End-to-end speech secognition toolkit

This is an E2E ASR toolkit modified from Espnet1 (version 0.9.9).
This is the official implementation of paper:
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI
This is also the official implementation of paper:
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language Model
We achieve state-of-the-art results on two of the most popular Mandarin datasets (Aishell-1 and Aishell-2).
Please feel free to change / modify the code as you like. :)

Update

2021/12/29: Release the first version, which contains all MMI-related features, including MMI training criteria, MMI Prefix Score (for attention-based encoder-decoder, AED) and MMI Alignment Score (For neural transducer, NT).
2022/1/6: Release the word-level N-gram LM scorer.
2022/1/12: We update the instructions to build the environment. We also release the trained NT model for Aishell-1 for quick performance check. We update the guildline to run our code.

Environment:

The main dependencies of this code can be divided into three part: kaldi, espnet and k2
Please follow the instructions in build_env.sh to build the environment.
Note the script cannot run automatically and you need to run it line-by-line.

Results

Currently we have released examples on Aishell-1 and Aishell-2 datasets.
With MMI training & decoding methods and the word-level N-gram LM. We achieve results on Aishell-1 and Aishell-2 as below. All results are in CER%
The model file of Aishell-1 NT system is here for quick performance check.

Test set	Aishell-1-dev	Aishell-1-test	Aishell-2-ios	Aishell-2-android	Aishell-2-mic
AED	4.73	5.32	5.73	6.56	6.53
AED + MMI + Word Ngram	4.08	4.45	5.26	6.22	5.92
NT	4.41	4.81	5.70	6.75	6.58
NT + MMI + Word Ngram	3.86	4.18	5.06	6.08	5.98

(example on Librispeech is not fully prepared)

Get Start

Take Aishell-1 as an example. Working process for other examples are very similar.

step 1: clone the code and link kaldi

conda activate lfmmi
git clone https://github.com/jctian98/e2e_lfmmi E2E-ASR-Framework # clone and RENAME
cd E2E-ASR-Framework
ln -s <path-to-kaldi> kaldi                                       # link kaldi

step 2: prepare data, lexicon and LMs. Before you run, please set the datadir in prepare.sh

cd egs/aishell1
bash prepare.sh

step 3: model training. You should split the data before start the training.
You can skip this step and download our trained model here

python3 espnet_utils/splitjson.py -p <ngpu> dump/train_sp/deltafalse/data.json
bash nt.sh --stop_stage 1

step 4: decode

bash nt.sh --stage 2 --mmi-weight 0.2 --word-ngram-weight 0.4

Several Hint:

Please change the paths in path.sh accordingly before you start
Please change the data to config your data path in prepare.sh
Our code runs in DDP style and requires some global variables. Before you start, you need to set them manually. We assume Pytorch distributed API works well on your machine.

export HOST_GPU_NUM=x       # number of GPUs on each host
export HOST_NUM=x           # number of hosts
export NODE_NUM=x           # number of GPUs in total (on all hosts)
export INDEX=x              # index of this host
export CHIEF_IP=xx.xx.xx.xx # IP of the master host

You may encounter some problem about k2. Try to delete data/lang_phone/Linv.pt (in training) and data/word_3gram/G.pt(in decoding) and re-generate them again.
Multiple choices are available during decoding (we take nt.sh as an example, but the usage of aed.sh is the same).
To use the MMI-related scorers, you need train the model with MMI auxiliary criterion;

To use MMI Prefix Score (in AED) or MMI Alignment score (in NT):

bash nt.sh --stage 2 --mmi-weight 0.2

To use any external LM, you need to train them in advance (as implemented in prepare.sh)

To use word-level N-gram LM:

bash nt.sh --stage 2 --word-ngram-weight 0.4

To use character-level N-gram LM:

bash nt.sh --stage 2 --ngram-weight 1.0

To use neural network LM:

bash nt.sh --stage 2 --lm-weight 1.0

Reference

kaldi: https://github.com/kaldi-asr/kaldi
Espent: https://github.com/espnet/espnet
k2-fsa: https://github.com/k2-fsa/k2

Citations

@article{tian2021consistent,  
  title={Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI},  
  author={Tian, Jinchuan and Yu, Jianwei and Weng, Chao and Zhang, Shi-Xiong and Su, Dan and Yu, Dong and Zou, Yuexian},  
  journal={arXiv preprint arXiv:2112.02498},  
  year={2021}  
}  
@misc{tian2022improving,
      title={Improving Mandarin End-to-End Speech Recognition with Word N-gram Language Model}, 
      author={Jinchuan Tian and Jianwei Yu and Chao Weng and Yuexian Zou and Dong Yu},
      year={2022},
      eprint={2201.01995},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Authorship

Jinchuan Tian; tianjinchuan@stu.pku.edu.cn or tyriontian@tencent.com
Jianwei Yu; tomasyu@tencent.com (supervisor)
Chao Weng; cweng@tencent.com
Yuexian Zou; zouyx@pku.edu.cn

Sundy1219 / e2e_lfmmi