CN117634508A

CN117634508A - Cloud computing generation type translation method, device and storage medium for live video broadcast

Info

Publication number: CN117634508A
Application number: CN202311705481.1A
Authority: CN
Inventors: 汪辉; 赵勇
Original assignee: Anhui Future Creative Technology Co ltd
Current assignee: Anhui Future Creative Technology Co ltd
Priority date: 2023-12-12
Filing date: 2023-12-12
Publication date: 2024-03-01

Abstract

The invention provides a cloud computing generation type translation method for live video broadcasting, which comprises the following steps: capturing a real-time live video stream and transmitting the real-time live video stream to a cloud server; preprocessing the video stream after the video stream reaches the cloud; the preprocessed text data is sent to a generated AI translation model for real-time translation; converting the translated text into a corresponding output format according to the setting of a user; the processed video stream is transmitted back to the user side through the cloud server together with the translation result; real-time translation includes: determining a suitable deep learning model architecture; extracting key features from the corpus; training a translation model by using the preprocessed data; optimizing the accuracy and efficiency of translation by adjusting model parameters; verifying the translation effect of the model on an independent test set; and integrating the model into a cloud platform, and testing the performance of the whole system. The invention adopts the generated AI model and is based on the neural network of deep learning, thereby remarkably improving the processing speed and efficiency in the translation process.

Description

Cloud computing generation type translation method, device and storage medium for live video broadcast

Technical Field

The invention belongs to the technical field of live video broadcast, and particularly relates to a cloud computing generation type translation method, a device and a storage medium for live video broadcast.

Background

Conventional real-time translation systems, while providing relatively timely translation services in some cases, often suffer from significant delay problems in high-load live environments. This delay is due primarily to the reliance on more traditional translation algorithms, such as rule or statistical based models, which tend to require more processing time when dealing with complex language constructs and fast changing live content. In addition, these traditional models often suffer from deficiencies in nature and accuracy of translation. Especially when dealing with professional terms, regional slang or culture-specific expressions, they cannot understand and adapt to different contexts and cultural backgrounds as human translation does, resulting in hard translation results and even misunderstanding.

Existing cloud computing translation solutions also exhibit significant shortcomings in terms of resource utilization efficiency. These systems often fail to take full advantage of the elastic and dynamic resource allocation capabilities provided by cloud computing environments, resulting in inefficient utilization of resources and a corresponding increase in cost. This inefficiency is further accentuated, especially when faced with the need for large-scale live viewers. In addition, the stability and efficiency of these systems in handling large real-time data streams is also an important issue. The specificity of a live environment requires that the translation system have a high degree of real-time processing power and stability to ensure the continuity and overall quality of the viewer experience. However, it is often difficult to meet these requirements in the prior art in maintaining high performance operation.

Existing real-time video translation systems often perform poorly in terms of multilingual and dialect support. Many systems can only support a few mainstream languages, which often have difficulty providing high quality translation services for a small population of languages or languages with complex grammars and expressions. The application and popularization of the systems in the global scope are limited, finally, the prior art also shows the defects in the aspects of self-adaptive learning and updating, the using habit and the expression mode of the language can change along with the time, but a plurality of existing translation systems cannot effectively adapt to the changes, so that the translation quality gradually decreases, on the basis, the applicant proposes a method for generating an AI translation model to a cloud computing platform through integrating advanced generation, solving the defects in the aspects of processing speed, translation naturalness and accuracy, resource utilization rate, real-time processing capacity, multi-language support, self-adaptive learning and updating and the like in the prior art, the integration not only improves the quality and efficiency of translation, but also enhances the expandability and flexibility of the system, particularly in a live environment, the application of the technology can greatly improve the user experience, meet the requirements of different languages and cultural background audiences, and continuously-changing market demands of translation services can be ensured through continuously learning and adapting to new language usage and expression modes, and generating the AI model can be popularized and used in the global scope.

Disclosure of Invention

The invention mainly provides a cloud computing generation type translation method, a cloud computing generation type translation device and a storage medium for live video broadcast, which are used for solving the technical problems of the prior art of real-time video translation, such as processing speed, translation naturalness and accuracy, resource utilization rate, real-time processing capacity, multi-language support, self-adaptive learning and updating and the like.

The technical scheme adopted for solving the technical problems is as follows:

the cloud computing generation type translation method for live video broadcast comprises the following steps:

capturing a real-time live video stream and transmitting the real-time live video stream to a cloud server;

preprocessing the video stream after the video stream reaches the cloud;

the preprocessed text data is sent to a generated AI translation model for real-time translation;

converting the translated text into a corresponding output format according to the setting of a user;

the processed video stream is transmitted back to the user side through the cloud server together with the translation result;

wherein, generating an AI translation model for real-time translation includes the following steps:

determining a suitable deep learning model architecture;

extracting key features from the corpus;

training a translation model by using the preprocessed data;

Optimizing the accuracy and efficiency of translation by adjusting model parameters;

verifying the translation effect of the model on an independent test set;

and integrating the model into a cloud platform, and testing the performance of the whole system.

The further improvement is that: the preprocessing process for the video stream comprises the following steps:

processing text data and processing audio data;

carrying out format unification and structured storage on the preprocessed data;

the text data processing comprises removing nonstandard characters, deleting repeated content and eliminating irrelevant information;

audio data processing includes denoising and normalization of audio signals.

The further improvement is that: the step of determining the appropriate deep learning model architecture includes:

the self-attention mechanism allows each position in the input sequence to interact with other positions, capturing global dependencies, whose calculation formula is as follows:

wherein Q, K, V is Query (Query), key (Key) and Value (Value), respectively, dk is the dimension of the Key;

by implementing the sparsification strategy, the interaction quantity between (Q\) and (K\) which need to be calculated is reduced, and the overall calculation complexity is reduced.

The further improvement is that: the number and dimension configuration of the multi-head attention are dynamically adjusted according to the characteristics and the scale of the input data, so that the adaptability and the efficiency of the model are improved.

The further improvement is that: the step of training the translation model by using the preprocessed data comprises the following steps:

the loss function is used to evaluate the differences between model predictions and actual data, and is expressed as follows:

where yi is the one-hot encoding of the real tag and pi is the probability of model prediction;

in the training process, the loss function predicts the time sequence consistency, semantic accuracy and content consistency between the key evaluation model and the actual live broadcast content;

training is carried out through real-time data flow so as to ensure that the model adapts to dynamic changes in a live broadcast environment;

the response speed of the model to real-time change is improved by adjusting the training strategy of the model;

finally, the model is continuously monitored and adjusted to adapt to the continuous change of the live content and the new language habit, and the high quality and fluency of translation are ensured.

The further improvement is that: the steps of optimizing the accuracy and efficiency of translation by adjusting model parameters include:

the update rules of the optimizer can be expressed as:

where θ is a model parameter, η is a learning rate,and->The estimates of the first and second moments, respectively, e being a small constant that prevents zero removal;

the adaptability of the enhancement model to real-time performance, context continuity and content diversity is introduced;

The loss function is more suitable for evaluating the accuracy and fluency of live video translation by adjusting algorithm parameters and introducing new evaluation standards.

The further improvement is that: the step of verifying the translation effect of the model on the independent test set includes:

the test set is independent from the training set, and covers a wide range of language cases and scenes, so as to ensure the comprehensiveness and fairness of the evaluation result, and the specific steps are as follows:

firstly, the data sources of the test set should be diversified, and different languages, dialects, accents and expression modes and various live content types are covered;

secondly, ensuring that the scene in the test set matches with the live environment in the real world, including different background noise levels, speaker numbers and style diversity;

meanwhile, the test set should be updated periodically to contain emerging language usage and live trends.

The further improvement is that: integrating the model into a cloud platform, wherein the step of testing the overall system performance comprises the following steps:

optimizing use of cloud resources to increase translation processing speed and reduce latency, and/or

And a dynamic resource allocation technology is adopted, and the computing resources are automatically adjusted according to the real-time translation requirement.

The invention also provides a cloud computing generation type translation device for live video broadcast, which comprises the following steps:

The video stream capturing module captures a real-time live video stream and transmits the real-time live video stream to the cloud server;

the preprocessing module is used for preprocessing the video stream after the video stream reaches the cloud;

the translation processing module is used for sending the preprocessed text data into the generated AI translation model for real-time translation;

the post-processing module converts the translated text into a corresponding output format according to the setting of a user;

the output module is used for transmitting the processed video stream together with the translation result back to the user side through the cloud server;

wherein, the translation processing module includes:

the model selection module is used for determining a proper deep learning model architecture;

the feature extraction module is used for extracting key features from the corpus;

the model training module is used for training a translation model by using the preprocessed data;

the optimizing and parameter adjusting module optimizes the accuracy and efficiency of translation by adjusting model parameters;

the model verification module is used for verifying the translation effect of the model on the independent test set;

and integrating the test module, integrating the model into a cloud platform, and testing the performance of the whole system.

The further improvement is that: the preprocessing module comprises the following steps:

processing text data and processing audio data;

audio data processing includes denoising and normalization of audio signals.

The further improvement is that: the model selection module comprises:

the self-attention mechanism module allows each position in the input sequence to interact with other positions, thereby capturing a global dependency relationship, and the calculation formula is as follows:

The further improvement is that: the model training module comprises:

a loss function for evaluating the difference between model predictions and actual data, the expression of which is as follows:

The further improvement is that: the optimizing and parameter adjusting module comprises the following steps:

the update rules of the optimizer can be expressed as:

The further improvement is that: the model verification module comprises the following steps:

The further improvement is that: the integrated test module comprises:

The invention also provides a computer readable storage medium comprising computer-executable instructions that, when run on a computer, cause the computer to perform the cloud computing generated translation method of live video streaming as described in any one of the above.

Compared with the prior art, the invention has the beneficial effects that:

compared with the traditional rule or statistics-based translation model, the generated AI model adopted by the scheme, especially the deep learning-based neural network, can more effectively process the complexity and the context change of the natural language, the improvement not only improves the accuracy of translation, especially when professional terms and slang terms are processed, but also enables the translation to be smoother and more natural, and by disposing the technology on a cloud computing platform, the cloud computing system effectively utilizes the high expandability and the strong computing capacity of cloud computing, and remarkably improves the processing speed and the processing efficiency in the translation process, so that the system can cope with a high-load live broadcast environment, and ensures the real-time property and the fluency of live broadcast translation.

The application of the generated AI model enables the translation system of the proposal to have self-learning and self-adapting capabilities, which is not possessed by the traditional translation system, which means that the system can continuously learn and adapt to new language usage and expression modes, the translation quality is not reduced and is reversely increased along with the time, in addition, the optimized use of cloud computing resources of the proposal reduces the overall operation cost, and simultaneously improves the resource utilization rate and the system stability, which is particularly important when processing a large number of concurrent translation requests, and the stability and the reliability of the system under high load can be maintained.

And the proposal supports translation of multiple languages and dialects, which benefits from the high flexibility and learning capability of the generated AI model, and the characteristic enables the system to adapt to various different language environments and provide a wider application range. In general, the technical improvements and differences bring the technical effects of simpler structure, higher efficiency and lower load, improve the accuracy and naturalness of translation, widen the application range, and jointly promote the practical application of the live translation technology, thereby providing better experience for users, especially in multi-language and high-load live broadcast environments.

The invention will be explained in detail below with reference to the drawings and specific embodiments.

Drawings

FIG. 1 is a flow chart of the data processing of the present invention;

FIG. 2 is a flow chart of the creation of a translation model of the present invention;

Detailed Description

In order that the invention may be more fully understood, a more particular description of the invention will be rendered by reference to the appended drawings, in which several embodiments of the invention are illustrated, but which may be embodied in different forms and are not limited to the embodiments described herein, which are, on the contrary, provided to provide a more thorough and complete disclosure of the invention.

The core structure of the cloud computing generation type translation technology for live video broadcasting comprises a hardware environment and detailed realization logic, and front-end and background processing are covered.

Hardware environment: the technical scheme is deployed in a high-performance cloud computing environment, which comprises a plurality of server nodes, wherein the nodes are specially configured to process a large number of concurrent translation requests. These servers have high-speed processors and large-capacity storage space, and can rapidly process complex data analysis and translation tasks. The servers are connected through a high-speed network, so that the rapid transmission of data among different processing nodes is ensured.

The realization logic: front end: user Interface (UI) designs focus on providing an intuitive and easy-to-use operating experience that enables a user to easily select a target language and adjust translation settings. The front end is also responsible for capturing real-time video streams and transmitting them to the cloud computing platform for processing. The front-end interface also displays translation results, including subtitles or speech output, as well as providing feedback and setup adjustments.

The method comprises the following steps: the core of the background is to receive and process the real-time video stream transmitted from the front end. The video stream is first pre-processed, including separation of video from audio and speech recognition, to convert the audio to text. The text is then fed into the generative AI translation model for processing. The models are based on the latest deep learning technology, and can efficiently and accurately complete multi-language translation tasks. The translated content is repackaged, possibly in audio format or in subtitle format, and then sent back to the front end for viewing by the user.

The whole background processing flow optimizes the efficiency of data processing and translation, and ensures that quick response can be maintained even under high load. Meanwhile, the background system also comprises dynamic resource management and load balancing mechanisms which can dynamically adjust resource allocation according to real-time load conditions, so that the performance of the whole system is maximized. Through the efficient hardware deployment and detailed implementation logic, the application proposal not only can provide high-quality real-time translation service, but also ensures the stability and reliability of the system, thereby being an ideal choice for live platform and users.

Referring to fig. 1, a data processing flow chart of the present application is specifically as follows:

video stream capture: the system captures a live video stream in real time. This step is typically implemented by the software of the live platform, which accesses the live signal source, captures the video content, and transmits it to the cloud server. This process needs to guarantee high speed and low delay to ensure real-time of video content.

Pretreatment: after the video stream reaches the cloud, preprocessing is performed first. This involves separating the video and audio and then performing speech recognition on the audio portion to convert the speech to text. The method also comprises the steps of segmenting the text and primarily cleaning, such as removing noise data, so as to ensure the quality of input data received by the translation model.

Translation processing: the preprocessed text data is sent to the generated AI translation model for real-time translation. These models use deep learning techniques that can process and translate multiple languages and dialects, providing accurate and natural translation results in view of context and semantics.

Post-treatment: the translated text is converted into a corresponding output format according to the user's settings. This may be converting text to audio in the target language and synchronizing with video, or generating corresponding subtitles. In this process, the style and position of the subtitle may also be adjusted according to the preference of the user.

And (3) outputting: and finally, the processed video stream, together with the translation result, is transmitted back to the user side through the cloud server. The user will receive the video content with the translation in real time on the live platform, whether audio translation in hearing or subtitle display in visual.

Referring to fig. 1 and 2, the detailed steps of the present invention are as follows:

step 1, data acquisition:

the data acquisition is a vital ring in the cloud computing generation type translation technology of live video broadcast, and directly affects the performance and accuracy of a translation system. This process involves a number of complex steps, each to ensure that the data collected is both rich and highly relevant and reliable.

The choice of data source is the basis of the data acquisition process. The goal is to collect text and audio data in multiple languages and dialects, therefore, the data sources must be broad, including public databases, social media platforms, news websites, professional forums, and the like. These data sources not only provide text data in multiple languages, but also include rich audio samples that cover different accents, speech speeds, and intonation, which are critical to training a highly adaptive translation model.

Web crawlers and automation technologies play an important role in the data collection process, and by using advanced web crawler technologies, the system is able to automatically collect text data from the internet. The collection of audio data is more focused on diversity, especially samples at different accents and speech rates. This not only ensures the breadth of the data, but also greatly improves the efficiency of data collection.

Step 2, preprocessing a corpus: and cleaning the collected data, removing noise and unifying the formats.

In the cloud computing generation type translation technology of live video, the process of preprocessing a corpus is of great importance, and the quality of input data is ensured, so that the accuracy and efficiency of final translation are directly affected. This process involves multiple steps, each employing elaborate methods and algorithms to ensure data quality.

For collected text data, preprocessing includes removing elements that are not in a consistent format, correcting spelling errors, deleting duplicates, and culling subject-independent information. This step is presented in the following pseudo code:

for each text sample:

removal of non-standard characters

Correcting misspellings

Deleting duplicate content

The key steps in rejecting extraneous information in processing audio data include denoising and normalization of the audio signal. Denoising typically uses spectral subtraction or Wiener filtering algorithms. For example, the basic idea of spectral subtraction is to subtract an estimated noise spectrum from the spectrum of a signal containing speech. The basic formula is as follows:

Y(f)＝X(f)α·N(f)

wherein Y (f) is the denoised signal spectrum, X (f) is the original signal spectrum, N (f) is the noise spectrum, and alpha is a preset coefficient for controlling the denoising intensity.

Normalization of the audio signal involves adjusting the sampling rate, bit depth and channel number of the audio to ensure that all audio data is in the same standard format for subsequent processing. For example, the uniform sampling rate is expressed as:

for each audio sample:

if the sample rate of the sample is not the standard value, it is converted to a standard sample rate.

For features extracted from audio, such as mel-frequency cepstral coefficients (MFCCs), they provide a compressed representation of the audio signal, which is effective in capturing features of speech. The computation of the MFCC involves a number of steps including pre-emphasis, framing, window function processing, fast fourier transform, mel filter bank processing, and taking the logarithm, discrete Cosine Transform (DCT). These steps work together to convert the original audio signal into a set of coefficients representing its spectral characteristics.

Finally, for all the preprocessed data, format unification and structured storage are required to facilitate subsequent feature extraction and model training. For example, text data is stored in a structured JSON format, and audio data is converted into a uniform waveform format or spectral feature representation.

Through the strict preprocessing flow, the data quality of the corpus is ensured, and a solid foundation is provided for subsequent deep learning model training and real-time translation. The preprocessing steps not only improve the consistency and usability of data, but also provide more accurate and rich input information for the translation model, thereby improving the accuracy and fluency of translation.

In the technical scheme, the process of cleaning the collected data is not limited to the traditional noise removal and unified data format, but is further innovated on the basis. First, we have employed more advanced noise recognition algorithms that can more accurately distinguish valid information from noise in the data, particularly when dealing with complex or low quality data. Secondly, a machine learning technology is introduced to optimize the data cleaning process, which means that the system not only removes noise according to preset rules, but also learns and adapts to the characteristics of different data, thereby realizing finer and personalized data processing. Furthermore, data format unification is no longer a simple format conversion, but rather involves optimization of the data structure to more effectively support subsequent data analysis and processing work. The innovations improve the efficiency and quality of data cleaning, and lay a solid foundation for subsequent data analysis and application. Compared with the prior art, the technical scheme has remarkable improvement in the aspects of accuracy of noise processing, intelligent degree of data processing and data format optimization, and the innovation points enable the whole data processing flow to be more efficient and reliable.

Step 3, model selection: a suitable deep learning model architecture, such as a sequence-to-sequence model, is determined.

In the cloud computing generation type translation technology of live video broadcast, model selection is a key step, and determines the infrastructure and performance of a translation system. For this purpose, a deep learning based sequence-to-sequence (Seq 2 Seq) model architecture is typically chosen, in particular an improved transducer model is typically employed which performs well in handling language translation tasks.

The transducer model is a deep learning architecture based on a self-attention mechanism, and is particularly suitable for processing long-sequence data. The core is a self-attention mechanism that allows the model to take the context of the entire sequence into account when processing each word. The basic structure of the transducer model comprises two parts: an Encoder (Encoder) and a Decoder (Decoder). The key formulas and algorithms are as follows:

1. self-attention mechanism:

the self-attention mechanism allows each location in the input sequence to interact with other locations, capturing global dependencies. The calculation formula is as follows:

where Q, K, V is the Query (Query), key (Key), and Value (Value), respectively, and dk is the dimension of the Key.

2. Encoder and decoder layers:

an encoder: comprising a plurality of identical layers, each having two sublayers, one being a multi-headed self-attention mechanism and the other being a simple, fully-connected feed-forward network.

A decoder: also included are multiple identical layers, but in addition to the same two sub-layers as in the encoder, there is an additional sub-layer to achieve attention to the encoder output.

3. Position coding:

since the transducer does not contain recursion and convolution, in order for the model to utilize the order information of the words, it is necessary to add position coding to the input sequence. The formula of the position code is:

PE(pos,2i)＝sin(pos/100002i/dmodel)

PE(pos,2i+1)＝cos(pos/100002i/dmodel)

where pos is the position and i is the dimension.

4. Multi-head attention (Multi-HeadAttention):

the transducer uses multiple heads of attention at the self-attention layer to enhance the attention capabilities of the model. Each head learns different attention information at different locations. The calculation formula is as follows:

MultiHead(Q,K,V)＝Concat(head1,…,headh)WO

where each header=attention (QWiQ, KWiK, VWiV), WO, wiQ, wiK, wiV is a trainable parameter matrix.

Through these mechanisms, the transducer model can effectively process complex language translation tasks, and particularly when processing long sentences and complex grammar structures, the transducer model has obvious advantages compared with the traditional model. In live video translation, these properties of the transducer model make it an ideal choice, which can provide a fast and accurate translation output. The highly parallelized structure of the model also makes the model particularly suitable for deployment in a cloud computing environment, and can effectively utilize computing resources of a cloud platform to support large scale.

In the technical scheme, innovation in determining a proper deep learning model architecture is mainly embodied on an adaptive model selection mechanism. This mechanism does not simply select a fixed deep learning architecture, but rather dynamically determines the model architecture that best suits the current task by analyzing characteristics (e.g., size, complexity, variability, etc.) of the input data. For example, for a text translation task, the system would first evaluate the length, language complexity, and semantic richness of the text and then select the best model, such as a sequence-to-sequence model or an attention mechanism model, based on these parameters. The key of the method is to evaluate the data characteristics by using advanced algorithm and combine a library containing a plurality of model architectures, thereby realizing automatic selection of the optimal model architecture of different tasks. In addition, this mechanism includes continuous learning and tuning functions, i.e., the system adjusts the model selection strategy based on the performance of historical tasks to accommodate changing data and task requirements. Compared with the prior art, the technical scheme is innovative in that a more flexible, efficient and intelligent method for determining the deep learning model architecture is provided, so that the processing efficiency is improved, and the accuracy and adaptability of the model for processing different tasks are improved.

In this solution, the computational efficiency of optimizing the self-attention mechanism is reflected by improving the algorithm and the computational method instead of simply modifying the existing self-attention computational formula. Such optimization is not necessarily embodied in traditional mathematical formulas, but is achieved through algorithmic level innovations including, but not limited to:

1. and (3) sparsification treatment: in conventional self-attention mechanisms, the attention score is calculated for each element of all input sequences, which can result in a significant amount of computation in a large data set. The sparsification process reduces the amount of computation by computing the attention scores of only a portion of the key elements, for example by selecting the most important elements by some mechanism (e.g., based on gradient or importance scores).

2. Low rank approximation: in calculating the self-attention, the original calculation process can be approximated by a low-rank matrix decomposition method, so that the required calculation resources can be reduced, especially when a large-scale matrix is processed.

3. Dynamic multi-head attention configuration: this involves dynamically adjusting the number and configuration of multi-headed attentives based on the nature and scale of the input data, rather than using a fixed setting. This has the advantage that the computational efficiency and performance of the model can be optimized according to the specific application scenario and data characteristics.

These optimization methods are implemented through innovations in algorithms and computational strategies, and are not necessarily created based on entirely new mathematical formulas.

Step 4, feature extraction: key features such as grammar structure and semantic information are extracted from the corpus.

In the cloud computing generation type translation technology of live video, feature extraction is a key link and is responsible for extracting features which are critical to translation, such as grammar structures, semantic information and the like, from a corpus. Successful execution of this step is critical to improving translation accuracy and efficiency.

The feature extraction is mainly divided into two parts: text feature extraction and audio feature extraction.

1. Text feature extraction:

text feature extraction focuses on extracting grammatical and semantic features of a language from text data.

Natural Language Processing (NLP) techniques such as part-of-speech tagging, named Entity Recognition (NER), syntactic dependency analysis, etc. are used to obtain structural and semantic information of text.

Statistical methods such as TF-IDF (word frequency-inverse document frequency) can also be used to identify keywords and phrases.

Another approach is to use Word embedding techniques (e.g., word2Vec, gloVe) to convert text into a vector space model to capture Word-to-Word relationships. The basic formula for word embedding can be expressed as:

Where w is the word,is a vector representation thereof.

2. Extracting audio characteristics:

audio feature extraction involves converting an audio signal into a numerical form capable of representing its features.

One common method is mel-frequency cepstral coefficient (MFCC). The MFCC extracts short-time energy and spectral features of the audio signal, which are particularly effective for speech recognition. The computation of the MFCC involves a number of steps including pre-emphasis, framing, window function processing, fast Fourier Transform (FFT), mel filter bank processing, taking the logarithm, and Discrete Cosine Transform (DCT). The basic formula can be expressed as:

MFCC＝DCT(log(Mel-Spectrogram(x)))

where x is the audio signal and Mel-Spectrogram (x) is the spectrogram after processing through the Mel-filter bank.

3. Combining text and audio features:

in the translation model, combining text and audio features can improve the accuracy and naturalness of the translation. For example, the grammatical and semantic features of text may be combined with the rhythmic and intonation features of audio to better understand and translate the original content.

Through the methods and algorithms, the features extracted from the corpus not only contain basic information of texts and audios, but also contain rich context information and deep language features. These features are critical to training efficient, accurate translation models, which enable the models to better understand and handle the transitions between multiple languages. In live video translation, extraction of these features provides a solid basis for improving translation quality, adapting to different language environments, and handling various complex contexts.

Step 5, model training: the translation model is trained using the preprocessed data.

Model training is a vital link in real-time live video cloud computing generated translation technology, where pre-processed data is used to train deep learning translation models. This process includes several key steps and algorithms used.

1. Initializing and preparing data:

before training begins, the parameters of the model are first initialized. This typically involves setting initial values of weights, typically using methods such as Xavier initialization or He initialization.

Preparing the data involves dividing the preprocessed data into training, validation and test sets. In this way, model parameters can be adjusted during the training process while evaluating the generalization ability of the model.

2. Selecting a model architecture:

a task-appropriate deep learning model architecture, such as a transducer-based sequence-to-sequence (Seq 2 Seq) model, is selected.

At the heart of the transducer model is a self-attention mechanism that allows the model to better understand the relationships between each element in the sequence and other elements.

Although the use of a deep learning model architecture has been determined in step 3, the specific choice of which model (e.g., the transducer-based Seq2Seq model) is based on the specific needs and characteristics of the translation task, and this choice is part of the overall translation processing framework, ensuring that the chosen model is most efficient in handling the specific translation task.

3. Defining a loss function and an optimizer:

in the cloud computing generated translation technique of live video, a loss function is used that is different from the traditional model, and is specifically designed to handle highly dynamic and variable live content. Such a loss function takes into account characteristics specific to live video, such as solidity, uncertainty, and diversity of content. The loss function herein emphasizes the processing of time sensitivity, and the adaptability to real-time changing content, as compared to conventional loss functions. The method can effectively evaluate the difference between the model prediction and the actual live broadcast data, particularly in terms of processing language conversion and semantic understanding. In addition, the loss function also considers the continuity and the context correlation of the live broadcast content, ensures that the translation is accurate, smooth and natural, and is suitable for a live broadcast scene in real time. Thus, while the basic principle is similar to the traditional loss function, i.e. evaluating the difference between prediction and reality, innovative adjustments are made in terms of processing methods and emphasis to accommodate the special requirements of live video translation in real time.

Implementing such a loss function tailored for live video translation first requires defining the structure and parameters of the loss function, ensuring that it is capable of paying particular attention to time sensitivity and changes in real-time content. In practice, deep learning models, such as recurrent neural networks or transducers, can be employed that are capable of processing sequence data and capturing time dependencies. Then, in the training process, the loss function predicts the time sequence consistency, semantic accuracy and content consistency between the key evaluation model prediction and the actual live content. Training can be performed through real-time data streams to ensure that the model adapts to dynamic changes in the live environment. In addition, the training strategy of the model needs to be adjusted, for example, continuous sample training in a short time window is performed, so that the response speed of the model to real-time change is improved. Finally, the model is continuously monitored and adjusted to adapt to the continuous change of the live content and the new language habit, and the high quality and fluency of translation are ensured. The method not only improves the accuracy and naturalness of translation, but also ensures that the translation process is matched with the real-time property and consistency of the live content.

The loss function is used to evaluate the differences between model predictions and actual data, and for the translation task, the loss function is as follows:

where yi is the one-hot encoding of the real label and pi is the probability of model prediction.

An optimizer is selected to adjust the weights of the model to reduce the value of the loss function. Common optimizers include Adam, SGD, and the like.

4. Training cycle:

during the training process, the model may traverse the training data multiple times. Each traversal is referred to as one epoch.

In each epoch, the model will propagate forward and backward across the training set. The forward propagation is used to calculate the output and loss, and the backward propagation is used to calculate the gradient of the loss function with respect to the model parameters.

The use optimizer adjusts the model parameters according to the gradient to reduce the value of the loss function.

5. Regularization and parameter tuning:

to prevent overfitting, regularization techniques, such as L2 regularization, dropout, etc., may need to be used.

Parameter tuning is an important step involving adjusting parameters such as learning rate, batch size, model layer number, etc. to find the best model performance.

6. Model verification and testing:

model performance is periodically evaluated on the validation set during training to monitor training progress and prevent overfitting.

After training is completed, the final performance of the model is evaluated using the test set.

Through the steps and the algorithm, a high-performance translation model can be effectively trained. The model can accurately translate the source language into the target language, and simultaneously maintain the fluency and naturalness of the language. In real-time live video translation applications, this trained model not only provides high quality translation, but also maintains stability and flexibility in handling complex language environments and different language styles.

Step 6, optimizing and adjusting parameters: and the accuracy and efficiency of translation are optimized by adjusting model parameters.

In the cloud computing generation type translation technology of live video, the sixth step of optimization and parameter adjustment are key links for ensuring that a translation model achieves optimal performance. This process involves fine tuning of the parameters of the model and the use of various optimization algorithms and techniques to improve the accuracy and efficiency of the translation.

1. Parameter adjustment:

adjusting model parameters is the core of the optimization process. This includes learning rate, batch size, number of model layers, hidden layer size, etc.

The learning rate determines the magnitude of the model weight adjustment. Too much learning rate may lead to unstable training, while too little learning rate may slow the training process.

Batch size affects the speed and memory consumption of model training. Larger batches may provide more stable gradient estimates, but may increase memory requirements.

2. Using an optimization algorithm:

an optimization algorithm such as Adam or SGD is used to adjust the weights of the model to minimize the loss function. Adam optimizers combine the advantages of momentum and RMSprop, often perform well in many deep learning tasks.

The update rules of Adam optimizer can be expressed as:

where θ is a model parameter, η is a learning rate,and->Is an estimate of the first and second moments, respectively, e is a small constant that prevents division by zero.

3. Regularization technique:

to avoid overfitting, regularization techniques may be applied. L2 regularization (weight decay) and Dropout are common regularization methods. L2 regularization works by adding a square term of the weight to the loss function, whose formula is:

Loss _regularized ＝Loss _original +λ∑θ ²

where λ is the regularization coefficient.

4. Early stop (EarlyStopping):

early stop is a technique to avoid overfitting that stops training when performance on the validation set is no longer improving.

This may prevent the model from overfitting on the training data while preserving the generalization ability on the unseen data.

5. Searching super parameters:

Hyper-parametric searching is a key step in finding the best model performance. Common methods include grid searching, random searching, and bayesian optimization. These methods find parameter combinations that optimize the performance of the model by searching in a predefined parameter space.

Through the optimization and parameter adjustment technologies, the performance of the translation model can be obviously improved. The optimized model not only has better translation accuracy, but also is more efficient and stable in processing large-scale and complex language data. In real-time live video translation applications, this means faster response times and higher quality translation output, thereby improving user experience and satisfaction.

The method for adjusting the loss function is realized by introducing specific algorithms which are improved and customized to the prior art so as to adapt to the special requirements of live video translation. This improvement is mainly reflected in the ability of the enhancement model to adapt to real-time, context consistency and content diversity. By adjusting algorithm parameters and introducing new evaluation criteria, such as taking into account time delays and contextual relevance, these algorithms make the loss function more suitable for evaluating the accuracy and fluency of live video translations. Such improvements ensure that the penalty function not only measures the literal accuracy of the translation, but also takes into account the special nature and challenges of live broadcasting in real time. However, this process is not necessarily directly embodied by the update rules of the optimizer. The optimizer is mainly responsible for the updating process of the model parameters, while the adjustment of the loss function usually involves training objectives and evaluation criteria of the model. In practice, these adjustments may be reflected in the construction of the loss function itself, the training strategy of the model, and the metrics used for model evaluation. For example, the adjustment and optimization of the penalty function may be achieved by introducing a time window dependent penalty term, or increasing an evaluation index for context consistency.

Step 7, model verification: the translation effect of the model was verified on a separate test set.

In real-time live video cloud computing generated translation techniques, model verification is a key element in validating model performance and translation accuracy. This step ensures the validity and accuracy of the translation model in practical applications by making a comprehensive assessment of the model on a separate test set.

1. Test set preparation:

the test set should be independent of the training set and cover a wide range of language cases and scenarios to ensure the comprehensiveness and fairness of the assessment results. Test data should include multiple languages and dialects, terms and expressions in different fields.

2. And (3) evaluation index selection:

common evaluation indexes include BLEU (bilingual evaluation of substitution rate), ROUGE (recall-oriented evaluation summary), METEOR (metric evaluation of consistency of translation output), and the like.

For example, the BLEU score calculation formula is:

where N is typically 4, indicating 1-gram to 4-gram accuracy.

3. Performance test:

performance testing includes not only accuracy of translation, but also response time and resource consumption of the model.

By simulating the actual application scene, the stability of the model in high load and multitasking is ensured.

4. Error analysis:

the translation errors are analyzed in detail to identify which types of translation tasks the model performs poorly.

Analysis may include linguistic (e.g., grammar, semantic errors) and technical (e.g., coding problems, data imbalances) angles. 5. Feedback loop:

and feeding back the test result to the model training and optimizing process to guide the subsequent model adjustment and data processing.

Specific optimizations are performed on the identified problems, such as adding training data for a specific language, adjusting model structures, etc.

Through the steps, the performance and accuracy of the translation model can be comprehensively verified. Model verification not only ensures translation quality, but also provides basis for continuous improvement of the model. In a live video cloud computing generated translation application, this means higher user satisfaction and better translation experience. The strictly verified model can be accurately translated between various languages and dialects, and can be kept efficient and stable even in complex and changeable real-time scenes.

In the cloud computing generation type translation technology of live video broadcast, the test set and the training set are ensured to be independent and cover wide language use cases and scenes, and the implementation is realized by carefully designing and selecting test data. First, the test set data sources should be diverse, covering different languages, dialects, accents, and expressions, as well as various live content types (e.g., news, sports, entertainment, etc.). Second, it is ensured that the scene in the test set matches the live environment in the real world, including different background noise levels, speaker count, and style diversity. In addition, the test set should be updated periodically to contain emerging language usage and live trends. By the method, the test set can cover various situations possibly occurring in the whole field, and the comprehensiveness and fairness of the evaluation result are ensured. In addition, proper statistical methods and performance indicators (such as accuracy, recall, and F1 score) are used to comprehensively evaluate translation quality, thereby ensuring objectivity and reliability of the evaluation result. Such a testing strategy is critical because it ensures that the translation model will exhibit high efficiency and accuracy in a variety of live video scenarios.

Step 8, integration test: and integrating the model into a cloud platform, and testing the performance of the whole system.

In the cloud computing generation type translation technology of live video broadcast, an integration test is a key link, and the integration test relates to integrating a trained and optimized translation model into a cloud platform and comprehensively testing the performance of the whole system. The process not only ensures the efficient operation of the translation model, but also verifies the stability, reliability and expandability of the system, and ensures that the user requirements can be met in practical application.

1. Model deployment and configuration:

and deploying the trained translation model to a cloud server. This includes serialization of the model, uploading to the cloud, and configuration and initialization on the cloud server.

Configuration for cloud servers includes setting computing resources (e.g., CPU, GPU), memory size, and network bandwidth. Relevant software environments, such as deep learning frameworks and dependency libraries, are configured according to the requirements of the model.

2. Performance test:

performance testing of the system includes evaluating translation speed, response time, and processing power of the system.

High concurrency scenarios were simulated using stress testing and load testing to verify the performance of the system at a large number of user visits. For example, the expansion capability and load balancing of the system may be tested by increasing the number of simulated concurrent requests.

3. And (3) verifying accuracy and consistency:

and verifying whether the translation accuracy of the model on the cloud platform meets the expectations. This can be done by making predictions over the test set and using metrics such as BLEU or ROUGE to evaluate translation quality.

The consistency of the system on different cloud server instances is checked, and each instance is ensured to provide translation services with the same quality.

4. Reliability and fault tolerance test:

the fault tolerance of the system is tested, such as simulating network delay, server fault and the like, so that the system can continue to operate when part of components are in problem.

And carrying out data backup and recovery tests to ensure that service can be quickly recovered when faults occur.

5. Security assessment:

and carrying out security tests on the system, including data encryption, access control, verification of security measures for preventing DDoS attacks and the like. Checking whether the system has potential security holes, such as data leakage or unauthorized access, and performing corresponding repair.

6. Extensibility testing:

the scalability of the system, including testing of the auto-expansion mechanism, is assessed to ensure that the system automatically increases computing resources as demand increases. And testing the influence of cloud server clusters of different scales on the system performance, and determining an optimal resource configuration scheme.

Through the integration test, the overall performance of the translation system in the cloud environment can be comprehensively evaluated, so that the real-time translation requirement of a user can be met when the system is actually deployed and operated. Successful completion of this stage provides a solid foundation for commercial deployment and stable operation of the translation system, ensuring consistency and high standards of user experience.

In the integrated testing stage of the overall system performance, special attention is paid to the performance of the model in the cloud platform environment. Innovations may include optimizing the use of cloud resources to increase translation processing speed and reduce latency. For example, dynamic resource allocation techniques may be employed to automatically adjust computing resources based on real-time translation requirements. Furthermore, specific optimization strategies may be implemented for different types of live content and network conditions to ensure that efficient translation quality is maintained in different situations. The integrated test not only focuses on the performance of a single model, but also covers the performance of the whole cloud platform, ensures that the whole link from video capturing to final output can be stably and efficiently operated, and the integrated test of the step 8 focuses on the performance of the whole system, and ensures the high efficiency and stability of the cloud platform when processing real-time translation tasks through innovative technology.

Step 9, developing a user interface: the user interface is designed to include language selection, caption display, etc.

In the cloud computing generation type translation technology of live video broadcast, user interface development is an important link for ensuring that a user can easily access and use translation services. An effective user interface should be simple and intuitive while providing the necessary functions such as language selection, subtitle display, etc. The following are key considerations and implementation steps for user interface development:

1. interface design:

an intuitive user interface is designed to ensure that the user can easily understand and operate. The interface should include clear indications and a compact layout.

A language selection function is provided to enable a user to select a source language and a target language. This is typically accomplished through a drop down menu in which all supported languages are listed.

2. Caption display:

a subtitle display function is developed to enable a user to see real-time translated text in a live video stream. The subtitles should be clearly readable, properly positioned, and not interfere with the video content.

The subtitle synchronization mechanism needs to be accurate, ensures that the subtitle is synchronized with the audio, and provides smooth viewing experience.

3. Interactivity enhancement:

interactive functions are implemented, such as allowing the user to adjust the font size, color, and background of the subtitle to accommodate different viewing needs.

Providing a user feedback mechanism allows the user to report translation errors or to make improvement suggestions, which is important for continued improvement of translation quality.

4. And (3) adaptive design:

ensuring that the user interface can be well shown on different devices, including screens and operating systems of different sizes. This requires the interface design to be responsive and adaptable.

For mobile devices, touch operations and interface element sizes are optimized to ensure ease of operation on small screens as well.

5. Performance considerations:

the interface design should take into account performance to ensure fast loading and smooth interaction. Optimizing front-end resources, such as reducing picture size, using efficient caching policies.

Asynchronous loading techniques, such as AJAX, are used to enable updating of interface content without reloading the entire page.

6. Security and privacy:

data security and user privacy are considered when designing a user interface. Ensuring that all user data is transferred and stored securely. Appropriate authentication and authorization mechanisms are implemented to protect user data from unauthorized access.

Through the steps, a user interface which is friendly to users, comprehensive in function and excellent in performance can be developed, and an effective user interaction platform is provided for live video translation. This not only improves the user experience, but also helps to expand the application range and user base of the translation service.

In the cloud computing generated translation technique of live video, the user interface development in step 9 contains some innovative elements. These innovations focus primarily on improving the user interaction experience and enhancing functionality availability. First, interface design pays attention to intuitiveness and usability, ensuring that a user can quickly select a desired language and adjust subtitle settings. Secondly, an intelligent language identification function is introduced, so that the system is allowed to automatically identify and recommend languages possibly required by a user, and the steps of manual selection are reduced. In addition, caption display has also been innovated, such as a dynamic caption adjusting function, which automatically adjusts the size, color and position of the caption according to the change of the video content, so as to provide a clearer visual experience. Also, to increase the accuracy and readability of subtitles, we have introduced real-time subtitle correction techniques that can correct errors or unclear expressions in translation on the fly. The innovations not only improve the intuitiveness and the operation convenience of the user interface, but also enhance the accuracy of the translation result and the overall satisfaction of the user experience.

Implementing these innovative functions is first the implementation of intelligent language identification functions. This can be achieved by integrating advanced speech recognition techniques, where the system automatically analyzes the speech input of the user or the language in the video content and recommends the corresponding language options, reducing the steps that the user manually selects. Then, for the innovation of caption display, a dynamic caption adjusting algorithm can be developed, and the algorithm can automatically adjust the size, color and position of the caption according to the characteristics of video content (such as picture change and keyword occurrence frequency) so as to provide a clearer visual experience. Furthermore, implementing real-time subtitle correction techniques requires the integration of natural language processing tools that can analyze subtitle content in real-time, identify and correct interpretation errors or unclear expressions. The integration of the functions not only improves the intuitiveness and the operation convenience of the user interface, but also remarkably improves the overall satisfaction of user experience by improving the translation accuracy and the readability. The whole implementation process needs to closely combine the professional knowledge of a plurality of fields such as software development, user interface design, natural language processing and the like, ensures that the innovative functions can be seamlessly integrated into the existing system, and provides better quality service for users.

Step 10, accessing real-time video stream: mechanisms for video stream capture and transmission were developed.

In the cloud computing generation type translation technology of live video broadcast, real-time video stream access is a key step, and relates to the development of a mechanism for capturing and transmitting video streams so as to ensure that video contents can be smoothly accessed into a translation system. This link requires consideration of various aspects of capturing, encoding, transmitting, and receiving video data.

1. Video capture:

the capturing function of the video stream can be realized by integrating the existing video conference system or live broadcast software, or developing a custom video capturing module.

For the custom capture module, a video capture API, such as WebRTC, is required, which is a free open source item that supports web browsers for real-time communication (RTC).

2. Video coding and compression:

video data typically requires encoding and compression to reduce the size of the transmitted data. Common video coding standards include h.264, h.265, etc.

A suitable compression algorithm, such as VP9 or AV1, is applied to reduce bandwidth requirements while guaranteeing video quality.

3. Real-time transmission mechanism:

and realizing real-time transmission of video streams. Protocols such as RTMP (real time messaging protocol) or HLS (HTTP live streaming) are used to support real time transmission of streaming media.

In view of network delay and bandwidth limitations, adaptive streaming needs to be implemented, and video quality is dynamically adjusted according to the network conditions of the user.

4. Video stream reception and processing:

and the receiving function of the video stream is realized at the cloud. This requires integration with the computing resources and storage services of the cloud platform to process and store the received video data.

The received video stream is subjected to necessary processing, such as format conversion, resolution adjustment, etc., to accommodate subsequent translation processing procedures.

5. Buffering and synchronization:

the buffer mechanism of the video stream is realized to reduce the influence caused by network fluctuation and keep the smooth playing of the video.

The synchronization mechanism ensures the synchronization of video and audio, and particularly ensures the synchronism of audio translation and video pictures in the translation process.

6. Safety and stability:

the transmission security of the video stream is ensured, and the encrypted transmission is realized, such as using TLS/SSL encryption.

Fault tolerance mechanisms are designed to ensure that video streams can be quickly restored when the network is unstable or service is interrupted.

Through the steps, an efficient, stable and safe real-time video stream access mechanism can be established, and strong back-end support is provided for live video broadcasting. This not only ensures smooth access and processing of video content, but also provides a smooth and high quality viewing experience for the end user.

In the cloud computing generated translation technique of live video, step 10 involves developing a mechanism for video stream capture and transmission, which includes several innovative elements. Firstly, we use efficient video stream coding and decoding techniques, which not only reduce the delay in data transmission, but also ensure video quality, providing a clear video stream even in bandwidth limited situations. Second, to enhance the stability and reliability of the video stream, we implement an adaptive flow control mechanism that can dynamically adjust the quality and size of the video stream according to network conditions. In addition, we have developed an intelligent routing system for real-time monitoring and optimizing of video transmission paths, ensuring that video data is always kept at maximum efficiency and minimum delay during transmission. The innovations not only enable capturing and transmitting of video streams to be more efficient and stable, but also provide powerful technical support for guaranteeing continuity and instantaneity of the translation process.

The detailed implementation steps of the step 10 in the cloud computing generation type translation technology of live video broadcast comprise the following steps: firstly, developing a high-efficiency video stream coding and decoding technology, which reduces delay in data transmission by optimizing a coding and decoding algorithm, ensures video quality, and can still provide clear video streams particularly under the condition of limited network bandwidth; then implementing an adaptive flow control mechanism, wherein the mechanism dynamically adjusts the quality and the size of the video stream according to the network conditions monitored in real time so as to cope with network fluctuation and improve the stability and the reliability of the video stream; then developing an intelligent routing system, wherein the system optimizes the routing of the data stream by monitoring the network condition and the video transmission path in real time, reduces the transmission delay and ensures the high efficiency of video data transmission; the steps are combined, so that the capturing and transmitting of the video stream are more efficient and stable, and solid technical support of continuity and instantaneity is provided for the translation process, so that the overall system performance and user experience are greatly improved.

Step 11, voice recognition: the speech in the video is converted to text.

In the cloud computing generation type translation technology of live video broadcast, a voice recognition link plays a crucial role. It is responsible for accurately converting the speech content in the video into text, a precondition for achieving efficient translation. Key steps and considerations for speech recognition are as follows:

1. voice capture:

it is necessary to extract the speech signal from the video stream. This typically involves decoding of the video stream and separation of the audio tracks. Ensuring that the audio quality is high enough for efficient speech recognition. This may require some preprocessing steps such as noise reduction and echo cancellation.

2. Selecting a voice recognition technology:

the choice of the appropriate speech recognition technique is critical. Currently, the most common is an Automatic Speech Recognition (ASR) system based on deep learning. These systems typically use Recurrent Neural Networks (RNNs) or long-term memory networks (LSTM) to process time-series data of the speech signal.

3. Model training and tuning:

to improve recognition accuracy, a large amount of labeled speech data is required to train the recognition model.

The model is tuned to accommodate different languages, accents and speaking modes. This may include fine tuning the model to accommodate a particular user population or scenario.

4. And (3) real-time processing:

realizing the real-time processing of the voice signal. This requires the system to be able to process the input speech quickly and generate a corresponding text output. In consideration of the real-time requirement, the calculation efficiency and response speed of the model need to be optimized.

5. Text post-processing:

the generated text may require further processing such as insertion and formatting of punctuation marks. For the identified erroneous or uncertain parts, a post-processing algorithm may be used for correction.

6. Integration and synchronization:

the voice recognition module is integrated with the live video and translation system. Ensuring that the speech-to-text conversion is synchronized with the video content. To provide a better user experience, real-time display of subtitles can be achieved, matching the speech recognition output.

Through the steps, an efficient, accurate and safe voice recognition system can be established, and a solid foundation is provided for live video translation. The translation process is automatic and efficient, and the accuracy of translated contents and the overall experience of users are greatly improved.

In the cloud computing generation type translation technology of live video, a voice recognition stage comprises a plurality of innovative elements, and the voice recognition stage is mainly focused on improving recognition accuracy and adapting to different languages and dialects. Firstly, we use deep neural network models, which can more accurately recognize voices in multiple languages and dialects through special training, and can maintain high accuracy even in a live environment with more background noise. Secondly, in order to cope with the rapid speech speed and the spoken language expression in live broadcast, we optimize the real-time processing capacity of the model and ensure that the conversion from voice to text is both rapid and accurate. In addition, a context awareness algorithm is introduced, so that the recognition accuracy of words or phrases can be improved according to the context. The innovations not only promote the accuracy and adaptability of voice recognition, but also ensure the fluency and continuity of the translation process, and lay a solid foundation for subsequent translation processing.

Step 12, real-time translation processing: and translating the text in real time by using the trained model.

In the cloud computing generation type translation technology of live video, real-time translation processing is a core link for converting voice in video into different language texts. This process involves real-time translation of the extracted text using a trained deep learning model to provide accurate, fluent multi-lingual subtitles. The key steps and techniques of the real-time translation process are considered as follows:

1. translation model selection:

the model architecture is chosen for real-time translation, typically based on a neural network sequence-to-sequence (Seq 2 Seq) model, such as a transducer model. These models are capable of handling long sentences and complex language constructs while maintaining high translation accuracy and fluency.

2. Model optimization:

the model is optimized to accommodate real-time translation requirements. The goal of the optimization is to reduce the response time and computational resource consumption of the model while maintaining translation accuracy. Model compression, quantization, distillation and other techniques can be employed to reduce model size and increase operational speed.

3. The real-time processing flow comprises the following steps:

an efficient real-time process flow is achieved. The text is input into the translation model immediately after the text is output by the speech recognition module. It is necessary to ensure that the translation process is performed quickly to maintain synchronization with the live content.

4. And (3) data flow management:

the data stream is managed to ensure continuity and real-time. In case of high network delay or instability, the problem of packet loss or delay needs to be properly handled. A buffering mechanism may be implemented to balance the impact of network fluctuations on real-time translation.

5. Subtitle synchronization and display:

ensuring that the translated text is synchronized with the video content. This involves a time stamping process to translate the text in order to properly align the subtitles with the video pictures. The display of subtitles should take into account readability, including font size, color and background, and proper location on the screen.

6. Fault tolerant mechanism:

fault tolerant mechanisms are implemented to handle errors and anomalies that may occur during translation. For example, when the model cannot accurately translate certain words or phrases, the corresponding processing strategies should be applied. System performance can be tracked and potential problems identified through logging and monitoring.

By implementing the steps, the real-time translation process can effectively convert the voice in the live video into the text of multiple languages, and provide instant and accurate subtitles for the audience, thereby greatly enhancing the accessibility and interactivity of the video content. This not only improves the user experience, but also provides strong support for cross-language communication.

In the cloud computing generation type translation technology of live video broadcast, a real-time translation processing stage comprises key innovation. Advanced deep learning models, such as adaptive transformers, are adopted, so that not only are different languages and dialects better understood, but also optimized translation can be performed according to real-time property and specific context of live content. In addition, we implement a real-time learning mechanism that allows the model to constantly learn and adapt during processing, thereby improving the accuracy and naturalness of the translation. We have also integrated advanced semantic understanding techniques to ensure that translations are not only literally accurate, but also able to capture and convey subtle emotions and contexts in the original language. These innovations ensure that the translation process is both fast and accurate, and can meet the strict requirements for real-time and high-quality translation in live video.

Step 13, synchronous output: the translated content is synchronized into the video stream.

In the cloud computing generation type translation technology of live video, synchronous output is a key process for synchronizing translated text content with an original video stream. This ensures that the viewer can see the translated subtitle synchronized with the video content, thereby achieving a seamless viewing experience. The key steps and techniques for implementing synchronous output include:

1. Timestamp alignment:

it is first necessary to ensure that the translated text is time aligned with the corresponding audio portion of the video stream. This typically involves adding a timestamp to the translated text that matches the timestamp of the video frame.

The alignment of the time stamps may be achieved by analyzing the frame rate of the video stream and the sampling rate of the audio. And ensuring that each translation text is played synchronously with the corresponding video part.

2. And (3) subtitle synthesis:

and fusing the translated text into a video stream as a subtitle. This involves the layout, format and style design of the subtitles to ensure the readability of the subtitles and the viewing experience of the viewer.

Subtitles are dynamically generated and displayed using specialized subtitle rendering tools or software, such as CSS and JavaScript techniques.

3. And (3) stream media coding:

the video stream into which the subtitles are synthesized is encoded for transmission through a network. In view of the real-time requirements, the encoding process needs to be fast enough to reduce the delay.

Efficient encoding techniques, such as h.264 or h.265, are used to ensure that the video stream reduces bandwidth occupation as much as possible while maintaining quality.

4. And (3) real-time transmission:

and realizing real-time transmission of the video stream after the subtitle fusion. Real-time streaming protocols such as RTMP or HLS are used to ensure timely and continuous transmission of video content to the viewer.

For different network conditions, an adaptive bit rate stream is implemented to optimize the viewing experience.

5. And (3) synchronous verification:

in the video streaming process, synchronization verification is continuously performed. This includes checking whether the time of subtitle display is consistent with the video and audio streams.

Any synchronization problems are detected and corrected using feedback mechanisms, such as viewer feedback or automatic detection tools.

6. User interaction:

user interaction functions are provided that allow the viewer to adjust the manner in which the subtitles are displayed, such as font size, color, or switch subtitles, as desired. An easy-to-use interface is realized, enabling the viewer to easily control the subtitle setting.

Through the steps, the synchronous output process can ensure that the translated content and the video stream are perfectly synchronous, and smooth and seamless subtitle display is provided, so that the viewing experience of audiences is enhanced. This is critical for the contact and understanding of video content by multi-language viewers, especially in terms of live international, news stories or educational content.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims

1. The cloud computing generation type translation method for live video broadcast is characterized by comprising the following steps of:

preprocessing the video stream after the video stream reaches the cloud;

determining a suitable deep learning model architecture;

extracting key features from the corpus;

training a translation model by using the preprocessed data;

verifying the translation effect of the model on an independent test set;

2. The cloud computing generation type translation method for live video broadcast according to claim 1, wherein the video stream preprocessing process comprises the following steps:

processing text data and processing audio data;

audio data processing includes denoising and normalization of audio signals.

3. The method for cloud computing generated translation of a live video stream as defined in claim 1 wherein the step of determining a suitable deep learning model architecture comprises:

4. A live video cloud computing generated translation method according to claim 3, wherein the number and dimension configuration of multiple head attentives are dynamically adjusted according to the characteristics and scale of input data to improve the adaptability and efficiency of the model.

5. The cloud computing generated translation method for live video as claimed in claim 1, wherein the step of training the translation model using the preprocessed data comprises:

6. The method for generating translation by cloud computing for live video broadcast according to claim 1, wherein the step of optimizing the accuracy and efficiency of the translation by adjusting model parameters comprises:

the update rules of the optimizer can be expressed as:

7. The cloud computing generated translation method of claim 1, wherein the step of verifying the translation effect of the model on the independent test set comprises:

8. The method for cloud computing generated translation of live video as claimed in claim 1, wherein the step of integrating the model into the cloud platform and testing the overall system performance comprises:

optimizing the use of cloud resources to increase translation processing speed and reduce latency, and/or employing dynamic resource allocation techniques to automatically adjust computing resources based on real-time translation requirements.

9. Cloud computing generation type translation device of live video, characterized by comprising:

wherein, the translation processing module includes:

10. A computer-readable storage medium comprising computer-executable instructions that, when run on a computer, cause the computer to perform the live video cloud computing generated translation method of any of claims 1-8.