Most transcription apps use a two-step process: ASR transcription followed by LLM cleanup. AI Transcription Notepad sends audio directly to multimodal AI models that transcribe and format in a single pass.

The AI hears tone, pauses, and emphasis. Verbal commands like "scratch that" or "new paragraph" work naturally.

• Cost-effective — 848 transcriptions for $1.17 (~1.4¢ per 1,000 words)
• Fast — Single API call with local preprocessing
• Smart cleanup — Removes filler words, adds punctuation, formats output
• Global hotkeys — Record from anywhere, even when minimized
• Flexible output — App window, clipboard, or inject directly at cursor
• Translation — Translate to 30+ languages in the same API call

	Online Documentation Full documentation site with guides, reference, and troubleshooting.
	User Manual v3 (PDF) Complete 27-page guide covering installation, configuration, hotkey setup, and troubleshooting.

1. Download from Releases (AppImage, .deb, or Windows installer)
2. Add your OpenRouter API key (get one here)
3. Press Record, speak naturally, press Transcribe
4. Get clean, formatted text

# Or run from source
git clone https://github.com/danielrosehill/AI-Transcription-Notepad.git
cd AI-Transcription-Notepad && ./run.sh

AI Transcription Notepad combines local preprocessing with cloud transcription for optimal cost and quality.

flowchart LR
    subgraph LOCAL["Local Preprocessing"]
        direction LR
        A[Record<br/>48kHz] --> B[AGC<br/>Normalize]
        B --> C[VAD<br/>Remove Silence]
        C --> D[Compress<br/>16kHz mono]
    end

    subgraph CLOUD["Cloud Transcription"]
        direction LR
        E[Prompt<br/>Concatenation] --> F[Gemini API<br/>Audio + Prompt]
        F --> G[Formatted<br/>Text]
    end

    D --> E

    style LOCAL fill:#e8f5e9,stroke:#4caf50,stroke-width:2px
    style CLOUD fill:#e3f2fd,stroke:#2196f3,stroke-width:2px

AI Transcription Notepad uses a layered prompt architecture where instructions are concatenated at transcription time. This allows flexible, modular control over output formatting.

flowchart TB
    subgraph FOUNDATION["Foundation Layer (Always Applied)"]
        F1[Remove filler words]
        F2[Add punctuation]
        F3[Fix grammar & spelling]
        F4[Honor verbal commands]
        F5[Handle background audio]
    end

    subgraph FORMAT["Format Layer"]
        FMT[Email / Todo / Meeting Notes<br/>Blog / Documentation / AI Prompt]
    end

    subgraph STYLE["Style Layer"]
        S1[Formality<br/>Casual → Professional]
        S2[Verbosity<br/>None → Maximum reduction]
    end

    subgraph PERSONAL["Personalization"]
        P1[Email signatures]
        P2[User name]
    end

    FOUNDATION --> FORMAT
    FORMAT --> STYLE
    STYLE --> PERSONAL
    PERSONAL --> OUTPUT[Final Prompt]

    style FOUNDATION fill:#fff3e0,stroke:#ff9800,stroke-width:2px
    style FORMAT fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
    style STYLE fill:#e8f5e9,stroke:#4caf50,stroke-width:2px
    style PERSONAL fill:#e3f2fd,stroke:#2196f3,stroke-width:2px

Prompt Stacks let you save and combine multiple prompt layers for recurring workflows:

Create custom stacks in the Prompt Stacks tab, then apply them with a single click.

OpenRouter is the sole provider. It offers per-key cost tracking, low latency, and access to Gemini 3 models via an OpenAI-compatible API.

See Technology Stack for details.

Real usage from ~2,000 transcriptions shows excellent performance with OpenRouter's Gemini models:

Anonymized usage data available in data/.

This software was developed through AI-human collaboration. Code was generated by Claude Opus 4.5 under my direction—I designed the architecture and specified requirements while Claude wrote the implementation.

• Audio-Multimodal-AI-Resources — Curated list of audio-capable multimodal models
• Audio-Understanding-Test-Prompts — Test prompts for evaluating audio understanding

MIT