# Tiny Example1.`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.## Steps- Prepare the data ```bash bash local/data.sh ``` `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.- Train your own ASR 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- Evaluate an existing model ```bash bash local/test.sh ``` `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:- Export jit model ```bash bash local/export.sh ckpt_path saved_jit_model_path ```