Implementation of Time Delay Neural Network (TDNN) and Factorized TDNN (TDNN-F) in PyTorch, available as layers which can be used directly.

For using (no development required)

pip install pytorch-tdnn

To install for development, clone the repository, and then run the following from within the root directory.

pip install -e .

from pytorch_tdnn.tdnn import TDNN as TDNNLayer

tdnn = TDNNLayer(
  512, # input dim
  512, # output dim
  [-3,0,3], # context
)

y = tdnn(x)

Here, x should have the shape (batch_size, input_dim, sequence_length).

Note: The context list should follow these constraints:

• The length of the list should be 2 or an odd number.
• If the length is 2, it should be of the form [-1,1] or [-3,3], but not [-1,3], for example.
• If the length is an odd number, they should be evenly spaced with a 0 in the middle. For example, [-3,0,3] is allowed, but [-3,-1,0,1,3] is not.

from pytorch_tdnn.tdnnf import TDNNF as TDNNFLayer

tdnnf = TDNNFLayer(
  512, # input dim
  512, # output dim
  256, # bottleneck dim
  1, # time stride
)

y = tdnnf(x, semi_ortho_step=True)

The argument semi_ortho_step determines whether to take the step towards semi- orthogonality for the constrained convolutional layers in the 3-stage splicing. If this call is made from within a forward() function of an nn.Module class, it can be set as follows to approximate Kaldi-style training where the step is taken once every 4 iterations:

import random
semi_ortho_step = self.training and (random.uniform(0,1) < 0.25)

Note: Time stride should be greater than or equal to 0. For example, if the time stride is 1, a context of [-1,1] is used for each stage of splicing.

• The TDNN implementation is based on: https://github.com/jonasvdd/TDNN and https://github.com/m-wiesner/nnet_pytorch.
• Semi-orthogonal convolutions used in TDNN-F are based on: https://github.com/cvqluu/Factorized-TDNN.
• Thanks to Matthew Wiesner for helpful discussions about the implementations.

This repository aims to wrap up these implementations in easy-installable PyPi packages, which can be used directly in PyTorch based neural network training.

If you find any bugs in the code, please raise an Issue, or email me at [email protected].