I Built an Open Source Audio Processing Ecosystem — From a Pure Go FLAC Encoder to AI Noise Removal

I work with audio a lot — music collections, audiobooks, voice messages, podcast recordings. Every task needed a different tool, a different installation headache, and half of them required FFmpeg as a dependency. So I started building my own tools. One turned into two, two turned into six, and now it's a full ecosystem: audiotools.dev — open source audio tools in Go and Python, plus browser-based utilities that process audio without uploading anything. Here's the technical story behind each one. The Ecosystem The project covers four main areas: Audio conversion — format conversion without FFmpeg (Go) Speech transcription — bulk voice-to-text with Whisper (Python) Music identification — batch Shazam recognition with auto-tagging (Python) Audiobook cleaning — AI noise/music removal with neural networks (Python) Plus two browser-based tools on the website: an audio converter (WASM) and real-time voice-to-text (Web Speech API). go-audio-converter: Why I Wrote a FLAC Encoder in Pure Go Git