---
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

```python
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`](https://github.com/DJRHails/voxpath)
via:

```bash
.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.