| name | workflow-podcast-pipeline |
| description | Raw podcast recordings → polished, loudness-compliant (EBU R128 / Spotify / Apple Podcasts / ACX), caption-accurate (Whisper), chapter-tagged deliverables across MP3 / M4A / video-podcast. Covers multi-mic capture, AI denoise, sidechain ducking, auto-chaptering, multi-language subtitles, ID3/MP4 metadata, and batch release. Use when the user says "publish a podcast episode", "normalize loudness to −16 LUFS", "auto-chapter", "podcast subtitles", "batch process episodes", "interview diarization", or anything podcast-production-related. |
| argument-hint | ["episode"] |
Workflow — Podcast Pipeline
What: Take a raw multi-mic recording and ship a broadcast-clean, caption-complete, chapter-tagged, loudness-compliant episode to every podcast platform.
Skills used
media-whisper, media-demucs, media-denoise-ai, media-subtitle-sync, media-sox, ffmpeg-audio-filter, ffmpeg-audio-fx, ffmpeg-audio-spatial, media-ffmpeg-normalize, ffmpeg-detect, ffmpeg-metadata, ffmpeg-subtitles, ffmpeg-captions, media-batch, media-cloud-upload, media-imagemagick.
Pipeline
Step 1 — Capture + concat multi-source
Join separate mic files into aligned tracks via amix / amerge. Auto time-align against a clap or common audio marker.
Step 2 — AI denoise per stem BEFORE mixing
media-denoise-ai:
- DeepFilterNet per mic (general use, 48 kHz mono).
- RNNoise for steady-state hum.
- Resemble Enhance for speech clarity.
Step 3 — Classical audio polish
SoX or ffmpeg-audio-filter: highpass=f=80, parametric EQ boost at ~3 kHz, compand for consistent level.
Step 4 — Music bed + ducking
Sidechain compression (sidechaincompress) ducks music under voice. Order matters: key input (voice) FIRST, compressed signal (music) SECOND.
Step 5 — Transcription
media-whisper:
large-v3 (3 GB) for best quality.base.en (140 MB) CPU-friendly; gap to medium (1.5 GB) is small on clean studio audio.
Use --word_timestamps True (faster-whisper) or --max-len 1 --split-on-word (whisper.cpp) for word-accurate subs.
Step 6 — Auto-sync drifted subs (optional)
media-subtitle-sync: alass first, ffsubsync as fallback.
Step 7 — Auto-chapter
- Silence-based —
ffmpeg-detect silencedetect at -35 dB min-duration 5 s.
- Diarization-based — pyannote.audio or simple-diarizer (external to Whisper).
Step 8 — Loudness normalize
media-ffmpeg-normalize EBU R128 two-pass:
| Target | LUFS |
|---|
| Spotify Master | −14 |
| Apple Podcasts | −16 |
| ACX audiobooks | −19 |
| General podcast | −16 to −19 |
Step 9 — Package deliverables
- MP3 —
id3v2_version 3, UTF-16 text, cover 1400–3000 px square ≤ 500 KB.
- M4A / AAC — Apple Podcasts. Chapters via
@chpl atom (ffmpeg writes automatically).
- Video podcast —
showwaves static waveform or pre-mix video track.
Step 10 — Batch + publish
media-batch (GNU parallel) for N-episode throughput. media-cloud-upload to host + YouTube.
Variants
- Interview podcast — pyannote diarization, SRT with speaker labels.
- Music podcast / DJ set —
media-demucs stem isolation for promo clips.
- Binaural / ASMR —
ffmpeg-audio-spatial sofalizer HRTF, azimuth/elevation per track.
- Video podcast with scene detection — multi-camera auto-cut at detected scenes.
- Transcript → blog post — Whisper + external LLM formatting.
- Multi-language release — transcribe original, translate externally, mux multiple sub tracks with language metadata.
- AI-generated intro — TTS branded intro + Riffusion bed + mix. See
workflow-ai-generation.
Gotchas
- Whisper wants 16 kHz mono — resamples internally, but explicit
-ar 16000 -ac 1 is cleaner.
- Demucs wants 44.1 or 48 kHz STEREO. Mono input → degraded stems.
- DeepFilterNet: 16 kHz or 48 kHz MONO only. Mix inputs at 48 kHz mono.
- RNNoise: 48 kHz mono 16-bit, strict. Resample first or it fails silently.
loudnorm -target -14 is Spotify Master, NOT podcasts. Podcasts want −16 (Apple) or −19 (ACX).- Single-pass
loudnorm uses guardrails — two-pass measures then applies. For compliance, use media-ffmpeg-normalize (two-pass).
loudnorm applies a limiter — over-loudnormed audio sounds compressed. Tune lra to preserve dynamics.- Sidechain compression order: key input FIRST, compressed signal SECOND. Wrong order = wrong ducking.
- Whisper hallucinates on silence.
silenceremove leading/trailing before transcription.
- Whisper timestamps end-of-chunk by default. Word-level requires
--word_timestamps.
- Whisper
--language auto is fragile on accents. Specify explicitly.
- MP3 ID3v2.4 vs v2.3: Apple Podcasts requires v2.3 + UTF-16 text.
-id3v2_version 3.
- Cover art: 1400–3000 px square, < 500 KB JPEG/PNG. Apple rejects larger.
- MP3 chapter metadata uses CTOC + CHAP frames. ffmpeg writes from
-metadata chapters but older players ignore.
- M4A chapter metadata uses
@chpl atoms — ffmpeg writes automatically.
- RSS enclosures need exact file size + MIME type. Post-publish file change = hash mismatch → listeners re-download.
- Serialize loudness normalization if sharing GPU with other jobs — two parallel
loudnorm = memory contention.
alass fails on already-synced audio (returns input unchanged). --force won't help.ffsubsync pattern-matches audio to existing subtitle anchors. Naive use on very-drifted subs = drift amplification.
Example — 60-minute interview → deliverables
Multi-mic input → DeepFilterNet per mic → amix → EQ + compand → music bed with sidechain duck → Whisper large-v3 word-timestamps → alass subtitle sync → silencedetect auto-chapter → loudnorm −16 LUFS two-pass → MP3 + M4A with chapter atoms + cover art → upload.
Related
workflow-audio-production — deeper audio-chain recipes.workflow-ai-generation — AI intro / voice clone / thumbnails.workflow-analysis-quality — loudness compliance verification.
