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Transformer/Conformer ASR with Librispeech ASR2

This example contains code used to train a u2 model (Transformer or Conformer model) with Librispeech dataset and use some functions in kaldi.

To use this example, you need to install Kaldi first.

Overview

All the scripts you need are in run.sh. There are several stages in run.sh, and each stage has its function.

Stage Function
0 Process data. It includes:
(1) Download the dataset
(2) Calculate the CMVN of the train dataset
(3) Get the vocabulary file
(4) Get the manifest files of the train, development and test dataset
(5) Get the sentencepiece model
1 Train the model
2 Get the final model by averaging the top-k models, set k = 1 means to choose the best model
3 Test the final model performance
4 Join ctc decoder and use transformer language model to score
5 Get ctc alignment of test data using the final model
6 Calculate the perplexity of transformer language model

You can choose to run a range of stages by setting stage and stop_stage .

For example, if you want to execute the code in stage 2 and stage 3, you can run this script:

bash run.sh --stage 2 --stop_stage 3

Or you can set stage equal to stop-stage to only run one stage. For example, if you only want to run stage 0, you can use the script below:

bash run.sh --stage 0 --stop_stage 0

The document below will describe the scripts in run.sh in detail.

The Environment Variables

The path.sh contains the environment variables.

. ./path.sh
. ./cmd.sh

This script needs to be run first. And another script is also needed:

source ${MAIN_ROOT}/utils/parse_options.sh

It will support the way of using --variable value in the shell scripts.

The Local Variables

Some local variables are set in run.sh. gpus denotes the GPU number you want to use. If you set gpus=, it means you only use CPU. stage denotes the number of the stage you want to start from in the experiments. stop stage denotes the number of the stage you want to end at in the experiments. conf_path denotes the config path of the model. dict_path denotes the path of the vocabulary file. avg_num denotes the number K of top-K models you want to average to get the final model. ckpt denotes the checkpoint prefix of the model, e.g. "transformer"

You can set the local variables (except ckpt) when you use run.sh

For example, you can set the gpus and avg_num when you use the command line.:

bash run.sh --gpus 0,1 --avg_num 10

Stage 0: Data Processing

To use this example, you need to process data firstly and you can use stage 0 in run.shto do this. The code is shown below:

 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # prepare data
     bash ./local/data.sh || exit -1
 fi

Stage 0 is for processing the data.

If you only want to process the data. You can run

bash run.sh --stage 0 --stop_stage 0

You can also just run these scripts in your command line.

. ./path.sh
. ./cmd.sh
bash ./local/data.sh

After processing the data, the data directory will look like this:

data/
├── dev
├── dev_clean
├── dev-clean.meta
├── dev_org
├── dev_other
├── dev-other.meta
├── lang_char
├── manifest.dev
├── manifest.dev-clean
├── manifest.dev-clean.raw
├── manifest.dev-other
├── manifest.dev-other.raw
├── manifest.dev.raw
├── manifest.test-clean
├── manifest.test-clean.raw
├── manifest.test-other
├── manifest.test-other.raw
├── manifest.test.raw
├── manifest.train
├── manifest.train-clean-100.raw
├── manifest.train-clean-360.raw
├── manifest.train-other-500.raw
├── manifest.train.raw
├── temp1
├── temp2
├── temp3
├── test_clean
├── test-clean.meta
├── test_other
├── test-other.meta
├── train_960
├── train_960_org
├── train_clean_100
├── train-clean-100.meta
├── train_clean_360
├── train-clean-360.meta
├── train_other_500
├── train-other-500.meta
├── train_sp
└── train_sp_org

Stage 1: Model Training

If you want to train the model. you can use stage 1 in run.sh. The code is shown below.

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
 fi

If you want to train the model, you can use the script below to execute stage 0 and stage 1:

bash run.sh --stage 0 --stop_stage 1

or you can run these scripts in the command line (only use CPU).

. ./path.sh
. ./cmd.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/transformer.yaml transformer

Stage 2: Top-k Models Averaging

After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the last K models and average the parameters of the models to get the final model. We can use stage 2 to do this, and the code is shown below:

 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # avg n best model
     avg.sh lastest exp/${ckpt}/checkpoints ${avg_num}
 fi

The avg.sh is in the ../../../utils/ which is define in the path.sh. If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:

bash run.sh --stage 0 --stop_stage 2

or you can run these scripts in the command line (only use CPU).

. ./path.sh
. ./cmd.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/transformer.yaml transformer
avg.sh best exp/transformer/checkpoints 10

Stage 3: Model Testing

Stage 3 is to evaluate the model performance with an attention rescore decoder. The code of this stage is shown below:

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    # attetion resocre decoder
    ./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi

If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :

bash run.sh --stage 0 --stop_stage 3

or you can run these scripts in the command line (only use CPU).

. ./path.sh
. ./cmd.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/transformer.yaml transformer
avg.sh latest exp/transformer/checkpoints 10
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/transformer.yaml data/train_960_unigram5000_units.txt exp/transformer/checkpoints/avg_10

Stage 4: Model Testing with Join CTC Decoder

Stage 4 is to evaluate the model performance with the join ctc decoder. The code of this stage is shown below:

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    # join ctc decoder, use transformerlm to score
    ./local/recog.sh  --ckpt_prefix exp/${ckpt}/checkpoints/${avg_ckpt}
fi

If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 4 :

bash run.sh --stage 0 --stop_stage 3
bash run.sh --stage 4 --stop_stage 4

or you can run these scripts in the command line (only use CPU).

. ./path.sh
. ./cmd.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/transformer.yaml transformer
avg.sh latest exp/transformer/checkpoints 10
./local/recog.sh  --ckpt_prefix exp/transformer/checkpoints/avg_10

Pretrained Model

You can get the pretrained models from this.

using the tar scripts to unpack the model and then you can use the script to test the model.

For example:

wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz
tar xzvf asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz
source path.sh
# If you have process the data and get the manifest file， you can skip the following 2 steps
bash local/data.sh --stage -1 --stop_stage -1
bash local/data.sh --stage 2 --stop_stage 2

CUDA_VISIBLE_DEVICES= ./local/test.sh conf/transformer.yaml exp/ctc/checkpoints/avg_10

The performance of the released models are shown here.

Compare with ESPNET we using 8gpu, but the model size (aheads4-adim256) small than it.

Stage 5: CTC Alignment

If you want to get the alignment between the audio and the text, you can use the ctc alignment. The code of this stage is shown below:

if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    # ctc alignment of test data
    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi

If you want to train the model, test it and do the alignment, you can use the script below to execute stage 0, stage 1, stage 2, stage 3, stage 4, and stage 5:

bash run.sh --stage 0 --stop_stage 5

or if you only need to train a model and do the alignment, you can use these scripts to escape stage 3(test stage):

bash run.sh --stage 0 --stop_stage 2
bash run.sh --stage 5 --stop_stage 5

or you can also use these scripts in the command line (only use CPU).

. ./path.sh
. ./cmd.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/transformer.yaml transformer
avg.sh best exp/transformer/checkpoints 20
CUDA_VISIBLE_DEVICES= ./local/align.sh conf/transformer.yaml data/train_960_unigram5000_units.txt exp/transformer/checkpoints/avg_10

Stage 6: Perplexity Calculation

This stage is for calculating the perplexity of the transformer language model. The code of this stage is shown below:

if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
    ./local/cacu_perplexity.sh || exit -1
fi

If you only want to calculate the perplexity of the transformer language model, you can use this script:

bash run.sh --stage 6 --stop_stage 6