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README.md
ASR(Automatic Speech Recognition)
Introduction
ASR, or Automatic Speech Recognition, refers to the problem of getting a program to automatically transcribe spoken language (speech-to-text).
This demo is an implementation to recognize text from a specific audio file. 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
), and the sample rate must be same as the model's.
Here are sample files for this demo that can be downloaded:
wget https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
3. Usage
-
Command Line(Recommended)
paddlespeech asr --input ./zh.wav
Usage:
paddlespeech asr --help
Arguments:
input
(required): Audio file to recognize.model
: Model type of asr task. Default:conformer_wenetspeech
.lang
: Model language. Default:zh
.sample_rate
: Sample rate of the model. Default:16000
.config
: Config of asr 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:
[2021-12-08 13:12:34,063] [ INFO] [utils.py] [L225] - ASR Result: 我认为跑步最重要的就是给我带来了身体健康
-
Python API
import paddle from paddlespeech.cli import ASRExecutor asr_executor = ASRExecutor() text = asr_executor( model='conformer_wenetspeech', lang='zh', sample_rate=16000, config=None, # Set `config` and `ckpt_path` to None to use pretrained model. ckpt_path=None, audio_file='./zh.wav', device=paddle.get_device()) print('ASR Result: \n{}'.format(text))
Output:
ASR Result: 我认为跑步最重要的就是给我带来了身体健康
4.Pretrained Models
Here is a list of pretrained models released by PaddleSpeech that can be used by command and python api:
Model | Language | Sample Rate |
---|---|---|
conformer_wenetspeech | zh | 16000 |
transformer_aishell | zh | 16000 |