PaddleSpeech/docs/source/asr/quick_start.md

# Quick Start of Speech-To-Text
Several shell scripts provided in `./examples/tiny/local` will help us to quickly give it a try, for most major modules, including data preparation, model training, case inference and model evaluation, with a few public dataset (e.g. [LibriSpeech](http://www.openslr.org/12/), [Aishell](http://www.openslr.org/33)). Reading these examples will also help you to understand how to make it work with your own data.

Some of the scripts in `./examples` are not configured with GPUs. If you want to train with 8 GPUs, please modify `CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7`. If you don't have any GPU available, please set `CUDA_VISIBLE_DEVICES=` to use CPUs instead. Besides, if out-of-memory problem occurs, just reduce `batch_size` to fit.

Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org/12/) for instance.

- Go to directory

    ```bash
    cd examples/tiny
    ```
    Notice that this is only a toy example with a tiny sampled subset of LibriSpeech. If you would like to try with the complete dataset (would take several days for training), please go to `examples/librispeech` instead.
- Source env
    ```bash
    source path.sh
    ```
    **Must do this before you start to do anything.**
    Set `MAIN_ROOT` as project dir. Using defualt `deepspeech2` model as `MODEL`, you can change this in the script.
- Main entrypoint
    ```bash
    bash run.sh
    ```
    This is just a demo, please make sure every `step` works well before next `step`.

More detailed information are provided in the following sections. Wish you a happy journey with the *DeepSpeech on PaddlePaddle* ASR engine!

## Training a model

The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell/local```. As mentioned above, please execute ```sh data.sh```, ```sh train.sh```and```sh test.sh```to do data preparation, training, and testing correspondingly.


## Evaluate a Model
To evaluate a model's performance quantitatively, please run:
```bash
CUDA_VISIBLE_DEVICES=0 bash local/test.sh
```
The error rate (default: word error rate; can be set with `error_rate_type`) will be printed.

We provide two types of CTC decoders: *CTC greedy decoder* and *CTC beam search decoder*. The *CTC greedy decoder* is an implementation of the simple best-path decoding algorithm, selecting at each timestep the most likely token, thus being greedy and locally optimal. The [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) otherwise utilizes a heuristic breadth-first graph search for reaching a near global optimality; it also requires a pre-trained KenLM language model for better scoring and ranking. The decoder type can be set with argument `decoding_method`.