File size: 3,305 Bytes
b42b672 | 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 | """Kaggle notebook body: hold out one voice, then measure generalisation.
The previous student fitted the seven voices it was trained on to within 1.19 pp
of the teacher. That is in-distribution. The case that matters for user
uploads is a voice the student has never heard, so this run trains on six and
evaluates the seventh.
a07 (伊芙琳) is held out. a08 stays in training as the in-distribution control,
so the two columns separate "fitted" from "generalised".
Also measures the int4 quantisation of the same student, since that decides
which bundle ships.
traj 6 voices x 120 texts, 2 GPUs ~55 min
train 4-step student ~25 min
export ONNX fp32 + int4 ~5 min
eval a07 + a08, teacher vs student ~15 min
eval_int4 same cells through the int4 graph ~12 min
"""
import os
import subprocess
import sys
import time
from notebook_eval import BUNDLE, MODEL, ROOT, WEIGHTS, pull, stage_deps, stage_repo
BUNDLE_REPO = "idefinity/webfm-eval"
SHARDS = [["a01", "a03", "a04"], ["a05", "a06", "a08"]]
HOLDOUT = "a07"
EVAL_VOICES = "a07,a08"
def run(tag: str, extra: dict, gpu: int = 0) -> int:
env = dict(os.environ, WORK_ROOT=str(ROOT), HOLDOUT=HOLDOUT, **extra)
env["CUDA_VISIBLE_DEVICES"] = str(gpu)
env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
log = open(ROOT / f"holdout_{tag}.log", "w")
p = subprocess.Popen([sys.executable, str(BUNDLE / "kaggle/train_voices.py")],
env=env, stdout=log, stderr=subprocess.STDOUT, cwd=str(ROOT))
rc = p.wait()
print(f"[{tag}] rc={rc}", flush=True)
if rc != 0:
print((ROOT / f"holdout_{tag}.log").read_text()[-2500:])
return rc
def main() -> None:
stage_repo()
stage_deps()
pull(WEIGHTS, MODEL)
pull(BUNDLE_REPO, BUNDLE, repo_type="dataset")
# Guard: confirm the holdout filter works before spending an hour.
rc = run("smoke", {"STAGE": "traj", "VOICES": "a08", "SMOKE": "1", "N_TEXTS": "2"})
if rc != 0:
raise SystemExit("traj path broken")
if (ROOT / "train/traj/a07").exists() and any((ROOT / "train/traj/a07").iterdir()):
raise SystemExit("holdout voice leaked into trajectories")
print("== trajectories (6 voices) ==", flush=True)
procs = []
for gpu, shard in enumerate(SHARDS):
env = dict(os.environ, WORK_ROOT=str(ROOT), STAGE="traj", HOLDOUT=HOLDOUT,
VOICES=",".join(shard), CUDA_VISIBLE_DEVICES=str(gpu))
log = open(ROOT / f"traj_gpu{gpu}.log", "w")
procs.append(subprocess.Popen([sys.executable, str(BUNDLE / "kaggle/train_voices.py")],
env=env, stdout=log, stderr=subprocess.STDOUT, cwd=str(ROOT)))
while any(p.poll() is None for p in procs):
time.sleep(60)
n = len(list((ROOT / "train/traj").glob("*/*.pt")))
print(f"[{time.strftime('%H:%M')}] trajectories={n}", flush=True)
print("traj exits:", [p.returncode for p in procs])
if run("final", {"STAGE": "all", "TRAIN_STEPS": "1500", "BATCH": "16",
"EVAL_VOICES": EVAL_VOICES}) != 0:
raise SystemExit("train/export/eval failed")
run("int4", {"STAGE": "eval_int4", "EVAL_VOICES": EVAL_VOICES})
if __name__ == "__main__":
main()
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