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Speech Translation

Introduction

Speech translation is the process by which conversational spoken phrases are instantly translated and spoken aloud in a second language.

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.

Usage

1. Installation

pip install paddlespeech

2. Prepare Input File

Input of this demo should be a WAV file(.wav).

Here are sample files for this demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav

3. Usage (not support for Windows now)

Command Line(Recommended)
```
paddlespeech st --input ./en.wav
```
Usage:
```
paddlespeech st --help
```
Arguments:
- input(required): Audio file to recognize and translate.
- model: Model type of st task. Default: fat_st_ted.
- src_lang: Source language. Default: en.
- tgt_lang: Target language. Default: zh.
- sample_rate: Sample rate of the model. Default: 16000.
- config: Config of st task. Use pretrained model when it is None. Default: None.
- ckpt_path: Model checkpoint. Use pretrained model when it is None. Default: None.
- device: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
Output:
```
[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 。']
```

Python API

import paddle
from paddlespeech.cli import STExecutor

st_executor = STExecutor()
text = st_executor(
    model='fat_st_ted',
    src_lang='en',
    tgt_lang='zh',
    sample_rate=16000,
    config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
    ckpt_path=None,
    audio_file='./en.wav',
    device=paddle.get_device())
print('ST Result: \n{}'.format(text))

Output:

ST Result:
['我 在 这栋 建筑 的 古老 门上 敲门 。']

4.Pretrained Models

Here is a list of pretrained models released by PaddleSpeech that can be used by command and python api:

Model	Source Language	Target Language
fat_st_ted	en	zh

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