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PaddleSpeech/examples/tiny/asr0/README.md

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# 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.
## 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
```