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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- embeddings |
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- Speaker |
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- Verification |
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- Identification |
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- NAS |
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- TDNN |
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- pytorch |
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datasets: |
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- voxceleb1 |
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- voxceleb2 |
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metrics: |
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- EER |
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- minDCF: |
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- p_target: 0.01 |
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--- |
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# EfficientTDNN |
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Model Version are listed as follows. |
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- **Dynamic Kernel**: The model enables various kernel sizes in {1,3,5}, `kernel/kernel.torchparams`. |
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- **Dynamic Depth**: The model enables additional various depth in {2,3,4} based on **Dynamic Kernel** version, `depth/depth.torchparams`. |
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- **Dynamic Width 1**: The model enable additional various width in [0.5, 1.0] based on **Dynamic Depth** version, `width1/width1.torchparams`. |
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- **Dynamic Width 2**: The model enable additional various width in [0.25, 0.5] based on **Dynamic Width 1** version, `width2/width2.torchparams`. |
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Furthermore, some subnets are given in the form of the weights of batchnorm corresponding to their trained supernets as follows. |
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- **Dynamic Kernel** |
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1. `kernel/kernel.max.bn.tar` |
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2. `kernel/kernel.Kmin.bn.tar` |
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- **Dynamic Depth** |
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1. `depth/depth.max.bn.tar` |
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2. `depth/depth.Kmin.bn.tar` |
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3. `depth/depth.Dmin.bn.tar` |
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4. `depth/depth.3.512.5.5.3.3.1536.bn.tar` |
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5. `depth/depth.ecapa-tdnn.3.512.512.512.512.5.3.3.3.1536.bn.tar` |
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- **Dynamic Width 1** |
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1. `width1/width1.torchparams` |
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2. `width1/width1.max.bn.tar` |
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3. `width1/width1.Kmin.bn.tar` |
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4. `width1/width1.Dmin.bn.tar` |
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5. `width1/width1.C1min.bn.tar` |
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6. `width1/width1.3.383.256.256.256.5.3.3.3.768.bn.tar` |
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- **Dynamic Width 2** |
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1. `width2/width2.max.bn.tar` |
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2. `width2/width2.Kmin.bn.tar` |
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3. `width2/width2.Dmin.bn.tar` |
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4. `width2/width2.C1min.bn.tar` |
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5. `width2/width2.C2min.bn.tar` |
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6. `width2/width2.3.384.3.1152.bn.tar` |
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7. `width2/width2.3.256.256.384.384.1.3.5.3.1152.bn.tar` |
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8. `width2/width2.2.256.256.256.3.3.3.400.bn.tar` |
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The tag is described as follows. |
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- max: `(4, [512, 512, 512, 512, 512], [5, 5, 5, 5, 5], 1536)` |
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- Kmin: `(4, [512, 512, 512, 512, 512], [1, 1, 1, 1, 1], 1536)` |
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- Dmin: `(2, [512, 512, 512], [1, 1, 1], 1536)` |
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- C1min: `(2, [256, 256, 256], [1, 1, 1], 768)` |
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- C2min: `(2, [128, 128, 128], [1, 1, 1], 384)` |
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More details about EfficentTDNN can be found in the paper [EfficientTDNN](https://arxiv.org/abs/2103.13581). |
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