ESPnet: end-to-end speech processing toolkit

ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition, and end-to-end text-to-speech. ESPnet uses chainer and pytorch 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.

Key Features

Hybrid CTC/attention based end-to-end ASR
- Fast/accurate training with CTC/attention multitask training
- CTC/attention joint decoding to boost monotonic alignment decoding
Encoder: VGG-like CNN + BLSTM or pyramid BLSTM
Attention: Dot product, location-aware attention, variants of multihead (pytorch only)
Incorporate RNNLM/LSTMLM trained only with text data
Flexible network architecture thanks to chainer and pytorch
Kaldi style complete recipe
- Support numbers of ASR benchmarks (WSJ, Switchboard, CHiME-4, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, etc.)
State-of-the-art performance in Japanese/Chinese benchmarks (comparable/superior to hybrid DNN/HMM and CTC)
Moderate performance in standard English benchmarks
Tacotron2 based end-to-end TTS (new!)

Requirements

Python2.7+
Cuda 8.0 or 9.1 (for the use of GPU)
Cudnn 6+ (for the use of GPU)
NCCL 2.0+ (for the use of multi-GPUs)
PyTorch 0.3.x (no support for PyTorch 0.4.x)
Chainer 4.x+

Installation

Step 1) setting of the environment

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

If you want to use multiple GPUs, you should install nccl and set paths in your .bashrc or .bash_profile appropriately, for example:

CUDAROOT=/path/to/cuda
NCCL_ROOT=/path/to/nccl

export CPATH=$NCCL_ROOT/include:$CPATH
export LD_LIBRARY_PATH=$NCCL_ROOT/lib/:$CUDAROOT/lib64:$LD_LIBRARY_PATH
export LIBRARY_PATH=$NCCL_ROOT/lib/:$LIBRARY_PATH
export CUDA_HOME=$CUDAROOT
export CUDA_PATH=$CUDAROOT

Step 2-A) installation with compiled Kaldi

Install Python libraries and other required tools using system python and virtualenv

$ cd tools
$ make KALDI=/path/to/kaldi

or using local miniconda

$ cd tools
$ make KALDI=/path/to/kaldi -f conda.mk

Step 2-B) installation including Kaldi installation

Install Kaldi, Python libraries and other required tools using system python and virtualenv

$ cd tools
$ make -j

or using local miniconda

$ cd tools
$ make -f conda.mk -j

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 CMU Census Database (AN4) recipe.

$ cd egs/an4/asr1

Once move to the directory, then, execute the following main script with a chainer backend:

$ ./run.sh --backend chainer

or execute the following main script with a pytorch backend:

$ ./run.sh --backend pytorch

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 or pytorch.
Recognition and scoring

The training progress (loss and accuracy for training and validation data) can be monitored with the following command

$ tail -f exp/${expdir}/train.log

With the default verbose (=0), it gives you the following information

epoch       iteration   main/loss   main/loss_ctc  main/loss_att  validation/main/loss  validation/main/loss_ctc  validation/main/loss_att  main/acc    validation/main/acc  elapsed_time  eps
:
:
6           89700       63.7861     83.8041        43.768                                                                                   0.731425                         136184        1e-08
6           89800       71.5186     93.9897        49.0475                                                                                  0.72843                          136320        1e-08
6           89900       72.1616     94.3773        49.9459                                                                                  0.730052                         136473        1e-08
7           90000       64.2985     84.4583        44.1386        72.506                94.9823                   50.0296                   0.740617    0.72476              137936        1e-08
7           90100       81.6931     106.74         56.6462                                                                                  0.733486                         138049        1e-08
7           90200       74.6084     97.5268        51.6901                                                                                  0.731593                         138175        1e-08
     total [#################.................................] 35.54%
this epoch [#####.............................................] 10.84%
     91300 iter, 7 epoch / 20 epochs
   0.71428 iters/sec. Estimated time to finish: 2 days, 16:23:34.613215.

Use of GPU

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

# use single gpu
$ ./run.sh --ngpu 1

# use multi-gpu
$ ./run.sh --ngpu 3

# if you want to specify gpus, set CUDA_VISIBLE_DEVICES as follows
# (Note that if you use slurm, this specification is not needed)
$ CUDA_VISIBLE_DEVICES=0,1,2 ./run.sh --ngpu 3

# use cpu
$ ./run.sh --ngpu 0

Default setup uses CPU (--ngpu 0).

Note that if you want to use multi-gpu, the installation of nccl is required before setup.

Error due to ACS (Multiple GPUs)

When using multiple GPUs, if the training freezes or lower performance than expected is observed, verify that PCI Express Access Control Services (ACS) are disabled. Larger discussions can be found at: link1 link2 link3. To disable the PCI Express ACS follow instructions written here. You need to have a ROOT user access or request to your administrator for it.

Docker Container

To work inside a docker container, execute run.sh located inside the docker directory. It will build a container and execute the main program specified by the following GPU, ASR example, and outside directory information, as follows:

$ cd docker
$ ./run.sh --docker_gpu 0 --docker_egs chime4/asr1 --docker_folders /export/corpora4/CHiME4/CHiME3 --dlayers 1 --ngpu 1

Optionally, you can set the CUDA and CUDNN version with the arguments --docker_cuda and --docker_cudnn respectively (default version set at CUDA=9.0 and CUDNN=7). The docker container can be built based on the CUDA and CUDNN version installed in your computer if you empty this arguments. The arguments required for the docker configuration have a prefix "--docker" (e.g., --docker_gpu, --docker_egs, --docker_folders). run.sh accept all normal ESPnet arguments, which must be followed by these docker arguments. Multiple GPUs should be specified with the following options:

$ cd docker
$ ./run.sh --docker_gpu 0,1,2 --docker_egs chime5/asr1 --docker_folders /export/corpora4/CHiME5 --ngpu 3

Note that all experimental files and results are created under the normal example directories (egs/<example>/).

Setup in your cluster

Change cmd.sh according to your cluster setup. If you run experiments with your local machine, please use default cmd.sh. For more information about cmd.sh see http://kaldi-asr.org/doc/queue.html. It supports Grid Engine (queue.pl), SLURM (slurm.pl), etc.

Error due to matplotlib

If you have the following error (or other numpy related errors),

RuntimeError: module compiled against API version 0xc but this version of numpy is 0xb
Exception in main training loop: numpy.core.multiarray failed to import
Traceback (most recent call last):
;
:
from . import _path, rcParams
ImportError: numpy.core.multiarray failed to import

Then, please reinstall matplotlib with the following command:

$ cd egs/an4/asr1
$ . ./path.sh
$ pip install pip --upgrade; pip uninstall matplotlib; pip --no-cache-dir install matplotlib

CTC, attention, and hybrid CTC/attention

ESPnet can completely switch the mode from CTC, attention, and hybrid CTC/attention

# hybrid CTC/attention (default)
#  --mtlalpha 0.5 and --ctc_weight 0.3 in most cases
$ ./run.sh

# CTC mode
$ ./run.sh --mtlalpha 1.0 --ctc_weight 1.0 --recog_model loss.best

# attention mode
$ ./run.sh --mtlalpha 0.0 --ctc_weight 0.0

The CTC training mode does not output the validation accuracy, and the optimum model is selected with its loss value (i.e., --recog_model loss.best). About the effectiveness of the hybrid CTC/attention during training and recognition, see [1] and [2].

Results

We list the character error rate (CER) and word error rate (WER) of major ASR tasks.

	CER (%)	WER (%)
WSJ dev93	4.9	10.9
WSJ eval92	3.1	7.1
CSJ eval1	8.5	N/A
CSJ eval2	6.1	N/A
CSJ eval3	6.8	N/A
HKUST train_dev	29.7	N/A
HKUST dev	28.3	N/A
Librispeech dev_clean	2.7	7.2
Librispeech test_clean	2.6	7.1

Chainer and Pytorch backends

	Chainer	Pytorch
Performance	◎	○
Speed	○	◎
Multi-GPU	supported	supported
VGG-like encoder	supported	no support
RNNLM integration	supported	supported
#Attention types	3 (no attention, dot, location)	12 including variants of multihead
TTS recipe suuport	no support	supported

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

tbright17/espnet