How to Scale Audio Annotation: Diarization, Transcription, and Automation with Encord

The webinar hosted by Encord delved into the complexities and challenges of developing production-ready audio AI systems, highlighting that while audio data is abundant, the difficulty arises from the intricate and expensive labeling process. Audio is inherently complex due to its temporal nature and issues such as overlapping voices and background noise, which complicate accurate transcription and diarization. Encord’s approach emphasizes the use of automation as a force multiplier, leveraging task agents and models like Whisper and Pyannote to handle initial transcription tasks, thus allowing human annotators to focus on refining machine-generated labels. The webinar underscored the importance of waveform-based labeling for precision and how workflow design, including confidence-based routing and active learning, drives significant improvements in model performance. The upcoming release of the Agents Catalog in Encord aims to further simplify automation by offering a library of agents to streamline integration and enhance workflow efficiency, making it accessible to teams with varying levels of machine learning infrastructure expertise.