WeNet On-device ASR Android Demo

需要用提取码下载

fxzWenet\app\src\main\assets\final.zip

fxzWenet\app\release\app-release.apk

fxzWenet\app\build\pytorch_android-1.10.0.aar

fxzWenet\app\build\wenet-openfst-android-1.0.2.aar

fxzWenet\app\build\intermediates

This Android demo shows we can run on-device streaming ASR with WeNet. You can download our prebuilt APK or build your APK from source code.

Prebuilt APK

• Chinese ASR Demo APK, with model trained on AIShell data 离线
• English ASR Demo APK, with model trained on GigaSpeech data

Build your APK from source code

1) Build model

You can use our pretrained model (click the following link to download):

中文(WenetSpeech) | English(GigaSpeech)

Or you can train your own model using WeNet training pipeline on your data.

2) Build APK

When your model is ready, put final.zip and units.txt into Android assets (app/src/main/assets) folder, then just build and run the APK. Here is a gif demo, which shows how our on-device streaming e2e ASR runs with low latency. Please note the wifi and data has been disabled in the demo so there is no network connection ^_^.

Compute the RTF

Step 1, connect your Android phone, and use adb push 把文件从电脑移到手机上的指令 command to push your model, wav scp, and waves to the sdcard.

Step 2, build the binary and the APK with Android Studio directly, or with the commands as follows:

cd runtime/android
./gradlew build

Step 3, push your binary and the dynamic library to /data/local/tmp as follows:

adb push app/.cxx/cmake/release/arm64-v8a/decoder_main /data/local/tmp
adb push app/build/pytorch_android-1.10.0.aar/jni/arm64-v8a/* /data/local/tmp

Step 4, change to the directory /data/local/tmp of your phone, and export the library path by:

adb shell
cd /data/local/tmp
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:.

Step 5, execute the same command as the x86 demo to run the binary to decode and compute the RTF.