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README.md

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+---
+license: cc0-1.0
+task_categories:
+- audio-classification
+tags:
+- speaker-embeddings
+- speaker-recognition
+- pyannote
+---
+
+# Pre-computed speaker embeddings
+
+Pre-computed 512-dim L2-normalized speaker embeddings extracted with
+[`pyannote/embedding`](https://huggingface.co/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
+
+```python
+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`](https://github.com/DJRHails/voxpath)
+via:
+
+```bash
+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.