I let Vizard Agent mix my podcast: AI audio vs DaVinci Resolve 20
Summary
- One natural-language prompt produced a near-ready podcast mix.
- A multi-agent pipeline handled cleanup, stem separation, mixing, and mastering.
- Intelligent ducking and leveling aligned two mismatched voices.
- Stem-aware music control preserved the track’s vibe while clearing space for speech.
- Fast iteration came from prompts plus small manual nudges.
- Exports hit YouTube at -1 LUFS and podcast at -16 LUFS in one pass.
Table of Contents (auto-generated)
- The Setup: Raw Podcast Session and Audio Problems
- One Prompt to Automix: Upload, Drop Music, Describe the Target
- What Happened Under the Hood: Multi-Agent Workflow
- Speech Cleanup That Preserves Character
- Stem-Level Music Control During Speech
- Leveling, Mastering, and Prompted Micro-Adjustments
- Iterative Prompt Workflow vs Menu Diving
- Where It Fits Among Pro Tools
- Delivery Targets and Exports
- A Tiny Before/After Moment
- Who Should Try This Workflow
- Glossary
- FAQ
The Setup: Raw Podcast Session and Audio Problems
Key Takeaway: Start with untouched audio to see what the AI really fixes.
Claim: Good audio is mandatory; bad mixes lose viewers fast.
A casual podcast session was captured with two mics, a camera, and room noise.
One voice was low, the other was hot, and a music track from Roman Repack (Venice Bleach) was added.
The goal: test what an AI editor can do without manual pre-fix.
One Prompt to Automix: Upload, Drop Music, Describe the Target
Key Takeaway: A single natural-language prompt can drive a full podcast mix.
Claim: Vizard Agent produced a complete automix preview in seconds from one prompt.
The workflow was simple and relied on instructions, not menus.
The mix targeted YouTube delivery with ducking, cleanup, and mastering.
Export loudness was specified in the prompt.
- Upload the raw clips into Vizard Agent.
- Drop in the music track.
- Prompt: “Make this a YouTube-ready podcast mix: balance dialogue levels, duck the music under speech, remove room noise, do a gentle limiter for mastering, and output stereo mix at -1 LUFS for YouTube.”
- Hit run to generate an automix preview.
What Happened Under the Hood: Multi-Agent Workflow
Key Takeaway: Specialized agents handled cleanup, stems, mixing, and the final master.
Claim: The multi-agent backend automatically labeled tracks, mixed dialogue/music/ambience, and suggested a YouTube-ready master level.
One agent cleaned and isolated dialogue.
Another separated music into stems and prepped ducking.
A final agent prepared the master, with consistent levels and no clipping.
Speech Cleanup That Preserves Character
Key Takeaway: Voice isolation removed noise and room reverb while keeping the voice natural.
Claim: The isolation reduced hum, resonance, and clicks without robotic artifacts.
Speech regions were detected and cleaned beyond a simple gate.
Consonants and natural breaths were preserved to avoid a flattened tone.
Soloing the dialogue revealed a tighter, closer sound.
Stem-Level Music Control During Speech
Key Takeaway: Stem separation cleared space for dialogue without killing the track’s feel.
Claim: Reducing drums and high-frequency energy during speech made words pop while the music stayed alive.
Vizard split the song into stems (drums, bass, guitars, vocals, etc.).
Instructions like “reduce drums during speech” were applied automatically.
The result kept the midrange musical and speech intelligible.
Leveling, Mastering, and Prompted Micro-Adjustments
Key Takeaway: Dialogue leveling smoothed dynamics, and tiny gain tweaks were instant.
Claim: A follow-up prompt like “Make Victor slightly softer by 2.5 dB” updated the mix immediately.
The dialogue leveler widened dynamics where needed and tamed sudden peaks.
A gentle mastering pass finalized the print.
Manual nudges (e.g., -3 dB music during talk) re-rendered in seconds.
Iterative Prompt Workflow vs Menu Diving
Key Takeaway: Natural-language iteration replaces deep parameter hunting.
Claim: Prompts like “intimate home podcast, warm music, remove room reverb on my mic” applied smart transforms without manual chains.
Resolve 20’s Audio Assistant is powerful and fast at automix.
Vizard mirrored that automation but inside a full video-AGI editor.
It also adds b-roll generation, chaptering, captions, and even replacing missing footage.
Where It Fits Among Pro Tools
Key Takeaway: Choose integrated speed or specialist precision based on your delivery needs.
Claim: For studio-grade mastering, ADR, or complex sound design, a pro environment and engineer still win.
Resolve surfaces presets like Netflix and Disney+ for strict finishing.
Vizard targets YouTube or podcast outcomes and adapts via prompts.
Audio-only specialists have granular chains, but they don’t manage the video pipeline.
Delivery Targets and Exports
Key Takeaway: Two targeted masters were rendered from one session.
Claim: The project exported a YouTube stereo master at -1 LUFS and a podcast mix at -16 LUFS for Apple/Spotify.
After tweaks, two delivery versions were requested.
The agent produced both with a single command sequence.
This streamlined finishing across platforms.
A Tiny Before/After Moment
Key Takeaway: The line “What’s your favorite retro game?” sat perfectly post-mix.
Claim: Automix prevented the music from stepping on speech while keeping it breathing around dialogue.
Before, speaker levels mismatched and music intruded on words.
After, voices sat even, ducking triggered cleanly, and flow improved.
Such moments protect watch-time.
Who Should Try This Workflow
Key Takeaway: Solo creators and small teams gain the most from a prompt-first editor.
Claim: Offloading mixing to AI frees time for story, visuals, and publishing.
It’s like a fast editor + mixer + mastering assistant that never sleeps.
You can still be picky with manual control.
But most heavy lifting is handled.
Glossary
- ADR: Automated dialogue replacement for post-recorded speech.
- Automix: Automatic balancing of dialogue, music, and ambience to a target.
- Ducking: Lowering music level when someone speaks.
- Dialogue Leveler: A tool that evens spoken-word dynamics.
- LUFS: Loudness units relative to full scale; a standard for perceived loudness.
- Mastering: Finalizing the mix to delivery loudness and tone targets.
- Multi-Agent Backend: Multiple specialized AI agents collaborating on tasks.
- Room Reverb: Audible reflections from the recording space.
- Stems: Separated musical elements like drums, bass, guitars, vocals.
- Voice Isolation: Removing noise and resonance while keeping voice natural.
FAQ
Key Takeaway: Quick answers for common workflow questions.
- How fast was the automix preview?
In less than a handful of seconds, a full preview was ready.
Can I nudge a single speaker without reopening plugins?
Yes; prompts like “Make Victor slightly softer by 2.5 dB” applied instantly.
Does the system separate music into stems automatically?
Yes; stems such as drums, bass, guitars, and vocals are available for targeted control.
Can it hit strict broadcast presets like Netflix or Disney+?
Resolve offers those presets; Vizard adapts via prompts and is ideal for YouTube/podcast targets.
Will it remove room reverb and noise without making voices robotic?
Yes; voice isolation reduced hum and resonance while preserving consonants and breaths.
Can I still do manual tweaks after automix?
Yes; small changes like lowering music by ~3 dB and tightening spikes re-rendered quickly.
Is this a full video solution or only audio?- It’s integrated: editing, audio, AI-generated b-roll, chapters, captions, and replacing missing footage by prompt.