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 + BiRNN (LSTM/GRU) or sub-sampling BiRNN (LSTM/GRU)
Attention: Dot product, location-aware attention, variants of multihead
Incorporate RNNLM/LSTMLM trained only with text data
Batch GPU decoding
Tacotron2 based end-to-end TTS
Flexible network architecture thanks to chainer and pytorch
Kaldi style complete recipe
- Support numbers of ASR recipes (WSJ, Switchboard, CHiME-4/5, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, REVERB, etc.)
- Support numbers of TTS recipes with a similar manner to the ASR recipe (LJSpeech, Librispeech, M-AILABS, etc.)
- Support speech translation recipes (Fisher callhome Spanish to English, IWSLT'18)
- Support speech separation and recognition recipe (WSJ-2mix)
State-of-the-art performance in several benchmarks (comparable/superior to hybrid DNN/HMM and CTC)
Flexible front-end processing thanks to kaldiio and HDF5 support
Tensorboard based monitoring

Requirements

Python 2.7+, 3.7+ (mainly support Python3.7+)
protocol buffer (for the sentencepiece, you need to install via package manager e.g. sudo apt-get install libprotobuf9v5 protobuf-compiler libprotobuf-dev. See details Installation of https://github.com/google/sentencepiece/blob/master/README.md)
PyTorch 0.4.1, 1.0.0
gcc>=4.9 for PyTorch1.0.0
Chainer 5.0.0

Optionally, GPU environment requires the following libraries:

Cuda 8.0, 9.0, 9.1, 10.0 depending on each DNN library
Cudnn 6+
NCCL 2.0+ (for the use of multi-GPUs)

Installation

Step 1) setting of the environment for GPU support

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

using miniconda (default)

Install Python libraries and other required tools with miniconda

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

You can also specify the Python (PYTHON_VERSION default 3.7), PyTorch (TH_VERSION default 1.0.0) and CUDA versions (CUDA_VERSION default 10.0), for example:

$ cd tools
$ make KALDI=/path/to/kaldi PYTHON_VERSION=3.6 TH_VERSION=0.4.1 CUDA_VERSION=9.0

using existing python

If you do not want to use miniconda, you need to specify your python interpreter to setup virtualenv

$ cd tools
$ make KALDI=/path/to/kaldi PYTHON=/usr/bin/python2.7

Step 2-B) installation including Kaldi installation

Install Kaldi, Python libraries and other required tools with miniconda

$ cd tools
$ make -j 10

As seen above, you can also specify the Python and CUDA versions, and Python path (based on virtualenv), for example:

$ cd tools
$ make -j 10 PYTHON_VERSION=3.6 TH_VERSION=0.4.1 CUDA_VERSION=9.0

$ cd tools
$ make -j 10 PYTHON=/usr/bin/python2.7

Step 2-C) installation for CPU-only

To install in a terminal that does not have a GPU installed, just clear the version of CUPY as follows:

$ cd tools
$ make CUPY_VERSION='' -j 10

This option is enabled for any of the install configuration.

Step 3) installation check

You can check whether the install is succeeded via the following commands

$ cd tools
$ make check_install

or make check_install CUPY_VERSION='' if you do not have a GPU on your terminal. If you have no warning, ready to run the recipe!

If there are some problems in python libraries, you can re-setup only python environment via following commands

$ cd tools
$ make clean_python
$ make python

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.

In addition Tensorboard events are automatically logged in the tensorboard/${expname} folder. Therefore, when you install Tensorboard, you can easily compare several experiments by using

$ tensorboard --logdir tensorboard

and connecting to the given address (default : localhost:6006). This will provide the following information: Note that we would not include the installation of Tensorboard to simplify our installation process. Please install it manually (pip install tensorflow; pip install tensorboard) when you want to use Tensorboard.

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

go to docker/ and follow README.md instructions there.

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 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 model.loss.best). About the effectiveness of the hybrid CTC/attention during training and recognition, see [2] and [3].

Results

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

	CER (%)	WER (%)
WSJ dev93	3.2	7.0
WSJ eval92	2.1	4.7
CSJ eval1	6.6	N/A
CSJ eval2	4.8	N/A
CSJ eval3	5.0	N/A
Aishell dev	6.8	N/A
Aishell test	8.0	N/A
HKUST train_dev	28.8	N/A
HKUST dev	27.4	N/A
Librispeech dev_clean	N/A	4.0
Librispeech test_clean	N/A	4.0

Note that the performance of the CSJ, HKUST, and Librispeech tasks was significantly improved by using the wide network (#units = 1024) and large subword units if necessary reported by RWTH.

Chainer and Pytorch backends

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

References

[1] Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, and Tsubasa Ochiai, "ESPnet: End-to-End Speech Processing Toolkit," Proc. Interspeech'18, pp. 2207-2211 (2018)

[2] 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)

[3] 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

Citation

@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={ESPnet: End-to-End Speech Processing Toolkit},
year=2018,
booktitle={Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

sknadig/espnet