Exporting the thunder-speech quartznet model to use inside android. The same process can be done to other models in the library, like citrinet or wav2vec and the exported torchscript model will have the same interface, meaning that the android code doesn't require modification.
This is a direct port of the official speech recognition example from pytorch, here.
- thunder-speech (Optional)
- Python 3.8+ (Optional)
- Android Pytorch library 1.8
- Android Studio 4.0.1 or later
First, run the following commands on a Terminal:
git clone https://github.com/scart97/quartznet-android
cd quartznet-android
If you don't have PyTorch installed or want to have a quick try of the demo app, you can download the scripted quartznet model here, then move the model.pt file to the app/src/main/assets/ folder.
Be aware that the downloadable model file was created with PyTorch 1.9.0, matching the PyTorch Android library 1.9.0 specified in the project's build.gradle file as implementation 'org.pytorch:pytorch_android:1.9.0'. If you use a different version of PyTorch to create your model by following the instructions below, make sure you specify the same PyTorch Android library version in the build.gradle file to avoid possible errors caused by the version mismatch.
Start Android Studio, open the project, build and run the app on an Android device. After the app runs, tap the Start button and start saying something; after a couple of seconds, the model will infer to recognize your speech. Only basic decoding of the recognition result from an array of floating numbers of logits to a list of tokens is provided in this demo app, but it is easy to see, without further post-processing, whether the model can recognize your utterances.