PaddleSpeech/examples/aishell/asr3/README.md

Wav2vec2ASR with Aishell

This example contains code used to finetune wav2vec2.0 model with Aishell dataset

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) Download the pretrained wav2vec2 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	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 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 stages 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. "wav2vec2ASR"

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 20

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/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.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.meta
|-- train.csv
|-- dev.csv
|-- test.csv

Stage 0 also downloads the Chinese pre-trained wav2vec2 model.

mkdir -p exp/wav2vec2
wget -P exp/wav2vec2 https://paddlespeech.bj.bcebos.com/wav2vec/chinese-wav2vec2-large.pdparams

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/wav2vec2ASR.yaml wav2vec2ASR

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. Note: We only train one epoch for wav2vec2ASR, thus the avg_num is set to 1.

 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # avg n best model
     avg.sh best exp/${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/wav2vec2ASR.yaml wav2vec2ASR
avg.sh best exp/wav2vec2ASR/checkpoints 1

Stage 3: Model Testing

The test stage is to evaluate the model performance. The code of 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} ${decode_conf_path} exp/${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 (only use CPU).

. ./path.sh
. ./cmd.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/wav2vec2ASR.yaml wav2vec2ASR
avg.sh best exp/wav2vec2ASR/checkpoints 1
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/wav2vec2ASR.yaml conf/tuning/decode.yaml exp/wav2vec2ASR/checkpoints/avg_1

Pretrained Model

You can get the pretrained wav2vec2ASR 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/asr3/wav2vec2ASR-large-aishell1_ckpt_1.4.0.model.tar.gz
tar xzvf wav2vec2ASR-large-aishell1_ckpt_1.4.0.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/wav2vec2ASR.yaml conf/tuning/decode.yaml exp/wav2vec2ASR/checkpoints/avg_1

The performance of the released models are shown in here.

Stage 4: Single Audio File Inference

In 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 4 ] && [ ${stop_stage} -ge 4 ]; then
     # test a single .wav file
     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
 fi

you 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/asr3/wav2vec2ASR-large-aishell1_ckpt_1.4.0.model.tar.gz
tar xzvf wav2vec2ASR-large-aishell1_ckpt_1.4.0.model.tar.gz

You can download the audio demo:

wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.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 of the audio demo by running the script below.

CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/wav2vec2ASR.yaml conf/tuning/decode.yaml exp/wav2vec2ASR/checkpoints/avg_1 data/demo_002_en.wav