Whisper app updates (#192)

Author: kirklandsign

whisper/android/WhisperApp/README.md

@@ -1,24 +1,26 @@
 # Whisper Demo App
 
-This app runs the Whisper model with the Qualcomm backend in ExecuTorch. The model itself runs on the Qualcomm HTP (Hexagon Tensor Processor), an AI accelerator in Qualcomm SoCs. You need an Android smartphone with a [supported Qualcomm chipset](https://github.com/pytorch/executorch/tree/main/backends/qualcomm).
+This app runs the Whisper model in ExecuTorch.
 
-### Build the ExecuTorch Android library
+## Build the ExecuTorch Android library
 
 Build the [ExecuTorch Android library with QNN backend](https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md).
 
-### Export the audio processing and model .pte files
+## Export the audio processing and model .pte files
 
 There are two steps, audio processing and the Whisper model (encoder+decoder), which are both done via ExecuTorch.
 
-1) Run the script `extension/audio/mel_spectrogram.py` to export `whisper_preprocess.pte`
-2) Run the script `examples/qualcomm/oss_scripts/whisper/whisper.py` to export `whisper_qnn_16a8w.pte`
+1. Run `extension/audio/mel_spectrogram.py` to export `whisper_preprocess.pte`
+2. Run `examples/qualcomm/oss_scripts/whisper/whisper.py` to export `whisper_qnn_16a8w.pte`
 
-Move these two .pte files along with `tokenizer.json` to `/data/local/tmp/whisper` on device.
+Move these two `.pte` files along with `tokenizer.json` to `/data/local/tmp/whisper` on device.
 
-### Run app
-
-Open WhisperApp in Android studio. Copy the Android library `executorch.aar` which should have audio JNI bindings and the Qualcomm HTP libraries, into `app/libs`.
+## Run the app
 
+1. Open WhisperApp in Android Studio
+2. Copy the Android library `executorch.aar` (with audio JNI bindings) into `app/libs`
+3. Build and run on device
 
+## Demo
 
 https://github.com/user-attachments/assets/ff8c71c5-b734-4ed4-8382-70a429830665