Add native Android app scaffold (M0): whisper.cpp engine via JNI

- android/: Gradle/Kotlin project (AGP 9.4, Compose, NDK 27.1), monorepo
  subdir as planned; whisper.cpp v1.9.3 vendored via pinned fetch script
- core/whisper: JNI wrapper (beam size, threads, language) + WhisperEngine
  Kotlin API mirroring the desktop engine contract
- app (M0 scope): in-app GGML model download from Hugging Face, engine
  load, WAV picker with resampling, on-device transcription, share sheet
- build validated: assembleDebug OK, libwhisper_jni.so + ggml packaged

android/README.md

@@ -0,0 +1,62 @@
+# MeetRec for Android
+
+Native Android app with the same functionality as the desktop meetrec:
+record meetings and transcribe them on-device with Whisper. Nothing leaves
+the phone — no cloud, no telemetry.
+
+Status: **M0 (engine proof)** — the app can download a GGML model, load it
+via JNI (whisper.cpp, CPU/NEON) and transcribe a picked WAV file. Recording,
+live transcript, and output files arrive in later milestones (see the
+milestone plan in the project docs).
+
+## Requirements
+
+- Android Studio (or: SDK Platform 36, Build Tools 36, NDK 27.1, CMake 3.22.1)
+- JDK 17+
+- Device running Android 10+ (developed against a Fairphone 6 / Snapdragon
+  7s Gen 3, Android 15+)
+
+## Build
+
+```sh
+cd android
+./tools/fetch-whisper.sh     # vendors whisper.cpp v1.9.3 into third_party/
+./gradlew :app:assembleDebug # or open the android/ folder in Android Studio
+adb install -r app/build/outputs/apk/debug/app-debug.apk
+```
+
+`third_party/whisper.cpp` is gitignored — the fetch script pins the exact
+release tag so the JNI layer never breaks on upstream churn.
+
+## Test on device (M0)
+
+1. Launch **MeetRec**, pick a model (`tiny` is fine for a first test) and tap
+   **Download** (model comes from Hugging Face; tiny is ~75 MB).
+2. Tap **Load engine**.
+3. Tap **Pick WAV file** and choose a 16 kHz mono WAV for best results
+   (other PCM WAVs are resampled automatically).
+4. Tap **Transcribe** — segments with timestamps appear; share via the
+   share sheet.
+
+Expect roughly realtime transcription with `tiny`/`base` on the
+Fairphone 6's CPU; `small` is noticeably slower — use it for final passes
+only (the live/final split comes with the recorder milestones).
+
+## Performance notes
+
+- The engine runs on CPU via NEON, using up to 4 threads.
+- GPU/NPU acceleration (e.g. Snapdragon NPU via the QNN backend) is a
+  stretch goal, not wired up yet.
+
+## Module layout
+
+```
+app/          Compose UI (model download, WAV picker, transcript view)
+core/whisper/ whisper.cpp JNI wrapper: CMake build + LibWhisper.kt +
+              WhisperEngine.kt (the on-device transcription API)
+tools/        fetch-whisper.sh — vendor the pinned whisper.cpp release
+```
+
+Models live in the app's private storage (`filesDir/models`), shared files
+with the desktop app's `~/.cache/meetrec/whisper-cpp` naming
+(ggml-tiny.bin … ggml-large-v3.bin).