README.md

metadata

license: cc0-1.0
task_categories:
  - audio-classification
tags:
  - speaker-embeddings
  - speaker-recognition
  - redimnet

Pre-computed speaker embeddings

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.

Contents

• 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 (accent / age / gender / source URL) — populated for 0 / 251 speakers
  • n_speakers, source for provenance

Loading

import numpy as np
data = np.load("librispeech-multi.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:

.venv/bin/python scripts/experiments/binary_endtask_speaker_eval.py extract \
    --out .data/corpus/librispeech-multi.pyannote-embedding.npz

The build streams the source audio, embeds valid (≥ 3 s) utterances with pyannote/embedding (512-dim), 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.