ForestLee / RapidASR

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.

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 wenetspeech/s0 and tested with recognize-onnx.py.

• Download Bidirectional model

• Download:

  • URL：https://pan.baidu.com/s/1BTR-uR_8WWBFpvOisNR_PA
  • Extract code：9xjz

Sample Rate: 16000Hz

Sample Depth: 16bits

Channel: single

Build

• Linux

Visual studio 2019 & cmake 3.20

cd thirdpart
build_win.cmd x86|x64

TBD

• Windows

Notice:

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

For so/dll/c++ interface, it complies with GPL V2.

For python interface, it belongs to Apache license.

For a commercial license, please contact us: [e-mail] (commercial license only).

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