Architecture: ResNet100 with SimAM (Simple, Parameter-Free Attention Module) basic blocks and Attentive Statistics Pooling (ASP)
Block schedule: [6, 16, 24, 3] = 49 SimAM basic blocks
Embedding dimension: 256
Parameters: ~50.2M
Input: Log mel-filterbank features [batch, frames, 80] (80 mel bins, 25 ms frame length, 10 ms frame shift, 16 kHz)
Output: 256-dimensional speaker embedding [batch, 256]

Weight Format

Weights are stored as model.safetensors with PyTorch-native key naming:

Prefix	Description
`front.conv1.`, `front.bn1.`	Stem (3×3 conv, no maxpool)
`front.layer{1-4}.{n}.conv{1,2}.*`	ResNet basic blocks (49 total)
`front.layer{2-4}.0.downsample.*`	1×1 channel/stride projection
`pooling.attention.*`	ASP attention (Conv1d 5120→128→5120)
`bottleneck.*`	Linear 10240→256

BatchNorm parameters include weight, bias, running_mean, running_var (folded format). The num_batches_tracked bookkeeping tensor is omitted.

Usage

python

import torch
from safetensors.torch import load_file

state_dict = load_file("model.safetensors")
# Load into SimAM_ResNet100_ASP(in_planes=64, embed_dim=256, acoustic_dim=80)
# model.load_state_dict(state_dict, strict=True)

# Input: [B, T, 80] log-mel fbank
# Output: [B, 256] speaker embedding

Source

Original checkpoint: avg_model.pt from the lengyue233/wespeaker-voxblink2-samresnet100 mirror
Model code: wespeaker/models/samresnet.py
Project page: wenet.org.cn

Re-exported from PyTorch state dict to safetensors format.

vpermilp/wespeaker-voxblink2-samresnet100

WeSpeaker VoxBlink2 SimAMResNet100 (ASP)

Model Description

Weight Format

Usage

Source

vpermilp/
wespeaker-voxblink2-samresnet100