# Speaker diarization

A speaker diarization model modified based on pyannote/speaker-diarization-3.0, using speechbrain/spkrec-ecapa-voxceleb for speaker embedding, with better performance in certain tests

This is an audio segmentation model capable of detecting speaker changes, voice activity, and overlapping speech, suitable for audio analysis in multi-speaker scenarios.

Japanese automatic speech recognition model based on Whisper, integrating speaker separation and punctuation addition functions

This is a speaker diarization model fine-tuned from the pyannote/segmentation-3.0 base model, specifically optimized for Japanese telephone conversation scenarios.

This is an audio processing model for speaker diarization, capable of detecting speech activity, overlapping speech, and multiple speakers.

The speaker diarization pipeline of Pyannote.audio, used to automatically detect speaker changes in audio and segment speech segments.

This is a powerset-encoded speaker diarization model capable of processing 10-second audio clips to identify multiple speakers and their overlapping speech.

This is an end-to-end speaker diarization model that supports voice activity detection, overlap speech detection, and resegmentation tasks.

Speaker diarization model based on pyannote.audio 2.0 for automatic detection of speaker changes and speech activity in audio

Speaker diarization model based on pyannote.audio 2.1.1, used for automatic detection of speaker changes and overlap speech in audio

A pre-trained model for detecting overlapped speech in audio, capable of identifying time segments where two or more speakers are active simultaneously.