To load pretrained model you need to define model_name (size), dataset it was trained on, train_type whether it is pretrain-only (ptn), large-margin finetuning (ft_lm), finetuning with cutmix+mixup augmentations (ft_mix).
import torch
model_name='M' # ~b3-b4 size
train_type='ft_mix'
dataset='vb2+vox2+cnc'
model = torch.hub.load('IDRnD/ReDimNet', 'ReDimNet',
model_name=model_name,
train_type=train_type,
dataset=dataset)All models configurations with corresponding metrics can be found in following table:
| Model name (size) | Train dataset | Train type | Vox1-O EER(%) | Vox1-E EER(%) | Vox1-H EER(%) | SITW EER(%) | VOICES EER(%) | CN-Celeb EER(%) |
|---|---|---|---|---|---|---|---|---|
| M | vb2+vox2+cnc | ft_mix | 0.835 | 0.745 | 1.284 | 1.203 | 2.703 | 7.474 |
| M | vb2 | ptn | 1.319 | 1.128 | 2.000 | 1.482 | 4.116 | 9.012 |
| S | vb2+vox2+cnc | ft_mix | 0.936 | 0.874 | 1.510 | 1.310 | 2.774 | 8.043 |
| S | vb2 | ptn | 1.542 | 1.408 | 2.505 | 1.781 | 3.987 | 9.592 |
| b0 | vox2 | ft_lm | 1.16 | 1.25 | 2.20 | - | - | - |
| b0 | vox2 | ptn | - | - | - | - | - | - |
| b1 | vox2 | ft_lm | 0.85 | 0.97 | 1.73 | - | - | - |
| b1 | vox2 | ptn | - | - | - | - | - | - |
| b2 | vox2 | ft_lm | 0.57 | 0.76 | 1.32 | - | - | - |
| b2 | vox2 | ptn | - | - | - | - | - | - |
| b3 | vox2 | ft_lm | 0.50 | 0.73 | 1.33 | - | - | - |
| b3 | vox2 | ptn | - | - | - | - | - | - |
| b4 | vox2 | ft_lm | 0.51 | 0.68 | 1.26 | - | - | - |
| b4 | vox2 | ptn | - | - | - | - | - | - |
| b5 | vox2 | ft_lm | 0.43 | 0.61 | 1.08 | - | - | - |
| b5 | vox2 | ptn | - | - | - | - | - | - |
| b6 | vox2 | ft_lm | 0.40 | 0.55 | 1.05 | - | - | - |
| b6 | vox2 | ptn | - | - | - | - | - | - |
| Model | Params | GMACs | LM | AS-Norm | Vox1-O EER(%) | Vox1-E EER(%) | Vox1-H EER(%) |
|---|---|---|---|---|---|---|---|
| ⬦ ReDimNet-B0 | 1.0M | 0.43 | ✓ | ✗ | 1.16 | 1.25 | 2.20 |
| ⬥ ReDimNet-B0 | ✓ | ✓ | 1.07 | 1.18 | 2.01 | ||
| NeXt-TDNN-l (C=128,B=3) | 1.6M | 0.29* | ✗ | ✓ | 1.10 | 1.24 | 2.12 |
| NeXt-TDNN (C=128,B=3) | 1.9M | 0.35* | ✗ | ✓ | 1.03 | 1.17 | 1.98 |
| ⬦ ReDimNet-B1 | 2.2M | 0.54 | ✓ | ✗ | 0.85 | 0.97 | 1.73 |
| ⬥ ReDimNet-B1 | ✓ | ✓ | 0.73 | 0.89 | 1.57 | ||
| ECAPA (C=512) | 6.4M | 1.05 | ✗ | ✓ | 0.94 | 1.21 | 2.20 |
| NeXt-TDNN-l (C=256,B=3) | 6.0M | 1.13* | ✗ | ✓ | 0.81 | 1.04 | 1.86 |
| CAM++ | 7.2M | 1.15 | ✓ | ✗ | 0.71 | 0.85 | 1.66 |
| NeXt-TDNN (C=256,B=3) | 7.1M | 1.35* | ✗ | ✓ | 0.79 | 1.04 | 1.82 |
| ⬦ ReDimNet-B2 | 4.7M | 0.90 | ✓ | ✗ | 0.57 | 0.76 | 1.32 |
| ⬥ ReDimNet-B2 | ✓ | ✓ | 0.52 | 0.74 | 1.27 | ||
| ECAPA (C=1024) | 14.9M | 2.67 | ✓ | ✗ | 0.98 | 1.13 | 2.09 |
| DF-ResNet56 | 4.5M | 2.66 | ✗ | ✓ | 0.96 | 1.09 | 1.99 |
| Gemini DF-ResNet60 | 4.1M | 2.50* | ✗ | ✓ | 0.94 | 1.05 | 1.80 |
| ⬦ ReDimNet-B3 | 3.0M | 3.00 | ✓ | ✗ | 0.50 | 0.73 | 1.33 |
| ⬥ ReDimNet-B3 | ✓ | ✓ | 0.47 | 0.69 | 1.23 | ||
| ResNet34 | 6.6M | 4.55 | ✓ | ✗ | 0.82 | 0.93 | 1.68 |
| Gemini DF-ResNet114 | 6.5M | 5.00 | ✗ | ✓ | 0.69 | 0.86 | 1.49 |
| ⬦ ReDimNet-B4 | 6.3M | 4.80 | ✓ | ✗ | 0.51 | 0.68 | 1.26 |
| ⬥ ReDimNet-B4 | ✓ | ✓ | 0.44 | 0.64 | 1.17 | ||
| Gemini DF-ResNet183 | 9.2M | 8.25 | ✗ | ✓ | 0.60 | 0.81 | 1.44 |
| DF-ResNet233 | 12.3M | 11.17 | ✗ | ✓ | 0.58 | 0.76 | 1.44 |
| ⬦ ReDimNet-B5 | 9.2M | 9.87 | ✓ | ✗ | 0.43 | 0.61 | 1.08 |
| ⬥ ReDimNet-B5 | ✓ | ✓ | 0.39 | 0.59 | 1.05 | ||
| ResNet293 | 23.8M | 28.10 | ✓ | ✗ | 0.53 | 0.71 | 1.30 |
| ECAPA2 | 27.1M | 187.00* | ✓ | ✗ | 0.44 | 0.62 | 1.15 |
| ⬦ ReDimNet-B6 | 15.0M | 20.27 | ✓ | ✗ | 0.40 | 0.55 | 1.05 |
| ⬥ ReDimNet-B6 | ✓ | ✓ | 0.37 | 0.53 | 1.00 |
* - means values have been estimated.