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: 5,579 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 | #!/usr/bin/env python3
"""FX-Encoder B2: Style transfer ACE-Step tracks with random MTG reference."""
import os, sys, json, random, time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent / "FXEncoder" / "mixing_style_transfer"))
from networks.architectures import FXencoder, TCNModel
import torch
import torchaudio
import soundfile as sf
import numpy as np
from collections import OrderedDict
ACESTEP_DIR = Path("/ssd_data/dataset/haim_dataset/fake/acestep/samples")
MTG_DIR = Path("/ssd_data/dataset/haim_dataset/real/MTG")
OUT_DIR = Path("/ssd_data/dataset/haim_dataset/B_hybrid/B2_human_mastered_ai_dsp")
WEIGHTS = Path("/ssd_data/dataset/haim_dataset/FXEncoder/weights")
TARGET = 6000
SR = 44100
SEG_LEN = SR * 10
CFG_ENC = {
"channels": [16,32,64,128,256,256,512,512,1024,1024,2048,2048],
"kernels": [25,25,15,15,10,10,10,10,5,5,5,5],
"strides": [4,4,2,2,2,2,2,2,2,2,1,1],
"dilation": [1,1,1,1,1,1,1,1,1,1,1,1],
"bias": True, "norm": "batch", "conv_block": "res", "activation": "relu",
}
CFG_CONV = {
"condition_dimension": 2048, "nblocks": 14, "dilation_growth": 2,
"kernel_size": 15, "channel_width": 128, "stack_size": 15, "causal": False,
}
def load_audio(path):
wav, sr = torchaudio.load(str(path))
if wav.shape[0] == 1: wav = wav.repeat(2, 1)
elif wav.shape[0] > 2: wav = wav[:2, :]
if sr != SR: wav = torchaudio.functional.resample(wav, sr, SR)
return wav
def main():
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load models
enc = FXencoder(CFG_ENC).to(device)
conv = TCNModel(nparams=2048, ninputs=2, noutputs=2,
nblocks=14, dilation_growth=2, kernel_size=15,
channel_width=128, stack_size=15, cond_dim=2048, causal=False).to(device)
for name, model, path in [("enc", enc, WEIGHTS/"FXencoder.pt"), ("conv", conv, WEIGHTS/"MixFXcloner.pt")]:
ckpt = torch.load(str(path), map_location=device)
state = OrderedDict()
for k, v in ckpt["model"].items():
state[k[7:] if k.startswith("module.") else k] = v
model.load_state_dict(state)
model.eval()
print("Models loaded")
# Get file lists
ai_files = sorted(ACESTEP_DIR.glob("*.mp3"))
ref_files = sorted(MTG_DIR.glob("*.mp3"))
random.seed(int(time.time()))
OUT_DIR.mkdir(parents=True, exist_ok=True)
meta_path = OUT_DIR / "metadata.jsonl"
existing = len(list(OUT_DIR.glob("*.wav")))
if existing >= TARGET:
print(f"Already at {existing}/{TARGET}")
return
print(f"Existing: {existing}, processing {TARGET - existing} more")
meta_f = open(meta_path, "a", encoding="utf-8")
done = existing
t0 = time.time()
with torch.no_grad():
for i, ai_path in enumerate(ai_files):
if done >= TARGET:
break
fname = f"B2_fx_{ai_path.stem}.wav"
out_path = OUT_DIR / fname
if out_path.exists():
done += 1
continue
for attempt in range(3):
try:
ref_path = random.choice(ref_files)
inp = load_audio(ai_path)
ref = load_audio(ref_path)
# Extract FX embedding from reference
ref_gpu = ref.unsqueeze(0).to(device)
embs = []
for s in range(0, ref_gpu.shape[2], SEG_LEN):
seg = ref_gpu[:, :, s:s+SEG_LEN]
if seg.shape[2] < SEG_LEN:
seg = torch.nn.functional.pad(seg, (0, SEG_LEN - seg.shape[2]))
embs.append(enc(seg))
fx_emb = torch.mean(torch.stack(embs), dim=0)
# Apply to input
inp_gpu = inp.unsqueeze(0).to(device)
T = inp_gpu.shape[2]
out_segs = []
for s in range(0, T, SEG_LEN):
seg = inp_gpu[:, :, s:s+SEG_LEN]
actual = seg.shape[2]
if actual < SEG_LEN:
seg = torch.nn.functional.pad(seg, (0, SEG_LEN - actual))
out = conv(seg, fx_emb)
out_segs.append(out[:, :, :actual].cpu())
result = torch.clamp(torch.cat(out_segs, dim=2).squeeze(0), -1, 1)
sf.write(str(out_path), result.numpy().T, SR)
meta = {
"track_id": f"B2_fx_{ai_path.stem}",
"filename": fname,
"input_source": ai_path.name,
"reference_source": ref_path.name,
"method": "fx_encoder_style_transfer",
}
# Per-track JSON
with open(OUT_DIR / f"B2_fx_{ai_path.stem}.json", "w", encoding="utf-8") as jf:
json.dump(meta, jf, ensure_ascii=False, indent=2)
meta_f.write(json.dumps(meta, ensure_ascii=False) + "\n")
meta_f.flush()
done += 1
if done % 50 == 0:
elapsed = time.time() - t0
eta = (TARGET - done) * elapsed / max(done - existing, 1)
print(f"[{done}/{TARGET}] ETA: {eta/3600:.1f}h")
except Exception as e:
print(f"Error {ai_path.name} (attempt {attempt+1}): {e}")
torch.cuda.empty_cache()
if attempt < 2:
continue
break
meta_f.close()
print(f"Done: {done}/{TARGET}")
if __name__ == "__main__":
main()
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