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: 5,175 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | #!/usr/bin/env python3
import argparse
from pathlib import Path
from typing import Any
from uuid import uuid4
import numpy as np
import torch
from scipy.io import wavfile
from tqdm import tqdm
from transformers import AutoProcessor, MusicgenForConditionalGeneration
from utils import *
MODEL_MAP = {
"small": "facebook/musicgen-small",
"medium": "facebook/musicgen-medium",
"large": "facebook/musicgen-large",
}
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--variant", choices=["small", "medium", "large"], default="small"
)
parser.add_argument("--target", type=int, default=2000)
parser.add_argument(
"--output-dir",
type=Path,
default=None,
help="Default: FAKE_DIR/A_opensource/musicgen_{variant}",
)
parser.add_argument(
"--batch-size",
type=int,
default=8,
help="Recommended: 8 (small), 4 (medium), 2 (large) on 24GB VRAM",
)
parser.add_argument("--device", choices=["cuda", "cpu"], default="cuda")
return parser.parse_args()
def to_int16(audio: np.ndarray):
audio = np.nan_to_num(audio.astype(np.float32), nan=0.0, posinf=0.0, neginf=0.0)
peak = np.max(np.abs(audio)) if audio.size else 0.0
if peak > 1.0:
audio = audio / peak
return np.int16(np.clip(audio, -1.0, 1.0) * 32767)
def main():
args = parse_args()
device = "cuda" if args.device == "cuda" and torch.cuda.is_available() else "cpu"
output_dir = args.output_dir or (
FAKE_DIR / "A_opensource" / f"musicgen_{args.variant}"
)
output_dir.mkdir(parents=True, exist_ok=True)
if not ensure_disk_space():
raise RuntimeError("Insufficient disk space before generation start.")
model_id = MODEL_MAP[args.variant]
processor: Any = AutoProcessor.from_pretrained(model_id)
model: Any = MusicgenForConditionalGeneration.from_pretrained(model_id)
model.to(device)
sampling_rate = int(model.config.audio_encoder.sampling_rate)
meta_mgr = MetadataManager(output_dir)
existing = meta_mgr.get_count()
if existing >= args.target:
print(f"Target already reached: {existing}/{args.target}")
return
prompts = get_diverse_prompts(args.target)
progress = tqdm(
total=args.target, initial=existing, desc=f"MusicGen-{args.variant}"
)
generated_this_run = 0
for batch_start in range(existing, args.target, max(1, args.batch_size)):
batch_prompts = prompts[
batch_start : min(args.target, batch_start + max(1, args.batch_size))
]
model_inputs = processor(text=batch_prompts, padding=True, return_tensors="pt")
model_inputs = {k: v.to(device) for k, v in model_inputs.items()}
with torch.inference_mode():
generated = model.generate(**model_inputs, max_new_tokens=1503)
wav_batch = torch.as_tensor(generated).detach().cpu().numpy()
for i, prompt in enumerate(batch_prompts):
waveform = wav_batch[i]
if waveform.ndim > 1:
waveform = np.mean(waveform, axis=0)
track_id = str(uuid4())
filename = f"{track_id}.wav"
file_path = output_dir / filename
wavfile.write(file_path, sampling_rate, to_int16(waveform))
info = get_audio_info(file_path)
if (
not info
or info.get("duration_sec", 0.0) <= 0.5
or info.get("file_size_bytes", 0) <= 1024
):
file_path.unlink(missing_ok=True)
continue
md5_hash = compute_md5(file_path)
meta = TrackMetadata(
track_id=track_id,
filename=filename,
category="A_opensource",
subcategory=f"musicgen_{args.variant}",
source_platform="musicgen",
source_type="open-source",
model_name="MusicGen",
model_version=args.variant,
collection_method="generate",
audio_format="wav",
prompt=prompt,
md5_hash=md5_hash,
duration_sec=info.get("duration_sec"),
sample_rate=info.get("sample_rate"),
channels=info.get("channels"),
bitrate_kbps=info.get("bitrate_kbps"),
file_size_bytes=info.get("file_size_bytes"),
)
meta_mgr.add_track(meta)
generated_this_run += 1
progress.update(1)
if meta_mgr.get_count() % 50 == 0:
if not ensure_disk_space():
meta_mgr.update_summary()
raise RuntimeError("Low disk space, stopping generation.")
meta_mgr.update_summary()
if meta_mgr.get_count() >= args.target:
break
if meta_mgr.get_count() >= args.target:
break
meta_mgr.update_summary()
progress.close()
print(
f"Generated {generated_this_run} tracks. Total: {meta_mgr.get_count()}/{args.target}"
)
if __name__ == "__main__":
main()
|