![]() ![]() ![]() In an increasingly multimedia world with a plethora of file formats, data annotation tools must be able to handle every single file format that you may work with on any given day.Īudio annotations can be used to develop speech recognition technology further. It’s essential that the tooling used by researchers, academics, enterprise data scientists, and machine learning professionals can keep up with the needs of the industry at large. The world of data labeling and annotation has come leaps and bounds recently. This excerpt specifies value="$csv" in the TimeSeries Object tag.Special thanks to Brandon Martel and Nate Kartchner for your expertise and contributions to this post. For example, the following excerpt of a time series labeling configuration. Use the data object to reference the value of the data specified by the Object tag in your labeling configuration. The JSON format for pre-annotations must match the labeling configuration used for your data labeling project. See how to add results to the predictions array. A predictions array that contains the pre-annotation results for the different types of labeling.This can be a URL to an audio file, a pre-signed cloud storage link to an image, plain text, a reference to a CSV file stored in Label Studio, or something else. A data object which references the source of the data that the pre-annotations apply to.Label Studio JSON format for pre-annotations must contain two sections: Check this common video tutorial showing how to convert a submitted annotation to a prediction: JSON format for pre-annotations The Label Studio ML backend also outputs tasks in this format. ![]() ![]() To import predicted labels into Label Studio, you must use the Basic Label Studio JSON format and set up your tasks with the predictions JSON key. You can import pre-annotated tasks into Label Studio using the UI or using the API. To generate interactive pre-annotations with a machine learning model while labeling, see Set up machine learning with Label Studio. ![]()
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