RECCoon 1.0.0: rebrand to Tom Cooks, Parakeet-only, GPLv3, F-Droid-ready

1 files changed, 75 insertions, 27 deletions

diff --git a/README.md b/README.md
index 8785d1c..812afd2 100644
--- a/README.md
+++ b/README.md
@@ -4,22 +4,34 @@ A minimal Android app that records uncompressed WAV audio and transcribes it
 on device.
 
 - **Format:** 44.1 kHz, 16-bit PCM (stereo when the device supports it), no compression
+- **Compact mode:** an optional switch saves a small AAC-LC `.m4a` instead of the WAV
+  (~22 MB/hour mono instead of ~317 MB); transcription still works because the
+  decoder streams the M4A back to PCM (`PcmAudioSource`)
 - **Controls:** one big round button — tap to start recording (timer starts), tap again to stop and save
 - **Meters:** live scrolling waveform plus left/right peak level meters with a clip indicator while recording
 - **Mono switch:** optionally record a single channel (half the file size)
+- **Swap L/R:** exchange the stereo channels when a device reports them inverted in
+  landscape; applies to the meters and the saved file
+- **Mic picker:** choose Auto, the built-in mic, or a specific input (Bluetooth SCO,
+  USB, wired headset ...) — the BT-Mic-Force feature folded into RECCoon
+- **Live monitoring:** an optional **Monitor** switch plays the microphone through a
+  chosen output (built-in speaker, wired, USB, Bluetooth ...). Use headphones — the
+  speaker causes feedback. A dedicated thread and a bounded queue keep a slow output
+  from ever stalling the recording
+- **Discard:** stop a recording and throw it away without saving
 - **Markers:** place labelled cue points while recording; they are embedded in the WAV (`cue ` + `LIST/adtl`) and shown in the player
 - **Silence:** optional Do Not Disturb total silence (no notifications, no vibration) while recording
 - **Recordings list:** scrollable list at the bottom of the main screen, newest first
 - **Player:** scrubber, ±10 s jumps, speed control, delete / rename / share / export, no autoplay
 - **Transcription:** on-device, offline speech recognition with
   [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) using multilingual
-  Whisper — works with **English and Italian**
-- **Name:** RECCoon · **by** wuhei · **version** 1.4.0-alpha
+  **NVIDIA Parakeet v3** — works with English, Italian and 23 other languages
+- **Name:** RECCoon · **by** Tom Cooks · **version** 1.0.0
 - **Icon:** raccoon with a microphone
 
 ## Requirements
 
-- Android 8.0 (API 26) or newer, arm64-v8a or x86_64
+- Android 8.0 (API 26) or newer, arm64-v8a
 - `RECORD_AUDIO` permission
 - On Android 9 and older, `WRITE_EXTERNAL_STORAGE` is also requested
 - Internet access the first time a transcription model is downloaded
@@ -37,16 +49,19 @@ Outputs:
 - `app/build/outputs/apk/debug/app-debug.apk`
 - `app/build/outputs/apk/release/app-release.apk`
 
-A prebuilt copy is available at `dist/RECCoon-1.4.0-alpha.apk`. Note that the
-sherpa-onnx AAR in `app/libs/` and the APK in `dist/` are **not** tracked in
-git because they are large; see `TODO.md`.
+A prebuilt copy is available at `dist/RECCoon-1.0.0.apk` (release, signed with
+the debug key). The APK in `dist/` is **not** tracked in git. The sherpa-onnx
+runtime comes from [JitPack](https://jitpack.io) (an F-Droid-trusted Maven
+repository), so no prebuilt binary lives in this repository.
 
 ## Recording
 
-- `WavRecorder` uses `AudioRecord` (MIC source) to capture 16-bit PCM at
-  44100 Hz into a temporary file, then rewrites a correct 44-byte RIFF/WAVE
-  header when recording stops. It uses stereo when available and falls back to
-  mono, and reports per-channel peak levels to the UI.
+- `WavRecorder` uses `AudioRecord` (MIC source, or `VOICE_COMMUNICATION` for
+  Bluetooth SCO) to capture 16-bit PCM at 44100 Hz into a temporary file, then
+  rewrites a correct 44-byte RIFF/WAVE header when recording stops. It uses
+  stereo when available and falls back to mono, and reports per-channel peak
+  levels to the UI. A selected input device is applied with
+  `AudioRecord.setPreferredDevice`.
 - `RecordingService` is a microphone-typed foreground service started while
   recording, so capture continues when the app is in the background.
 - `MainActivity` handles the runtime permissions, the timer, the level meters
@@ -79,32 +94,65 @@ git because they are large; see `TODO.md`.
   (MediaStore or file), **Export** (writes `.txt` and `.srt` to Downloads) and
   **Delete** (removes the WAV and its sidecars).
 - Transcript words are clickable: tapping a word seeks the player to it. Word
-  timings come from Whisper token timestamps when available, otherwise the
+  timings come from the model's token timestamps when available, otherwise the
   segment is divided between its words.
 
 ## On-device transcription
 
-- `TranscriptionEngine` streams the WAV, resamples it to 16 kHz and segments it
-  with Silero VAD, then decodes each speech segment with a multilingual Whisper
-  model through sherpa-onnx. Streaming keeps memory bounded for long
-  recordings (offline Whisper only handles 30 s at a time).
-- `ModelRepository` downloads the models on first use into app-private storage:
+- `TranscriptionEngine` reads the recording through `PcmAudioSource`, which
+  parses WAV directly and decodes compressed files (AAC/M4A) with
+  `MediaExtractor` + `MediaCodec`, resamples to 16 kHz and segments the audio
+  with Silero VAD, then decodes each speech segment with the Parakeet v3
+  transducer through sherpa-onnx. Streaming keeps memory bounded for long
+  recordings.
+- `ModelRepository` downloads the model on first use:
 
   | Model | Size | Notes |
   |---|---|---|
-  | Whisper tiny (int8, multilingual) | ~99 MB | faster, lower accuracy |
-  | Whisper base (int8, multilingual) | ~154 MB | default, better Italian |
-
-  Both include the small Silero VAD model (~0.6 MB). Files come from the
-  sherpa-onnx Whisper model repos on Hugging Face and the sherpa-onnx GitHub
-  release.
-- Language can be forced to English (`en`) or Italian (`it`), or left on
-  **Auto detect**.
+  | Parakeet v3 (int8, 25 languages) | ~670 MB | only model, multilingual, large download |
+
+  It includes the small Silero VAD model (~0.6 MB). Files come from the
+  sherpa-onnx model repo on Hugging Face and the sherpa-onnx GitHub release.
+  Models are stored in the app-specific external folder
+  `/sdcard/Android/data/com.tomcooks.reccoon/files/models/` so they can be
+  side-loaded with `adb push` or copied over USB instead of downloaded in-app.
+- Parakeet v3 is a transducer, so it is loaded with `model_type="nemo_transducer"`
+  and handles language detection itself (no language picker). It needs ~670 MB of
+  storage and a large-heap device.
 - Transcripts are stored as JSON sidecars in app-private storage
   (`filesDir/transcripts/<recording>.json`).
 
+## Compact (compressed) recordings
+
+- The **Compressed (small M4A)** checkbox transcodes the finished WAV to
+  AAC-LC (48 kbps per channel) with `AudioCompressor` and saves that instead.
+  Markers are kept as a JSON sidecar. Use it when storage matters more than
+  bit-exact audio.
+- `tools/whistle-eval/` contains a desktop harness that compares Whisper,
+  Parakeet and Cactus Whistle; see its README for the results.
+
+## Live monitoring and discard
+
+- **Monitor** plays the incoming audio back through the selected output device while
+  recording. The output picker mirrors the mic picker (Auto, phone speaker, wired,
+  USB, Bluetooth). `LiveMonitor` owns an `AudioTrack` on its own thread with a small
+  bounded queue, so a slow output (for example Bluetooth A2DP) drops monitor chunks
+  instead of blocking the capture loop and corrupting the recording. Headphones are
+  strongly recommended — the phone speaker feeds back.
+- **Discard** stops the recording and deletes the temporary file without saving it,
+  for takes that are obviously bad.
+
+## License
+
+RECCoon is free software, released under the **GNU General Public License,
+version 3 or later** (see `LICENSE`). The Parakeet v3 model is
+[NVIDIA Parakeet TDT 0.6B v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3),
+licensed CC-BY-4.0.
+
 ## Native libraries and APK size
 
-The APK bundles the sherpa-onnx/onnxruntime native libraries for `arm64-v8a`
-and `x86_64`, so it is around 72 MB. Removing `x86_64` from the `abiFilters`
-in `app/build.gradle.kts` roughly halves the APK size for phone-only builds.
+The APK bundles the sherpa-onnx/onnxruntime native libraries only for
+`arm64-v8a` (`abiFilters` in `app/build.gradle.kts`), which keeps it around
+35–40 MB. Including `x86_64` as well would roughly double it; that is only
+needed for x86 emulators. The ~670 MB Parakeet model is downloaded at runtime,
+not bundled.