Top Speaker Diarization Libraries and APIs in 2022

Speaker Diarization is a process that identifies the number of speakers in an audio file and assigns their words to the correct speaker. It involves breaking down the audio into utterances, creating embeddings representative of each speaker's characteristics using Deep Learning models, determining the number of speakers, clustering utterance embeddings based on similarity, and finally labeling each utterance with a unique speaker label. This technology is useful for making transcriptions more readable and as an analytic tool to identify patterns or trends among individual speakers. Currently, Speaker Diarization models work best for asynchronous transcription and struggle with real-time transcription. The accuracy of these models can be affected by factors such as speaker talk time, conversational pace, and background noise.