Datasets

Preparing datasets is a complex and multidisciplinary task that requires careful planning and coordination between different experts. Our work has resulted in datasets that can contribute significantly to advances in artificial intelligence and machine learning. Last but not least, it is important to ensure that text datasets are maintained and updated. This includes regular data revisions, adding new situational text, terminology and updating annotations to keep the data relevant and useful for the project or service.

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Preparation of text datasets

Maximising AI performance

We have significant experience and process knowledge in preparing text data for AI. Our services include comprehensive creation and expert review of texts designed to retrain or extend your AI. Our datasets provide higher accuracy and relevance of outputs, which is essential for reliable and effective use of AI in practice. Our service will help improve the performance of your AI systems and adapt them to specific user requirements, and your tools will perform at their peak with our service.

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Audio datasets

We will process your sound recordings

The process of creating and preparing audio datasets involves several key steps, from the analysis and design of the technical way of publishing the data, through the determination of the technical form of the published data, to the actual publication and cataloguing of the datasets. The first step is to analyze and define the content of the dataset. This includes determining what audio recordings will be included in the set and how they will be structured. For example, if we are creating a speech recognition dataset, we need to specify the different languages, dialects and pronunciation types we want to include. Part of the process includes determining file formats, sampling rates, bit depths and other technical parameters to ensure that the dataset is compatible with different systems and applications.

We keep your audio datasets up to date and maintained

After publication, it is important to regularly maintain and update your dataset to keep it relevant and useful. This may include enriching audio recordings of previously unforeseen situations, adding specific or newly emerging terminology, or updating metadata to refine decision‑making.

Leveraging audio datasets to advance your technology

The importance of audio datasets is invaluable, especially for the development of speech recognition, speech synthesis, and other natural language processing‑based applications. These applications can be used not only for commercial purposes, but also to support education, accessibility and communication. Creating high‑quality audio datasets is therefore crucial for advancing language technology and artificial intelligence. It is a process that requires careful preparation, expertise and cooperation between different professionals. And it is thanks to these efforts that new horizons in human interaction with technology are opening up.

Vyžití zvukových datových sad pro rozvoj vašich technologií

Image data creation

Taking your AI tools to the next level

Part of the process of creating and preparing image datasets is image recognition after machine learning. The process of preparing image datasets involves several important steps to ensure that the data is properly organized, annotated and ready for further processing. The next step is the actual data preparation, which includes the collection of image material, its cleaning from unnecessary information, segmentation and annotation. Annotation is particularly important because it provides the necessary context for the images and allows machines to better understand the content of the dataset.

Synthetic Image Sets

Synthetic data can help overcome the limitations associated with the lack of real data or the ethical and privacy issues that can arise when collecting it. The creation and preparation of synthetic image data is a process that requires careful planning and sophisticated techniques. We define the targets and parameters that will need to be represented in the compiled datasets. This includes determining what information the dataset will contain and how it will be structured. It is also important to specify technical requirements for the data, such as file formats, image resolutions and annotation methods.

Datasets

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