Introducing Batch Diarization V2, a major upgrade to speaker labeling for pre-recorded audio.
Understanding what was said is only part of the story. You also need to know who said it.
Highlights:
🔹 Preferred 3.3X in human evaluation
🔹 Improved speaker attribution accuracy across real-world audio
🔹 Available today via the new diarize_model parameter
Get started:
Speaker attribution errors corrupt call analytics and meeting summaries downstream. The upgrade is opt-in through a single new parameter, so an existing batch transcription pipeline can compare old and new labeling without other changes.
