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 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:

• Python
• Linux
• Mac
• 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.

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: [email protected]

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