#annotation

Data-Speech provides efficient scripts for speech dataset annotation, emphasizing audio transformation and automated tagging essential for next-gen text-to-speech models. This toolset aids in crafting natural language labeled datasets, inspired by significant research on speech synthesis. It works seamlessly with renowned datasets like LibriTTS-R and MLS and integrates with the Parler-TTS library for both inference and training. Discover streamlined processes for annotating and generating detailed language prompts from speech data.

Label Studio Converter, now part of the Label Studio SDK, facilitates conversion of diverse annotation formats like JSON, CSV, COCO, and Pascal VOC XML for machine learning libraries. It is particularly effective for YOLO annotations, supporting both local and cloud storage configurations. This tool simplifies data importation into Label Studio, enhancing workflow efficiency. Suitable for developers in need of streamlined data preparation.

PyLabel is a Python package that assists in preparing image datasets for computer vision models like PyTorch and YOLOv5. It offers efficient conversion of annotation formats such as COCO to YOLO with minimal code. Users can analyze image datasets and strategically split them into training, test, and validation groups. Furthermore, PyLabel includes a Jupyter notebook-based tool supporting both manual and AI-assisted image labeling. It also allows easy visualization to verify annotations, developed as a project at UC Berkeley.

LabelBee is designed to optimize annotation experiences with its straightforward component library for quick application development. It supports seamless integration with a stand-alone rendering module, requiring minimal setup. Developers can easily access it through npm or yarn. Extensive documentation is provided to assist users at all technical levels, facilitating a smooth setup. Explore efficient and independent rendering capabilities with LabelBee.

This Kotlin compiler plugin provides an efficient way to redact sensitive details in `toString()` outputs, offering enhanced data privacy. Through the `@Redacted` annotation, it conceals confidential properties, supporting JVM, JS, and native targets. With its easy integration via Gradle, customization of redaction behaviors is straightforward. Although IDE support is unstable, it effectively protects sensitive data within logs or interfaces for multiplatform projects.