File size: 3,364 Bytes
ab7c6e3 | 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 | """Złączenie przez ensemble (logit-mix) DWÓCH modeli o WSPÓLNYM słowniku.
W każdym kroku: logits = alpha*A + (1-alpha)*B -> sampluj. Melodia wychodzi stylistycznie pomiędzy.
To jest baseline „płaskiego ważenia" (KMS5). Stitch reprezentacji = osobny eksperyment (E1).
Użycie:
python src/compose/fuse.py --a data/models/waltz_ckpt.pt --b data/models/reel_sv_ckpt.pt \
--alpha 0.5 --meter 3/4 --keys D,G,Emin --inst piano --out data/recordings/fuzja
"""
import argparse, sys, os, math
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
from torch.nn import functional as F
from core.gpt import GPT
from core.abc_to_midi import to_midi
from music21 import instrument as M
INST = {"piano": M.Piano, "violin": M.Violin, "none": None}
def load(path):
ck = torch.load(path, map_location="cpu", weights_only=False)
m = GPT(ck["config"]); m.load_state_dict(ck["model"]); m.eval()
return m, ck
def first_tune(raw):
out = []
for ln in raw.split("\n"):
if ln.startswith("X:") and out:
break
out.append(ln)
return "\n".join(out).strip()
@torch.no_grad()
def gen_mix(A, B, idx, n, alpha, temp, topk, block):
for _ in range(n):
ic = idx[:, -block:]
la, _ = A(ic); lb, _ = B(ic)
logits = (alpha * la[:, -1, :] + (1 - alpha) * lb[:, -1, :]) / temp
if topk:
v, _ = torch.topk(logits, topk)
logits[logits < v[:, [-1]]] = float("-inf")
probs = F.softmax(logits, dim=-1)
idx = torch.cat([idx, torch.multinomial(probs, 1)], dim=1)
return idx
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--a", required=True); ap.add_argument("--b", required=True)
ap.add_argument("--alpha", type=float, default=0.5)
ap.add_argument("--meter", default="3/4"); ap.add_argument("--keys", default="D,G,Emin")
ap.add_argument("--inst", default="piano", choices=list(INST))
ap.add_argument("--out", required=True)
ap.add_argument("--new", type=int, default=420); ap.add_argument("--temp", type=float, default=0.85)
ap.add_argument("--topk", type=int, default=18)
a = ap.parse_args()
sys.stdout.reconfigure(encoding="utf-8")
A, ckA = load(a.a); B, ckB = load(a.b)
assert ckA["stoi"] == ckB["stoi"], "Modele mają RÓŻNY słownik — najpierw wspólny słownik!"
stoi, itos = ckA["stoi"], ckA["itos"]
block = ckA["config"].block_size
os.makedirs(a.out, exist_ok=True)
inst_cls = INST[a.inst]
print(f"FUZJA α={a.alpha} (A={a.a} : B={a.b}) | wspólny słownik {len(stoi)} | -> {a.out}")
torch.manual_seed(20260621)
ok = 0
for i, key in enumerate(a.keys.split(","), 1):
seed = f"X:1\nM:{a.meter}\nK:{key}\n"
idx = torch.tensor([[stoi[c] for c in seed]])
gen = gen_mix(A, B, idx, a.new, a.alpha, a.temp, a.topk, block)[0].tolist()
tune = first_tune("".join(itos[t] for t in gen))
base = f"{a.out}/fuzja_{i}_{key}"
open(base + ".abc", "w", encoding="utf-8").write(tune + "\n")
good = to_midi(tune, base + ".mid", inst=inst_cls() if inst_cls else None)
ok += good
print(f" #{i} ({key}) [{'MIDI OK' if good else 'błąd'}] -> {base}.mid")
print(f"\ngotowe: {ok}/{len(a.keys.split(','))} fuzji w {a.out}/")
if __name__ == "__main__":
main()
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