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Transformer ASR with Timit

The phoneme-based continuous speech corpus is a collaboration between Texas Instruments, MIT, and SRI International. The Timit dataset has a voice sampling frequency of 16 khz and contains a total of 6,300 sentences, with 630 people from 8 major U.S. dialects speaking a given 10 sentences each, all sentences are manually segmented and marked at the phone level. Seventy percent of the speakers are male; most of the speakers are white adults.

Dataset

Download and Extract

Download TIMIT from it's official website and extract it to ~/datasets. Assume unzip the dataset in the directory ~/datasets/timit.

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
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 Get ctc alignment of test data using the final 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.

source path.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. avg_num denotes the number K of top-K models you want to average to get the final model. audio_file denotes the file path of the single file you want to infer in stage 5 ckpt denotes the checkpoint prefix of the model, e.g. "conformer" 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 0,1,2,3 --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.sh to do this. The code is shown below:

 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # prepare data
     bash ./local/timit_data_prep.sh ${TIMIT_path}
     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.

source path.sh
bash ./local/timit_data_prep.sh ${TIMIT_path}
bash ./local/data.sh

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

data/
|-- lang_char
|   `-- vocab.txt
|-- local
|   `-- dev_sph.flist
|   `-- dev_sph.scp
|   `-- dev.text
|   `-- dev.trans
|   `-- dev.uttids
|   `-- test_sph.flist
|   `-- test_sph.scp
|   `-- test.text
|   `-- test.trans
|   `-- test.uttids
|   `-- train_sph.flist
|   `-- train_sph.scp
|   `-- train.text
|   `-- train.trans
|   `-- train.uttids
|-- manifest.dev
|-- manifest.dev.raw
|-- manifest.test
|-- manifest.test.raw
|-- manifest.train
|-- manifest.train.raw
|-- mean_std.json
|-- test.meta

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.

source path.sh
bash ./local/timit_data_prep.sh ${TIMIT_path}
bash ./local/data.sh
CUDA_VISIBLE_DEVICES=${gpus} ./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 best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below:

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

The avg.shis 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.

bash ./local/timit_data_prep.sh ${TIMIT_path}
source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
avg.sh best exp/conformer/checkpoints 10

Stage 3: Model Testing

The test stage is to evaluate the model performance. The code of the test stage is shown below:

 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${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.

source path.sh
bash ./local/timit_data_prep.sh ${TIMIT_path}
bash ./local/data.sh
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
avg.sh best exp/transformer/checkpoints 10
CUDA_VISIBLE_DEVICES=0 ./local/test.sh conf/transformer.yaml exp/transformer/checkpoints/avg_10

Stage 4: 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 4 ] && [ ${stop_stage} -ge 4 ]; then
     # ctc alignment of test data
     CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${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, and stage 3 :

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

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 4 --stop_stage 4

or you can also use these scripts in the command line.

source path.sh
bash ./local/timit_data_prep.sh ${TIMIT_path}
bash ./local/data.sh
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
avg.sh best exp/transformer/checkpoints 10
# test stage is optional
CUDA_VISIBLE_DEVICES=0 ./local/test.sh conf/transformer.yaml exp/transformer/checkpoints/avg_10
CUDA_VISIBLE_DEVICES=0 ./local/align.sh conf/transformer.yaml exp/transformer/checkpoints/avg_10