This repository contains TF2.x based implementation for this paper. The default setup, which is a character-based model, achieves 11.66% and 28.31% WERs on LibriSpeech test-clean and test-other sets respectively. These WERs can easily be improved by using:

• large vocabulary (subword unit is one way to achieve this)
• data augmentation (SpecAugment is one such technique)
• regularization or limiting model capacity

• Pysoundfile
• Librosa
• Tensorflow 2.x
• Warp-transducer

• Input: Sequence of 80-dimensional filterbank features using 25msec window length and 10msec stride
• Output: 1K WPM
• Dataset: 960 hours of LibriSpeech

• Adam optimizer
• Transformer LR schedule with 15K warmup steps and peak LR 0.0025
• L2 regularization on all trainable weights
• Variational noise added to decoder for regularization

• RNN-Transducer based architecuture

• Acoustic encoder is proposed in the paper, prediction network and joint network is based on LSTM layers as used in this paper

• Acoustic encoder: It consists of multiple convolution blocks (23 in all the experiments), where each block C_i is made up of multiple depth-wise separable convolution layers. A convolution block is shown below with details for all 23 blocks in the following table.

  Block Id	#Conv Layers	#Output Channels	Kernel Size	Other
  C0	1	256 x α	5	No residual
  C1-C2	5	256 x α	5
  C3	5	256 x α	5	Stride is 2
  C4-C6	5	256 x α	5
  C7	5	256 x α	5	Stride is 2
  C8-C10	5	256 x α	5
  C11-C13	5	512 x α	5
  C14	5	512 x α	5	Stride is 2
  C15-C21	5	512 x α	5
  C22	1	640 x α	5	No residual

  Stride of 2 in a convolution block means last convolution layer in that block has a stride of 2, rest of them have stride of 1. SE is squeeze and excitation layer as shown below

  3 different model variations with global context are shown below. The authors also experiment with context sizes of None, 256, 512 and 1024. Currently, the implementation allows either global context or no context at all.

  Model	α	#Params(M)
  Small	0.5	10.8
  Medium	1.0	31.4
  Large	2.0	112.7

• Label encoder: Single LSTM layer with input dimension 640 and width 2048

• (Optional/Not implemented) RNN-LM: 3 LSTM layers of width 4096

• Mask parameter F = 27
• 10 time masks with maximum time-mask ratio, p_s = 0.05
• Maximum size of the time mask p_s * length-of-utterance
• Time warping is not used

• Swish activation, f(x) = x * σ ( β * x), works better than RELU. β = 1 is used in the paper
• Increasing context size in SE layer improves the model performance on test-other set. Model without any context also performs very well and is comparable with model performances with non-zero context size
• A progressive downsampling of 8 achieves good tradeoff between computational cost and model performance
• The proposed architecture is also effective on large scale dataset

Note: All the images, tables and details are taken from the original paper unless mentioned otherwise.