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acestep

AI music generation with ACE-Step 1.5 — background music, vocal tracks, covers, stem extraction for video production. Use when generating music, soundtracks, jingles, or working with audio stems. Triggers include background music, soundtrack, jingle, music generation, stem extraction, cover, style transfer, or musical composition tasks.

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karanmrn/karanagentskills星标 3(GitHub 3 + cccskills 0)
集成工具ElevenLabs

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  • SKILL.md9.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

# 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)

python 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.

# 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

python 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 --seed to lock randomness when iterating on prompt/lyrics

Scene Presets

PresetBPMKeyUse Case
corporate-bg110C MajorProfessional background, presentations
upbeat-tech128G MajorProduct launches, tech demos
ambient72D MajorOverview slides, reflective content
dramatic90D MinorReveals, announcements
tension85A MinorProblem statements, challenges
hopeful120C MajorSolution reveals, resolutions
cta135E MajorCall to action, closing energy
lofi85F MajorScreen 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

  1. Specificity over vagueness — describe instruments, mood, production style
  2. Avoid contradictions — don't request "classical strings" and "hardcore metal" simultaneously
  3. Repetition reinforces priority — repeat important elements for emphasis
  4. Sparse captions = more creative freedom — detailed captions constrain the model
  5. 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

ScenePresetDurationNotes
Titledramatic or ambient3-5sShort, mood-setting
Problemtension10-15sDark, unsettling
Solutionhopeful10-15sRelief, optimism
Demolofi or corporate-bg30-120sNon-distracting, matches demo length
Statsupbeat-tech8-12sBuilding credibility
CTActa5-10sMaximum energy, punchy
Creditsambient5-10sGentle fade-out

Timing Workflow

  1. Plan scene durations first (from voiceover script)
  2. Generate music to match: --duration <scene_seconds>
  3. Music duration is precise (within 0.1s of requested)
  4. For background music spanning multiple scenes: generate one long track

Combining with Voiceover

Background music should be mixed at 10-20% volume in Remotion:

<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

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