This example contains code used to train a u2 model (Transformer or Conformer model) with Aishell dataset
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 |
| 5 | Infer the single audio file |
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 3Or 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 0The document below will describe the scripts in run.sh in detail.
The path.sh contains the environment variables.
source path.shThis script needs to be run first. And another script is also needed:
source ${MAIN_ROOT}/utils/parse_options.shIt will support the way of using --variable value in the shell scripts.
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 --avg_num 20To 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/data.sh || exit -1
fiStage 0 is for processing the data.
If you only want to process the data. You can run
bash run.sh --stage 0 --stop_stage 0You can also just run these scripts in your command line.
source path.sh
bash ./local/data.shAfter processing the data, the data directory will look like this:
data/
|-- dev.meta
|-- lang_char
| `-- vocab.txt
|-- manifest.dev
|-- manifest.dev.raw
|-- manifest.test
|-- manifest.test.raw
|-- manifest.train
|-- manifest.train.raw
|-- mean_std.json
|-- test.meta
`-- train.metaIf 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}
fiIf 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 1or you can run these scripts in the command line (only use CPU).
source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformerAfter 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}
fiThe 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 2or you can run these scripts in the command line (only use CPU).
source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20The 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
fiIf 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 3or you can run these scripts in the command line (only use CPU).
source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20You can get the pretrained transformer or conformer 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/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz
tar xzvf asr1_transformer_aishell_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/transformer/checkpoints/avg_20
The performance of the released models
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
fiIf 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 4or 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 4or you can also use these scripts in the command line (only use CPU).
source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20
# test stage is optional
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20
CUDA_VISIBLE_DEVICES= ./local/align.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# test a single .wav file
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
fiyou can train the model by yourself using bash run.sh --stage 0 --stop_stage 3, or you can download the pretrained model through the script below:
wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz
tar xzvf transformer.model.tar.gzYou can download the audio demo:
wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result by running the script below.
CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/transformer.yaml exp/transformer/checkpoints/avg_20 data/demo_01_03.wav