kongaik/ecapa-tdnn

Unofficial implementation of the (ECAPA-TDNN model).

Usage:

from ecapa_tdnn import ECAPA_TDNN

# Input size: batch_size * seq_len * feat_dim
x = torch.zeros(2, 200, 80)
model = ECAPA_TDNN(in_channels=80, channels=512, embd_dim=192)
out = model(x)
print(model)
print(out.shape)    # should be [2, 192]

Note that we DON'T concatenate the last frame-wise layer with non-weighted mean and standard deviation, because it brings little improvment but significantly increases model parameters. As a result, this implementation basically equals the A.2 of Table 2 in the paper.