] The generative AI model generates voice data with the specified voice quality based on the text data and returns it to the server.

[0644] The server receives the generated audio data and stores it for further processing steps.

[0645] Step 5: Regenerate your media and prepare it for streaming

[0646] The server embeds the generated audio data into the original media file using a media editing library (e.g. FFmpeg). The input is the original media file and the generated audio data, and the output is a new media file.

[0647] The server converts the new media file, including the audio, into a streaming format (e.g., HLS, DASH) in real time. The input is the new media file, and the output is the streaming data.

[0648] The server generates a URL for the streaming data and prepares it for transmission to the terminal.

[0649] Step 6: User Views Media

[0650] The device obtains streaming data using the streaming URL received from the server. The input is the streaming URL, and the output is the streaming data.

[0651] The device receives the streaming data and buffers it for playback. The input is the streaming data and the output is the buffered data.

[0652] The user watches the media content with the translated voice through the terminal.

[0653] (Application example 1)

[0654] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0655] Conventional media translation systems have struggled to translate and generate speech in real time that matches the user's desired language and voice quality. They also lack features to improve the user experience, such as buffering streaming data and upload status notifications. The objective of this invention is to solve these problems and provide a system that supports multiple languages ​​and plays media in real time with a voice quality specified by the user.

[0656] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0657] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, means for allowing a user to specify a desired language and voice quality when uploading an arbitrary media file, and means for buffering the received streaming data to achieve smooth playback, thereby enabling a user to watch or listen to media content in a desired language and voice quality in real time.

[0658] "Means for receiving user-selected media files" refers to the devices and software used to transmit user-specified multimedia files to the server and for the server to receive them.

[0659] "Means for analyzing the audio of received media files and converting it into text data" refers to devices or software that extract the audio data from media files and convert it into text data using voice recognition technology.

[0660] "Means for translating text data into a specified target language" refers to a translation engine or software for converting parsed character data into another specified language.

[0661] "Means for generating voice data with a specified voice quality based on translated text data" refers to a generative AI model or voice synthesis technology that generates a voice file with a voice quality specified by the user based on translated text data.

[0662] "Means for incorporating generated audio data into a media file and streaming it in real time" means equipment or software that integrates newly generated audio data with the original media file and transmits and plays it to a user in real time.

[0663] "A means by which a user can specify a desired language and voice quality when uploading any media file" means an interface or software configuration feature that allows a user to select or specify the language into which a media file is to be translated and the voice quality to be used when uploading a media file.

[0664] "Means for buffering received streaming data to ensure smooth playback" refers to technology or software that temporarily stores streaming data transmitted in real time for playback, enabling uninterrupted playback.

[0665] This invention provides a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0666] 1. System Configuration

[0667] User's device

[0668] The user's device is used to select media files and enter the necessary information. In this example, a smartphone (iOS or Android) is used. The smartphone app is built with React Native and provides an interface where users can upload media files and specify the desired translation language and voice quality.

[0669] server

[0670] The server is built on Node.js and Express and offers the following features:

[0671] Receiving media files

[0672] Speech analysis and text conversion (using FFmpeg)

[0673] Translation (using Google Translate API or AWS Translate)

[0674] Speech generation (built in Python and using TensorFlow to run generative AI models such as Tacotron2 and WaveGlow)

[0675] Sending Streaming Data

[0676] Generative AI Models

[0677] The generative AI model generates voice data based on the translated text data in a voice quality specified by the user, specifically using Tacotron2 and WaveGlow.

[0678] 2. System Operation

[0679] Selecting and uploading media files

[0680] Using a smartphone app, users can select any media file and specify the desired translation language and voice quality, and this information is then uploaded from the device to the server.

[0681] Receiving media files and analyzing audio

[0682] The server receives the uploaded media file and first begins audio analysis. For audio analysis, it uses FFmpeg to extract the audio data from the media file and converts it into text data using speech recognition technology.

[0683] Language Translation

[0684] The server sends the text data to Google Translate API or AWS Translate, which translates it into the specified language, generating translated text data.

[0685] Speech generation and voice conversion

[0686] The translated text data is sent to a generative AI model (Tacotron2, WaveGlow), which generates voice data with the specified voice quality. This voice data is then returned to the server.

[0687] Regenerate media and prepare it for streaming

[0688] The generated audio data is embedded into the original media file, and the regenerated media file is converted into a streaming format in real time. The structured streaming data is then sent to the user's smartphone app.

[0689] User media viewing

[0690] The smartphone app receives and buffers the streaming data, allowing users to watch and listen to the media file in real time, integrated with audio generated in the specified translation language and voice quality.

[0691] 3. Specific Examples

[0692] Example 1: Watching a French movie in Japanese

[0693] 1. A user selects a French movie file on a smartphone app and specifies a Japanese translation and a "calm male voice."

[0694] 2. The movie file is uploaded to the server and audio analysis is performed.

[0695] 3. The French audio data is converted into text and then translated into Japanese using a translation API.

[0696] 4. The generative AI model generates speech with the specified voice quality based on the Japanese text.

[0697] 5. The generated audio is integrated into the original movie file and sent to the app as streaming data.

[0698] 6. Users can smoothly watch movies converted into Japanese audio.

[0699] Example 2: Example of a prompt statement

[0700] An example prompt for watching a French movie in Japanese is:

[0701] Upload file: france_movie.mp4

[0702] Source language: French

[0703] Target language: Japanese

[0704] Voice quality: Gentle male voice

[0705] As described above, this system allows users to enjoy media content in different languages ​​in real time with the voice quality of their choice through simple operations.

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Step 1:

[0708] The user uses the terminal to select any media file. An input interface is displayed, allowing the user to specify the desired translation language and voice quality. The user enters this information and clicks the upload button. The inputs at this time are the media file, source language, target language, and desired voice quality. Based on this input, the terminal sends the media file and the specified information to the server.

[0709] Step 2:

[0710] The server receives media files uploaded by users. It checks the contents of the received media files and prepares them for the next step. Specific operations include saving the files and retrieving metadata (file name, size, format, etc.). The input in this case is the uploaded media file, and the output is the saved media file and its metadata.

[0711] Step 3:

[0712] The server extracts audio data from the stored media files. This process uses FFmpeg. The extracted audio data is then analyzed and converted into text data using speech recognition technology. This involves decoding the audio file and converting it using a speech recognition API (such as the Google Speech-to-Text API). The input is audio data, and the output is text data of the audio.

[0713] Step 4:

[0714] The server translates the obtained text data into the specified target language. Specifically, it translates the text data using Google Translate API or AWS Translate. The input is the original text data and the specified target language, and the output is the translated text data.

[0715] Step 5:

[0716] The server uses a generative AI model (such as Tacotron2 or WaveGlow) to generate voice data with a specified voice quality based on the translated text data. Specifically, the translated text data is input into the generative AI model to generate voice data. The input at this time is the translated text data and the specified voice quality, and the output is the generated voice data.

[0717] Step 6:

[0718] The server then incorporates the generated audio data into the original media file. Specifically, it uses FFmpeg to integrate the audio data into the media file and convert it into a streaming format. The input is the generated audio data and the original media file, and the output is the converted streaming file.

[0719] Step 7:

[0720] The server prepares to send streaming data to the terminal. Specifically, it uses a streaming protocol (such as HLS or DASH) to prepare the streaming data and starts sending it to the terminal. The input at this time is the converted streaming file, and the output is the streaming data.

[0721] Step 8:

[0722] The terminal buffers the streaming data received from the server in order to play it back. Once buffering is complete, it starts playing the media to the user. Specifically, it temporarily stores the received data and performs playback processing. The input at this time is streaming data, and the output is playback media that the user can view.

[0723] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0724] MODE FOR CARRYING OUT THE INVENTION

[0725] overview

[0726] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[0727] composition

[0728] The system is broadly composed of the following components:

[0729] 1. User's device: The device where the user selects media files and uploads them to the server.

[0730] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0731] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0732] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions, voice, etc. and sends that information to the server.

[0733] Program processing overview

[0734] 1. Select and upload media files

[0735] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[0736] The terminal sends the media file and the translation request parameters to the server.

[0737] 2. Emotion recognition

[0738] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[0739] 3. Receiving media files and analyzing audio

[0740] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[0741] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[0742] 4. Language Translation

[0743] The server automatically selects a translation language that is particularly suited to the user's emotion based on the recognized user emotion and the input translation request parameters.

[0744] The server passes the text data to a translation engine, which converts it into the selected target language in real time.

[0745] 5. Speech generation and voice conversion

[0746] The server passes the translated text data to a generative AI model, which generates voice data with the optimal voice quality based on the recognized user emotions.

[0747] The generative AI model creates the audio data and returns it to the server.

[0748] 6. Regenerate media and prepare for streaming

[0749] The server incorporates the generated audio data into the original media file, adding it as a new audio track.

[0750] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time.

[0751] 7. User Media Viewing

[0752] The terminal receives the streaming data from the server and buffers it.

[0753] Users can view and listen to translated media content in their preferred language and voice quality.

[0754] Specific examples

[0755] Example 1: Emotion-based video translation

[0756] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, but the emotion engine recognizes the emotion of "surprise" while watching.

[0757] 2. The device uploads the video file and translation request parameters to the server.

[0758] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[0759] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[0760] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0761] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[0762] Example 2: Emotion-based audio content

[0763] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[0764] 2. The terminal uploads the audio file and translation request parameters to the server.

[0765] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[0766] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[0767] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0768] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[0769] The system of the present invention provides a more personalized viewing experience by realizing real-time translation and speech generation that reflects the user's emotions.

[0770] The processing flow will be explained below.

[0771] Step 1:

[0772] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[0773] Step 2:

[0774] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[0775] Step 3:

[0776] The emotion engine monitors input data such as the user's facial expressions and voice, recognizes the user's emotions in real time, and sends the recognized emotion data to the server.

[0777] Step 4:

[0778] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[0779] Step 5:

[0780] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[0781] Step 6:

[0782] The server transmits the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[0783] Step 7:

[0784] The server takes into consideration the user's emotional data received from the emotion engine and sends it to the generative AI model to generate voice data based on the translated text data in a specified voice quality or a voice quality appropriate for the emotion.

[0785] Step 8:

[0786] The generative AI model generates voice data based on the translated text data in a voice quality that matches the specified voice quality or emotion.

[0787] Step 9:

[0788] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[0789] Step 10:

[0790] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[0791] Step 11:

[0792] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[0793] Step 12:

[0794] Users can watch and listen to translated media content on their devices in their preferred language and voice quality. Based on data from the emotion engine, the content is played in a specific voice quality and context that matches the user's emotions.

[0795] Through these steps, the system provides fast and accurate real-time translation and speech generation, enabling a new viewing experience that reflects users' emotions, and allowing users to enjoy foreign content in their native language in real time.

[0796] Example 2

[0797] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0798] Conventional translation systems could analyze the audio of a user-selected media file, convert it into text data, and translate it. However, the translated audio was fixed to a single voice quality, making it difficult to reproduce audio that reflected the user's emotions. Furthermore, they did not offer real-time streaming or dynamic voice quality changes in response to changes in the user's emotions. This resulted in a lack of a personalized listening experience and a lack of realism and familiarity in the translated audio.

[0799] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving a media file selected by a user; means for analyzing the audio of the received media file and converting it into text data; means for translating the text data into a specified translated language; means for generating audio data with a specified voice quality based on the translated text data; means for incorporating the generated audio data into a media file and streaming it in real time; and means for recognizing the user's emotions based on an emotion engine and dynamically changing the voice quality and the translated language. This makes it possible to generate audio that reflects the user's emotions and dynamically change the voice quality in real time, thereby providing a more personalized viewing experience.

[0800] A "user" is an entity that uses the system to select media files and direct translation and speech generation.

[0801] A "media file" is a digital data file containing audio or video that is selected by a user and that the system processes.

[0802] The "means for receiving" is a mechanism for capturing media files uploaded by users onto the server.

[0803] "Means for analyzing audio and converting it to text data" refers to algorithms or software that extract the audio portion of a media file and convert it into text format.

[0804] The "means for translating" is the algorithm or software used to convert the converted text data into the specified target language.

[0805] "Means for generating voice data" refers to algorithms or software, such as a generative AI model, that generates voice data with a specified voice quality based on translated text data.

[0806] The "means for embedding and streaming" is a mechanism for embedding the generated audio data into the original media file and transmitting it to the user's terminal in real time.

[0807] An "emotion engine" is software or a device that analyzes a user's facial expressions and voice to recognize emotions and provide that information to the system.

[0808] "Voice quality" refers to the characteristics of the timbre and tone of the generated voice data, and is an element that can be changed according to emotions and user instructions.

[0809] "Means for dynamic change" refers to a mechanism that allows the system to change settings and conditions in real time depending on the situation.

[0810] "Real-time" means that the system processes and provides results immediately with little to no delay.

[0811] MODE FOR CARRYING OUT THE INVENTION

[0812] Hardware and software used

[0813] Hardware: User devices (PCs, smartphones), servers (high-performance computing resources)

[0814] Software: Generative AI model, emotion recognition engine, translation engine, voice analysis module, streaming server

[0815] Detailed Description

[0816] This invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions, making it possible to dynamically change the voice quality and translation language of audio data based on the user's emotions.

[0817] System Configuration and Operation

[0818] 1. Select media files

[0819] The user uses the device to select the media file they want to translate. For example, the user selects the video file "lecture.mp4" from their PC.

[0820] The user inputs the source language (e.g., English), target language (e.g., Japanese), and desired voice quality (e.g., a gentle voice) through a dedicated interface.

[0821] 2. Upload media files

[0822] The terminal packages the selected media file and the translation request parameters and sends them to the server.

[0823] 3. Emotion recognition

[0824] The emotion engine monitors the user's facial expressions and voice in real time. For example, if the user makes a "surprise" expression, the emotion engine will recognize this.

[0825] The emotion engine transmits the recognized emotion information to the server.

[0826] 4. Receiving media files and analyzing audio

[0827] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[0828] The audio analysis module in the server analyzes the audio in the media file and converts it into text data. For example, the audio "Hello, welcome to the lecture" extracted from "lecture.mp4" is converted into the text "Hello, welcome to the lecture."

[0829] 5. Language Translation

[0830] The server selects the most suitable post-translation language based on the emotion information received from the emotion engine and the translation request parameters input by the user.

[0831] The server passes the text data to the translation engine, which translates it in real time. For example, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture."

[0832] 6. Speech generation and voice conversion

[0833] The server passes the translated text data to a generative AI model, which then generates speech data in the optimal voice quality based on the recognized emotion. For example, the translated text "Hello, welcome to the lecture" is generated in a voice quality appropriate for the emotion "surprise."

[0834] The generative AI model generates voice data and returns it to the server, for example, "Voice data: Hello, welcome to the lecture (surprised voice)."

[0835] 7. Regenerate media and prepare for streaming

[0836] The server incorporates the generated audio data into the original media file (e.g., video) and adds it as a new audio track.

[0837] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time, for example, converting the new media file into HLS (HTTP Live Streaming) format.

[0838] 8. User Viewing

[0839] The device receives streaming data from the server and buffers it. For example, it receives a new media file, "lecture_translated.m3u8."

[0840] Users can watch and listen to media content translated in their preferred language and voice quality. For example, they can watch a video with a voice saying, "Hello, welcome to the lecture (surprised voice)."

[0841] Specific examples

[0842] Example 1: Emotion-based video translation

[0843] 1. The user selects an English educational video from their device and specifies the Japanese translation and desired voice quality.

[0844] 2. The device uploads the video file "lecture.mp4" and the translation request parameters to the server.

[0845] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[0846] 4. The server analyzes the video file, translates the audio from English to Japanese, and changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[0847] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0848] 6. The device receives the streaming data, and the user watches the Japanese video with an astonishing voice quality.

[0849] Example 2: Emotion-based audio content

[0850] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[0851] 2. The terminal uploads the audio file and translation request parameters to the server.

[0852] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[0853] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[0854] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0855] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[0856] This system enables real-time translation and speech generation that reflects the user's emotions, providing a more personalized viewing experience.

[0857] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0858] Step 1: Select and upload media files

[0859] The user uses a terminal to select a media file to be translated. The input data is the selected media file and translation request parameters (e.g., source language, target language, desired voice quality). For example, the user selects the video file "lecture.mp4" from their PC, specifies English as the source language, Japanese as the target language, and a soft voice as the desired voice quality. This information is packaged on the terminal and uploaded to the server. The output is the media file received on the server and the translation request parameters.

[0860] Step 2: Emotion Recognition

[0861] The emotion engine monitors the user's facial expressions and voice in real time. The input data is the user's facial expressions and voice. For example, if the user makes a "surprise" expression, the emotion engine recognizes this and records "surprise" as emotional information. The emotion engine sends the recorded emotional information to the server. The output is the emotional information sent to the server.

[0862] Step 3: Receiving media files and analyzing audio

[0863] The server saves the media file and translation request parameters received from the terminal. The input data is the media file "lecture.mp4" sent from the terminal and the translation request parameters (source language: English, target language: Japanese, desired voice quality: gentle). The audio analysis module in the server extracts the audio portion of the media file and converts that audio data into text data. Specifically, the audio extracted from "lecture.mp4" "Hello, welcome to the lecture" is converted into "text: Hello, welcome to the lecture". The output is the audio converted into text data.

[0864] Step 4: Language Translation

[0865] The server selects the most appropriate target language for translation based on the emotional information received from the emotion engine and the translation request parameters entered by the user. The input data is the text data "Hello, welcome to the lecture", the emotional information "surprise", and the translation request parameters (target language: Japanese). The server passes this text data to the translation engine, which translates it in real time. Specifically, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture". The output is the translated Japanese text data.

[0866] Step 5: Speech generation and voice conversion

[0867] The server passes the translated text data to a generative AI model, which generates speech data with the optimal voice quality based on the emotional information. The input data is the translated Japanese text data "Hello, welcome to the lecture," the emotional information "surprise," and the desired voice quality (gentle voice). Specifically, the translated text "Hello, welcome to the lecture" is generated with an emphasized voice quality appropriate for the emotion of "surprise." The generative AI model generates speech data and returns it to the server. The output is the generated speech data "Hello, welcome to the lecture (surprised voice)."

[0868] Step 6: Regenerate your media and prepare it for streaming

[0869] The server incorporates the generated audio data into the original media file and adds it as a new audio track. The input data is the generated audio data "Hello, welcome to the lecture (surprised voice)" and the original media file "lecture.mp4." Specifically, a new Japanese audio track is added to the original media file. The server converts the new media file into a streaming format and prepares it for transmission to the device in real time. For example, it converts the new media file into HLS (HTTP Live Streaming) format. The output is a new media file converted into a streaming format.

[0870] Step 7: User Viewing

[0871] The device receives streaming data from the server and buffers it. The input data is a new streaming media file, "lecture_translated.m3u8." The user watches and listens to the translated media content in their preferred language and voice quality. Specifically, the user watches the video with a voice saying, "Hello, welcome to the lecture (surprised voice)." The output is the user's viewing experience.

[0872] (Application example 2)

[0873] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0874] Conventional media file translation systems simply translate text data into different languages ​​and generate audio data in a specified voice quality. As a result, they do not provide a personalized viewing experience that reflects the user's emotions. There is a need for a system that can provide a more interactive and emotionally sensitive media experience by dynamically changing the voice quality and translation language of audio data according to the user's emotions.

[0875] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0876] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, and means for recognizing the user's emotions and reflecting that information in the audio data generation process, thereby enabling audio generation that reflects the user's emotions and real-time streaming of media files.

[0877] A "user" is an individual or organization that uses the system.

[0878] A "media file" is a digital data file that contains audio, video, or both.

[0879] The "receiving means" is a device or software that has the function of importing the specified media file into the server.

[0880] The "means for analyzing audio" refers to a technology or device that has the function of converting audio data in a received media file into text data.

[0881] "Text data" is character information analyzed from speech.

[0882] The "translation means" refers to software or a device that has the function of converting text data into a specified target language.

[0883] The "translated language" is the target language into which the original text data has been converted.

[0884] "Voice quality" refers to the characteristics of the sound or tone of the voice data.

[0885] "Means for generating voice data" refers to a technology or device that creates voice data with a specified voice quality based on translated text data.

[0886] An "incorporating means" is a technology or device that has the ability to add generated audio data to the original media file.

[0887] "Real-time streaming means" refers to a technology or device that has the ability to deliver generated media files to users without delay.

[0888] "Means for recognizing emotions" refers to technology or devices that have the function of determining the emotional state of a user from their facial expressions and voice.

[0889] "Means for reflecting in the voice data generation process" refers to a technology or device that reflects the recognized emotional information in the generation of voice.

[0890] overview

[0891] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[0892] System Configuration

[0893] The system is broadly composed of the following components:

[0894] 1. Device: The device where the user selects media files and uploads them to the server. This includes smartphones, tablets, PCs, etc.

[0895] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0896] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0897] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions and voice, and sends that information to the server. It includes input devices such as a camera and microphone.

[0898] Specific operation of the system

[0899] 1. Select and upload media files

[0900] The user selects a media file from the terminal and inputs the source language, target language, and desired voice quality information. The terminal then sends the media file and translation request parameters to the server.

[0901] 2. Emotion recognition

[0902] Using the device's built-in camera and microphone, the emotion engine recognizes the user's emotions from their facial expressions and voice, and sends the recognition results to the server.

[0903] 3. Speech analysis and translation

[0904] The server stores the received media file and prepares it for passing to the analysis module, which analyzes the audio of the media file and converts it into text data. The text data is then passed to the translation engine, which dynamically converts it into a translated language that matches the user's sentiment.

[0905] 4. Speech Generation

[0906] The translated text data is passed to a generative AI model, which generates new voice data with a voice quality that matches the user's emotions.

[0907] 5. Media Reassembly and Streaming

[0908] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, and the new media file is converted into a streaming format in real time and sent to the device.

[0909] Specific examples

[0910] Example 1: Emotion-based video translation

[0911] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, and while watching, the emotion engine recognizes the emotion of "surprise."

[0912] 2. The device uploads the video file and translation request parameters to the server.

[0913] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[0914] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[0915] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0916] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[0917] Prompt Sentence Examples

[0918] Example 1 (translation using the emotion of joy)

[0919] "The user is showing a happy expression. Select the English video 'Science Discovery' and translate it into Japanese. Generate it in a bright and friendly voice quality."

[0920] Example 2 (Audio content translation using surprise emotion)

[0921] "Translate the French podcast 'Le Monde Aujourd'hui' into English, selected by a user who expressed surprise. Generate an energetic voice."

[0922] Such a system will enable personalized translation and speech generation that is sensitive to the user's emotions, providing a richer and more interactive viewing experience.

[0923] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0924] Step 1:

[0925] The user selects a media file using a terminal and inputs the source language, target language, and desired voice quality information. The input data includes the media file, translation parameters, and voice quality parameters. The terminal sends this data to the server. The output is the media file and parameter information transferred to the server.

[0926] Step 2:

[0927] The device uses a built-in camera and microphone to transmit the user's facial expressions and voice in real time to the emotion engine. The emotion engine analyzes the data obtained from these input devices and recognizes the user's emotions. The output is the recognized emotion information, which is sent to the server.

[0928] Step 3:

[0929] The server stores the received media files and prepares them for passing to the audio analysis module, which converts the audio data in the media files into text data. The input is the media file and the output is text data.

[0930] Step 4:

[0931] The server passes the text data to a translation engine, which converts it into the specified target language. The user's emotional information is also used as input, and a translation appropriate to the emotion is performed. The input is text data and emotional information, and the output is translated text data.

[0932] Step 5:

[0933] The server passes the translated text data to a generative AI model, which generates voice data with the specified voice quality. The voice quality is adjusted based on emotional information. The input is the translated text data and emotional information, and the output is the generated voice data.

[0934] Step 6:

[0935] The server embeds the generated audio data into the original media file and adds it as a new audio track. This operation creates a new media file. The inputs are the original media file and the generated audio data, and the output is the new media file.

[0936] Step 7:

[0937] The server converts the new media file into a streaming format and sends it to the device in real time. The device receives the streaming data and buffers it for the user to view. The input is the new media file, and the output is the streaming data.

[0938] In this way, a system is realized that provides a personalized viewing experience based on the user's emotions.

[0939] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0940] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0941] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0942] [Third embodiment]

[0943] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0944] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0945] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0946] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0947] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0948] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0949] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0950] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0951] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0952] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0953] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0954] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0955] MODE FOR CARRYING OUT THE INVENTION

[0956] overview

[0957] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[0958] composition

[0959] The system is broadly composed of the following components:

[0960] 1. User's device: The device where the user selects media files and uploads them to the server.

[0961] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[0962] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[0963] Program processing overview

[0964] 1. Select and upload media files

[0965] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[0966] The terminal uploads the media file along with this information to the server.

[0967] 2. Receiving media files and analyzing audio

[0968] The server transfers the media files received from the terminal to the analysis module.

[0969] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[0970] 3. Language Translation

[0971] The server sends the analyzed text data to a translation engine and converts it into the specified target language.

[0972] The translation engine performs the translation work and generates translated text data.

[0973] 4. Speech generation and voice conversion

[0974] The server passes the translated text data to a generative AI model, which generates audio data with the specified voice quality.

[0975] The generative AI model creates the audio data and returns it to the server.

[0976] 5. Regenerate media and prepare for streaming

[0977] The server incorporates the generated audio data into the original media file and converts it into a streaming format in real time.

[0978] The server prepares to send streaming data to the terminal.

[0979] 6. User Media Viewing

[0980] The device receives streaming data from the server and buffers it for smooth playback.

[0981] The user watches and listens to the translated media content.

[0982] Specific examples

[0983] Example 1: Watching an English documentary in Japanese

[0984] 1. The user selects an English documentary video from their device and specifies the Japanese translation and desired voice quality (for example, a gentle female voice).

[0985] 2. The device uploads the video file and translation request parameters to the server.

[0986] 3. The server receives the video file, analyzes the audio using the analysis module, and converts the English audio into text data.

[0987] 4. The server translates the English text data into Japanese using a translation engine.

[0988] 5. The server uses the generated AI model to convert the Japanese text data into voice data with the specified voice quality.

[0989] 6. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[0990] 7. The device receives the streaming data and the user watches the documentary video in Japanese.

[0991] Example 2: Listening to a French radio program in German

[0992] 1. The user selects an audio file of a French radio program and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[0993] 2. The terminal uploads the audio file and translation request parameters to the server.

[0994] 3. The server receives the audio file, analyzes the audio using the analysis module, and converts the French audio into text data.

[0995] 4. The server translates the French text data into German using a translation engine.

[0996] 5. The server uses the generative AI model to convert the German text data into audio data with the specified voice quality.

[0997] 6. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[0998] 7. The device receives the streaming data and the user listens to the radio program in German.

[0999] Thus, the present invention is a system that provides a new viewing experience to a user by translating a media file selected by the user into another language in real time and playing it back in a specified voice quality.

[1000] The processing flow will be explained below.

[1001] Step 1:

[1002] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[1003] Step 2:

[1004] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[1005] Step 3:

[1006] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[1007] Step 4:

[1008] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[1009] Step 5:

[1010] The server passes the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[1011] Step 6:

[1012] The server passes the translated text data to the generative AI model, which generates voice data in the voice quality specified by the user. The generative AI model then creates synthetic voice data in the specified voice quality based on the translated text.

[1013] Step 7:

[1014] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[1015] Step 8:

[1016] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[1017] Step 9:

[1018] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[1019] Step 10:

[1020] Users can watch and listen to translated media content on their devices in their preferred language and voice quality, allowing them to enjoy foreign content in their native language, such as Japanese, in real time.

[1021] Through these steps, the system provides fast and accurate real-time translation and speech generation, bringing a new listening experience to users, while also providing a user-friendly interface and smooth operation.

[1022] Example 1

[1023] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1024] Conventional systems have the drawback of being complex and difficult to process in real time, as they require users to translate selected media files into other languages ​​and play them in a specified voice quality. Furthermore, the quality of the voice generation is inconsistent, resulting in an inconsistent user experience.

[1025] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1026] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for using an artificial intelligence model to generate audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into the original media file and converting it into a streaming format in real time for transmission, and means for the terminal to receive the streaming data and buffer it for playback. This streamlines the process from media file translation to audio playback, allowing users to enjoy a consistent, high-quality audio experience.

[1027] A "user" is a person who selects a media file and specifies translation requests and audio playback.

[1028] A "terminal" is a device operated by a user that selects and uploads media files, and receives and buffers streaming data.

[1029] A "server" is a computing device that parses media files, translates them, generates audio, and transmits streaming data.

[1030] A "media file" is a file of digital content, including audio and video.

[1031] "Analysis" refers to the process of converting the audio data in a media file into text data.

[1032] "Text data" refers to character information converted from audio data.

[1033] "Translation" refers to the process of converting text data into a specified language.

[1034] The "translated language" is the language to be translated into, and is the output language desired by the user.

[1035] "Voice quality" refers to the characteristics of the voice when generating voice data, and is the voice characteristics (for example, gender, tone of voice, etc.) designated by the user.

[1036] "Artificial intelligence model" refers to a machine learning model used to generate audio data from text data.

[1037] "Generation" refers to the process of creating audio data using an artificial intelligence model.

[1038] "Audio data" refers to digital data of the generated audio.

[1039] "Embedding" refers to the process of integrating generated audio data into the original media file.

[1040] "Real-time" refers to providing immediate processing and results without delay.

[1041] "Streaming" refers to the continuous delivery and playback of media files over the Internet.

[1042] "Buffering" is the process of temporarily storing a portion of streaming data to ensure smooth playback.

[1043] MODE FOR CARRYING OUT THE INVENTION

[1044] Overall structure

[1045] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[1046] Hardware and software used

[1047] The main hardware and software used to implement the system are as follows:

[1048] User devices: PCs, smartphones, tablets, etc.

[1049] Server: Cloud or on-premise server with high-performance computing resources

[1050] Speech analysis library: Google Speech-to-Text API, Microsoft Azure Speech-to-Text, etc.

[1051] Translation libraries: Google Translate API, DeepL, etc.

[1052] Speech generation libraries: such as OpenAI's speech generation API

[1053] Media editing libraries: FFmpeg, etc.

[1054] Process Overview

[1055] 1. Select and upload media files

[1056] The user selects a media file through the device's UI and specifies the source language, target language, and desired voice quality.

[1057] The terminal packages the selected media file and the translation request parameters and sends the package to the server in an HTTP request.

[1058] 2. Receiving media files and analyzing audio

[1059] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters.

[1060] An analysis module (for example, Google Speech-to-Text API) in the server converts the audio data of the media file into text data.

[1061] 3. Language Translation

[1062] The server sends the parsed text data to a translation library (e.g., Google Translate API or DeepL), which translates the text data into the specified target language.

[1063] 4. Speech generation and voice conversion

[1064] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API), which generates voice data with the specified voice quality.

[1065] 5. Regenerate media and prepare for streaming

[1066] The server embeds the generated audio data into the original media file, converts it into a streaming format (e.g., HLS or DASH) in real time, and transmits it.

[1067] 6. User Media Viewing

[1068] The terminal buffers the streaming data received from the server, allowing the user to watch the media file with smoothly translated audio.

[1069] Specific examples

[1070] Example 1: Watching an English documentary in Japanese

[1071] 1. The user selects an English documentary video on the device's UI and specifies the Japanese translation and desired voice quality (e.g., a soft female voice).

[1072] 2. The device combines the video file and translation request parameters into an HTTP request and uploads it to the server.

[1073] 3. The server receives the video file and converts the audio into text data using the Google Speech-to-Text API.

[1074] 4. The server uses the Google Translate API to translate the English text into Japanese.

[1075] 5. The server uses OpenAI's speech generation API to convert the Japanese text into audio data with the specified voice quality.

[1076] 6. The server uses FFmpeg to incorporate the new Japanese audio into the original video, convert it to HLS format, and generate a streaming URL for playback.

[1077] 7. The device receives the streaming URL and the user watches the documentary video with the new Japanese audio.

[1078] Example 2: Listening to a French radio program in German

[1079] 1. The user selects a French radio program on the device and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[1080] 2. The device bundles the audio file and translation request parameters into an HTTP request and uploads it to the server.

[1081] 3. The server receives the audio file and converts it into text using the Google Speech-to-Text API.

[1082] 4. The server uses DeepL to translate the French text into German.

[1083] 5. The server uses OpenAI's speech generation API to convert the German text into audio data with the specified voice quality.

[1084] 6. The server uses FFmpeg to embed the new German audio into the original audio file, convert it to HLS format, and generate a streaming URL.

[1085] 7. The device receives the streaming URL and the user listens to the radio program with the new German voice.

[1086] This allows users to experience real-time translation of selected media files and playback in the voice quality they specify.

[1087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1088] Step 1: Select and upload media files

[1089] The user selects a media file through the device's UI. As input, the user specifies the media file (e.g., a video file), the source language, the target language, and the desired voice quality.

[1090] The device packages the selected media file and the translation request parameters and creates an HTTP request. The output is an HTTP request that is sent to the server.

[1091] The device executes this HTTP request and uploads the translation request along with the media file to the server.

[1092] Step 2: Receiving media files and analyzing audio

[1093] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters. The input is the HTTP request, and the output is the media file and translation request parameters.

[1094] The server uses an analysis module (e.g., Google Speech-to-Text API) to convert the audio data of the media file into text data. The input is the media file, and the output is text data.

[1095] The server temporarily stores the converted text data and prepares it for the next processing step.

[1096] Step 3: Language Translation

[1097] The server sends the parsed text data to a translation library (e.g., Google Translate API, DeepL). The input is the text data and translation request parameters, and the output is the translated text data.

[1098] The translation library converts the text data into the specified target language and generates translated text data.

[1099] The server receives the translated text data and temporarily stores it.

[1100] Step 4: Speech generation and voice conversion

[1101] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API). The input is the translated text data and voice quality information, and the output is the generated voice data.

[1102] The generative AI model generates voice data with the specified voice quality based on the text data and returns it to the server.

[1103] The server receives the generated audio data and stores it for further processing steps.

[1104] Step 5: Regenerate your media and prepare it for streaming

[1105] The server embeds the generated audio data into the original media file using a media editing library (e.g. FFmpeg). The input is the original media file and the generated audio data, and the output is a new media file.

[1106] The server converts the new media file, including the audio, into a streaming format (e.g., HLS, DASH) in real time. The input is the new media file, and the output is the streaming data.

[1107] The server generates a URL for the streaming data and prepares it for transmission to the terminal.

[1108] Step 6: User Views Media

[1109] The device obtains streaming data using the streaming URL received from the server. The input is the streaming URL, and the output is the streaming data.

[1110] The device receives the streaming data and buffers it for playback. The input is the streaming data and the output is the buffered data.

[1111] The user watches the media content with the translated voice through the terminal.

[1112] (Application example 1)

[1113] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1114] Conventional media translation systems have struggled to translate and generate speech in real time that matches the user's desired language and voice quality. They also lack features to improve the user experience, such as buffering streaming data and upload status notifications. The objective of this invention is to solve these problems and provide a system that supports multiple languages ​​and plays media in real time with a voice quality specified by the user.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1116] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, means for allowing a user to specify a desired language and voice quality when uploading an arbitrary media file, and means for buffering the received streaming data to achieve smooth playback, thereby enabling a user to watch or listen to media content in a desired language and voice quality in real time.

[1117] "Means for receiving user-selected media files" refers to the devices and software used to transmit user-specified multimedia files to the server and for the server to receive them.

[1118] "Means for analyzing the audio of received media files and converting it into text data" refers to devices or software that extract the audio data from media files and convert it into text data using voice recognition technology.

[1119] "Means for translating text data into a specified target language" refers to a translation engine or software for converting parsed character data into another specified language.

[1120] "Means for generating voice data with a specified voice quality based on translated text data" refers to a generative AI model or voice synthesis technology that generates a voice file with a voice quality specified by the user based on translated text data.

[1121] "Means for incorporating generated audio data into a media file and streaming it in real time" means equipment or software that integrates newly generated audio data with the original media file and transmits and plays it to a user in real time.

[1122] "A means by which a user can specify a desired language and voice quality when uploading any media file" means an interface or software configuration feature that allows a user to select or specify the language into which a media file is to be translated and the voice quality to be used when uploading a media file.

[1123] "Means for buffering received streaming data to ensure smooth playback" refers to technology or software that temporarily stores streaming data transmitted in real time for playback, enabling uninterrupted playback.

[1124] This invention provides a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[1125] 1. System Configuration

[1126] User's device

[1127] The user's device is used to select media files and enter the necessary information. In this example, a smartphone (iOS or Android) is used. The smartphone app is built with React Native and provides an interface where users can upload media files and specify the desired translation language and voice quality.

[1128] server

[1129] The server is built on Node.js and Express and offers the following features:

[1130] Receiving media files

[1131] Speech analysis and text conversion (using FFmpeg)

[1132] Translation (using Google Translate API or AWS Translate)

[1133] Speech generation (built in Python and using TensorFlow to run generative AI models such as Tacotron2 and WaveGlow)

[1134] Sending Streaming Data

[1135] Generative AI Models

[1136] The generative AI model generates voice data based on the translated text data in a voice quality specified by the user, specifically using Tacotron2 and WaveGlow.

[1137] 2. System Operation

[1138] Selecting and uploading media files

[1139] Using a smartphone app, users can select any media file and specify the desired translation language and voice quality, and this information is then uploaded from the device to the server.

[1140] Receiving media files and analyzing audio

[1141] The server receives the uploaded media file and first begins audio analysis. For audio analysis, it uses FFmpeg to extract the audio data from the media file and converts it into text data using speech recognition technology.

[1142] Language Translation

[1143] The server sends the text data to Google Translate API or AWS Translate, which translates it into the specified language, generating translated text data.

[1144] Speech generation and voice conversion

[1145] The translated text data is sent to a generative AI model (Tacotron2, WaveGlow), which generates voice data with the specified voice quality. This voice data is then returned to the server.

[1146] Regenerate media and prepare it for streaming

[1147] The generated audio data is embedded into the original media file, and the regenerated media file is converted into a streaming format in real time. The structured streaming data is then sent to the user's smartphone app.

[1148] User media viewing

[1149] The smartphone app receives and buffers the streaming data, allowing users to watch and listen to the media file in real time, integrated with audio generated in the specified translation language and voice quality.

[1150] 3. Specific Examples

[1151] Example 1: Watching a French movie in Japanese

[1152] 1. A user selects a French movie file on a smartphone app and specifies a Japanese translation and a "calm male voice."

[1153] 2. The movie file is uploaded to the server and audio analysis is performed.

[1154] 3. The French audio data is converted into text and then translated into Japanese using a translation API.

[1155] 4. The generative AI model generates speech with the specified voice quality based on the Japanese text.

[1156] 5. The generated audio is integrated into the original movie file and sent to the app as streaming data.

[1157] 6. Users can smoothly watch movies converted into Japanese audio.

[1158] Example 2: Example of a prompt statement

[1159] An example prompt for watching a French movie in Japanese is:

[1160] Upload file: france_movie.mp4

[1161] Source language: French

[1162] Target language: Japanese

[1163] Voice quality: Gentle male voice

[1164] As described above, this system allows users to enjoy media content in different languages ​​in real time with the voice quality of their choice through simple operations.

[1165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1166] Step 1:

[1167] The user uses the terminal to select any media file. An input interface is displayed, allowing the user to specify the desired translation language and voice quality. The user enters this information and clicks the upload button. The inputs at this time are the media file, source language, target language, and desired voice quality. Based on this input, the terminal sends the media file and the specified information to the server.

[1168] Step 2:

[1169] The server receives media files uploaded by users. It checks the contents of the received media files and prepares them for the next step. Specific operations include saving the files and retrieving metadata (file name, size, format, etc.). The input in this case is the uploaded media file, and the output is the saved media file and its metadata.

[1170] Step 3:

[1171] The server extracts audio data from the stored media files. This process uses FFmpeg. The extracted audio data is then analyzed and converted into text data using speech recognition technology. This involves decoding the audio file and converting it using a speech recognition API (such as the Google Speech-to-Text API). The input is audio data, and the output is text data of the audio.

[1172] Step 4:

[1173] The server translates the obtained text data into the specified target language. Specifically, it translates the text data using Google Translate API or AWS Translate. The input is the original text data and the specified target language, and the output is the translated text data.

[1174] Step 5:

[1175] The server uses a generative AI model (such as Tacotron2 or WaveGlow) to generate voice data with a specified voice quality based on the translated text data. Specifically, the translated text data is input into the generative AI model to generate voice data. The input at this time is the translated text data and the specified voice quality, and the output is the generated voice data.

[1176] Step 6:

[1177] The server then incorporates the generated audio data into the original media file. Specifically, it uses FFmpeg to integrate the audio data into the media file and convert it into a streaming format. The input is the generated audio data and the original media file, and the output is the converted streaming file.

[1178] Step 7:

[1179] The server prepares to send streaming data to the terminal. Specifically, it uses a streaming protocol (such as HLS or DASH) to prepare the streaming data and starts sending it to the terminal. The input at this time is the converted streaming file, and the output is the streaming data.

[1180] Step 8:

[1181] The terminal buffers the streaming data received from the server in order to play it back. Once buffering is complete, it starts playing the media to the user. Specifically, it temporarily stores the received data and performs playback processing. The input at this time is streaming data, and the output is playback media that the user can view.

[1182] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1183] MODE FOR CARRYING OUT THE INVENTION

[1184] overview

[1185] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[1186] composition

[1187] The system is broadly composed of the following components:

[1188] 1. User's device: The device where the user selects media files and uploads them to the server.

[1189] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[1190] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[1191] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions, voice, etc. and sends that information to the server.

[1192] Program processing overview

[1193] 1. Select and upload media files

[1194] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[1195] The terminal sends the media file and the translation request parameters to the server.

[1196] 2. Emotion recognition

[1197] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[1198] 3. Receiving media files and analyzing audio

[1199] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[1200] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[1201] 4. Language Translation

[1202] The server automatically selects a translation language that is particularly suited to the user's emotion based on the recognized user emotion and the input translation request parameters.

[1203] The server passes the text data to a translation engine, which converts it into the selected target language in real time.

[1204] 5. Speech generation and voice conversion

[1205] The server passes the translated text data to a generative AI model, which generates voice data with the optimal voice quality based on the recognized user emotions.

[1206] The generative AI model creates the audio data and returns it to the server.

[1207] 6. Regenerate media and prepare for streaming

[1208] The server incorporates the generated audio data into the original media file, adding it as a new audio track.

[1209] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time.

[1210] 7. User Media Viewing

[1211] The terminal receives the streaming data from the server and buffers it.

[1212] Users can view and listen to translated media content in their preferred language and voice quality.

[1213] Specific examples

[1214] Example 1: Emotion-based video translation

[1215] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, but the emotion engine recognizes the emotion of "surprise" while watching.

[1216] 2. The device uploads the video file and translation request parameters to the server.

[1217] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[1218] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[1219] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1220] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[1221] Example 2: Emotion-based audio content

[1222] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[1223] 2. The terminal uploads the audio file and translation request parameters to the server.

[1224] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[1225] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[1226] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[1227] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[1228] The system of the present invention provides a more personalized viewing experience by realizing real-time translation and speech generation that reflects the user's emotions.

[1229] The processing flow will be explained below.

[1230] Step 1:

[1231] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[1232] Step 2:

[1233] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[1234] Step 3:

[1235] The emotion engine monitors input data such as the user's facial expressions and voice, recognizes the user's emotions in real time, and sends the recognized emotion data to the server.

[1236] Step 4:

[1237] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[1238] Step 5:

[1239] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[1240] Step 6:

[1241] The server transmits the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[1242] Step 7:

[1243] The server takes into consideration the user's emotional data received from the emotion engine and sends it to the generative AI model to generate voice data based on the translated text data in a specified voice quality or a voice quality appropriate for the emotion.

[1244] Step 8:

[1245] The generative AI model generates voice data based on the translated text data in a voice quality that matches the specified voice quality or emotion.

[1246] Step 9:

[1247] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[1248] Step 10:

[1249] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[1250] Step 11:

[1251] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[1252] Step 12:

[1253] Users can watch and listen to translated media content on their devices in their preferred language and voice quality. Based on data from the emotion engine, the content is played in a specific voice quality and context that matches the user's emotions.

[1254] Through these steps, the system provides fast and accurate real-time translation and speech generation, enabling a new viewing experience that reflects users' emotions, and allowing users to enjoy foreign content in their native language in real time.

[1255] Example 2

[1256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1257] Conventional translation systems could analyze the audio of a user-selected media file, convert it into text data, and translate it. However, the translated audio was fixed to a single voice quality, making it difficult to reproduce audio that reflected the user's emotions. Furthermore, they did not offer real-time streaming or dynamic voice quality changes in response to changes in the user's emotions. This resulted in a lack of a personalized listening experience and a lack of realism and familiarity in the translated audio.

[1258] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving a media file selected by a user; means for analyzing the audio of the received media file and converting it into text data; means for translating the text data into a specified translated language; means for generating audio data with a specified voice quality based on the translated text data; means for incorporating the generated audio data into a media file and streaming it in real time; and means for recognizing the user's emotions based on an emotion engine and dynamically changing the voice quality and the translated language. This makes it possible to generate audio that reflects the user's emotions and dynamically change the voice quality in real time, thereby providing a more personalized viewing experience.

[1259] A "user" is an entity that uses the system to select media files and direct translation and speech generation.

[1260] A "media file" is a digital data file containing audio or video that is selected by a user and that the system processes.

[1261] The "means for receiving" is a mechanism for capturing media files uploaded by users onto the server.

[1262] "Means for analyzing audio and converting it to text data" refers to algorithms or software that extract the audio portion of a media file and convert it into text format.

[1263] The "means for translating" is the algorithm or software used to convert the converted text data into the specified target language.

[1264] "Means for generating voice data" refers to algorithms or software, such as a generative AI model, that generates voice data with a specified voice quality based on translated text data.

[1265] The "means for embedding and streaming" is a mechanism for embedding the generated audio data into the original media file and transmitting it to the user's terminal in real time.

[1266] An "emotion engine" is software or a device that analyzes a user's facial expressions and voice to recognize emotions and provide that information to the system.

[1267] "Voice quality" refers to the characteristics of the timbre and tone of the generated voice data, and is an element that can be changed according to emotions and user instructions.

[1268] "Means for dynamic change" refers to a mechanism that allows the system to change settings and conditions in real time depending on the situation.

[1269] "Real-time" means that the system processes and provides results immediately with little to no delay.

[1270] MODE FOR CARRYING OUT THE INVENTION

[1271] Hardware and software used

[1272] Hardware: User devices (PCs, smartphones), servers (high-performance computing resources)

[1273] Software: Generative AI model, emotion recognition engine, translation engine, voice analysis module, streaming server

[1274] Detailed Description

[1275] This invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions, making it possible to dynamically change the voice quality and translation language of audio data based on the user's emotions.

[1276] System Configuration and Operation

[1277] 1. Select media files

[1278] The user uses the device to select the media file they want to translate. For example, the user selects the video file "lecture.mp4" from their PC.

[1279] The user inputs the source language (e.g., English), target language (e.g., Japanese), and desired voice quality (e.g., a gentle voice) through a dedicated interface.

[1280] 2. Upload media files

[1281] The terminal packages the selected media file and the translation request parameters and sends them to the server.

[1282] 3. Emotion recognition

[1283] The emotion engine monitors the user's facial expressions and voice in real time. For example, if the user makes a "surprise" expression, the emotion engine will recognize this.

[1284] The emotion engine transmits the recognized emotion information to the server.

[1285] 4. Receiving media files and analyzing audio

[1286] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[1287] The audio analysis module in the server analyzes the audio in the media file and converts it into text data. For example, the audio "Hello, welcome to the lecture" extracted from "lecture.mp4" is converted into the text "Hello, welcome to the lecture."

[1288] 5. Language Translation

[1289] The server selects the most suitable post-translation language based on the emotion information received from the emotion engine and the translation request parameters input by the user.

[1290] The server passes the text data to the translation engine, which translates it in real time. For example, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture."

[1291] 6. Speech generation and voice conversion

[1292] The server passes the translated text data to a generative AI model, which then generates speech data in the optimal voice quality based on the recognized emotion. For example, the translated text "Hello, welcome to the lecture" is generated in a voice quality appropriate for the emotion "surprise."

[1293] The generative AI model generates voice data and returns it to the server, for example, "Voice data: Hello, welcome to the lecture (surprised voice)."

[1294] 7. Regenerate media and prepare for streaming

[1295] The server incorporates the generated audio data into the original media file (e.g., video) and adds it as a new audio track.

[1296] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time, for example, converting the new media file into HLS (HTTP Live Streaming) format.

[1297] 8. User Viewing

[1298] The device receives streaming data from the server and buffers it. For example, it receives a new media file, "lecture_translated.m3u8."

[1299] Users can watch and listen to media content translated in their preferred language and voice quality. For example, they can watch a video with a voice saying, "Hello, welcome to the lecture (surprised voice)."

[1300] Specific examples

[1301] Example 1: Emotion-based video translation

[1302] 1. The user selects an English educational video from their device and specifies the Japanese translation and desired voice quality.

[1303] 2. The device uploads the video file "lecture.mp4" and the translation request parameters to the server.

[1304] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[1305] 4. The server analyzes the video file, translates the audio from English to Japanese, and changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[1306] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1307] 6. The device receives the streaming data, and the user watches the Japanese video with an astonishing voice quality.

[1308] Example 2: Emotion-based audio content

[1309] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[1310] 2. The terminal uploads the audio file and translation request parameters to the server.

[1311] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[1312] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[1313] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[1314] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[1315] This system enables real-time translation and speech generation that reflects the user's emotions, providing a more personalized viewing experience.

[1316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1317] Step 1: Select and upload media files

[1318] The user uses a terminal to select a media file to be translated. The input data is the selected media file and translation request parameters (e.g., source language, target language, desired voice quality). For example, the user selects the video file "lecture.mp4" from their PC, specifies English as the source language, Japanese as the target language, and a soft voice as the desired voice quality. This information is packaged on the terminal and uploaded to the server. The output is the media file received on the server and the translation request parameters.

[1319] Step 2: Emotion Recognition

[1320] The emotion engine monitors the user's facial expressions and voice in real time. The input data is the user's facial expressions and voice. For example, if the user makes a "surprise" expression, the emotion engine recognizes this and records "surprise" as emotional information. The emotion engine sends the recorded emotional information to the server. The output is the emotional information sent to the server.

[1321] Step 3: Receiving media files and analyzing audio

[1322] The server saves the media file and translation request parameters received from the terminal. The input data is the media file "lecture.mp4" sent from the terminal and the translation request parameters (source language: English, target language: Japanese, desired voice quality: gentle). The audio analysis module in the server extracts the audio portion of the media file and converts that audio data into text data. Specifically, the audio extracted from "lecture.mp4" "Hello, welcome to the lecture" is converted into "text: Hello, welcome to the lecture". The output is the audio converted into text data.

[1323] Step 4: Language Translation

[1324] The server selects the most appropriate target language for translation based on the emotional information received from the emotion engine and the translation request parameters entered by the user. The input data is the text data "Hello, welcome to the lecture", the emotional information "surprise", and the translation request parameters (target language: Japanese). The server passes this text data to the translation engine, which translates it in real time. Specifically, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture". The output is the translated Japanese text data.

[1325] Step 5: Speech generation and voice conversion

[1326] The server passes the translated text data to a generative AI model, which generates speech data with the optimal voice quality based on the emotional information. The input data is the translated Japanese text data "Hello, welcome to the lecture," the emotional information "surprise," and the desired voice quality (gentle voice). Specifically, the translated text "Hello, welcome to the lecture" is generated with an emphasized voice quality appropriate for the emotion of "surprise." The generative AI model generates speech data and returns it to the server. The output is the generated speech data "Hello, welcome to the lecture (surprised voice)."

[1327] Step 6: Regenerate your media and prepare it for streaming

[1328] The server incorporates the generated audio data into the original media file and adds it as a new audio track. The input data is the generated audio data "Hello, welcome to the lecture (surprised voice)" and the original media file "lecture.mp4." Specifically, a new Japanese audio track is added to the original media file. The server converts the new media file into a streaming format and prepares it for transmission to the device in real time. For example, it converts the new media file into HLS (HTTP Live Streaming) format. The output is a new media file converted into a streaming format.

[1329] Step 7: User Viewing

[1330] The device receives streaming data from the server and buffers it. The input data is a new streaming media file, "lecture_translated.m3u8." The user watches and listens to the translated media content in their preferred language and voice quality. Specifically, the user watches the video with a voice saying, "Hello, welcome to the lecture (surprised voice)." The output is the user's viewing experience.

[1331] (Application example 2)

[1332] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] Conventional media file translation systems simply translate text data into different languages ​​and generate audio data in a specified voice quality. As a result, they do not provide a personalized viewing experience that reflects the user's emotions. There is a need for a system that can provide a more interactive and emotionally sensitive media experience by dynamically changing the voice quality and translation language of audio data according to the user's emotions.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1335] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, and means for recognizing the user's emotions and reflecting that information in the audio data generation process, thereby enabling audio generation that reflects the user's emotions and real-time streaming of media files.

[1336] A "user" is an individual or organization that uses the system.

[1337] A "media file" is a digital data file that contains audio, video, or both.

[1338] The "receiving means" is a device or software that has the function of importing the specified media file into the server.

[1339] The "means for analyzing audio" refers to a technology or device that has the function of converting audio data in a received media file into text data.

[1340] "Text data" is character information analyzed from speech.

[1341] The "translation means" refers to software or a device that has the function of converting text data into a specified target language.

[1342] The "translated language" is the target language into which the original text data has been converted.

[1343] "Voice quality" refers to the characteristics of the sound or tone of the voice data.

[1344] "Means for generating voice data" refers to a technology or device that creates voice data with a specified voice quality based on translated text data.

[1345] An "incorporating means" is a technology or device that has the ability to add generated audio data to the original media file.

[1346] "Real-time streaming means" refers to a technology or device that has the ability to deliver generated media files to users without delay.

[1347] "Means for recognizing emotions" refers to technology or devices that have the function of determining the emotional state of a user from their facial expressions and voice.

[1348] "Means for reflecting in the voice data generation process" refers to a technology or device that reflects the recognized emotional information in the generation of voice.

[1349] overview

[1350] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[1351] System Configuration

[1352] The system is broadly composed of the following components:

[1353] 1. Device: The device where the user selects media files and uploads them to the server. This includes smartphones, tablets, PCs, etc.

[1354] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[1355] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[1356] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions and voice, and sends that information to the server. It includes input devices such as a camera and microphone.

[1357] Specific operation of the system

[1358] 1. Select and upload media files

[1359] The user selects a media file from the terminal and inputs the source language, target language, and desired voice quality information. The terminal then sends the media file and translation request parameters to the server.

[1360] 2. Emotion recognition

[1361] Using the device's built-in camera and microphone, the emotion engine recognizes the user's emotions from their facial expressions and voice, and sends the recognition results to the server.

[1362] 3. Speech analysis and translation

[1363] The server stores the received media file and prepares it for passing to the analysis module, which analyzes the audio of the media file and converts it into text data. The text data is then passed to the translation engine, which dynamically converts it into a translated language that matches the user's sentiment.

[1364] 4. Speech Generation

[1365] The translated text data is passed to a generative AI model, which generates new voice data with a voice quality that matches the user's emotions.

[1366] 5. Media Reassembly and Streaming

[1367] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, and the new media file is converted into a streaming format in real time and sent to the device.

[1368] Specific examples

[1369] Example 1: Emotion-based video translation

[1370] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, and while watching, the emotion engine recognizes the emotion of "surprise."

[1371] 2. The device uploads the video file and translation request parameters to the server.

[1372] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[1373] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[1374] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1375] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[1376] Prompt Sentence Examples

[1377] Example 1 (translation using the emotion of joy)

[1378] "The user is showing a happy expression. Select the English video 'Science Discovery' and translate it into Japanese. Generate it in a bright and friendly voice quality."

[1379] Example 2 (Audio content translation using surprise emotion)

[1380] "Translate the French podcast 'Le Monde Aujourd'hui' into English, selected by a user who expressed surprise. Generate an energetic voice."

[1381] Such a system will enable personalized translation and speech generation that is sensitive to the user's emotions, providing a richer and more interactive viewing experience.

[1382] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1383] Step 1:

[1384] The user selects a media file using a terminal and inputs the source language, target language, and desired voice quality information. The input data includes the media file, translation parameters, and voice quality parameters. The terminal sends this data to the server. The output is the media file and parameter information transferred to the server.

[1385] Step 2:

[1386] The device uses a built-in camera and microphone to transmit the user's facial expressions and voice in real time to the emotion engine. The emotion engine analyzes the data obtained from these input devices and recognizes the user's emotions. The output is the recognized emotion information, which is sent to the server.

[1387] Step 3:

[1388] The server stores the received media files and prepares them for passing to the audio analysis module, which converts the audio data in the media files into text data. The input is the media file and the output is text data.

[1389] Step 4:

[1390] The server passes the text data to a translation engine, which converts it into the specified target language. The user's emotional information is also used as input, and a translation appropriate to the emotion is performed. The input is text data and emotional information, and the output is translated text data.

[1391] Step 5:

[1392] The server passes the translated text data to a generative AI model, which generates voice data with the specified voice quality. The voice quality is adjusted based on emotional information. The input is the translated text data and emotional information, and the output is the generated voice data.

[1393] Step 6:

[1394] The server embeds the generated audio data into the original media file and adds it as a new audio track. This operation creates a new media file. The inputs are the original media file and the generated audio data, and the output is the new media file.

[1395] Step 7:

[1396] The server converts the new media file into a streaming format and sends it to the device in real time. The device receives the streaming data and buffers it for the user to view. The input is the new media file, and the output is the streaming data.

[1397] In this way, a system is realized that provides a personalized viewing experience based on the user's emotions.

[1398] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1399] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1400] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1401] [Fourth embodiment]

[1402] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1403] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1404] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1405] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1406] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1407] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1408] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1409] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1410] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1411] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1412] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1413] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1414] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1415] MODE FOR CARRYING OUT THE INVENTION

[1416] overview

[1417] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[1418] composition

[1419] The system is broadly composed of the following components:

[1420] 1. User's device: The device where the user selects media files and uploads them to the server.

[1421] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[1422] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[1423] Program processing overview

[1424] 1. Select and upload media files

[1425] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[1426] The terminal uploads the media file along with this information to the server.

[1427] 2. Receiving media files and analyzing audio

[1428] The server transfers the media files received from the terminal to the analysis module.

[1429] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[1430] 3. Language Translation

[1431] The server sends the analyzed text data to a translation engine and converts it into the specified target language.

[1432] The translation engine performs the translation work and generates translated text data.

[1433] 4. Speech generation and voice conversion

[1434] The server passes the translated text data to a generative AI model, which generates audio data with the specified voice quality.

[1435] The generative AI model creates the audio data and returns it to the server.

[1436] 5. Regenerate media and prepare for streaming

[1437] The server incorporates the generated audio data into the original media file and converts it into a streaming format in real time.

[1438] The server prepares to send streaming data to the terminal.

[1439] 6. User Media Viewing

[1440] The device receives streaming data from the server and buffers it for smooth playback.

[1441] The user watches and listens to the translated media content.

[1442] Specific examples

[1443] Example 1: Watching an English documentary in Japanese

[1444] 1. The user selects an English documentary video from their device and specifies the Japanese translation and desired voice quality (for example, a gentle female voice).

[1445] 2. The device uploads the video file and translation request parameters to the server.

[1446] 3. The server receives the video file, analyzes the audio using the analysis module, and converts the English audio into text data.

[1447] 4. The server translates the English text data into Japanese using a translation engine.

[1448] 5. The server uses the generated AI model to convert the Japanese text data into voice data with the specified voice quality.

[1449] 6. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1450] 7. The device receives the streaming data and the user watches the documentary video in Japanese.

[1451] Example 2: Listening to a French radio program in German

[1452] 1. The user selects an audio file of a French radio program and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[1453] 2. The terminal uploads the audio file and translation request parameters to the server.

[1454] 3. The server receives the audio file, analyzes the audio using the analysis module, and converts the French audio into text data.

[1455] 4. The server translates the French text data into German using a translation engine.

[1456] 5. The server uses the generative AI model to convert the German text data into audio data with the specified voice quality.

[1457] 6. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[1458] 7. The device receives the streaming data and the user listens to the radio program in German.

[1459] Thus, the present invention is a system that provides a new viewing experience to a user by translating a media file selected by the user into another language in real time and playing it back in a specified voice quality.

[1460] The processing flow will be explained below.

[1461] Step 1:

[1462] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[1463] Step 2:

[1464] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[1465] Step 3:

[1466] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[1467] Step 4:

[1468] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[1469] Step 5:

[1470] The server passes the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[1471] Step 6:

[1472] The server passes the translated text data to the generative AI model, which generates voice data in the voice quality specified by the user. The generative AI model then creates synthetic voice data in the specified voice quality based on the translated text.

[1473] Step 7:

[1474] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[1475] Step 8:

[1476] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[1477] Step 9:

[1478] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[1479] Step 10:

[1480] Users can watch and listen to translated media content on their devices in their preferred language and voice quality, allowing them to enjoy foreign content in their native language, such as Japanese, in real time.

[1481] Through these steps, the system provides fast and accurate real-time translation and speech generation, bringing a new listening experience to users, while also providing a user-friendly interface and smooth operation.

[1482] Example 1

[1483] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] Conventional systems have the drawback of being complex and difficult to process in real time, as they require users to translate selected media files into other languages ​​and play them in a specified voice quality. Furthermore, the quality of the voice generation is inconsistent, resulting in an inconsistent user experience.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1486] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for using an artificial intelligence model to generate audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into the original media file and converting it into a streaming format in real time for transmission, and means for the terminal to receive the streaming data and buffer it for playback. This streamlines the process from media file translation to audio playback, allowing users to enjoy a consistent, high-quality audio experience.

[1487] A "user" is a person who selects a media file and specifies translation requests and audio playback.

[1488] A "terminal" is a device operated by a user that selects and uploads media files, and receives and buffers streaming data.

[1489] A "server" is a computing device that parses media files, translates them, generates audio, and transmits streaming data.

[1490] A "media file" is a file of digital content, including audio and video.

[1491] "Analysis" refers to the process of converting the audio data in a media file into text data.

[1492] "Text data" refers to character information converted from audio data.

[1493] "Translation" refers to the process of converting text data into a specified language.

[1494] The "translated language" is the language to be translated into, and is the output language desired by the user.

[1495] "Voice quality" refers to the characteristics of the voice when generating voice data, and is the voice characteristics (for example, gender, tone of voice, etc.) designated by the user.

[1496] "Artificial intelligence model" refers to a machine learning model used to generate audio data from text data.

[1497] "Generation" refers to the process of creating audio data using an artificial intelligence model.

[1498] "Audio data" refers to digital data of the generated audio.

[1499] "Embedding" refers to the process of integrating generated audio data into the original media file.

[1500] "Real-time" refers to providing immediate processing and results without delay.

[1501] "Streaming" refers to the continuous delivery and playback of media files over the Internet.

[1502] "Buffering" is the process of temporarily storing a portion of streaming data to ensure smooth playback.

[1503] MODE FOR CARRYING OUT THE INVENTION

[1504] Overall structure

[1505] The present invention relates to a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[1506] Hardware and software used

[1507] The main hardware and software used to implement the system are as follows:

[1508] User devices: PCs, smartphones, tablets, etc.

[1509] Server: Cloud or on-premise server with high-performance computing resources

[1510] Speech analysis library: Google Speech-to-Text API, Microsoft Azure Speech-to-Text, etc.

[1511] Translation libraries: Google Translate API, DeepL, etc.

[1512] Speech generation libraries: such as OpenAI's speech generation API

[1513] Media editing libraries: FFmpeg, etc.

[1514] Process Overview

[1515] 1. Select and upload media files

[1516] The user selects a media file through the device's UI and specifies the source language, target language, and desired voice quality.

[1517] The terminal packages the selected media file and the translation request parameters and sends the package to the server in an HTTP request.

[1518] 2. Receiving media files and analyzing audio

[1519] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters.

[1520] An analysis module (for example, Google Speech-to-Text API) in the server converts the audio data of the media file into text data.

[1521] 3. Language Translation

[1522] The server sends the parsed text data to a translation library (e.g., Google Translate API or DeepL), which translates the text data into the specified target language.

[1523] 4. Speech generation and voice conversion

[1524] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API), which generates voice data with the specified voice quality.

[1525] 5. Regenerate media and prepare for streaming

[1526] The server embeds the generated audio data into the original media file, converts it into a streaming format (e.g., HLS or DASH) in real time, and transmits it.

[1527] 6. User Media Viewing

[1528] The terminal buffers the streaming data received from the server, allowing the user to watch the media file with smoothly translated audio.

[1529] Specific examples

[1530] Example 1: Watching an English documentary in Japanese

[1531] 1. The user selects an English documentary video on the device's UI and specifies the Japanese translation and desired voice quality (e.g., a soft female voice).

[1532] 2. The device combines the video file and translation request parameters into an HTTP request and uploads it to the server.

[1533] 3. The server receives the video file and converts the audio into text data using the Google Speech-to-Text API.

[1534] 4. The server uses the Google Translate API to translate the English text into Japanese.

[1535] 5. The server uses OpenAI's speech generation API to convert the Japanese text into audio data with the specified voice quality.

[1536] 6. The server uses FFmpeg to incorporate the new Japanese audio into the original video, convert it to HLS format, and generate a streaming URL for playback.

[1537] 7. The device receives the streaming URL and the user watches the documentary video with the new Japanese audio.

[1538] Example 2: Listening to a French radio program in German

[1539] 1. The user selects a French radio program on the device and specifies the German translation and the desired voice quality (e.g., a deep male voice).

[1540] 2. The device bundles the audio file and translation request parameters into an HTTP request and uploads it to the server.

[1541] 3. The server receives the audio file and converts it into text using the Google Speech-to-Text API.

[1542] 4. The server uses DeepL to translate the French text into German.

[1543] 5. The server uses OpenAI's speech generation API to convert the German text into audio data with the specified voice quality.

[1544] 6. The server uses FFmpeg to embed the new German audio into the original audio file, convert it to HLS format, and generate a streaming URL.

[1545] 7. The device receives the streaming URL and the user listens to the radio program with the new German voice.

[1546] This allows users to experience real-time translation of selected media files and playback in the voice quality they specify.

[1547] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1548] Step 1: Select and upload media files

[1549] The user selects a media file through the device's UI. As input, the user specifies the media file (e.g., a video file), the source language, the target language, and the desired voice quality.

[1550] The device packages the selected media file and the translation request parameters and creates an HTTP request. The output is an HTTP request that is sent to the server.

[1551] The device executes this HTTP request and uploads the translation request along with the media file to the server.

[1552] Step 2: Receiving media files and analyzing audio

[1553] The server analyzes the HTTP request received from the terminal and extracts the media file and translation request parameters. The input is the HTTP request, and the output is the media file and translation request parameters.

[1554] The server uses an analysis module (e.g., Google Speech-to-Text API) to convert the audio data of the media file into text data. The input is the media file, and the output is text data.

[1555] The server temporarily stores the converted text data and prepares it for the next processing step.

[1556] Step 3: Language Translation

[1557] The server sends the parsed text data to a translation library (e.g., Google Translate API, DeepL). The input is the text data and translation request parameters, and the output is the translated text data.

[1558] The translation library converts the text data into the specified target language and generates translated text data.

[1559] The server receives the translated text data and temporarily stores it.

[1560] Step 4: Speech generation and voice conversion

[1561] The server passes the translated text data and desired voice quality information to a generative AI model (e.g., OpenAI's voice generation API). The input is the translated text data and voice quality information, and the output is the generated voice data.

[1562] The generative AI model generates voice data with the specified voice quality based on the text data and returns it to the server.

[1563] The server receives the generated audio data and stores it for further processing steps.

[1564] Step 5: Regenerate your media and prepare it for streaming

[1565] The server embeds the generated audio data into the original media file using a media editing library (e.g. FFmpeg). The input is the original media file and the generated audio data, and the output is a new media file.

[1566] The server converts the new media file, including the audio, into a streaming format (e.g., HLS, DASH) in real time. The input is the new media file, and the output is the streaming data.

[1567] The server generates a URL for the streaming data and prepares it for transmission to the terminal.

[1568] Step 6: User Views Media

[1569] The device obtains streaming data using the streaming URL received from the server. The input is the streaming URL, and the output is the streaming data.

[1570] The device receives the streaming data and buffers it for playback. The input is the streaming data and the output is the buffered data.

[1571] The user watches the media content with the translated voice through the terminal.

[1572] (Application example 1)

[1573] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1574] Conventional media translation systems have struggled to translate and generate speech in real time that matches the user's desired language and voice quality. They also lack features to improve the user experience, such as buffering streaming data and upload status notifications. The objective of this invention is to solve these problems and provide a system that supports multiple languages ​​and plays media in real time with a voice quality specified by the user.

[1575] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1576] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, means for allowing a user to specify a desired language and voice quality when uploading an arbitrary media file, and means for buffering the received streaming data to achieve smooth playback, thereby enabling a user to watch or listen to media content in a desired language and voice quality in real time.

[1577] "Means for receiving user-selected media files" refers to the devices and software used to transmit user-specified multimedia files to the server and for the server to receive them.

[1578] "Means for analyzing the audio of received media files and converting it into text data" refers to devices or software that extract the audio data from media files and convert it into text data using voice recognition technology.

[1579] "Means for translating text data into a specified target language" refers to a translation engine or software for converting parsed character data into another specified language.

[1580] "Means for generating voice data with a specified voice quality based on translated text data" refers to a generative AI model or voice synthesis technology that generates a voice file with a voice quality specified by the user based on translated text data.

[1581] "Means for incorporating generated audio data into a media file and streaming it in real time" means equipment or software that integrates newly generated audio data with the original media file and transmits and plays it to a user in real time.

[1582] "A means by which a user can specify a desired language and voice quality when uploading any media file" means an interface or software configuration feature that allows a user to select or specify the language into which a media file is to be translated and the voice quality to be used when uploading a media file.

[1583] "Means for buffering received streaming data to ensure smooth playback" refers to technology or software that temporarily stores streaming data transmitted in real time for playback, enabling uninterrupted playback.

[1584] This invention provides a system that translates user-selected media files in real time and plays them in a specified voice quality. The system operates by integrating the user's device, a server, and a generative AI model.

[1585] 1. System Configuration

[1586] User's device

[1587] The user's device is used to select media files and enter the necessary information. In this example, a smartphone (iOS or Android) is used. The smartphone app is built with React Native and provides an interface where users can upload media files and specify the desired translation language and voice quality.

[1588] server

[1589] The server is built on Node.js and Express and offers the following features:

[1590] Receiving media files

[1591] Speech analysis and text conversion (using FFmpeg)

[1592] Translation (using Google Translate API or AWS Translate)

[1593] Speech generation (built in Python and using TensorFlow to run generative AI models such as Tacotron2 and WaveGlow)

[1594] Sending Streaming Data

[1595] Generative AI Models

[1596] The generative AI model generates voice data based on the translated text data in a voice quality specified by the user, specifically using Tacotron2 and WaveGlow.

[1597] 2. System Operation

[1598] Selecting and uploading media files

[1599] Using a smartphone app, users can select any media file and specify the desired translation language and voice quality, and this information is then uploaded from the device to the server.

[1600] Receiving media files and analyzing audio

[1601] The server receives the uploaded media file and first begins audio analysis. For audio analysis, it uses FFmpeg to extract the audio data from the media file and converts it into text data using speech recognition technology.

[1602] Language Translation

[1603] The server sends the text data to Google Translate API or AWS Translate, which translates it into the specified language, generating translated text data.

[1604] Speech generation and voice conversion

[1605] The translated text data is sent to a generative AI model (Tacotron2, WaveGlow), which generates voice data with the specified voice quality. This voice data is then returned to the server.

[1606] Regenerate media and prepare it for streaming

[1607] The generated audio data is embedded into the original media file, and the regenerated media file is converted into a streaming format in real time. The structured streaming data is then sent to the user's smartphone app.

[1608] User media viewing

[1609] The smartphone app receives and buffers the streaming data, allowing users to watch and listen to the media file in real time, integrated with audio generated in the specified translation language and voice quality.

[1610] 3. Specific Examples

[1611] Example 1: Watching a French movie in Japanese

[1612] 1. A user selects a French movie file on a smartphone app and specifies a Japanese translation and a "calm male voice."

[1613] 2. The movie file is uploaded to the server and audio analysis is performed.

[1614] 3. The French audio data is converted into text and then translated into Japanese using a translation API.

[1615] 4. The generative AI model generates speech with the specified voice quality based on the Japanese text.

[1616] 5. The generated audio is integrated into the original movie file and sent to the app as streaming data.

[1617] 6. Users can smoothly watch movies converted into Japanese audio.

[1618] Example 2: Example of a prompt statement

[1619] An example prompt for watching a French movie in Japanese is:

[1620] Upload file: france_movie.mp4

[1621] Source language: French

[1622] Target language: Japanese

[1623] Voice quality: Gentle male voice

[1624] As described above, this system allows users to enjoy media content in different languages ​​in real time with the voice quality of their choice through simple operations.

[1625] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1626] Step 1:

[1627] The user uses the terminal to select any media file. An input interface is displayed, allowing the user to specify the desired translation language and voice quality. The user enters this information and clicks the upload button. The inputs at this time are the media file, source language, target language, and desired voice quality. Based on this input, the terminal sends the media file and the specified information to the server.

[1628] Step 2:

[1629] The server receives media files uploaded by users. It checks the contents of the received media files and prepares them for the next step. Specific operations include saving the files and retrieving metadata (file name, size, format, etc.). The input in this case is the uploaded media file, and the output is the saved media file and its metadata.

[1630] Step 3:

[1631] The server extracts audio data from the stored media files. This process uses FFmpeg. The extracted audio data is then analyzed and converted into text data using speech recognition technology. This involves decoding the audio file and converting it using a speech recognition API (such as the Google Speech-to-Text API). The input is audio data, and the output is text data of the audio.

[1632] Step 4:

[1633] The server translates the obtained text data into the specified target language. Specifically, it translates the text data using Google Translate API or AWS Translate. The input is the original text data and the specified target language, and the output is the translated text data.

[1634] Step 5:

[1635] The server uses a generative AI model (such as Tacotron2 or WaveGlow) to generate voice data with a specified voice quality based on the translated text data. Specifically, the translated text data is input into the generative AI model to generate voice data. The input at this time is the translated text data and the specified voice quality, and the output is the generated voice data.

[1636] Step 6:

[1637] The server then incorporates the generated audio data into the original media file. Specifically, it uses FFmpeg to integrate the audio data into the media file and convert it into a streaming format. The input is the generated audio data and the original media file, and the output is the converted streaming file.

[1638] Step 7:

[1639] The server prepares to send streaming data to the terminal. Specifically, it uses a streaming protocol (such as HLS or DASH) to prepare the streaming data and starts sending it to the terminal. The input at this time is the converted streaming file, and the output is the streaming data.

[1640] Step 8:

[1641] The terminal buffers the streaming data received from the server in order to play it back. Once buffering is complete, it starts playing the media to the user. Specifically, it temporarily stores the received data and performs playback processing. The input at this time is streaming data, and the output is playback media that the user can view.

[1642] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1643] MODE FOR CARRYING OUT THE INVENTION

[1644] overview

[1645] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[1646] composition

[1647] The system is broadly composed of the following components:

[1648] 1. User's device: The device where the user selects media files and uploads them to the server.

[1649] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[1650] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[1651] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions, voice, etc. and sends that information to the server.

[1652] Program processing overview

[1653] 1. Select and upload media files

[1654] The user selects a media file from the terminal and inputs information on the source language, target language, and desired voice quality.

[1655] The terminal sends the media file and the translation request parameters to the server.

[1656] 2. Emotion recognition

[1657] The emotion engine recognizes emotions from the user's facial expressions and voice and sends that information to the server.

[1658] 3. Receiving media files and analyzing audio

[1659] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[1660] An analysis module in the server analyzes the audio of the media file and converts it into text data.

[1661] 4. Language Translation

[1662] The server automatically selects a translation language that is particularly suited to the user's emotion based on the recognized user emotion and the input translation request parameters.

[1663] The server passes the text data to a translation engine, which converts it into the selected target language in real time.

[1664] 5. Speech generation and voice conversion

[1665] The server passes the translated text data to a generative AI model, which generates voice data with the optimal voice quality based on the recognized user emotions.

[1666] The generative AI model creates the audio data and returns it to the server.

[1667] 6. Regenerate media and prepare for streaming

[1668] The server incorporates the generated audio data into the original media file, adding it as a new audio track.

[1669] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time.

[1670] 7. User Media Viewing

[1671] The terminal receives the streaming data from the server and buffers it.

[1672] Users can view and listen to translated media content in their preferred language and voice quality.

[1673] Specific examples

[1674] Example 1: Emotion-based video translation

[1675] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, but the emotion engine recognizes the emotion of "surprise" while watching.

[1676] 2. The device uploads the video file and translation request parameters to the server.

[1677] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[1678] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[1679] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1680] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[1681] Example 2: Emotion-based audio content

[1682] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[1683] 2. The terminal uploads the audio file and translation request parameters to the server.

[1684] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[1685] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[1686] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[1687] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[1688] The system of the present invention provides a more personalized viewing experience by realizing real-time translation and speech generation that reflects the user's emotions.

[1689] The processing flow will be explained below.

[1690] Step 1:

[1691] The user selects a media file (video or audio) through the terminal interface and inputs the source language, target language, and desired voice quality.

[1692] Step 2:

[1693] The device sends the user-selected media file and specified translation parameters (source language, target language, desired voice quality) to the server. The device monitors the file upload progress in real time and displays it to the user.

[1694] Step 3:

[1695] The emotion engine monitors input data such as the user's facial expressions and voice, recognizes the user's emotions in real time, and sends the recognized emotion data to the server.

[1696] Step 4:

[1697] The server saves the media file received from the device and notifies the device that the reception is complete. The server prepares to pass the media file to the analysis module.

[1698] Step 5:

[1699] The server's analysis module puts the media file through an audio analysis process and converts the audio data into text data, using speech recognition technology to extract text information from the audio.

[1700] Step 6:

[1701] The server transmits the generated text data to a translation engine, which converts the data into the specified target language in real time. The translation engine translates the text data and generates text data in the new language.

[1702] Step 7:

[1703] The server takes into consideration the user's emotional data received from the emotion engine and sends it to the generative AI model to generate voice data based on the translated text data in a specified voice quality or a voice quality appropriate for the emotion.

[1704] Step 8:

[1705] The generative AI model generates voice data based on the translated text data in a voice quality that matches the specified voice quality or emotion.

[1706] Step 9:

[1707] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, which can then be played in the user's preferred language and voice quality.

[1708] Step 10:

[1709] The server converts the newly created media file into a streaming format and prepares it for transmission to the device in real time. The server transfers the data to the user using the appropriate protocol.

[1710] Step 11:

[1711] The device receives the streaming data from the server and buffers the received data, allowing the media file to be played smoothly.

[1712] Step 12:

[1713] Users can watch and listen to translated media content on their devices in their preferred language and voice quality. Based on data from the emotion engine, the content is played in a specific voice quality and context that matches the user's emotions.

[1714] Through these steps, the system provides fast and accurate real-time translation and speech generation, enabling a new viewing experience that reflects users' emotions, and allowing users to enjoy foreign content in their native language in real time.

[1715] Example 2

[1716] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1717] Conventional translation systems could analyze the audio of a user-selected media file, convert it into text data, and translate it. However, the translated audio was fixed to a single voice quality, making it difficult to reproduce audio that reflected the user's emotions. Furthermore, they did not offer real-time streaming or dynamic voice quality changes in response to changes in the user's emotions. This resulted in a lack of a personalized listening experience and a lack of realism and familiarity in the translated audio.

[1718] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving a media file selected by a user; means for analyzing the audio of the received media file and converting it into text data; means for translating the text data into a specified translated language; means for generating audio data with a specified voice quality based on the translated text data; means for incorporating the generated audio data into a media file and streaming it in real time; and means for recognizing the user's emotions based on an emotion engine and dynamically changing the voice quality and the translated language. This makes it possible to generate audio that reflects the user's emotions and dynamically change the voice quality in real time, thereby providing a more personalized viewing experience.

[1719] A "user" is an entity that uses the system to select media files and direct translation and speech generation.

[1720] A "media file" is a digital data file containing audio or video that is selected by a user and that the system processes.

[1721] The "means for receiving" is a mechanism for capturing media files uploaded by users onto the server.

[1722] "Means for analyzing audio and converting it to text data" refers to algorithms or software that extract the audio portion of a media file and convert it into text format.

[1723] The "means for translating" is the algorithm or software used to convert the converted text data into the specified target language.

[1724] "Means for generating voice data" refers to algorithms or software, such as a generative AI model, that generates voice data with a specified voice quality based on translated text data.

[1725] The "means for embedding and streaming" is a mechanism for embedding the generated audio data into the original media file and transmitting it to the user's terminal in real time.

[1726] An "emotion engine" is software or a device that analyzes a user's facial expressions and voice to recognize emotions and provide that information to the system.

[1727] "Voice quality" refers to the characteristics of the timbre and tone of the generated voice data, and is an element that can be changed according to emotions and user instructions.

[1728] "Means for dynamic change" refers to a mechanism that allows the system to change settings and conditions in real time depending on the situation.

[1729] "Real-time" means that the system processes and provides results immediately with little to no delay.

[1730] MODE FOR CARRYING OUT THE INVENTION

[1731] Hardware and software used

[1732] Hardware: User devices (PCs, smartphones), servers (high-performance computing resources)

[1733] Software: Generative AI model, emotion recognition engine, translation engine, voice analysis module, streaming server

[1734] Detailed Description

[1735] This invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions, making it possible to dynamically change the voice quality and translation language of audio data based on the user's emotions.

[1736] System Configuration and Operation

[1737] 1. Select media files

[1738] The user uses the device to select the media file they want to translate. For example, the user selects the video file "lecture.mp4" from their PC.

[1739] The user inputs the source language (e.g., English), target language (e.g., Japanese), and desired voice quality (e.g., a gentle voice) through a dedicated interface.

[1740] 2. Upload media files

[1741] The terminal packages the selected media file and the translation request parameters and sends them to the server.

[1742] 3. Emotion recognition

[1743] The emotion engine monitors the user's facial expressions and voice in real time. For example, if the user makes a "surprise" expression, the emotion engine will recognize this.

[1744] The emotion engine transmits the recognized emotion information to the server.

[1745] 4. Receiving media files and analyzing audio

[1746] The server stores the media files received from the device and prepares them for passing to the audio analysis module.

[1747] The audio analysis module in the server analyzes the audio in the media file and converts it into text data. For example, the audio "Hello, welcome to the lecture" extracted from "lecture.mp4" is converted into the text "Hello, welcome to the lecture."

[1748] 5. Language Translation

[1749] The server selects the most suitable post-translation language based on the emotion information received from the emotion engine and the translation request parameters input by the user.

[1750] The server passes the text data to the translation engine, which translates it in real time. For example, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture."

[1751] 6. Speech generation and voice conversion

[1752] The server passes the translated text data to a generative AI model, which then generates speech data in the optimal voice quality based on the recognized emotion. For example, the translated text "Hello, welcome to the lecture" is generated in a voice quality appropriate for the emotion "surprise."

[1753] The generative AI model generates voice data and returns it to the server, for example, "Voice data: Hello, welcome to the lecture (surprised voice)."

[1754] 7. Regenerate media and prepare for streaming

[1755] The server incorporates the generated audio data into the original media file (e.g., video) and adds it as a new audio track.

[1756] The server converts the new media file into a streaming format and prepares it for transmission to the device in real time, for example, converting the new media file into HLS (HTTP Live Streaming) format.

[1757] 8. User Viewing

[1758] The device receives streaming data from the server and buffers it. For example, it receives a new media file, "lecture_translated.m3u8."

[1759] Users can watch and listen to media content translated in their preferred language and voice quality. For example, they can watch a video with a voice saying, "Hello, welcome to the lecture (surprised voice)."

[1760] Specific examples

[1761] Example 1: Emotion-based video translation

[1762] 1. The user selects an English educational video from their device and specifies the Japanese translation and desired voice quality.

[1763] 2. The device uploads the video file "lecture.mp4" and the translation request parameters to the server.

[1764] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[1765] 4. The server analyzes the video file, translates the audio from English to Japanese, and changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[1766] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1767] 6. The device receives the streaming data, and the user watches the Japanese video with an astonishing voice quality.

[1768] Example 2: Emotion-based audio content

[1769] 1. The user selects a French podcast and the emotion engine recognizes the "Relaxed" state.

[1770] 2. The terminal uploads the audio file and translation request parameters to the server.

[1771] 3. The emotion engine recognizes the user's "relaxed" emotion and sends it to the server.

[1772] 4. The server translates the French text data into German and generates audio data in a relaxed voice quality.

[1773] 5. The server incorporates the new German audio data into the radio program file and sends it to the terminal in streaming format.

[1774] 6. The device receives the streaming data and the user listens to the German podcast in a voice quality suitable for a relaxed state.

[1775] This system enables real-time translation and speech generation that reflects the user's emotions, providing a more personalized viewing experience.

[1776] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1777] Step 1: Select and upload media files

[1778] The user uses a terminal to select a media file to be translated. The input data is the selected media file and translation request parameters (e.g., source language, target language, desired voice quality). For example, the user selects the video file "lecture.mp4" from their PC, specifies English as the source language, Japanese as the target language, and a soft voice as the desired voice quality. This information is packaged on the terminal and uploaded to the server. The output is the media file received on the server and the translation request parameters.

[1779] Step 2: Emotion Recognition

[1780] The emotion engine monitors the user's facial expressions and voice in real time. The input data is the user's facial expressions and voice. For example, if the user makes a "surprise" expression, the emotion engine recognizes this and records "surprise" as emotional information. The emotion engine sends the recorded emotional information to the server. The output is the emotional information sent to the server.

[1781] Step 3: Receiving media files and analyzing audio

[1782] The server saves the media file and translation request parameters received from the terminal. The input data is the media file "lecture.mp4" sent from the terminal and the translation request parameters (source language: English, target language: Japanese, desired voice quality: gentle). The audio analysis module in the server extracts the audio portion of the media file and converts that audio data into text data. Specifically, the audio extracted from "lecture.mp4" "Hello, welcome to the lecture" is converted into "text: Hello, welcome to the lecture". The output is the audio converted into text data.

[1783] Step 4: Language Translation

[1784] The server selects the most appropriate target language for translation based on the emotional information received from the emotion engine and the translation request parameters entered by the user. The input data is the text data "Hello, welcome to the lecture", the emotional information "surprise", and the translation request parameters (target language: Japanese). The server passes this text data to the translation engine, which translates it in real time. Specifically, the English text "Hello, welcome to the lecture" is translated into Japanese as "Hello, welcome to the lecture". The output is the translated Japanese text data.

[1785] Step 5: Speech generation and voice conversion

[1786] The server passes the translated text data to a generative AI model, which generates speech data with the optimal voice quality based on the emotional information. The input data is the translated Japanese text data "Hello, welcome to the lecture," the emotional information "surprise," and the desired voice quality (gentle voice). Specifically, the translated text "Hello, welcome to the lecture" is generated with an emphasized voice quality appropriate for the emotion of "surprise." The generative AI model generates speech data and returns it to the server. The output is the generated speech data "Hello, welcome to the lecture (surprised voice)."

[1787] Step 6: Regenerate your media and prepare it for streaming

[1788] The server incorporates the generated audio data into the original media file and adds it as a new audio track. The input data is the generated audio data "Hello, welcome to the lecture (surprised voice)" and the original media file "lecture.mp4." Specifically, a new Japanese audio track is added to the original media file. The server converts the new media file into a streaming format and prepares it for transmission to the device in real time. For example, it converts the new media file into HLS (HTTP Live Streaming) format. The output is a new media file converted into a streaming format.

[1789] Step 7: User Viewing

[1790] The device receives streaming data from the server and buffers it. The input data is a new streaming media file, "lecture_translated.m3u8." The user watches and listens to the translated media content in their preferred language and voice quality. Specifically, the user watches the video with a voice saying, "Hello, welcome to the lecture (surprised voice)." The output is the user's viewing experience.

[1791] (Application example 2)

[1792] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1793] Conventional media file translation systems simply translate text data into different languages ​​and generate audio data in a specified voice quality. As a result, they do not provide a personalized viewing experience that reflects the user's emotions. There is a need for a system that can provide a more interactive and emotionally sensitive media experience by dynamically changing the voice quality and translation language of audio data according to the user's emotions.

[1794] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1795] In this invention, the server includes means for receiving a media file selected by a user, means for analyzing the audio of the received media file and converting it into text data, means for translating the text data into a specified translation language, means for generating audio data with a specified voice quality based on the translated text data, means for incorporating the generated audio data into a media file and streaming it in real time, and means for recognizing the user's emotions and reflecting that information in the audio data generation process, thereby enabling audio generation that reflects the user's emotions and real-time streaming of media files.

[1796] A "user" is an individual or organization that uses the system.

[1797] A "media file" is a digital data file that contains audio, video, or both.

[1798] The "receiving means" is a device or software that has the function of importing the specified media file into the server.

[1799] The "means for analyzing audio" refers to a technology or device that has the function of converting audio data in a received media file into text data.

[1800] "Text data" is character information analyzed from speech.

[1801] The "translation means" refers to software or a device that has the function of converting text data into a specified target language.

[1802] The "translated language" is the target language into which the original text data has been converted.

[1803] "Voice quality" refers to the characteristics of the sound or tone of the voice data.

[1804] "Means for generating voice data" refers to a technology or device that creates voice data with a specified voice quality based on translated text data.

[1805] An "incorporating means" is a technology or device that has the ability to add generated audio data to the original media file.

[1806] "Real-time streaming means" refers to a technology or device that has the ability to deliver generated media files to users without delay.

[1807] "Means for recognizing emotions" refers to technology or devices that have the function of determining the emotional state of a user from their facial expressions and voice.

[1808] "Means for reflecting in the voice data generation process" refers to a technology or device that reflects the recognized emotional information in the generation of voice.

[1809] overview

[1810] The present invention combines a system that translates user-selected media files in real time and plays them in a specified voice quality with an emotion engine that recognizes the user's emotions. This system can dynamically change the voice quality and translation language of the audio data based on the user's emotions.

[1811] System Configuration

[1812] The system is broadly composed of the following components:

[1813] 1. Device: The device where the user selects media files and uploads them to the server. This includes smartphones, tablets, PCs, etc.

[1814] 2. Server: Computing resources for analyzing received media files and generating audio data using translation and generative AI models.

[1815] 3. Generative AI model: An artificial intelligence model that generates voice data with a specified voice quality based on translated text data.

[1816] 4. Emotion engine: A device that recognizes emotions from the user's facial expressions and voice, and sends that information to the server. It includes input devices such as a camera and microphone.

[1817] Specific operation of the system

[1818] 1. Select and upload media files

[1819] The user selects a media file from the terminal and inputs the source language, target language, and desired voice quality information. The terminal then sends the media file and translation request parameters to the server.

[1820] 2. Emotion recognition

[1821] Using the device's built-in camera and microphone, the emotion engine recognizes the user's emotions from their facial expressions and voice, and sends the recognition results to the server.

[1822] 3. Speech analysis and translation

[1823] The server stores the received media file and prepares it for passing to the analysis module, which analyzes the audio of the media file and converts it into text data. The text data is then passed to the translation engine, which dynamically converts it into a translated language that matches the user's sentiment.

[1824] 4. Speech Generation

[1825] The translated text data is passed to a generative AI model, which generates new voice data with a voice quality that matches the user's emotions.

[1826] 5. Media Reassembly and Streaming

[1827] The server then incorporates the generated audio data into the original media file, adding it as a new audio track, and the new media file is converted into a streaming format in real time and sent to the device.

[1828] Specific examples

[1829] Example 1: Emotion-based video translation

[1830] 1. A user selects an English educational video on their device, specifies the Japanese translation and desired voice quality, and while watching, the emotion engine recognizes the emotion of "surprise."

[1831] 2. The device uploads the video file and translation request parameters to the server.

[1832] 3. The emotion engine recognizes the user's "surprise" emotion and sends it to the server.

[1833] 4. The server analyzes the video file and translates the audio from English to Japanese, but changes the voice quality to emphasize it based on the "surprise" from the emotion engine.

[1834] 5. The server incorporates the new Japanese audio data into the original video file and sends it to the terminal in streaming format.

[1835] 6. The device receives the streaming data and the user watches the Japanese video with a voice quality appropriate for the emotion of surprise.

[1836] Prompt Sentence Examples

[1837] Example 1 (translation using the emotion of joy)

[1838] "The user is showing a happy expression. Select the English video 'Science Discovery' and translate it into Japanese. Generate it in a bright and friendly voice quality."

[1839] Example 2 (Audio content translation using surprise emotion)

[1840] "Translate the French podcast 'Le Monde Aujourd'hui' into English, selected by a user who expressed surprise. Generate an energetic voice."

[1841] Such a system will enable personalized translation and speech generation that is sensitive to the user's emotions, providing a richer and more interactive viewing experience.

[1842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1843] Step 1:

[1844] The user selects a media file using a terminal and inputs the source language, target language, and desired voice quality information. The input data includes the media file, translation parameters, and voice quality parameters. The terminal sends this data to the server. The output is the media file and parameter information transferred to the server.

[1845] Step 2:

[1846] The device uses a built-in camera and microphone to transmit the user's facial expressions and voice in real time to the emotion engine. The emotion engine analyzes the data obtained from these input devices and recognizes the user's emotions. The output is the recognized emotion information, which is sent to the server.

[1847] Step 3:

[1848] The server stores the received media files and prepares them for passing to the audio analysis module, which converts the audio data in the media files into text data. The input is the media file and the output is text data.

[1849] Step 4:

[1850] The server passes the text data to a translation engine, which converts it into the specified target language. The user's emotional information is also used as input, and a translation appropriate to the emotion is performed. The input is text data and emotional information, and the output is translated text data.

[1851] Step 5:

[1852] The server passes the translated text data to a generative AI model, which generates voice data with the specified voice quality. The voice quality is adjusted based on emotional information. The input is the translated text data and emotional information, and the output is the generated voice data.

[1853] Step 6:

[1854] The server embeds the generated audio data into the original media file and adds it as a new audio track. This operation creates a new media file. The inputs are the original media file and the generated audio data, and the output is the new media file.

[1855] Step 7:

[1856] The server converts the new media file into a streaming format and sends it to the device in real time. The device receives the streaming data and buffers it for the user to view. The input is the new media file, and the output is the streaming data.

[1857] In this way, a system is realized that provides a personalized viewing experience based on the user's emotions.

[1858] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1859] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1860] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1861] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1862] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1863] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1864] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1865] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1866] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1867] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1868] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1869] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1870] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1871] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1872] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1873] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1874] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1875] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1876] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1877] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1878] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1879] The following is further disclosed regarding the above embodiment.

[1880] (Claim 1)

[1881] means for receiving a user-selected media file;

[1882] A means for analyzing the audio of the received media file and converting it into text data;

[1883] means for translating the text data into a specified target language;

[1884] means for generating voice data with a specified voice quality based on the translated text data;

[1885] means for incorporating the generated audio data into a media file and streaming it in real time;

[1886] A system including:

[1887] (Claim 2)

[1888] 10. The system of claim 1, further comprising means for notifying a user of the upload status of a media file.

[1889] (Claim 3)

[1890] 10. The system of claim 1, further comprising means for buffering the generated audio data during playback.

[1891] "Example 1"

[1892] (Claim 1)

[1893] means for receiving a user-selected media file;

[1894] A means for analyzing the audio of the received media file and converting it into text data;

[1895] means for translating the text data into a specified target language;

[1896] a means for using an artificial intelligence model to generate speech data with a specified voice quality based on the translated text data;

[1897] a means for incorporating the generated audio data into the original media file, converting the data into a streaming format in real time, and transmitting the stream;

[1898] means for receiving the streaming data and buffering it for playback at the terminal;

[1899] A system including:

[1900] (Claim 2)

[1901] 10. The system of claim 1, further comprising means for notifying a user of the upload status of a media file.

[1902] (Claim 3)

[1903] 10. The system of claim 1, further comprising means for buffering the generated audio data during playback.

[1904] "Application Example 1"

[1905] (Claim 1)

[1906] means for receiving a user-selected media file;

[1907] A means for analyzing the audio of the received media file and converting it into text data;

[1908] means for translating the text data into a specified target language;

[1909] means for generating voice data with a specified voice quality based on the translated text data;

[1910] means for incorporating the generated audio data into a media file and streaming it in real time;

[1911] A means for users to specify their preferred language and voice quality when uploading any media file;

[1912] A means for buffering the received streaming data to realize smooth playback;

[1913] A system including:

[1914] (Claim 2)

[1915] 10. The system of claim 1, further comprising means for notifying a user of the status of an uploaded media file.

[1916] (Claim 3)

[1917] 10. The system of claim 1, further comprising means for buffering the translated audio data during playback.

[1918] "Example 2: Combining Emotion Engines"

[1919] (Claim 1)

[1920] means for receiving a user-selected media file;

[1921] A means for analyzing the audio of the received media file and converting it into text data;

[1922] means for translating the text data into a specified target language;

[1923] means for generating voice data with a specified voice quality based on the translated text data;

[1924] means for incorporating the generated audio data into a media file and streaming it in real time;

[1925] A means for recognizing a user's emotion based on an emotion engine and dynamically changing the voice quality and the translated language;

[1926] A system including:

[1927] (Claim 2)

[1928] 10. The system of claim 1, further comprising means for notifying a user of the upload status of a media file.

[1929] (Claim 3)

[1930] 10. The system of claim 1, further comprising means for buffering the generated audio data during playback.

[1931] "Application example 2 when combining emotion engines"

[1932] (Claim 1)

[1933] means for receiving a user-selected media file;

[1934] A means for analyzing the audio of the received media file and converting it into text data;

[1935] means for translating the text data into a specified target language;

[1936] means for generating voice data with a specified voice quality based on the translated text data;

[1937] means for incorporating the generated audio data into a media file and streaming it in real time;

[1938] A means for recognizing a user's emotion and reflecting the emotion in the process of generating voice data;

[1939] A system including:

[1940] (Claim 2)

[1941] 10. The system of claim 1, further comprising means for notifying a user of the upload status of a media file.

[1942] (Claim 3)

[1943] 10. The system of claim 1, further comprising means for buffering the generated audio data during playback. [Explanation of symbols]

[1944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a user-selected media file; A means for analyzing the audio of the received media file and converting it into text data; means for translating the text data into a specified target language; means for generating voice data with a specified voice quality based on the translated text data; means for incorporating the generated audio data into a media file and streaming it in real time; A system including:

2. 10. The system of claim 1, further comprising means for notifying a user of the upload status of a media file.

3. 10. The system of claim 1, further comprising means for buffering the generated audio data during playback.

Images