Datasets:
Tasks:
Audio Classification
Formats:
parquet
Size:
1K - 10K
ArXiv:
Tags:
arxiv:2606.01686
music
ai-generated-music
ai-generated-music-detection
plagiarism-detection
ace-step
License:
File size: 6,465 Bytes
b347b70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #!/usr/bin/env python3
"""Direct ACE-Step generation using repo's AceStepHandler."""
import sys
import os
import shutil
from pathlib import Path
REPO_DIR = Path("/ssd_data/dataset/ai_music_dataset/models/ACE-Step-1.5")
sys.path.insert(0, str(REPO_DIR))
os.chdir(REPO_DIR)
from acestep.handler import AceStepHandler
from tqdm import tqdm
PROMPTS = [
"Upbeat pop song with catchy synth melody and female vocals",
"Smooth jazz trio with piano, upright bass, and brushed drums",
"Classical orchestral piece with sweeping strings and French horns",
"EDM festival banger with heavy bass drops and saw-wave synths",
"Acoustic folk ballad with fingerpicked guitar and harmonica",
"K-pop dance track with layered vocal harmonies and electronic beats",
"Lo-fi hip-hop with vinyl crackle, mellow keys, and boom-bap drums",
"Heavy metal with distorted guitars, double bass drums, and growling vocals",
"R&B slow jam with silky vocals, Rhodes piano, and 808 bass",
"Country song with steel guitar, fiddle, and storytelling lyrics",
"Reggaeton with dembow rhythm, Latin percussion, and autotune vocals",
"Ambient electronic with evolving pads, field recordings, and reverb",
"Funk groove with slap bass, wah guitar, and tight horn section",
"Bossa nova with nylon guitar, soft percussion, and breathy vocals",
"Trap beat with hi-hat rolls, 808 sub bass, and dark melody",
"Indie rock with jangly guitars, driving drums, and earnest vocals",
"Cinematic epic with choir, taiko drums, and orchestral brass",
"Disco with four-on-the-floor kick, funky bass, and string stabs",
"Blues shuffle with overdriven guitar, walking bass, and organ",
"Synthwave with arpeggiated synths, gated reverb drums, and retro pads",
"Latin salsa with congas, timbales, piano montuno, and brass",
"Chill electronic with soft pads, gentle beats, and atmospheric textures",
"Progressive rock with odd time signatures, synth solos, and dynamic changes",
"Gospel choir with powerful vocals, organ, and hand claps",
"Drum and bass with fast breakbeats, deep sub bass, and chopped vocals",
"Afrobeat with polyrhythmic drums, talking drum, and horn riffs",
"New age meditation music with crystal bowls, gentle flute, and nature sounds",
"Punk rock with fast power chords, shouted vocals, and crashing cymbals",
"Swing jazz with walking bass, big band horns, and brush drums",
"Minimal techno with hypnotic loops, subtle variations, and deep kick",
"Grunge with heavy distortion, angsty vocals, and loud-quiet dynamics",
"Tropical house with steel drums, marimba, and laid-back beats",
"Chamber music with string quartet playing romantic era melodies",
"Hip-hop with boom-bap drums, scratching, and conscious rap flow",
"Psychedelic rock with wah pedal, phaser effects, and extended jams",
"Celtic folk with tin whistle, bodhrán drum, and fiddle",
"Future bass with supersaw chords, pitched vocals, and heavy sidechain",
"Musical theater ballad with piano accompaniment and emotional vocals",
"Dubstep with wobble bass, half-time drums, and aggressive synths",
"World fusion with sitar, tabla, and electronic production",
]
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--target", type=int, default=5000)
parser.add_argument("--output-dir", type=str, required=True)
parser.add_argument("--duration", type=float, default=60.0)
args = parser.parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
existing = len(list(output_dir.glob("*.wav")) + list(output_dir.glob("*.mp3")))
if existing >= args.target:
print(f"Already at target: {existing}/{args.target}")
return
import os
# Don't fake VRAM limit — let it use real available memory
handler = AceStepHandler()
checkpoint_dir = REPO_DIR / "checkpoints"
dit_model_path = str(checkpoint_dir / "acestep-v15-turbo")
print(f"Initializing ACE-Step handler (~6GB VRAM mode)...")
print(f" DiT model: {dit_model_path}")
handler.initialize_service(
project_root=str(REPO_DIR),
config_path=dit_model_path,
device="auto",
offload_to_cpu=True,
offload_dit_to_cpu=False,
)
generated = existing
pbar = tqdm(total=args.target, initial=existing, desc="ACE-Step")
while generated < args.target:
prompt = PROMPTS[generated % len(PROMPTS)]
try:
result = handler.generate_music(
captions=prompt,
inference_steps=8,
guidance_scale=7.0,
audio_duration=args.duration,
batch_size=1,
task_type="text2music",
use_random_seed=True,
)
# Result should contain audio files or data
if result is None:
continue
# Check if result has audio data
if hasattr(result, 'audio_paths') and result.audio_paths:
for ap in result.audio_paths:
src = Path(ap)
if src.exists():
dst = output_dir / f"acestep_{generated:05d}{src.suffix}"
shutil.move(str(src), str(dst))
generated += 1
pbar.update(1)
elif hasattr(result, 'audios') and result.audios:
import soundfile as sf
for audio_data in result.audios:
dst = output_dir / f"acestep_{generated:05d}.wav"
if isinstance(audio_data, tuple):
sf.write(str(dst), audio_data[0], audio_data[1])
generated += 1
pbar.update(1)
else:
# Try to find generated files in temp/output dirs
for wav in sorted(REPO_DIR.glob("output*/*.wav"), key=lambda p: p.stat().st_mtime):
dst = output_dir / f"acestep_{generated:05d}.wav"
shutil.move(str(wav), str(dst))
generated += 1
pbar.update(1)
break
except Exception as e:
print(f"Error at {generated}: {e}")
continue
pbar.close()
print(f"Done. Generated {generated} tracks in {output_dir}")
if __name__ == "__main__":
main()
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