MediaSeg

Product Introduction

1. Definition: MediaSeg is a local macOS desktop utility and command-line tool, developed primarily in Python with a PySide6 (Qt) graphical interface. It belongs to the category of media file processing software, specifically designed as a high-quality file splitter for large video files like MP4 and WEBM.
2. Core Value Proposition: MediaSeg exists to solve the upload-size-limit problem for users of size-restricted platforms, particularly Google NotebookLM. Its core purpose is to split large media files into configurable, upload-ready chunks while preserving the original media quality, leveraging local processing to ensure privacy and control.

Main Features

1. Intelligent Chunk Sizing Algorithm: MediaSeg employs a target-based splitting strategy that uses FFmpeg's stream copy mode (-c copy) to avoid re-encoding. It calculates optimal split points to keep resulting chunks below a user-configurable maximum size (default 200MB), aiming to keep each chunk's size within 90%-98% of the target for efficient uploads. This quality-preserving media splitter ensures no loss in fidelity.
2. Dual Interface (GUI & CLI): The product offers a PySide6 graphical user interface with drag-and-drop support, a collapsible session log, and an output folder selector for intuitive use. For automation and scripting, it provides a Python CLI entry point (mediaseg.py) that accepts file paths and parameters like --max-size, making it suitable for batch processing workflows.
3. Format Conversion & Processing: MediaSeg supports MP4 files natively and WEBM files via conversion. For WEBM inputs, it utilizes macOS VideoToolbox hardware acceleration to convert to MP4 before splitting, a process that is CPU-intensive but quality-focused. The tool performs duration probing via ffprobe and generates sequentially named output files in a timestamped folder.
4. Local-First and Minimal Dependency: The tool operates entirely on the user's local machine, processing local files with FFmpeg/FFprobe as its sole external dependency. This local media processing design ensures no data is uploaded to third-party servers, addressing privacy concerns. Installation is streamlined via a Python virtual environment and Homebrew.

Problems Solved

1. Pain Point: Users frequently encounter file upload size limitations on platforms like NotebookLM, which cannot ingest large video recordings (e.g., long lectures, meetings, training sessions). Traditional methods often involve lossy re-compression or complex manual splitting with command-line FFmpeg, which is time-consuming and risks quality degradation.
2. Target Audience: The primary users are content creators, researchers, educators, and professionals who work with long-form video and audio recordings. Specifically, it targets NotebookLM users, podcasters, online course instructors, and anyone needing to prepare large media files for AI analysis or knowledge management platforms.
3. Use Cases: Essential scenarios include splitting long meeting recordings for archival, chunking lecture videos before uploading to NotebookLM for summarization, preparing tutorial footage for platforms with 100MB/200MB limits, and dividing podcast episodes for distribution on size-restricted services. It is a critical upload prep tool for the AI-powered research workflow.

Unique Advantages

1. Differentiation: Unlike generic video editors or online splitters, MediaSeg is a specialized, lightweight utility built for one purpose: fast, quality-preserving splitting for uploads. It avoids the complexity of full editors like DaVinci Resolve and the privacy risk of web-based tools. Its target-range optimization (90%-98%) produces more predictable and efficient chunks than simple binary splitting.
2. Key Innovation: The core innovation is the integrated, intelligent splitting logic that combines FFmpeg stream copy with a target-based sizing algorithm in an accessible package. The fully AI-assisted development model, which produced a stable product in just two days, also demonstrates a modern, efficient approach to software creation. The use of macOS VideoToolbox for hardware-accelerated conversion is a platform-specific optimization.

Frequently Asked Questions (FAQ)

1. Will splitting my video with MediaSeg reduce the quality? No. MediaSeg prioritizes quality preservation by using FFmpeg's stream copy mode (-c copy) for MP4 files, which re-muxes the streams without re-encoding. For WEBM conversion, it uses high-quality settings via macOS VideoToolbox. The goal is to maintain the original quality within the smaller chunks.
2. What are the system requirements to run MediaSeg? MediaSeg requires an Apple Silicon Mac running macOS 15 Sequoia or later. It needs Python 3.13+, FFmpeg and FFprobe installed (easily via Homebrew: brew install ffmpeg), and the PySide6 Python library for the GUI. The MediaSeg.app can be built via provided scripts.
3. Can MediaSeg handle audio-only files like MP3 or WAV? Currently, MediaSeg supports MP4 and WEBM video files. Audio-only formats (e.g., MP3, WAV, MOV, MKV) are listed as planned future additions. The focus is on video files for tools like NotebookLM, but the core logic is adaptable.
4. How is the output organized, and are the filenames logical? MediaSeg automatically creates an output folder named with the source file and a timestamp (e.g., MeetingVideo_20260614-101523/). Inside, files are sequentially numbered (e.g., MeetingVideo_001.mp4, MeetingVideo_002.mp4) for easy identification and ordering.
5. Is MediaSeg free to use, and how is it distributed? Yes, MediaSeg is an open-source utility hosted on GitHub. You can run it from source using the Python CLI or GUI, or use the provided scripts (build_public.sh, build_private.sh) to package it as a standalone macOS application for personal or internal distribution.