| --- |
| 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. |
|
|
