ACE-Step 1.5 Music Generation
Open-source music generation (MIT license) via tools/music_gen.py. Runs on RunPod serverless.
Requires RUNPOD_API_KEY and RUNPOD_ACESTEP_ENDPOINT_ID in .env (run --setup to create endpoint).
Quick Reference
bash# Basic generation python tools/music_gen.py --prompt "Upbeat tech corporate" --duration 60 --output bg.mp3 # With musical control python tools/music_gen.py --prompt "Calm ambient piano" --duration 30 --bpm 72 --key "D Major" --output ambient.mp3 # Scene presets (video production) python tools/music_gen.py --preset corporate-bg --duration 60 --output bg.mp3 python tools/music_gen.py --preset tension --duration 20 --output problem.mp3 python tools/music_gen.py --preset cta --brand digital-samba --duration 15 --output cta.mp3 # Vocals with lyrics python tools/music_gen.py --prompt "Indie pop jingle" --lyrics "[verse]\nBuild it better\nShip it faster" --duration 30 --output jingle.mp3 # Cover / style transfer python tools/music_gen.py --cover --reference theme.mp3 --prompt "Jazz piano version" --duration 60 --output jazz_cover.mp3 # Stem extraction python tools/music_gen.py --extract vocals --input mixed.mp3 --output vocals.mp3 # List presets python tools/music_gen.py --list-presets
Creating a Song (Step by Step)
1. Instrumental background track (simplest)
bashpython tools/music_gen.py --prompt "Upbeat indie rock, driving drums, jangly guitar" --duration 60 --bpm 120 --key "G Major" --output track.mp3
2. Song with vocals and lyrics
Write lyrics in a temp file or pass inline. Use structure tags to control song sections.
bash# Write lyrics to a file first (recommended for longer songs) cat > /tmp/lyrics.txt << 'LYRICS' [Verse 1] Walking through the morning light Coffee in my hand feels right Another day to build and dream Nothing's ever what it seems [Chorus - anthemic] WE KEEP MOVING FORWARD Through the noise and doubt We keep moving forward That's what it's about [Verse 2] Screens are glowing late at night Shipping code until it's right The deadline's close but so are we Almost there, just wait and see [Chorus - bigger] WE KEEP MOVING FORWARD Through the noise and doubt We keep moving forward That's what it's about [Outro - fade] (Moving forward...) LYRICS # Generate the song python tools/music_gen.py \ --prompt "Upbeat indie rock anthem, male vocal, driving drums, electric guitar, studio polish" \ --lyrics "$(cat /tmp/lyrics.txt)" \ --duration 60 \ --bpm 128 \ --key "G Major" \ --output my_song.mp3
3. Using a preset for video background
bashpython tools/music_gen.py --preset tension --duration 20 --output problem_scene.mp3
Key tips for good results
- Caption = overall style (genre, instruments, mood, production quality)
- Lyrics = temporal structure (verse/chorus flow, vocal delivery)
- UPPERCASE in lyrics = high vocal intensity
- Parentheses = background vocals: "We rise (together)"
- Keep 6-10 syllables per line for natural rhythm
- Don't describe the melody in the caption — describe the sound and feeling
- Use
--seedto lock randomness when iterating on prompt/lyrics
Scene Presets
| Preset | BPM | Key | Use Case |
|---|---|---|---|
corporate-bg | 110 | C Major | Professional background, presentations |
upbeat-tech | 128 | G Major | Product launches, tech demos |
ambient | 72 | D Major | Overview slides, reflective content |
dramatic | 90 | D Minor | Reveals, announcements |
tension | 85 | A Minor | Problem statements, challenges |
hopeful | 120 | C Major | Solution reveals, resolutions |
cta | 135 | E Major | Call to action, closing energy |
lofi | 85 | F Major | Screen recordings, coding demos |
Task Types
text2music (default)
Generate music from text prompt + optional lyrics.
cover
Style transfer from reference audio. Control blend with --cover-strength (0.0-1.0):
- 0.2 — Loose style inspiration (more creative freedom)
- 0.5 — Balanced style transfer
- 0.7 — Close to original structure (default)
- 1.0 — Maximum fidelity to source
extract
Stem separation — isolate individual tracks from mixed audio.
Tracks: vocals, drums, bass, guitar, piano, keyboard, strings, brass, woodwinds, other
repaint (future)
Regenerate a specific time segment within existing audio while preserving the rest.
lego (future, requires base model)
Generate individual instrument tracks within an existing audio context.
complete (future, requires base model)
Extend partial compositions by adding specified instruments.
Prompt Engineering
Caption Writing — Layer Dimensions
Write captions by layering multiple descriptive dimensions rather than single-word descriptions.
Dimensions to include:
- Genre/Style: pop, rock, jazz, electronic, lo-fi, synthwave, orchestral
- Emotion/Mood: melancholic, euphoric, dreamy, nostalgic, intimate, tense
- Instruments: acoustic guitar, synth pads, 808 drums, strings, brass, piano
- Timbre: warm, crisp, airy, punchy, lush, polished, raw
- Era: "80s synth-pop", "modern indie", "classical romantic"
- Production: lo-fi, studio-polished, live recording, cinematic
- Vocal: breathy, powerful, falsetto, raspy, spoken word (or "instrumental")
Good: "Slow melancholic piano ballad with intimate female vocal, warm strings building to powerful chorus, studio-polished production" Bad: "Sad song"
Key Principles
- Specificity over vagueness — describe instruments, mood, production style
- Avoid contradictions — don't request "classical strings" and "hardcore metal" simultaneously
- Repetition reinforces priority — repeat important elements for emphasis
- Sparse captions = more creative freedom — detailed captions constrain the model
- Use metadata params for BPM/key — don't write "120 BPM" in the caption, use
--bpm 120
Lyrics Formatting
Structure tags (use in lyrics, not caption):
[Intro] [Verse] [Chorus] [Bridge] [Outro] [Instrumental] [Guitar Solo] [Build] [Drop] [Breakdown]
Vocal control (prefix lines or sections):
[raspy vocal] [whispered] [falsetto] [powerful belting] [harmonies] [ad-lib]
Energy indicators:
- UPPERCASE = high intensity ("WE RISE ABOVE")
- Parentheses = background vocals ("We rise (together)")
- Keep 6-10 syllables per line within sections for natural rhythm
Example — Tech Product Jingle:
[Verse] Build it better, ship it faster Every feature tells a story [Chorus - anthemic] THIS IS YOUR PLATFORM Your vision, your stage Digital Samba, every page [Outro - fade] (Build it better...)
Video Production Integration
Music for Scene Types
| Scene | Preset | Duration | Notes |
|---|---|---|---|
| Title | dramatic or ambient | 3-5s | Short, mood-setting |
| Problem | tension | 10-15s | Dark, unsettling |
| Solution | hopeful | 10-15s | Relief, optimism |
| Demo | lofi or corporate-bg | 30-120s | Non-distracting, matches demo length |
| Stats | upbeat-tech | 8-12s | Building credibility |
| CTA | cta | 5-10s | Maximum energy, punchy |
| Credits | ambient | 5-10s | Gentle fade-out |
Timing Workflow
- Plan scene durations first (from voiceover script)
- Generate music to match:
--duration <scene_seconds> - Music duration is precise (within 0.1s of requested)
- For background music spanning multiple scenes: generate one long track
Combining with Voiceover
Background music should be mixed at 10-20% volume in Remotion:
tsx<Audio src={staticFile('voiceover.mp3')} volume={1} /> <Audio src={staticFile('bg-music.mp3')} volume={0.15} />
For music under narration: use instrumental presets (corporate-bg, ambient, lofi).
For music-forward scenes (title, CTA): can use higher volume or vocal tracks.
Brand Consistency
Use --brand <name> to load hints from brands/<name>/brand.json.
Use --cover --reference brand_theme.mp3 to create variations of a brand's sonic identity.
For consistent sound across a project: fix the seed (--seed 42) and vary only duration/prompt.
Technical Details
- Output: 48kHz MP3/WAV/FLAC
- Duration range: 10-600 seconds
- BPM range: 30-300
- Inference: ~2-3s on GPU (turbo, 8 steps), ~40-60s on Mac MPS
- Turbo model: 8 steps, no CFG needed, fast and good quality
- Shift parameter: 3.0 recommended for turbo (improves quality)
When NOT to use ACE-Step
- Voice cloning — use Qwen3-TTS or ElevenLabs instead
- Sound effects — use ElevenLabs SFX (
tools/sfx.py) - Speech/narration — use voiceover tools, not music gen
- Stem extraction from video — extract audio first with FFmpeg, then use
--extract
