| """Kaggle notebook body: distil the flow head on the seven mined voices. |
| |
| Order matters. A smoke pass (5 texts, 5 optimizer steps) validates the |
| trajectory and training code paths before spending an hour generating 840 |
| trajectories; the earlier failures in this project each cost a full session. |
| |
| smoke traj x5 on one GPU, train x5 steps ~4 min |
| traj 7 voices x 120 texts, sharded over 2 GPUs ~70 min |
| train 4-step student on all shards ~25 min |
| export ONNX fp32 + int4 ~5 min |
| eval 40 sentences x 2 voices x {teacher, stu} ~25 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", "a07", "a08"]] |
| EVAL_VOICES = "a08,a01" |
|
|
|
|
| def run(tag: str, env_extra: dict, gpu: int = 0) -> int: |
| env = dict(os.environ, WORK_ROOT=str(ROOT), **env_extra) |
| if gpu is not None: |
| env["CUDA_VISIBLE_DEVICES"] = str(gpu) |
| log = open(ROOT / f"train_{tag}.log", "w") |
| p = subprocess.Popen([sys.executable, str(BUNDLE / "kaggle/train_voices.py")], |
| env=env, stdout=log, stderr=subprocess.STDOUT, cwd=str(ROOT)) |
| return p.wait() |
|
|
|
|
| def main() -> None: |
| stage_repo() |
| stage_deps() |
| pull(WEIGHTS, MODEL) |
| pull(BUNDLE_REPO, BUNDLE, repo_type="dataset") |
|
|
| print("== smoke: traj x5, train x5 ==", flush=True) |
| rc = run("smoke_traj", {"STAGE": "traj", "VOICES": "a08", "SMOKE": "1", "N_TEXTS": "5"}) |
| print("smoke traj rc", rc) |
| rc = run("smoke_train", {"STAGE": "train", "TRAIN_STEPS": "5", "BATCH": "4"}) |
| print("smoke train rc", rc) |
| if rc != 0: |
| print((ROOT / "train_smoke_train.log").read_text()[-3000:]) |
| raise SystemExit("training path failed in smoke") |
|
|
| print("== full trajectories ==", flush=True) |
| procs = [] |
| for gpu, shard in enumerate(SHARDS): |
| env = dict(os.environ, WORK_ROOT=str(ROOT), STAGE="traj", |
| 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]) |
|
|
| print("== train + export + eval ==", flush=True) |
| rc = run("final", {"STAGE": "all", "TRAIN_STEPS": "1500", "BATCH": "16", |
| "EVAL_VOICES": EVAL_VOICES}, gpu=0) |
| print("final rc", rc) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
