ReDimNet2: Scaling Speaker Verification via Time-Pooled Dimension Reshaping

Anonymous submission to Interspeech 2026

This repository contains the official implementation and pretrained weights for the paper "ReDimNet2: Scaling Speaker Verification via Time-Pooled Dimension Reshaping".

Overview

ReDimNet2 extends the ReDimNet dimension-reshaping framework by introducing pooling over the time dimension in the 1D processing pathway. This allows significantly more aggressive channel scaling without proportional compute increase, yielding a strictly better accuracy–efficiency Pareto front at every scale point.

Results

Trained on VoxCeleb2-dev

EER (%) on VoxCeleb1 cleaned protocols:

Model	Params	GMACs	Vox1-O	Vox1-E	Vox1-H
ReDimNet2-B0	1.1M	0.33	1.04	1.16	1.97
ReDimNet2-B1	2.1M	0.56	0.78	0.96	1.72
ReDimNet2-B2	3.6M	0.95	0.57	0.76	1.41
ReDimNet2-B3	4.1M	2.70	0.42	0.66	1.22
ReDimNet2-B4	6.6M	4.62	0.37	0.58	1.07
ReDimNet2-B5	8.9M	9.62	0.33	0.56	1.07
ReDimNet2-B6	12.3M	13.05	0.29	0.52	0.99

Trained on VoxBlink2 + VoxCeleb2

EER (%) on VoxCeleb1 cleaned protocols:

Model	Params	GMACs	Vox1-O	Vox1-E	Vox1-H	Vox1-B
ReDimNet2-B6	12.5M	13.05	0.17	0.34	0.68	0.95

EER (%) on out-of-domain cross-dataset benchmarks (train_type="lm", dataset="vb2+vox2_v0"):

Model	SITW (eval-core)	VOICES (eval)	CN-Celeb (multi-enroll)
ReDimNet2-B6	0.66	2.59	7.39

Quick Start

Via torch.hub

import torch
import torchaudio

# VoxCeleb2-only variant (default)
model = torch.hub.load("PalabraAI/redimnet2", "redimnet2",
                      model_name="b6", train_type="lm", pretrained=True)

# VoxBlink2 + VoxCeleb2 variant (stronger, B6 only)
model = torch.hub.load("PalabraAI/redimnet2", "redimnet2",
                      model_name="b6", train_type="lm",
                      dataset="vb2+vox2_v0", pretrained=True)
model.eval()

waveform, sr = torchaudio.load("audio.wav")
# waveform: (batch, samples), 16 kHz
with torch.no_grad():
    embedding = model(waveform)  # (batch, emb_dim)

Available model names: b0, b1, b2, b3, b4, b5, b6. Available train types: ptn (pretraining), lm (large-margin fine-tuning, recommended). Available datasets: vox2 (default), vb2+vox2_v0 (B6 only).

Local installation

git clone https://github.com/PalabraAI/redimnet2.git
cd redimnet2
pip install -r requirements.txt

from redimnet2 import ReDimNet2

model = ReDimNet2.from_pretrained("b3", train_type="lm")

Model Zoo

Pretrained weights are available as GitHub Release assets.

VoxCeleb2-dev

Config	Pretraining (ptn)	Large-Margin (lm)
B0	b0-vox2-ptn.pt	b0-vox2-lm.pt
B1	b1-vox2-ptn.pt	b1-vox2-lm.pt
B2	b2-vox2-ptn.pt	b2-vox2-lm.pt
B3	b3-vox2-ptn.pt	b3-vox2-lm.pt
B4	b4-vox2-ptn.pt	b4-vox2-lm.pt
B5	b5-vox2-ptn.pt	b5-vox2-lm.pt
B6	b6-vox2-ptn.pt	b6-vox2-lm.pt

VoxBlink2 + VoxCeleb2

Config	Pretraining (ptn)	Large-Margin (lm)
B6	b6-vb2+vox2_v0-ptn.pt	b6-vb2+vox2_v0-lm.pt

Roadmap

We are actively working on improving model stability and expanding the release. Planned updates:

• Release a new version of ReDimNet2 with improved normalization unified for both 1D and 2D feature map representations (agg_gnorm + dual_agg options, first used by the vb2+vox2_v0 B6 release)
• Release the training pipeline (VoxCeleb2)
• Release VoxBlink2 + VoxCeleb2 pretrained model (B6)
• Release VoxBlink2 + VoxCeleb2 pretrained models (B3, distilled B6 → B3)
• Release training recipes for VoxBlink2 + VoxCeleb2 training

Citation

@inproceedings{redimnet2_2026,
  title={ReDimNet2: Scaling Speaker Verification via Time-Pooled Dimension Reshaping},
  author={Anonymous},
  booktitle={Submitted to Interspeech 2026},
  year={2026}
}

License

MIT