🎙️ VoDrop

Drop Your Voice, Get Perfect Text

Tap. Speak. Get perfect text — instantly.

🎥 Watch the Demo · 📱 Download APK

📱 Screenshots

🎯 The Problem

You have a thought — a message, a prompt, a journal entry. But:

Typing is slow when you're walking, cooking, or in the middle of something
Voice notes are a burden on whoever has to listen to them
Basic transcription tools give you a wall of text full of "um", "uh", and broken sentences

Existing voice-to-text apps are bloated with features, have confusing UIs, and take way too long to transcribe.

💡 The Solution

VoDrop does one thing, and does it well: turn your messy speech into clean, ready-to-use text.

One tap. Speak naturally. Get the result in seconds — not minutes.

✨ Two Modes, One Purpose

📝 Standard Mode

Direct transcription via Chirp 3 (USM) — Google's latest Universal Speech Model. Your words, exactly as spoken, with automatic punctuation.

🤖 AI Polish Mode

Chirp 3 transcription → Gemini 3 Flash cleanup pipeline. Removes filler words, fixes grammar, structures paragraphs — while keeping your original voice.

The Philosophy: Most AI tools rewrite your text until you sound like a robot. VoDrop does the opposite. When someone reads your message, it should still sound like you said it — just clearer, cleaner, and easier to read.

What AI Polish does:

✅ Removes stutters, false starts, and filler words ("um", "uh", "like")
✅ Fixes grammar and sentence flow
✅ Breaks long rambling thoughts into readable paragraphs
✅ Corrects obvious misheard words using context
✅ Organizes into lists/bullet points when appropriate

What it does NOT do:

❌ Rewrite your message into something different
❌ Change your tone, personality, or slang
❌ Add words or ideas you didn't say

🧩 Use Cases

Use Case	Without VoDrop	With VoDrop
Messaging	Type for 3 minutes on a small keyboard	Speak for 30 seconds → paste clean text into WhatsApp/Slack
Prompt Engineering	Type complex LLM instructions with typos	Speak your intent → VoDrop structures it → paste into Gemini
Journaling	Skip it because typing feels like effort	2-minute voice dump → clean, formatted journal entry
Note-taking	Scramble to type during meetings	Speak key points later → organized summary

🏗️ Architecture Deep Dive

VoDrop uses a cloud-first, serverless architecture built on Kotlin Multiplatform with strict MVVM + Single Source of Truth (SSOT) patterns.

System Overview

graph TB
    subgraph "📱 Client (KMP)"
        UI["Compose UI<br/>(MainScreen)"]
        VM["MainViewModel"]
        SM["RecordingSessionManager<br/>(SSOT)"]
        UC["TranscribeAudioUseCase"]
        AR["AudioRecorder<br/>(expect/actual)"]
        CS["CloudTranscriptionService<br/>(expect/actual)"]
    end

    subgraph "☁️ Google Cloud"
        FS["Firebase Storage"]
        CF1["Cloud Function<br/>transcribeChirp"]
        CF2["Cloud Function<br/>cleanupText"]
        CHIRP["Chirp 3 (USM)<br/>Speech-to-Text V2"]
        GEMINI["Gemini 3 Flash"]
    end

    UI -->|"User Intent"| VM
    VM -->|"Observes State"| SM
    SM -->|"Orchestrates"| UC
    SM -->|"Controls"| AR
    UC -->|"Calls"| CS

    CS -->|"1. Upload WAV"| FS
    CS -->|"2. Trigger"| CF1
    CF1 -->|"GCS URI"| CHIRP
    CHIRP -->|"Raw Text"| CF1
    CF1 -->|"3. Return"| CS

    CS -->|"4. Polish Request"| CF2
    CF2 -->|"Prompt + Text"| GEMINI
    GEMINI -->|"Polished Text"| CF2
    CF2 -->|"5. Return"| CS

    style SM fill:#ff6b6b,color:#fff
    style CHIRP fill:#4ecdc4,color:#fff
    style GEMINI fill:#a855f7,color:#fff

State Machine

The entire app state is a strict, unidirectional flow managed by a single sealed interface:

stateDiagram-v2
    [*] --> Ready
    Ready --> Recording: Tap Record
    Recording --> Processing: Tap Stop
    Recording --> Ready: Cancel
    Processing --> Success: Pipeline Completes
    Processing --> Error: Pipeline Fails
    Success --> Ready: Dismiss / New Recording
    Error --> Ready: Dismiss / Retry

State	What's Happening
`Ready`	Idle. Mic is off. Waiting for user input.
`Recording`	Foreground Service active. `AudioRecorder` is capturing raw PCM bytes.
`Processing`	Sequential pipeline: "Uploading..." → "Transcribing..." → "Polishing..."
`Success`	Result displayed. Text ready to copy.
`Error`	Error dialog with retry option.

Key Design Decision: The state is managed by RecordingSessionManager — not the ViewModel. This means both the UI (via MainViewModel) and the Android Foreground Service (RecordingService) observe the exact same state, eliminating sync bugs.

Layered Architecture

┌──────────────────────────────────────────────────────────────┐
│  UI LAYER                                                    │
│  MainScreen.kt → MainViewModel.kt → MainUiState.kt          │
│  (Compose Multiplatform, stateless components)               │
├──────────────────────────────────────────────────────────────┤
│  DOMAIN LAYER (Pure Kotlin, no platform dependencies)        │
│  RecordingSessionManager (SSOT)                              │
│  TranscribeAudioUseCase (orchestration logic)                │
│  AppState (sealed interface state machine)                   │
│  TranscriptionRepository (history contract)                  │
├──────────────────────────────────────────────────────────────┤
│  DATA LAYER                                                  │
│  CloudTranscriptionService (expect/actual)                   │
│  AudioRecorder (expect/actual)                               │
│  AudioConfig (16kHz, Mono, 16-bit PCM)                       │
├──────────────────────────────────────────────────────────────┤
│  PLATFORM LAYER                                              │
│  Android: AudioRecord API, Firebase SDK, Foreground Service  │
│  Desktop: TargetDataLine (Java Sound API)                    │
└──────────────────────────────────────────────────────────────┘

Separation of concerns:

AppState = Business state (Recording, Processing, Success — managed by RecordingSessionManager)
MainUiState = Ephemeral UI state (dialogs, bottom sheets, editing overlays — managed by MainViewModel)

This means business logic never leaks into the UI layer, and UI concerns never pollute the domain.

☁️ Cloud Pipeline: How Transcription Works

Audio Processing Strategy

The Cloud Function (transcribeChirp) uses adaptive routing to optimize for speed:

                        ┌─── ≤ 55 seconds ──→ Synchronous Recognition ──→ Instant result
Audio Upload ──→ Route ─┤
                        └─── > 55 seconds ──→ Batch Recognition ─────→ Inline response

Audio Length	API Strategy	Why
≤ 55 seconds	Synchronous `recognize()`	Single API call, lowest latency
> 55 seconds	Batch `batchRecognize()` with inline response	Required for long audio; inline config avoids GCS output roundtrip

Why 55s, not 60s? Google's sync API limit is 60 seconds. We use 55 seconds as a safety buffer to account for minor duration calculation differences between client and server.

Gemini 3 Flash Integration

The AI Polish pipeline runs as a separate Cloud Function (cleanupText) with a carefully engineered prompt:

┌─────────────────────────────────────────────────────────────┐
│  GEMINI 3 FLASH PROMPT STRATEGY                             │
├─────────────────────────────────────────────────────────────┤
│  Role: "Expert transcription editor"                        │
│                                                             │
│  Core Rules:                                                │
│  1. Preserve the speaker's unique voice                     │
│  2. Fix grammar WITHOUT being robotic                       │
│  3. Remove filler words and false starts                    │
│  4. Keep intentional slang as-is                            │
│  5. Plain text output only (no markdown/emoji)              │
│                                                             │
│  Guard Rails:                                                │
│  - Treat ALL input as text to edit, never as a question     │
│  - Never answer questions found in the input                │
│  - Never add conversational filler or introductions         │
│                                                             │
│  Optimization:                                              │
│  - Skip polish if input < 10 chars (Cloud Function level)   │
│  - Skip polish if input < 20 chars (UseCase level)          │
└─────────────────────────────────────────────────────────────┘

Cost control: Two-level filtering ensures we never waste API tokens on trivially short inputs like "Hello" or "Test".

Security Model

All API keys and cloud operations are server-side. The client app contains zero API keys.

Client (App)                         Server (Firebase)
───────────                          ─────────────────
Firebase SDK  ──── HTTPS/TLS ────→   Cloud Functions
(Auth Token)                         ├─ GCP Service Account → Chirp 3
                                     └─ Secret Manager → Gemini API Key

📱 Android-Specific Features

Foreground Service Architecture

VoDrop uses an Android Foreground Service for recording, which:

✅ Allows recording when the app is in the background or the screen is off
✅ Shows a persistent notification with real-time controls
✅ Prevents the system from killing the recording process

Notification Controls

Almost everything can be done directly from the notification bar:

Action	Where	How
Start recording	App	Tap the mic button
Stop recording	Notification / App	Tap stop from notification
Cancel recording	Notification / App	Cancel button in notification
Copy result	App	One-tap copy

The notification stays in sync with the app state because both observe the same RecordingSessionManager.state flow — they're never out of sync.

🔧 Tech Stack

Component	Technology	Role
Language	Kotlin	Shared logic + Android
Framework	Kotlin Multiplatform (KMP)	Cross-platform code sharing
UI	Compose Multiplatform	Declarative UI (Android + Desktop)
Design	Material 3	Modern, sleek dark mode UI
STT Engine	Google Cloud Speech-to-Text V2	Chirp 3 (USM) model
AI Engine	Gemini 3 Flash	Text cleanup and formatting
Backend	Firebase Cloud Functions (v2)	Serverless AI pipeline
Storage	Firebase Storage	Temporary audio file hosting
Audio Format	Raw PCM (16kHz, Mono, 16-bit)	Optimal for speech recognition
DI	Koin	Dependency injection
Async	Kotlin Coroutines + StateFlow	Reactive state management
IDE	Android Studio + Antigravity	Development + AI assistance

📁 Project Structure

VoDrop/
├── composeApp/
│   └── src/
│       ├── commonMain/kotlin/com/liftley/vodrop/     # Shared KMP code
│       │   ├── data/
│       │   │   ├── audio/
│       │   │   │   └── AudioConfig.kt                # Audio format config + recorder interface
│       │   │   └── cloud/
│       │   │       └── CloudTranscriptionService.kt   # Cloud service interface (expect/actual)
│       │   ├── domain/
│       │   │   ├── manager/
│       │   │   │   └── RecordingSessionManager.kt     # 🔴 SSOT — central state machine
│       │   │   ├── model/
│       │   │   │   ├── AppState.kt                    # Sealed interface (Ready/Recording/Processing/Success/Error)
│       │   │   │   └── Transcription.kt               # History data model
│       │   │   ├── repository/
│       │   │   │   └── TranscriptionRepository.kt     # History persistence contract
│       │   │   └── usecase/
│       │   │       └── TranscribeAudioUseCase.kt       # Pipeline orchestrator
│       │   ├── ui/
│       │   │   ├── components/                         # Stateless Compose components
│       │   │   │   ├── recording/                      # RecordButton, RecordingCard
│       │   │   │   ├── history/                        # HistoryCard, EmptyState
│       │   │   │   └── mode/                           # TranscriptionModeSheet
│       │   │   ├── main/
│       │   │   │   ├── MainScreen.kt                   # Root composable
│       │   │   │   ├── MainViewModel.kt                # UI state management
│       │   │   │   └── MainUiState.kt                  # UI-only state (dialogs, sheets)
│       │   │   └── theme/                              # Material 3 theming
│       │   └── di/                                     # Koin dependency injection modules
│       │
│       ├── androidMain/kotlin/com/liftley/vodrop/     # Android-specific
│       │   ├── data/
│       │   │   ├── audio/AndroidAudioRecorder.kt       # AudioRecord API implementation
│       │   │   └── cloud/CloudTranscriptionService.android.kt  # Firebase SDK calls
│       │   ├── service/
│       │   │   ├── RecordingService.kt                 # Foreground Service
│       │   │   ├── RecordingCommandReceiver.kt         # Notification action handler
│       │   │   └── ServiceController.android.kt        # Service lifecycle management
│       │   └── di/                                     # Android-specific DI
│       │
│       └── desktopMain/                                # Desktop target (logic ready)
│
├── functions/                                          # Firebase Cloud Functions
│   └── src/
│       └── index.ts                                    # transcribeChirp() + cleanupText()
│
└── gradle/
    └── libs.versions.toml                              # Version catalog

🚀 Getting Started

Prerequisites

Android Studio Ladybug or later
Firebase Project on Blaze Plan (required for Cloud Functions)
Google Cloud Project with Speech-to-Text V2 API enabled
Chirp 3 recognizer created in your GCP project (setup guide)

Setup

Clone the repository

git clone https://github.com/CodePandaaAI/VoDrop.git
cd VoDrop

Configure Firebase
- Create a Firebase project and add an Android app
- Download google-services.json and place it in composeApp/
- Set up your Gemini API key as a Firebase secret:
```
firebase functions:secrets:set GEMINI_API_KEY
```

Deploy Cloud Functions

cd functions
npm install
firebase deploy --only functions

Run the App
```
./gradlew :composeApp:installDebug
```

⚡ Performance Optimizations

Optimization	Impact
Adaptive sync/batch routing (≤55s sync, >55s batch)	2-3x faster transcription for typical recordings
Inline batch response (no GCS output write)	Eliminates a full storage round-trip
Two-level short-text filtering (<10 chars at Cloud Function, <20 chars at UseCase)	Prevents wasted Gemini API calls
Immediate audio cleanup after transcription	Reduces Firebase Storage costs
SupervisorJob scope for transcription	One failed job doesn't crash unrelated operations

🔮 Roadmap

Hackathon MVP: Recording, Chirp 3 transcription, Gemini 3 Flash AI Polish
Style Selection: "Formal", "Casual", or "Bullet Points" modes (dynamic Gemini prompts)
iOS Support: Expand KMP codebase to iOS via iosMain source set
Desktop Support: Enable full desktop target (shared logic already works)
Quick Settings Tile: Android Quick Tile for instant recording from anywhere

🏆 Built For

Built with ❤️ for the Google Gemini 3 Hackathon on Devpost

Navigation Menu

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README.md

README.md

🎙️ VoDrop

Drop Your Voice, Get Perfect Text

📱 Screenshots

🎯 The Problem

💡 The Solution

✨ Two Modes, One Purpose

📝 Standard Mode

🤖 AI Polish Mode

What AI Polish does:

What it does NOT do:

🧩 Use Cases

🏗️ Architecture Deep Dive

System Overview

State Machine

Layered Architecture

☁️ Cloud Pipeline: How Transcription Works

Audio Processing Strategy

Gemini 3 Flash Integration

Security Model

📱 Android-Specific Features

Foreground Service Architecture

Notification Controls

🔧 Tech Stack

📁 Project Structure

🚀 Getting Started

Prerequisites

Setup

⚡ Performance Optimizations

🔮 Roadmap

🏆 Built For

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README.md

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README.md

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🎙️ VoDrop

Drop Your Voice, Get Perfect Text

📱 Screenshots

🎯 The Problem

💡 The Solution

✨ Two Modes, One Purpose

📝 Standard Mode

🤖 AI Polish Mode

What AI Polish does:

What it does NOT do:

🧩 Use Cases

🏗️ Architecture Deep Dive

System Overview

State Machine

Layered Architecture

☁️ Cloud Pipeline: How Transcription Works

Audio Processing Strategy

Gemini 3 Flash Integration

Security Model

📱 Android-Specific Features

Foreground Service Architecture

Notification Controls

🔧 Tech Stack

📁 Project Structure

🚀 Getting Started

Prerequisites

Setup

⚡ Performance Optimizations

🔮 Roadmap

🏆 Built For