ReDimNet2 B4 (VoxCeleb2-dev, lm) for MLX
MLX conversion of the official ReDimNet2 b4-vox2-lm.pt checkpoint
(large-margin fine-tuned (recommended)). It produces 192-dimensional speaker
embeddings from 16 kHz mono audio.
Usage
uv add git+https://github.com/avra-m3/speaker-embedding-mlx
import mlx.core as mx
from redimnet2_mlx import load_model
model = load_model("causal/redimnet2-b4-vox2-lm-mlx")
emb = model(mx.array(wav_16k_float32)[None]) # (1, 192)
Parity
Embeddings match the PyTorch reference to 4.1e-06 relative error (worst case, float32, MLX CPU backend). See the parity log.
License and citation
MIT, following the upstream ReDimNet2 release (Copyright (c) 2026 Palabra.ai). See third-party notices.
@inproceedings{redimnet2_2026,
title={ReDimNet2: Scaling Speaker Verification via Time-Pooled Dimension Reshaping},
author={Yakovlev, Ivan and Okhotnikov, Anton},
booktitle={Proceedings of Interspeech 2026},
year={2026},
eprint={2603.11841},
archivePrefix={arXiv},
}