update readme and git attr

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+*.torchparams filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,68 @@
+---
+language:
+- en
+license: mit
+tags:
+- embeddings
+- Speaker
+- Verification
+- Identification
+- NAS
+- TDNN
+- pytorch
+datasets:
+- voxceleb1
+- voxceleb2
+metrics:
+- EER
+- minDCF:
+  - p_target: 0.01
+---
+
+
 # EfficientTDNN
 
-Model Version
-- Maximum Size: One single model with the maximum size.
-- Dynamic Kernel: The model enables various kernel sizes in {1,3,5}.
-- Dynamic Depth: The model enables additional various depth in {2,3,4} based on **Dynamic Kernel** version.
-- Dynamic Width 1: The model enable additional various width in [0.5, 1.0] based on **Dynamic Depth** version.
-- Dynamic Width 2: The model enable additional various width in [0.25, 0.5] based on **Dynamic Width 1** version.
+Model Version are listed as follows.
+
+- **Dynamic Kernel**: The model enables various kernel sizes in {1,3,5}, `kernel/kernel.torchparams`.
+- **Dynamic Depth**: The model enables additional various depth in {2,3,4} based on **Dynamic Kernel** version, `depth/depth.torchparams`.
+- **Dynamic Width 1**: The model enable additional various width in [0.5, 1.0] based on **Dynamic Depth** version, `width1/width1.torchparams`.
+- **Dynamic Width 2**: The model enable additional various width in [0.25, 0.5] based on **Dynamic Width 1** version, `width2/width2.torchparams`.
+
+Furthermore, some subnets are given in the form of the weights of batchnorm corresponding to their trained supernets as follows.
+
+- **Dynamic Kernel**
+  1. `kernel/kernel.max.bn.tar`
+  2. `kernel/kernel.Kmin.bn.tar`
+- **Dynamic Depth**
+  1. `depth/depth.max.bn.tar`
+  2. `depth/depth.Kmin.bn.tar`
+  3. `depth/depth.Dmin.bn.tar`
+  4. `depth/depth.3.512.5.5.3.3.1536.bn.tar`
+  5. `depth/depth.ecapa-tdnn.3.512.512.512.512.5.3.3.3.1536.bn.tar`
+- **Dynamic Width 1**
+  1. `width1/width1.torchparams`
+  2. `width1/width1.max.bn.tar`
+  3. `width1/width1.Kmin.bn.tar`
+  4. `width1/width1.Dmin.bn.tar`
+  5. `width1/width1.C1min.bn.tar`
+  6. `width1/width1.3.383.256.256.256.5.3.3.3.768.bn.tar`
+- **Dynamic Width 2**
+  1. `width2/width2.max.bn.tar`
+  2. `width2/width2.Kmin.bn.tar`
+  3. `width2/width2.Dmin.bn.tar`
+  4. `width2/width2.C1min.bn.tar`
+  5. `width2/width2.C2min.bn.tar`
+  6. `width2/width2.3.384.3.1152.bn.tar`
+  7. `width2/width2.3.256.256.384.384.1.3.5.3.1152.bn.tar`
+  8. `width2/width2.2.256.256.256.3.3.3.400.bn.tar`
+
+The tag is described as follows.
 
-It was introduced by [EfficientTDNN](https://arxiv.org/abs/2103.13581).
+- max: `(4, [512, 512, 512, 512, 512], [5, 5, 5, 5, 5], 1536)`
+- Kmin: `(4, [512, 512, 512, 512, 512], [1, 1, 1, 1, 1], 1536)`
+- Dmin: `(2, [512, 512, 512], [1, 1, 1], 1536)`
+- C1min: `(2, [256, 256, 256], [1, 1, 1], 768)`
+- C2min: `(2, [128, 128, 128], [1, 1, 1], 384)`
 
-TODO upload weights.
+More details about EfficentTDNN can be found in the paper [EfficientTDNN](https://arxiv.org/abs/2103.13581).