VibeLine

VibeLine is a powerful voice memo processing system that uses AI to extract meaningful content from your voice recordings. It uses a flexible plugin system to generate various types of content like summaries, blog posts, app ideas, and action items.

Features

• 🎙️ Automatic voice memo transcription
• 🔌 Flexible plugin system for content extraction
• 🤖 AI-powered content generation using Ollama
• 📝 Built-in plugins for:
  • Summaries
  • Blog posts
  • App ideas
  • Action items/TODOs
• 🎯 Smart plugin matching based on transcript content
• 📁 Organized output directory structure

Installation

1. Clone the repository:

git clone https://github.com/yourusername/vibeline.git
cd vibeline

2. Create and activate a virtual environment:

python -m venv vibenv
source vibenv/bin/activate  # On Windows: vibenv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Copy the example environment file and edit it with your settings:

cp .env.example .env

5. Make sure you have Ollama installed and running.

Usage

Basic Usage

1. Place your voice memo (.m4a file) in the VoiceMemos directory
2. Process the voice memo:

./process.sh VoiceMemos/your_memo.m4a

This will:

1. Transcribe the audio
2. Extract content based on active plugins
3. Save outputs in organized directories

Directory Structure

VoiceMemos/
├── transcripts/     # Voice memo transcripts
├── summaries/       # Generated summaries
├── blog_posts/      # Generated blog posts
├── app_ideas/      # Generated app ideas
└── action_items/  # Generated action items

Plugin System

VibeLine uses a flexible YAML-based plugin system for content extraction. Each plugin is defined in a YAML file with the following structure:

name: plugin_name
description: What the plugin does
model: llama2  # Optional, falls back to ENV default
type: or       # Comparison type: 'and' or 'or'
run: matching  # When to run: 'always' or 'matching'
output_extension: .txt  # Optional, defaults to .txt
prompt: |
  Your prompt template here.
  Use {transcript} for the transcript content.
  Use {summary} for the summary content.

Plugin Types

• Run Types:

  • always: Plugin runs for every transcript
  • matching: Plugin runs only when keywords match

• Comparison Types:

  • or: Runs if any keyword matches
  • and: Runs only if all keywords match

Creating a New Plugin

1. Create a new YAML file in the plugins directory
2. Define the plugin configuration
3. Add your prompt template
4. The plugin will be automatically loaded on the next run

Example Plugin

name: blog_post
description: Generate draft blog posts from transcripts
model: llama2
type: or
run: matching
output_extension: .md
prompt: |
  Based on the following transcript, create a blog post...
  
  Transcript:
  {transcript}

Environment Variables

• OLLAMA_EXTRACT_MODEL: Default model for content extraction
• OLLAMA_SUMMARIZE_MODEL: Default model for summarization
• VOICE_MEMOS_DIR: Directory for voice memos (default: "VoiceMemos")

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.