| """Kaggle notebook body: is the student undertrained, or unable to generalise? |
| |
| Run 2 landed at +3.05 pp overall: +1.95 pp on a voice it trained on (a08) and |
| +4.15 pp on the held-out one (a07). The CUDA source confirms the CFG convention |
| matches our training target exactly, so the recipe is not the suspect. What is: |
| the loss was still falling at step 1500, and 1500 x batch 16 = 24,000 samples |
| against roughly 28,800 frames of data — under one epoch. |
| |
| So this run changes one thing: 4,000 steps. Same six voices, same a07 holdout, |
| same evaluation. |
| |
| It also packs the trajectory cache into traj.zip so the next ablation does not |
| have to spend 50 minutes regenerating it. |
| |
| traj 6 voices x 120 texts, 2 GPUs ~50 min |
| pack zip the cache ~2 min |
| train 4-step, 4000 steps ~65 min |
| eval a07 + a08, teacher vs student ~15 min |
| int4 same cells through int4 ~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" |
| 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"learn_{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"learn_{tag}.log").read_text()[-2500:]) |
| return rc |
|
|
|
|
| def main() -> None: |
| stage_repo() |
| stage_deps() |
| pull(WEIGHTS, MODEL) |
| pull(BUNDLE_REPO, BUNDLE, repo_type="dataset") |
|
|
| print("== trajectories (6 voices, a07 held out) ==", 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]) |
|
|
| |
| subprocess.run(f"cd {ROOT}/train && zip -qr ../traj.zip traj", shell=True, check=False) |
| print("cache size:", (ROOT / "traj.zip").stat().st_size // 1024 // 1024, "MB", flush=True) |
|
|
| if run("final", {"STAGE": "all", "TRAIN_STEPS": TRAIN_STEPS, "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() |
|
|
