* 2.x model

* model test pass

* fix data

* fix soundfile with flac support

* one thread dataloader test pass

* export feasture size
add trainer and utils
add setup model and dataloader
update travis using Bionic dist

* add venv; test under venv

* fix unittest; train and valid

* add train and config

* add config and train script

* fix ctc cuda memcopy error

* fix imports

* fix train valid log

* fix dataset batch shuffle shift start from 1
fix rank_zero_only decreator error
close tensorboard when train over
add decoding config and code

* test process can run

* test with decoding

* test and infer with decoding

* fix infer

* fix ctc loss
lr schedule
sortagrad
logger

* aishell egs

* refactor train
add aishell egs

* fix dataset batch shuffle and add batch sampler log
print model parameter

* fix model and ctc

* sequence_mask make all inputs zeros, which cause grad be zero, this is a bug of LessThanOp
add grad clip by global norm
add model train test notebook

* ctc loss
remove run prefix
using ord value as text id

* using unk when training
compute_loss need text ids
ord id using in test mode, which compute wer/cer

* fix tester

* add lr_deacy
refactor code

* fix tools

* fix ci
add tune
fix gru model bugs
add dataset and model test

* fix decoding

* refactor repo
fix decoding

* fix musan and rir dataset

* refactor io, loss, conv, rnn, gradclip, model, utils

* fix ci and import

* refactor model
add export jit model

* add deploy bin and test it

* rm uselss egs

* add layer tools

* refactor socket server
new model from pretrain

* remve useless

* fix instability loss and grad nan or inf for librispeech training

* fix sampler

* fix libri train.sh

* fix doc

* add license on cpp

* fix doc

* fix libri script

* fix install

* clip 5 wer 7.39, clip 400 wer 7.54, 1.8 clip 400 baseline 7.49

Refactor whole data preprocessor for DS2 (re-design classes, re-organize dir, add augmentaion interfaces etc.).

1. Refactor data preprocessor with new added class AudioSegment, SpeechSegment, TextFeaturizer, AudioFeaturizer, SpeechFeaturizer.
2. Add data augmentation interfaces and class AugmentorBase, AugmentationPipeline, VolumnPerturbAugmentor etc..
3. Seperate normalizer's mean and std computing from training, by adding FeatureNormalizer and a seperate tool compute_mean_std.py.
4. Re-organize directory.