mknotes/enhancement_proposals.md

mknotes Improvement Recommendations

This document outlines proposed improvements for the mknotes software, grouped by category.

Feature Enhancements

• Support for More Audio Formats ✅ (Partially implemented - WAV support added)

  • ✅ Added support for WAV files with automatic conversion to MP3 before processing
  • Extend support to include additional formats like FLAC, OGG, etc.
  • ✅ Updated the find_audio_files function in utils.py to recognize WAV extension

• Customizable Enhancement Prompts ✅ (Implemented)

  • ✅ Added CLI argument to select different prompt types (lecture, meeting, interview)
  • ✅ Moved prompts to separate files for easier customization
  • ✅ Created specialized prompts for different use cases (lectures, meeting minutes, interviews)
  • Allow users to provide their own custom prompts via configuration files.

• Batch Processing Controls

  • Add the ability to limit the number of files processed in one run.
  • Implement resume functionality for interrupted batch processing.

• Output Format Options

  • Support multiple output formats beyond Markdown (e.g., HTML, PDF, DOCX).
  • Add options for customizing Markdown styling.

• Caching Mechanism

  • Implement caching for OpenAI API calls to reduce costs and improve performance.
  • Store intermediate results to avoid reprocessing if enhancement fails.

Technical Improvements

• Robust Error Handling and Logging

  • Implement a proper logging system instead of print statements.
  • Add comprehensive error handling with appropriate recovery strategies.
  • Example: Add retry logic for API calls with exponential backoff.

• Configuration Management

  • Create a configuration system using YAML/JSON files.
  • Allow users to set default values for all parameters.
  • Support environment-specific configurations.

• API Key Management

  • Implement a more secure way to handle API keys.
  • Add support for API key rotation.

• Performance Optimization

  • Implement parallel processing for transcription of multiple files.
  • Add an option to use local models for offline processing.

• Testing Framework

  • Add unit tests for core functionality.
  • Implement integration tests for the complete workflow.

Code Structure Improvements

• Separation of Concerns ✅ (Partially implemented)

  • ✅ Moved prompts to separate files in a dedicated prompts directory.
  • Create a more abstract API client layer.

• Progress Tracking and Reporting

  • Enhance progress reporting beyond simple tqdm bars.
  • Add detailed statistics about processing time, token usage, etc.

• Plugin Architecture

  • Implement a plugin system to allow for custom transcription or enhancement modules.
  • Make it easier to switch between different AI models or services.

User Experience Enhancements

• Interactive Mode

  • Add an interactive mode where users can preview and edit enhanced notes before saving.
  • Implement a simple TUI (Text User Interface) for a better CLI experience.

• Web Interface

  • Create a simple web interface for users who prefer GUI over CLI.
  • Consider a lightweight Flask/FastAPI app that wraps the core functionality.

• Notification System

  • Add notifications for long-running processes (email, desktop notifications).
  • Implement a webhook system for integration with other tools.

Documentation Improvements

• Enhanced Documentation

  • Create comprehensive documentation with examples and use cases.
  • Add a troubleshooting guide for common issues.

• Sample Configurations

  • Provide sample configuration files for different use cases.
  • Include examples of custom prompts for different types of content.

Error Recovery and Resilience

• Robust Error Recovery

  • Implement proper error recovery mechanisms to prevent data loss
  • Save transcriptions before enhancement to avoid losing work if API fails
  • Add retry mechanism with exponential backoff for failed API calls
  • Handle partial failures gracefully in batch processing

• Process Persistence

  • Add checkpoint/resume functionality for interrupted batch processing
  • Save processing state to allow continuation from last successful file
  • Implement transaction-like processing (all-or-nothing for each file)

Memory and Performance Optimization

• Large File Handling

  • Implement streaming support for large audio files
  • Add chunked processing to avoid loading entire files into memory
  • Optimize memory usage for batch processing

• Parallel Processing

  • Implement concurrent transcription of multiple files
  • Add configurable worker threads/processes
  • Optimize API calls with batching where possible

Security Improvements

• API Key Security
  • Validate API key before starting batch processing
  • Implement secure storage for API keys (keyring integration)
  • Remove unnecessary API key parameter passing
  • Add support for multiple API keys with rotation

Advanced User Experience

• Preview and Planning

  • Add dry-run mode to preview what will be processed
  • Show estimated costs before processing
  • Implement interactive file selection mode

• Processing Statistics

  • Display comprehensive statistics after processing (tokens used, cost, time)
  • Add detailed progress reporting with ETA
  • Generate processing summary reports

• Flexible Processing Options

  • Add option to skip transcription and only enhance existing text files
  • Support for processing specific file patterns or date ranges
  • Implement file filtering based on duration or size

Code Quality Improvements

• Type Safety

  • Add type hints throughout the codebase
  • Implement proper data validation
  • Use dataclasses for configuration objects

• Dependency Management

  • Remove standard library modules from requirements.txt (argparse)
  • Pin dependency versions for reproducibility
  • Add optional dependencies for advanced features

• Configuration

  • Remove hardcoded values (e.g., "gpt-4.1" model name)
  • Make all parameters configurable
  • Support multiple configuration profiles

Extended Format Support

• Video File Support

  • Extract audio from video files (MP4, AVI, MOV, etc.)
  • Preserve video metadata in output
  • Option to generate subtitles

• Advanced Audio Features

  • Language detection and specification for transcription
  • Speaker diarization support
  • Audio preprocessing options (noise reduction, normalization)
  • Support for merging multiple audio files before processing

• Offline Capabilities

  • Support for custom Whisper model paths
  • Local LLM integration for enhancement
  • Fully offline mode with local models

Output Management

• Organization Features

  • Organize output by date, source, or custom categories
  • Preserve original file metadata
  • Generate index/summary files for processed batches
  • Support for incremental updates to existing notes

• Export Options

  • Export to multiple formats simultaneously
  • Custom templates for different output formats
  • Metadata embedding in output files

Integration Capabilities

• Cloud Storage

  • Direct integration with S3, Google Drive, Dropbox
  • Automatic backup of processed files
  • Cloud-based processing queue

• Note-Taking Apps

  • Direct export to Notion, Obsidian, Roam Research
  • Sync with existing note structures
  • Tag and categorization support

• Automation

  • Webhook notifications for processing completion
  • Custom post-processing script support
  • Integration with workflow automation tools (Zapier, IFTTT)
  • Watch folder functionality for automatic processing

• API and SDK

  • RESTful API for remote processing
  • Python SDK for programmatic access
  • Batch job scheduling support

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These recommendations are intended to guide future development and prioritization for mknotes. Each suggestion can be implemented independently or as part of a broader roadmap.