ESPnet: end-to-end speech processing toolkit

ESPnet is an end-to-end speech processing toolkit. ESPnet uses chainer as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments.

Installation

Install Kaldi, Python libraries and other required tools

$ cd tools
$ make -j

To use cuda (and cudnn), make sure to set paths in your .bashrc or .bash_profile appropriately.

CUDAROOT=/path/to/cuda

export PATH=$CUDAROOT/bin:$PATH
export LD_LIBRARY_PATH=$CUDAROOT/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=$CUDAROOT
export CUDA_PATH=$CUDAROOT

Execution of example scripts

Move to an example directory under the egs directory. We prepare several major ASR benchmarks including WSJ, CHiME-4, and TED. The following directory is an example of performing ASR experiment with the VoxForge Italian Corpus.

$ cd egs/voxforge/asr1

Once move to the directory, then, execute the following main script:

$ ./run.sh

With this main script, you can perform a full procedure of ASR experiments including

Data download
Data preparation (Kaldi style, see http://kaldi-asr.org/doc/data_prep.html)
Feature extraction (Kaldi style, see http://kaldi-asr.org/doc/feat.html)
Dictionary and JSON format data preparation
Training based on chainer.
Recognition and scoring

Use of GPU

If you use GPU in your experiment, set --gpu option in run.sh appropriately, e.g.,

$ ./run.sh --gpu 0

Default setup uses CPU (--gpu -1).

Setup in your cluster

Change cmd.sh according to your cluster setup. If you run experiments with your local machine, you don't have to change it. For more information about cmd.sh see http://kaldi-asr.org/doc/queue.html. It supports Grid Engine (queue.pl), SLURM (slurm.pl), etc.

Installation using Docker

For GPU support nvidia-docker should be installed.

For Execution use the command

$ cd egs/voxforge/asr1
$ ./run_in_docker.sh --gpu GPUID

If GPUID is set to -1, the program will run only CPU.

The file builds and loads the information into the Docker container. If any additional application is required, modify the Docker devel-file located at the tools folder.

To downgrade or use a private devel file, modify the name inside run_in_docker.sh

References (Please cite the following articles)

[1] Suyoun Kim, Takaaki Hori, and Shinji Watanabe, "Joint CTC-attention based end-to-end speech recognition using multi-task learning," Proc. ICASSP'17, pp. 4835--4839 (2017)

[2] Shinji Watanabe, Takaaki Hori, Suyoun Kim, John R. Hershey and Tomoki Hayashi, "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition," IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 8, pp. 1240-1253, Dec. 2017