Jackwaterveg
3 years ago
committed by
GitHub
parent
14d2cf9d74
commit
96fa8889be
@ -1,39 +1,230 @@
|
||||
# Tiny Example
|
||||
|
||||
1. `source path.sh`
|
||||
3. set `CUDA_VISIBLE_DEVICES` as you need.
|
||||
2. demo scrpt is `bash run.sh`. You can run commond separately as needed.
|
||||
# DeepSpeech2 offline/online ASR with Tiny
|
||||
|
||||
## Steps
|
||||
- Prepare the data
|
||||
This example contains code used to train a DeepSpeech2 offline or online model with Tiny dataset(a part of [[Librispeech dataset](http://www.openslr.org/resources/12)](http://www.openslr.org/resources/33))
|
||||
|
||||
```bash
|
||||
bash local/data.sh
|
||||
```
|
||||
## Overview
|
||||
|
||||
`data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
|
||||
All the scirpts you need are in the ```run.sh```. There are several stages in the ```run.sh```, and each stage has its function.
|
||||
|
||||
- Train your own ASR model
|
||||
| Stage | Function |
|
||||
| :---- | :----------------------------------------------------------- |
|
||||
| 0 | Process data. It includes: <br> (1) Download the dataset <br> (2) Caculate the CMVN of the train dataset <br> (3) Get the vocabulary file <br> (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 choose the best model |
|
||||
| 3 | Test the final model performance |
|
||||
| 4 | Export the static graph model |
|
||||
|
||||
```bash
|
||||
bash local/train.sh
|
||||
```
|
||||
|
||||
`train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
|
||||
|
||||
- Case inference with an existing model
|
||||
You can choose to run a range of stages by setting the ```stage``` and ```stop_stage ``` .
|
||||
|
||||
- Evaluate an existing model
|
||||
For example, if you want to execute the code in stage 2 and stage 3, you can run this script:
|
||||
|
||||
```bash
|
||||
bash local/test.sh
|
||||
```
|
||||
```bash
|
||||
bash run.sh --stage 2 --stop_stage 3
|
||||
```
|
||||
|
||||
`test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
|
||||
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
|
||||
bash run.sh --stage 0 --stop_stage 0
|
||||
```
|
||||
|
||||
- Export jit model
|
||||
|
||||
```bash
|
||||
bash local/export.sh ckpt_path saved_jit_model_path
|
||||
```
|
||||
|
||||
The document below will describe the scripts in the ```run.sh``` in detail.
|
||||
|
||||
## The environment variables
|
||||
|
||||
The path.sh contains the environment variable.
|
||||
|
||||
```bash
|
||||
source path.sh
|
||||
```
|
||||
|
||||
This script needs to be run firstly.
|
||||
|
||||
And another script is also needed:
|
||||
|
||||
```bash
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh
|
||||
```
|
||||
|
||||
It will support the way of using```--varibale value``` in the shell scripts.
|
||||
|
||||
|
||||
|
||||
## The local variables
|
||||
|
||||
Some local variables are set in the ```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 stage you want to start from in the expriments.
|
||||
```stop stage```denotes the number of stage you want to end at in the expriments.
|
||||
|
||||
```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.
|
||||
```model_type```denotes the model type: offline or online
|
||||
|
||||
```ckpt``` denotes the checkpoint prefix of the model, e.g. "deepspeech2"
|
||||
|
||||
You can set the local variables (except ```ckpt```) when you use the ```run.sh```
|
||||
|
||||
For example, you can set the ```gpus``` and ``avg_num`` when you use the command line.:
|
||||
|
||||
```bash
|
||||
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 the ```run.sh``` to do this. The code is shown below:
|
||||
|
||||
```bash
|
||||
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
|
||||
bash run.sh --stage 0 --stop_stage 0
|
||||
```
|
||||
|
||||
You can also just run these scripts in your command line.
|
||||
|
||||
```bash
|
||||
source path.sh
|
||||
bash ./local/data.sh
|
||||
```
|
||||
|
||||
After processing the data, the ``data`` directory will look like this:
|
||||
|
||||
```bash
|
||||
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
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Stage 1: Model training
|
||||
|
||||
If you want to train the model. you can use stage 1 in the ```run.sh```. The code is shown below.
|
||||
|
||||
```bash
|
||||
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
|
||||
bash run.sh --stage 0 --stop_stage 1
|
||||
```
|
||||
|
||||
or you can run these scripts in the command line (only use CPU).
|
||||
|
||||
```bash
|
||||
source path.sh
|
||||
bash ./local/data.sh
|
||||
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 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:
|
||||
|
||||
```bash
|
||||
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
|
||||
bash run.sh --stage 0 --stop_stage 2
|
||||
```
|
||||
|
||||
or you can run these scripts in the command line (only use CPU).
|
||||
|
||||
```bash
|
||||
source path.sh
|
||||
bash ./local/data.sh
|
||||
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2
|
||||
avg.sh best exp/deepspeech2/checkpoints 1
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Stage 3: Model Testing
|
||||
|
||||
The test stage is to evaluate the model performance.. The code of test stage is shown below:
|
||||
|
||||
```bash
|
||||
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
|
||||
bash run.sh --stage 0 --stop_stage 3
|
||||
```
|
||||
|
||||
or you can run these scripts in the command line (only use CPU).
|
||||
|
||||
```bash
|
||||
source path.sh
|
||||
bash ./local/data.sh
|
||||
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2
|
||||
avg.sh best exp/deepspeech2/checkpoints 1
|
||||
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1
|
||||
```
|
||||
|
||||
|
||||
## Stage 4: Static graph model Export
|
||||
|
||||
This stage is to transform the dynamic graph model to static graph model.
|
||||
|
||||
```bash
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
# export ckpt avg_n
|
||||
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
|
||||
fi
|
||||
```
|
||||
|
||||
If you already have a dynamic graph model, you can run this script:
|
||||
|
||||
```bash
|
||||
source path.sh
|
||||
./local/export.sh deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 exp/deepspeech2/checkpoints/avg_1.jit offline
|
||||
```
|
||||
|
||||
|
||||
Loading…
Reference in new issue