audio-production

Audio Production & Mastering Skill

A complete pipeline for transcribing, editing, mastering, and bundling spoken audio for Substack podcasts, YouTube replays, community calls, and dev logs.

When to Use

• Transcribing audio files (.mp3, .wav, .m4a, .webm) into clean Markdown with timestamps and speaker labels.
• Analyzing an unedited recording for dead air, mic feedback, sensitive disclosures, or rambling.
• Performing surgical cuts with smooth crossfades (acrossfade) so transitions sound completely natural.
• Mastering audio to broadcast/podcast standard (-16 LUFS, 80Hz highpass filter, adaptive noise suppression).
• Packaging deliverables: Always consolidate the cleaned audio and structured markdown transcript into a single unified directory (e.g. clean_session/ or dist/audio/).

🎙️ Transcription Engines

1. Cloud STT: OpenRouter microsoft/mai-transcribe-2

• Cost/Speed: ~$0.10/hr (half the price of Whisper Large v3 Turbo), 10x faster on long-form audio.
• Key Location: ~/projects/openrouter/.env_openrouter or $OPENROUTER_API_KEY.
• Endpoint: https://openrouter.ai/api/v1/audio/transcriptions
• 25MB request cap, handled automatically. OpenRouter's own docs cap a single transcription request at 25MB. A single transcribe_openrouter() call over that size will fail outright -- use transcribe_long_openrouter() for anything that might be long, which splits by measured bitrate (not a fixed duration guess, since a 32kbps voice memo and a 320kbps studio recording need very different chunk lengths to land under the same byte cap), transcribes each piece, and stitches the timestamps back onto the original file's timeline. Verified 2026-09-15 against a real 57-minute / 110MB / 257kbps file: 558 segments, zero backward-jumping timestamps across chunk boundaries, last segment's end time matched the file's true duration to five decimal places, transcribed in 64 seconds for about $0.10.

  from audio_pipeline import transcribe_long_openrouter
  result = transcribe_long_openrouter("recording.m4a")  # any length, any size
  # result = {"text": "...", "segments": [{"start": 0.0, "end": 7.56, "text": "..."}, ...]}
  

  Or from the CLI: python3 audio_pipeline.py -i recording.m4a --transcribe out.json. Only reach for the unwrapped transcribe_openrouter() directly when the input is already known to be under 15MB (its internal safety margin below the 25MB cap).