Monthly Archives: January 2023

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Revolutionizing Speech: How Technology and AI are Transforming Text-to-Speech with Human-like Voices


Technology and artificial intelligence (AI) have come a long way in recent years, and one area where this is particularly evident is in the field of text-to-speech (TTS) synthesis. TTS systems are designed to convert written text into spoken words, and today’s TTS systems are able to produce human-like voices with a high degree of accuracy.

One of the key advancements that has made this possible is the development of deep learning algorithms. These algorithms are able to analyze large amounts of data and learn to recognize patterns and relationships between different sounds and words. This allows TTS systems to generate speech that sounds more natural and human-like.

Another important factor is the use of large datasets of recorded speech. These datasets are used to train TTS systems, and the more data that is available, the more accurate the system can become. In addition, the use of sophisticated algorithms to analyze and process the speech data also helps to improve the quality of the generated speech.

One of the most popular TTS systems available today is Google’s DeepMind WaveNet. This system uses a deep neural network to generate speech that sounds very natural. It is able to produce speech in a wide range of languages and dialects, and it can even mimic specific individuals’ voices.

Another example is Amazon’s Polly, which uses advanced machine learning techniques to produce lifelike speech. It offers a variety of natural-sounding voices, including voices in multiple languages, and allows users to customize the speed, pitch and volume of the generated speech.

The TTS systems also have many practical applications, such as in virtual assistants, voice-controlled devices, and accessibility technology for people with hearing impairments. In the future, it’s likely that TTS technology will continue to improve, becoming even more natural and human-like, and finding new applications in a variety of fields.

In conclusion, the technology and artificial intelligence have made a great development in the field of text-to-speech synthesis. The use of deep learning algorithms and large datasets of recorded speech have improved the accuracy of TTS systems and made it possible to produce human-like voices. TTS technology is already being used in many practical applications, and it is likely to continue to evolve and improve in the future.

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Splitting Messages in SAP PO using a Java Mapping

Category:Programming,SAP,SAP PI/PO

Here is an example of a Java mapping in SAP PO that splits a message using the String.split() method:

import java.util.Arrays;


public class MessageSplitter extends AbstractTransformation {

    public void transform(TransformationInput input, TransformationOutput output) throws StreamTransformationException {
        try {
            // Get input and output payload
            InputPayload in = input.getInputPayload();
            OutputPayload out = output.getOutputPayload();

            // Read input message into a string
            byte[] bytes = new byte[in.getInputStream().available()];
            String inputMessage = new String(bytes);

            // Split message using a delimiter
            String delimiter = ",";
            String[] parts = inputMessage.split(delimiter);

            // Write the parts to the output payload
            for (String part : parts) {
        } catch (Exception e) {
            throw new StreamTransformationException(e.getMessage());

In this example, the input message is read into a string, then split using the comma (“,”) delimiter. The resulting parts are then written to the output payload, one after the other. You can change the delimiter to any string you want to use as a separator.

Please keep in mind that this is just an example and it may need further adaptation depending on the specific requirements of your integration scenario