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:
| #!/usr/bin/env python3 | |
| """Generate MusicGen large tracks to fill up to 2000.""" | |
| import json, os, torch | |
| import numpy as np | |
| from pathlib import Path | |
| from uuid import uuid4 | |
| from scipy.io import wavfile | |
| from tqdm import tqdm | |
| from transformers import AutoProcessor, MusicgenForConditionalGeneration | |
| OUT_DIR = Path("/ssd_data/dataset/haim_dataset/fake/musicgen/large") | |
| TARGET = 2000 | |
| PROMPTS = [ | |
| "epic orchestral film score", "lo-fi hip hop beats", "jazz piano trio", | |
| "heavy metal guitar riff", "ambient electronic soundscape", "acoustic folk ballad", | |
| "funk bass groove", "classical string quartet", "reggae dub", "edm festival drop", | |
| "bossa nova guitar", "cinematic trailer music", "blues harmonica solo", | |
| "k-pop dance track", "country western guitar", "synthwave retro", | |
| "celtic folk melody", "arabic oud music", "drum and bass", "chillwave dreampop", | |
| "gospel choir", "psychedelic rock", "minimal techno", "flamenco guitar", | |
| "bollywood dance music", "afrobeat percussion", "latin salsa", "grunge rock", | |
| "neo soul r&b", "baroque harpsichord", "trap beat with 808s", | |
| "new age meditation music", "punk rock energy", "smooth jazz saxophone", | |
| "progressive rock epic with time signature changes", | |
| "chiptune 8-bit video game music", "world music fusion", | |
| "deep house with warm pads", "indie rock with jangly guitars", | |
| "hip hop boom bap beat", | |
| ] | |
| def main(): | |
| # Count existing | |
| existing = len(list(OUT_DIR.glob("*.wav"))) + len(list(OUT_DIR.glob("*.mp3"))) | |
| remaining = TARGET - existing | |
| if remaining <= 0: | |
| print(f"Already at {existing}/{TARGET}") | |
| return | |
| print(f"Existing: {existing}, generating {remaining} more") | |
| # Load model with CPU offload to save GPU memory | |
| processor = AutoProcessor.from_pretrained("facebook/musicgen-large") | |
| model = MusicgenForConditionalGeneration.from_pretrained( | |
| "facebook/musicgen-large", | |
| torch_dtype=torch.float16, | |
| ).to("cuda") | |
| device = "cuda" | |
| sr = int(model.config.audio_encoder.sampling_rate) | |
| # Load existing metadata | |
| meta_path = OUT_DIR / "metadata.jsonl" | |
| existing_meta = [] | |
| if meta_path.exists(): | |
| with open(meta_path) as f: | |
| existing_meta = [json.loads(l) for l in f if l.strip()] | |
| added = 0 | |
| with open(meta_path, "a", encoding="utf-8") as meta_f: | |
| for i in tqdm(range(remaining), desc="MusicGen-large"): | |
| prompt = PROMPTS[(existing + i) % len(PROMPTS)] | |
| try: | |
| inputs = processor(text=[prompt], padding=True, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| audio = model.generate(**inputs, max_new_tokens=500) | |
| wav = audio[0, 0].cpu().float().numpy() | |
| del audio, inputs | |
| torch.cuda.empty_cache() | |
| # Save | |
| tid = str(uuid4()) | |
| fname = f"{tid}.wav" | |
| out_path = OUT_DIR / fname | |
| wav_int16 = np.int16(np.clip(wav, -1.0, 1.0) * 32767) | |
| wavfile.write(str(out_path), sr, wav_int16) | |
| meta = { | |
| "track_id": tid, | |
| "filename": fname, | |
| "category": "A_opensource", | |
| "subcategory": "musicgen_large", | |
| "source_platform": "musicgen", | |
| "model_name": "MusicGen", | |
| "model_version": "large", | |
| "duration_sec": len(wav) / sr, | |
| "sample_rate": sr, | |
| "prompt": prompt, | |
| "collection_method": "generate", | |
| } | |
| meta_f.write(json.dumps(meta, ensure_ascii=False) + "\n") | |
| meta_f.flush() | |
| added += 1 | |
| except Exception as e: | |
| import traceback | |
| print(f"Error: {e}") | |
| traceback.print_exc() | |
| torch.cuda.empty_cache() | |
| continue | |
| print(f"Done: added {added}, total {existing + added}/{TARGET}") | |
| if __name__ == "__main__": | |
| main() | |