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# Speech Translation
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## Introduction
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Speech translation is the process by which conversational spoken phrases are instantly translated and spoken aloud in a second language.
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This demo is an implementation to recognize text from a specific audio file and translate to target language. It can be done by a single command or a few lines in python using `PaddleSpeech`.
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## Usage
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### 1. Installation
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```bash
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pip install paddlespeech
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```
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### 2. Prepare Input File
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Input of this demo should be a WAV file(`.wav`).
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Here are sample files for this demo that can be downloaded:
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```bash
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wget https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
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```
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### 3. Usage
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- Command Line(Recommended)
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```bash
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paddlespeech st --input ~/en.wav
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```
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Usage:
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```bash
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paddlespeech st --help
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```
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Arguments:
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- `input`(required): Audio file to recognize and translate.
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- `model`: Model type of st task. Default: `fat_st_ted`.
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- `src_lang`: Source language. Default: `en`.
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- `tgt_lang`: Target language. Default: `zh`.
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- `sample_rate`: Sample rate of the model. Default: `16000`.
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- `config`: Config of st task. Use pretrained model when it is None. Default: `None`.
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- `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
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- `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
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Output:
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```bash
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[2021-12-09 11:13:03,178] [ INFO] [utils.py] [L225] - ST Result: ['我 在 这栋 建筑 的 古老 门上 敲门 。']
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```
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- Python API
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```python
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import paddle
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from paddlespeech.cli import STExecutor
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st_executor = STExecutor()
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text = st_executor(
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model='fat_st_ted',
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src_lang='en',
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tgt_lang='zh',
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sample_rate=16000,
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config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
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ckpt_path=None,
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audio_file='./en.wav',
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device=paddle.get_device())
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print('ST Result: \n{}'.format(text))
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```
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Output:
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```bash
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ST Result:
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['我 在 这栋 建筑 的 古老 门上 敲门 。']
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```
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### 4.Pretrained Models
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Here is a list of pretrained models released by PaddleSpeech that can be used by command and python api:
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| Model | Source Language | Target Language
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| :--- | :---: | :---: |
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| fat_st_ted| en| zh
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