Pre-computed 512-dim L2-normalized speaker embeddings extracted with
pyannote/embedding (512-dim) over
LibriSpeech train-clean-100 (all 251 speakers, 10 utterances each). 2510 utterances across 251 speakers, minimum 3 s duration.
librispeech-multi.pyannote-embedding.npz — numpy .npz archive with:
embeddings: (2510, 512) float32
speaker_ids: (2510,) string IDs from the source corpus
metadata_json: per-speaker metadata… See the full description on the dataset page:
https://huggingface.co/datasets/DJRHails/pyannote-embedding-librispeech-multi.
