audio-extraction

You can extract audio from video sources using yt-dlp to fetch the best-quality audio stream and ffmpeg to transcode it into MP3 format. This workflow preserves original audio quality while embedding ID3 metadata and cover art.

Loudness normalization adjusts the perceived volume across multiple audio files so everything sounds consistent. It analyzes and applies gain changes during the audio extraction process, ensuring uniform loudness throughout your personal music or podcast library.

Yes, batch processing allows you to convert multiple video files at once. You can split audio tracks by chapters, trim specific segments, and merge tracks together using ffmpeg-based transcoding pipelines without manual intervention.

Audio extraction supports podcast RSS retrieval, allowing you to automatically download and convert episodes from podcast feeds. The workflow integrates yt-dlp and ffmpeg to transcode episodes and apply consistent ID3 tagging for organized library management.

To produce speech-ready audio for transcription, extract the audio track from your video source and transcode it to mono 16kHz WAV format. This specific format is optimized for speech-to-text workflows and ensures accurate transcription results.

The best way to add ID3 metadata is by embedding thumbnails, generating artist/album/title tags, and applying consistent naming conventions during the audio extraction process. This improves searchability and organization within your personal library.