---
license: mit
library_name: voxpath
tags:
- speaker-recognition
- quantizer
- voxpath
- pyannote
---

# voxpath quantizer

Trained HierarchicalHashQuantizer (18 bits grouped into 6 display words of vocab size 8) that maps L2-normalized 192-dim speaker embeddings
from [`PalabraAI/redimnet2 (b6-vox2-lm, 192-dim, via torch.hub)`](https://huggingface.co/PalabraAI/redimnet2 (b6-vox2-lm, 192-dim, via torch.hub)) into
hierarchical word paths like `chalk.fjord.bismuth.elm`.

Use with [`voxpath`](https://github.com/DJRHails/voxpath) to assign
LM-readable speaker identities for diarization + transcription pipelines.

## Files

- `voxceleb+librispeech+commonvoice+voices.redimnet2-b6.hierarchical-hash.quantizer.json` — fitted quantizer state (0.1 MB).

## Training corpus

Trained on unique English-speaking speakers from VoxCeleb 2 + LibriSpeech + CommonVoice EN (HF streaming builds via `voxpath corpus build`), each utterance ≥3 s, embedded with ReDimNet2-B6 (vox2, large-margin), L2-normalized.

## Loading

```python
from huggingface_hub import hf_hub_download
from voxpath.hashing import HierarchicalHashQuantizer

path = hf_hub_download(
    "DJRHails/voxpath-hierarchical-hash-redimnet2-b6",
    "voxceleb+librispeech+commonvoice+voices.redimnet2-b6.hierarchical-hash.quantizer.json",
)
quantizer = HierarchicalHashQuantizer.load(path)

# Then, given a pyannote/embedding output `embedding` (shape (192,)):
voxpath = quantizer.quantize(embedding)
print(voxpath.to_tag())  # e.g. SPEAKER:halite.rill.bismuth.elm
```

## Why model-and-embedder-specific

Speaker embeddings don't translate across embedders — a quantizer
fitted on `pyannote/embedding` outputs has no meaning for embeddings
from `wespeaker`, `ECAPA-TDNN`, or anything else. The repo name pins
both the quantizer family (HierarchicalHashQuantizer) and the embedding model so users find
the right artifact at a glance.