| """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") |
|
|
| |
| 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() |
|
|
