Introduction

This is a pytorch_lightning reimplementation of the Transducer module from ESPnet.

Features

• Faster

Experientments on timit dataset, model = RNN-T, batchsize = 4, GPU = GTX1060. A training step during an epoch cost

Framework	Speed
pytorch	6.1 step/s
pytorch_lightning	6.6 step/s

• More intuitive

Only streaming Transducer model from ESPnet is included here. Currently support

InputLayer	Encoder	Decoder
Conv.	RNN	RNN
	Transformer	Transformer
	Conformer

Including papers

• [RNN-T 2012] Graves Alex, "Sequence Transduction with Recurrent Neural Networks", 2012.
• [RNN-T 2013] Graves Alex, et al., "Speech Recognition with Deep Recurrent Neural Networks", ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 38. 10.1109/ICASSP.2013.6638947, 2013.
• [T-T facebook] C.-F. Yeh, et al., "Transformertransducer: End-to-end speech recognition with self-attention", 2019.
• [T-T google] Q. Zhang, et al., "Transformer transducer: A streamable speech recognition model with transformer encoders and rnn-t loss", ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020.
• [Conformer-T] Gulati Anmol, et al, "Conformer: Convolution-Augmented Transformer for Speech Recognition", Interspeech 2020, 2020, pp. 5036–5040.

Environment

• kaldi
  Need to complie kaldi in advance. First clone kaldi locally, then compile tools and src according to INSTALL in their folder.
• pip install h5py kaldiio soundfile configargparse dataclasses typeguard

Usage

1. Link complied kaldi to the tools directory, e.g., <kaldi-root>=/home/usr_name/kaldi

cd ./tools
ln -s <kaldi-root>/tools

2. Create a new virtual environment from the system python

./setup_venv.sh $(command -v python3)

3. Configure the ceated virtual environment

make

4. Link steps and utils from wsj to the root directory

cd ..
ln -s <kaldi-root>/egs/wsj/s5/steps .
ln -s <kaldi-root>/egs/wsj/s5/utils .

5. Run

./run.sh 

6. Resume

If the training process is accidentally interrupted, you can resume training by changing the resume variable in front of the run.sh script, e.g.,

resume='exp/path/to/ckpt'

7. Data pre-process

The scripts of English corpus Timit and Mandarin corpus Aishell-1 are already in the local directory.

Thanks to

• ESPnet
• wrap rnnt

Contact me

Email: [email protected]