Introducing Batch Diarization V2

Batch Diarization V2 is a significant upgrade to speaker attribution technology, enhancing the accuracy of speaker labeling in pre-recorded audio across various domains such as contact centers, healthcare, and voice AI applications. The new model introduces expanded training data, an improved speaker embedding model, and better segmentation and clustering, leading to more precise speaker attribution and reduced labeling errors. Evaluations showed that Diarization V2 consistently outperformed its predecessor, Diarization V1, with human evaluators preferring the new version 3.3 times more often due to improved performance, particularly in challenging audio scenarios. The update is available via the new diarize_model parameter, allowing users to select between model versions without any changes in pricing or existing integrations, and is compatible with Deepgram's batch Speech-to-Text offerings.