File size: 1,945 Bytes
e6e274e | 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 | """Kaggle notebook body: release-candidate run at the operating point.
4000 steps was the best configuration; 8000 regressed. This run reproduces it
with the teacher seeded, and this time keeps the audio so the result can be
judged by ear rather than only by CER.
Produces:
train/student4.pt weights
train/dit4.onnx 412 MB fp32
train/dit4_int4.onnx 70 MB int4
train/eval_rows*.json teacher / student / int4 on a07 + a08
train/eval_wavs/*.wav teacher and student audio for the same cells
Uses the cached trajectories, so no regeneration.
"""
import os
import subprocess
import sys
from notebook_eval import BUNDLE, MODEL, ROOT, WEIGHTS, pull, stage_deps, stage_repo
BUNDLE_REPO = "idefinity/webfm-eval"
HOLDOUT = "a07"
EVAL_VOICES = "a07,a08"
TRAIN_STEPS = "4000"
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"final_{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"final_{tag}.log").read_text()[-2500:])
return rc
def main() -> None:
stage_repo()
stage_deps()
pull(WEIGHTS, MODEL)
pull(BUNDLE_REPO, BUNDLE, repo_type="dataset")
if not (BUNDLE / "traj.zip").exists():
raise SystemExit("traj.zip missing")
if run("main", {"STAGE": "all", "TRAIN_STEPS": TRAIN_STEPS, "BATCH": "16",
"EVAL_VOICES": EVAL_VOICES, "SAVE_WAVS": "1"}) != 0:
raise SystemExit("train/export/eval failed")
run("int4", {"STAGE": "eval_int4", "EVAL_VOICES": EVAL_VOICES})
if __name__ == "__main__":
main()
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