cvqluu / TDNN

Time delay neural network (TDNN) implementation in Pytorch using unfold method

TDNN

Simple Time Delay Neural Network (TDNN) implementation in Pytorch. Uses the unfold method to slide over an input sequence.

[1] https://www.danielpovey.com/files/2015_interspeech_multisplice.pdf

Factorized TDNN (TDNN-F)

I've also implemented the Factorized TDNN from Kaldi (TDNN-F) in PyTorch here: https://github.com/cvqluu/Factorized-TDNN

Usage

To recreate the TDNN part of the x-vector network in [2]:

from tdnn import TDNN

# Assuming 24 dim MFCCs per frame

frame1 = TDNN(input_dim=24, output_dim=512, context_size=5, dilation=1)
frame2 = TDNN(input_dim=512, output_dim=512, context_size=3, dilation=2)
frame3 = TDNN(input_dim=512, output_dim=512, context_size=3, dilation=3)
frame4 = TDNN(input_dim=512, output_dim=512, context_size=1, dilation=1)
frame5 = TDNN(input_dim=512, output_dim=1500, context_size=1, dilation=1)

# Input to frame1 is of shape (batch_size, T, 24)
# Output of frame5 will be (batch_size, T-14, 1500)

[2] https://www.danielpovey.com/files/2018_icassp_xvectors.pdf