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README.md
(简体中文|English)
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 it to the target language. It can be done by a single command or a few lines in python using PaddleSpeech
.
Usage
1. Installation
see installation.
You can choose one way from easy, meduim and hard to install paddlespeech.
2. Prepare Input File
The 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.st 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 |