# RapidASR

**Repository Path**: yuxb/RapidASR

## Basic Information

- **Project Name**: RapidASR
- **Description**: A Cross platform implementation of Wenet ASR inference. It's based on ONNXRuntime and Wenet. We provide a set of easier APIs to call wenet models.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2022-05-09
- **Last Updated**: 2022-05-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# RapidASR: a new member of RapidAI family.

Our vision is to offer an out-of-box engineering implementation for ASR.

A cpp implementation of recognize-onnx.py in [Wenet-asr](https://github.com/wenet-e2e/wenet) in which it implements the inference with ONNXRuntime. For a version of pure CPP code, we need to do a bit of work to rewrite some components.

### Special thanks to its original author SlyneD.

## Less is more. Less dependency, more usability.


Just offline mode, not support stream mode, aka separate files can be recognized.

## Supported modes:

- CTC_GREEDY_SEARCH
- CTC_RPEFIX_BEAM_SEARCH
- ATTENSION_RESCORING 

## Progress:

- [X] Python
- [x] Linux
- [x] Mac
- [x] Android
- [ ] Windows




## Models

The model is original from https://github.com/wenet-e2e/wenet/tree/main/examples/wenetspeech/s0 and tested with recognize-onnx.py.

Bidirectional model: http://mobvoi-speech-public.ufile.ucloud.cn/public/wenet/wenetspeech/20211025_conformer_bidecoder_exp.tar.gz

Download:
```
URL：https://pan.baidu.com/s/1BTR-uR_8WWBFpvOisNR_PA 
CODE：9xjz 

```
- Sample Rate:

16000Hz

- sample Depth:

16bits

- channel:

single

## Build

- Linux 

 TBD
- Windows
 ```
Visual studio 2019 & cmake 3.20



 cd thirdpart
 build_win.cmd x86|x64

 ```

## Notice:

The project is under the protection of GPL V2 and commercial license.

For a commercial license, please contact us: znsoft@163.com

## Commercial support

For a commercial user, we offer a library to resample input data including mp3, mp4, mkv  and so on.

Please visit: https://github.com/RapidAI/RapidAudioKit