DJRHails/pyannote-embedding-librispeech

Pre-computed speaker embeddings

Pre-computed 512-dim L2-normalized speaker embeddings extracted with `pyannote/embedding` over LibriSpeech train.100 + train.360 (1172 speakers via openslr/librispeech_asr). One utterance per speaker, minimum 3 s duration.

Contents

• —librispeech.pyannote-embedding.npz — numpy .npz archive with:
• —embeddings: (3507, 512) float32
• —speaker_ids: (3507,) string IDs from the source corpus
• —metadata_json: per-speaker metadata (accent / age / gender / source URL) — populated for 1172 / 3507 speakers
• —n_speakers, source for provenance

Loading

import numpy as np
data = np.load("librispeech.pyannote-embedding.npz", allow_pickle=True)
embeddings = data["embeddings"]            # (N, 512)
speaker_ids = list(data["speaker_ids"])    # length N

Regenerating

This file was produced by `voxpath` via:

voxpath corpus build commonvoice --max-speakers 3507 \
    --output librispeech.pyannote-embedding.npz

voxpath corpus build streams the source audio, embeds each speaker's first valid (≥ 3 s) utterance with pyannote/embedding, L2-normalises, and writes the .npz.

Why model-specific

Speaker embeddings are not portable across embedders. A wespeaker embedding and a pyannote/embedding embedding for the same audio lie in different spaces and can't be compared or quantized together. This repo is named after the embedding model so users can find the right artifact for their pipeline at a glance.